routine update on ch2 rough translation

ch1.md

@@ -10,7 +10,7 @@
 
 小量数据，可在单一机器上存储和处理，通常相对容易处理。然而，随着数据量或查询率的增加，需要将数据分布到多台机器上，这引入了许多挑战。随着应用程序需求的复杂化，仅在一个系统中存储所有数据已不再足够，可能需要结合多个提供不同功能的存储或处理系统。
 
-如果数据管理是开发应用程序的主要挑战之一，我们称这类应用为*数据密集型* [[1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Kouzes2009)]。而在*计算密集型*系统中，挑战在于并行处理一些非常大的计算，在数据密集型应用中，我们通常更关心的是如何存储和处理大数据量、管理数据变化、在出现故障和并发时确保一致性以及确保服务的高可用性。
+如果数据管理是开发应用程序的主要挑战之一，我们称这类应用为*数据密集型* [[1](ch01.html#Kouzes2009)]。而在*计算密集型*系统中，挑战在于并行处理一些非常大的计算，在数据密集型应用中，我们通常更关心的是如何存储和处理大数据量、管理数据变化、在出现故障和并发时确保一致性以及确保服务的高可用性。
 
 这类应用通常由提供常用功能的标准构建块构成。例如，许多应用需要：
 
@@ -43,7 +43,7 @@ Data is central to much application development today. With web and mobile apps,
 
 Small amounts of data, which can be stored and processed on a single machine, are often fairly easy to deal with. However, as the data volume or the rate of queries grows, it needs to be distributed across multiple machines, which introduces many challenges. As the needs of the application become more complex, it is no longer sufficient to store everything in one system, but it might be necessary to combine multiple storage or processing systems that provide different capabilities.
 
-We call an application *data-intensive* if data management is one of the primary challenges in developing the application [[1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Kouzes2009)]. While in *compute-intensive* systems the challenge is parallelizing some very large computation, in data-intensive applications we usually worry more about things like storing and processing large data volumes, managing changes to data, ensuring consistency in the face of failures and concurrency, and making sure services are highly available.
+We call an application *data-intensive* if data management is one of the primary challenges in developing the application [[1](ch01.html#Kouzes2009)]. While in *compute-intensive* systems the challenge is parallelizing some very large computation, in data-intensive applications we usually worry more about things like storing and processing large data volumes, managing changes to data, ensuring consistency in the face of failures and concurrency, and making sure services are highly available.
 
 Such applications are typically built from standard building blocks that provide commonly needed functionality. For example, many applications need to:
 
@@ -65,10 +65,10 @@ One of the key challenges with data systems is that different people need to do
 
 To help you understand what choices you can make, this chapter compares several contrasting concepts, and explores their trade-offs:
 
-- the difference between transaction processing and analytics ([“Transaction Processing versus Analytics”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_analytics));
-- pros and cons of cloud services and self-hosted systems ([“Cloud versus Self-Hosting”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_cloud));
-- when to move from single-node systems to distributed systems ([“Distributed versus Single-Node Systems”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_distributed)); and
-- balancing the needs of the business and the rights of the user ([“Data Systems, Law, and Society”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_compliance)).
+- the difference between transaction processing and analytics ([“Transaction Processing versus Analytics”](ch01.html#sec_introduction_analytics));
+- pros and cons of cloud services and self-hosted systems ([“Cloud versus Self-Hosting”](ch01.html#sec_introduction_cloud));
+- when to move from single-node systems to distributed systems ([“Distributed versus Single-Node Systems”](ch01.html#sec_introduction_distributed)); and
+- balancing the needs of the business and the rights of the user ([“Data Systems, Law, and Society”](ch01.html#sec_introduction_compliance)).
 
 Moreover, this chapter will provide you with terminology that we will need for the rest of the book.
 
@@ -76,11 +76,11 @@ Moreover, this chapter will provide you with terminology that we will need for t
 
 ### 术语：前端与后端
 
-我们在本书中将讨论的许多内容涉及*后端开发*。解释该术语：对于网络应用程序，客户端代码（在网页浏览器中运行）被称为*前端*，处理用户请求的服务器端代码被称为*后端*。移动应用与前端类似，它们提供用户界面，通常通过互联网与服务器端后端通信。前端有时会在用户设备上本地管理数据[[2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Kleppmann2019)]，但最大的数据基础设施挑战通常存在于后端：前端只需要处理一个用户的数据，而后端则代表*所有*用户管理数据。
+我们在本书中将讨论的许多内容涉及*后端开发*。解释该术语：对于网络应用程序，客户端代码（在网页浏览器中运行）被称为*前端*，处理用户请求的服务器端代码被称为*后端*。移动应用与前端类似，它们提供用户界面，通常通过互联网与服务器端后端通信。前端有时会在用户设备上本地管理数据[[2](ch01.html#Kleppmann2019)]，但最大的数据基础设施挑战通常存在于后端：前端只需要处理一个用户的数据，而后端则代表*所有*用户管理数据。
 
 后端服务通常可以通过 HTTP 访问；它通常包含一些应用程序代码，这些代码在一个或多个数据库中读写数据，有时还会与额外的数据系统（如缓存或消息队列）接口（我们可能统称为*数据基础设施*）。应用程序代码通常是*无状态的*（即，当它完成处理一个 HTTP 请求后，它会忘记该请求的所有信息），并且任何需要从一个请求传递到另一个请求的信息都需要存储在客户端或服务器端的数据基础设施中。
 
-Much of what we will discuss in this book relates to *backend development*. To explain that term: for web applications, the client-side code (which runs in a web browser) is called the *frontend*, and the server-side code that handles user requests is known as the *backend*. Mobile apps are similar to frontends in that they provide user interfaces, which often communicate over the Internet with a server-side backend. Frontends sometimes manage data locally on the user’s device [[2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Kleppmann2019)], but the greatest data infrastructure challenges often lie in the backend: a frontend only needs to handle one user’s data, whereas the backend manages data on behalf of *all* of the users.
+Much of what we will discuss in this book relates to *backend development*. To explain that term: for web applications, the client-side code (which runs in a web browser) is called the *frontend*, and the server-side code that handles user requests is known as the *backend*. Mobile apps are similar to frontends in that they provide user interfaces, which often communicate over the Internet with a server-side backend. Frontends sometimes manage data locally on the user’s device [[2](ch01.html#Kleppmann2019)], but the greatest data infrastructure challenges often lie in the backend: a frontend only needs to handle one user’s data, whereas the backend manages data on behalf of *all* of the users.
 
 A backend service is often reachable via HTTP; it usually consists of some application code that reads and writes data in one or more databases, and sometimes interfaces with additional data systems such as caches or message queues (which we might collectively call *data infrastructure*). The application code is often *stateless* (i.e., when it finishes handling one HTTP request, it forgets everything about that request), and any information that needs to persist from one request to another needs to be stored either on the client, or in the server-side data infrastructure.
 
@@ -89,7 +89,7 @@ A backend service is often reachable via HTTP; it usually consists of some appli
 
 ## 事务处理与分析
 
-如果你在企业中从事数据系统工作，你可能会遇到几种不同类型的处理数据的人。第一种是*后端工程师*，他们构建处理读取和更新数据请求的服务；这些服务通常直接或间接通过其他服务为外部用户提供服务（见[“微服务和无服务器”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_microservices)）。有时服务是供组织内部其他部分使用的。
+如果你在企业中从事数据系统工作，你可能会遇到几种不同类型的处理数据的人。第一种是*后端工程师*，他们构建处理读取和更新数据请求的服务；这些服务通常直接或间接通过其他服务为外部用户提供服务（见[“微服务和无服务器”](ch01.html#sec_introduction_microservices)）。有时服务是供组织内部其他部分使用的。
 
 除了管理后端服务的团队外，还有两个群体通常需要访问组织的数据：*商业分析师*，他们生成有关组织活动的报告以帮助管理层做出更好的决策（*商业智能*或*BI*），以及*数据科学家*，他们在数据中寻找新的见解或创建由数据分析和机器学习/AI支持的面向用户的产品功能（例如，电子商务网站上的“购买 X 的人也购买了 Y”推荐、风险评分或垃圾邮件过滤等预测分析，以及搜索结果的排名）。
 
@@ -98,11 +98,11 @@ A backend service is often reachable via HTTP; it usually consists of some appli
 - *业务系统*包括后端服务和数据基础设施，数据是在那里创建的，例如通过服务外部用户。在这里，应用程序代码根据用户的操作读取并修改其数据库中的数据。
 - *分析系统*满足商业分析师和数据科学家的需求。它们包含来自业务系统的数据的只读副本，并针对分析所需的数据处理类型进行了优化。
 
-正如我们将在下一节中看到的，出于充分的理由，操作和分析系统通常保持独立。随着这些系统的成熟，出现了两个新的专业角色：*数据工程师*和*分析工程师*。数据工程师是了解如何整合操作和分析系统的人，他们负责组织的数据基础设施的更广泛管理[[3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Reis2022)]。分析工程师建模和转换数据，使其对查询组织中数据的最终用户更有用[[4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Machado2023)]。
+正如我们将在下一节中看到的，出于充分的理由，操作和分析系统通常保持独立。随着这些系统的成熟，出现了两个新的专业角色：*数据工程师*和*分析工程师*。数据工程师是了解如何整合操作和分析系统的人，他们负责组织的数据基础设施的更广泛管理[[3](ch01.html#Reis2022)]。分析工程师建模和转换数据，使其对查询组织中数据的最终用户更有用[[4](ch01.html#Machado2023)]。
 
 许多工程师专注于操作或分析的一侧。然而，这本书涵盖了操作和分析数据系统，因为两者在组织内的数据生命周期中都扮演着重要的角色。我们将深入探讨用于向内部和外部用户提供服务的数据基础设施，以便你能更好地与这一界限另一侧的同事合作。
 
-If you are working on data systems in an enterprise, you are likely to encounter several different types of people who work with data. The first type are *backend engineers* who build services that handle requests for reading and updating data; these services often serve external users, either directly or indirectly via other services (see [“Microservices and Serverless”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_microservices)). Sometimes services are for internal use by other parts of the organization.
+If you are working on data systems in an enterprise, you are likely to encounter several different types of people who work with data. The first type are *backend engineers* who build services that handle requests for reading and updating data; these services often serve external users, either directly or indirectly via other services (see [“Microservices and Serverless”](ch01.html#sec_introduction_microservices)). Sometimes services are for internal use by other parts of the organization.
 
 In addition to the teams managing backend services, two other groups of people typically require access to an organization’s data: *business analysts*, who generate reports about the activities of the organization in order to help the management make better decisions (*business intelligence* or *BI*), and *data scientists*, who look for novel insights in data or who create user-facing product features that are enabled by data analysis and machine learning/AI (for example, “people who bought X also bought Y” recommendations on an e-commerce website, predictive analytics such as risk scoring or spam filtering, and ranking of search results).
 
@@ -111,7 +111,7 @@ Although business analysts and data scientists tend to use different tools and o
 - *Operational systems* consist of the backend services and data infrastructure where data is created, for example by serving external users. Here, the application code both reads and modifies the data in its databases, based on the actions performed by the users.
 - *Analytical systems* serve the needs of business analysts and data scientists. They contain a read-only copy of the data from the operational systems, and they are optimized for the types of data processing that are needed for analytics.
 
-As we shall see in the next section, operational and analytical systems are often kept separate, for good reasons. As these systems have matured, two new specialized roles have emerged: *data engineers* and *analytics engineers*. Data engineers are the people who know how to integrate the operational and the analytical systems, and who take responsibility for the organization’s data infrastructure more widely [[3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Reis2022)]. Analytics engineers model and transform data to make it more useful for end users querying data in an organization [[4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Machado2023)].
+As we shall see in the next section, operational and analytical systems are often kept separate, for good reasons. As these systems have matured, two new specialized roles have emerged: *data engineers* and *analytics engineers*. Data engineers are the people who know how to integrate the operational and the analytical systems, and who take responsibility for the organization’s data infrastructure more widely [[3](ch01.html#Reis2022)]. Analytics engineers model and transform data to make it more useful for end users querying data in an organization [[4](ch01.html#Machado2023)].
 
 Many engineers specialize on either the operational or the analytical side. However, this book covers both operational and analytical data systems, since both play an important role in the lifecycle of data within an organization. We will explore in-depth the data infrastructure that is used to deliver services both to internal and external users, so that you can work better with your colleagues on the other side of this divide.
 
@@ -134,7 +134,7 @@ In the early days of business data processing, a write to the database typically
 - 我们在最近的促销活动中卖出的香蕉比平时多多少？
 - 哪种品牌的婴儿食品最常与X品牌的尿布一起购买？
 
-这些类型的查询所产生的报告对于商业智能至关重要，帮助管理层决定下一步做什么。为了区分使用数据库的这种模式与事务处理的不同，它被称为*在线分析处理*（OLAP）[[5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Codd1993)]。OLTP和分析之间的区别并不总是明确的，但[表1-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#tab_oltp_vs_olap)列出了一些典型的特征。
+这些类型的查询所产生的报告对于商业智能至关重要，帮助管理层决定下一步做什么。为了区分使用数据库的这种模式与事务处理的不同，它被称为*在线分析处理*（OLAP）[[5](ch01.html#Codd1993)]。OLTP和分析之间的区别并不总是明确的，但[表1-1](ch01.html#tab_oltp_vs_olap)列出了一些典型的特征。
 
 | 属性     | 业务系统 (OLTP)    | 分析系统 (OLAP)   |
 |--------|----------------|---------------|
@@ -156,7 +156,7 @@ However, databases also started being increasingly used for analytics, which has
 - How many more bananas than usual did we sell during our latest promotion?
 - Which brand of baby food is most often purchased together with brand X diapers?
 
-The reports that result from these types of queries are important for business intelligence, helping the management decide what to do next. In order to differentiate this pattern of using databases from transaction processing, it has been called *online analytic processing* (OLAP) [[5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Codd1993)]. The difference between OLTP and analytics is not always clear-cut, but some typical characteristics are listed in [Table 1-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#tab_oltp_vs_olap).
+The reports that result from these types of queries are important for business intelligence, helping the management decide what to do next. In order to differentiate this pattern of using databases from transaction processing, it has been called *online analytic processing* (OLAP) [[5](ch01.html#Codd1993)]. The difference between OLTP and analytics is not always clear-cut, but some typical characteristics are listed in [Table 1-1](ch01.html#tab_oltp_vs_olap).
 
 | Property            | 运营系统 (OLTP)                                     | 分析系统 (OLAP)                               |
 |:--------------------|:------------------------------------------------|:------------------------------------------|
@@ -188,13 +188,13 @@ With operational systems, users are generally not allowed to construct custom SQ
 通常不希望商业分析师和数据科学家直接查询这些OLTP系统，原因有几个：
 
 - 感兴趣的数据可能分布在多个业务系统中，将这些数据集合并到单一查询中很困难（一个称为*数据孤岛*的问题）；
-- 适合OLTP的模式和数据布局不太适合分析（见[“星型和雪花型：分析的模式”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_analytics)）；
+- 适合OLTP的模式和数据布局不太适合分析（见[“星型和雪花型：分析的模式”](ch03.html#sec_datamodels_analytics)）；
 - 分析查询可能相当昂贵，如果在OLTP数据库上运行，将影响其他用户的性能；以及
 - OLTP系统可能位于一个不允许用户直接访问的单独网络中，出于安全或合规原因。
 
-与此相反，*数据仓库*是一个单独的数据库，分析师可以尽情查询，而不影响OLTP操作[[6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Chaudhuri1997)]。正如我们将在[即将提供链接]中看到的，数据仓库通常以与OLTP数据库非常不同的方式存储数据，以优化常见于分析的查询类型。
+与此相反，*数据仓库*是一个单独的数据库，分析师可以尽情查询，而不影响OLTP操作[[6](ch01.html#Chaudhuri1997)]。正如我们将在[即将提供链接]中看到的，数据仓库通常以与OLTP数据库非常不同的方式存储数据，以优化常见于分析的查询类型。
 
-数据仓库包含公司所有各种OLTP系统中的数据的只读副本。数据从OLTP数据库中提取（使用定期数据转储或持续更新流），转换成便于分析的模式，清理后，然后加载到数据仓库中。将数据获取到数据仓库的过程称为*提取-转换-加载*（ETL），并在[图1-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#fig_dwh_etl)中进行了说明。有时*转换*和*加载*的顺序被交换（即在数据仓库中加载后进行转换），这就变成了*ELT*。
+数据仓库包含公司所有各种OLTP系统中的数据的只读副本。数据从OLTP数据库中提取（使用定期数据转储或持续更新流），转换成便于分析的模式，清理后，然后加载到数据仓库中。将数据获取到数据仓库的过程称为*提取-转换-加载*（ETL），并在[图1-1](ch01.html#fig_dwh_etl)中进行了说明。有时*转换*和*加载*的顺序被交换（即在数据仓库中加载后进行转换），这就变成了*ELT*。
 
 At first, the same databases were used for both transaction processing and analytic queries. SQL turned out to be quite flexible in this regard: it works well for both types of queries. Nevertheless, in the late 1980s and early 1990s, there was a trend for companies to stop using their OLTP systems for analytics purposes, and to run the analytics on a separate database system instead. This separate database was called a *data warehouse*.
 
@@ -203,13 +203,13 @@ A large enterprise may have dozens, even hundreds, of operational transaction pr
 It is usually undesirable for business analysts and data scientists to directly query these OLTP systems, for several reasons:
 
 - the data of interest may be spread across multiple operational systems, making it difficult to combine those datasets in a single query (a problem known as *data silos*);
-- the kinds of schemas and data layouts that are good for OLTP are less well suited for analytics (see [“Stars and Snowflakes: Schemas for Analytics”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_analytics));
+- the kinds of schemas and data layouts that are good for OLTP are less well suited for analytics (see [“Stars and Snowflakes: Schemas for Analytics”](ch03.html#sec_datamodels_analytics));
 - analytic queries can be quite expensive, and running them on an OLTP database would impact the performance for other users; and
 - the OLTP systems might reside in a separate network that users are not allowed direct access to for security or compliance reasons.
 
-A *data warehouse*, by contrast, is a separate database that analysts can query to their hearts’ content, without affecting OLTP operations [[6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Chaudhuri1997)]. As we shall see in [Link to Come], data warehouses often store data in a way that is very different from OLTP databases, in order to optimize for the types of queries that are common in analytics.
+A *data warehouse*, by contrast, is a separate database that analysts can query to their hearts’ content, without affecting OLTP operations [[6](ch01.html#Chaudhuri1997)]. As we shall see in [Link to Come], data warehouses often store data in a way that is very different from OLTP databases, in order to optimize for the types of queries that are common in analytics.
 
-The data warehouse contains a read-only copy of the data in all the various OLTP systems in the company. Data is extracted from OLTP databases (using either a periodic data dump or a continuous stream of updates), transformed into an analysis-friendly schema, cleaned up, and then loaded into the data warehouse. This process of getting data into the data warehouse is known as *Extract–Transform–Load* (ETL) and is illustrated in [Figure 1-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#fig_dwh_etl). Sometimes the order of the *transform* and *load* steps is swapped (i.e., the transformation is done in the data warehouse, after loading), resulting in *ELT*.
+The data warehouse contains a read-only copy of the data in all the various OLTP systems in the company. Data is extracted from OLTP databases (using either a periodic data dump or a continuous stream of updates), transformed into an analysis-friendly schema, cleaned up, and then loaded into the data warehouse. This process of getting data into the data warehouse is known as *Extract–Transform–Load* (ETL) and is illustrated in [Figure 1-1](ch01.html#fig_dwh_etl). Sometimes the order of the *transform* and *load* steps is swapped (i.e., the transformation is done in the data warehouse, after loading), resulting in *ELT*.
 
 
 ![ddia 0308](img/ddia_0308.png)
@@ -219,62 +219,62 @@ The data warehouse contains a read-only copy of the data in all the various OLTP
 
 在某些情况下，ETL过程的数据来源是外部的SaaS产品，如客户关系管理（CRM）、电子邮件营销或信用卡处理系统。在这些情况下，你无法直接访问原始数据库，因为它只能通过软件供应商的API访问。将这些外部系统的数据引入你自己的数据仓库，可以启用SaaS API无法实现的分析。对于SaaS API的ETL通常由专业的数据连接服务实现，如Fivetran、Singer或AirByte。
 
-有些数据库系统提供*混合事务/分析处理*（HTAP），旨在在单一系统中同时启用OLTP和分析，无需从一个系统向另一个系统进行ETL [[7](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Ozcan2017)，[8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Prout2022)]。然而，许多HTAP系统内部由一个OLTP系统与一个独立的分析系统组成，这些系统通过一个公共界面隐藏——因此，理解这两者之间的区别对于理解这些系统的工作方式非常重要。
+有些数据库系统提供*混合事务/分析处理*（HTAP），旨在在单一系统中同时启用OLTP和分析，无需从一个系统向另一个系统进行ETL [[7](ch01.html#Ozcan2017)，[8](ch01.html#Prout2022)]。然而，许多HTAP系统内部由一个OLTP系统与一个独立的分析系统组成，这些系统通过一个公共界面隐藏——因此，理解这两者之间的区别对于理解这些系统的工作方式非常重要。
 
-此外，尽管存在HTAP，由于它们目标和要求的不同，事务性和分析性系统之间的分离仍然很常见。特别是，每个业务系统拥有自己的数据库被视为良好的实践（见[“微服务与无服务器”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_microservices)），导致有数百个独立的操作数据库；另一方面，一个企业通常只有一个数据仓库，这样业务分析师可以在单个查询中合并来自几个业务系统的数据。
+此外，尽管存在HTAP，由于它们目标和要求的不同，事务性和分析性系统之间的分离仍然很常见。特别是，每个业务系统拥有自己的数据库被视为良好的实践（见[“微服务与无服务器”](ch01.html#sec_introduction_microservices)），导致有数百个独立的操作数据库；另一方面，一个企业通常只有一个数据仓库，这样业务分析师可以在单个查询中合并来自几个业务系统的数据。
 
-业务系统和分析系统之间的分离是一个更广泛趋势的一部分：随着工作负载变得更加苛刻，系统变得更加专业化，并为特定工作负载优化。通用系统可以舒适地处理小数据量，但规模越大，系统趋向于变得更加专业化 [[9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Stonebraker2005fitsall)]。
+业务系统和分析系统之间的分离是一个更广泛趋势的一部分：随着工作负载变得更加苛刻，系统变得更加专业化，并为特定工作负载优化。通用系统可以舒适地处理小数据量，但规模越大，系统趋向于变得更加专业化 [[9](ch01.html#Stonebraker2005fitsall)]。
 
 In some cases the data sources of the ETL processes are external SaaS products such as customer relationship management (CRM), email marketing, or credit card processing systems. In those cases, you do not have direct access to the original database, since it is accessible only via the software vendor’s API. Bringing the data from these external systems into your own data warehouse can enable analyses that are not possible via the SaaS API. ETL for SaaS APIs is often implemented by specialist data connector services such as Fivetran, Singer, or AirByte.
 
-Some database systems offer *hybrid transactional/analytic processing* (HTAP), which aims to enable OLTP and analytics in a single system without requiring ETL from one system into another [[7](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Ozcan2017), [8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Prout2022)]. However, many HTAP systems internally consist of an OLTP system coupled with a separate analytical system, hidden behind a common interface—so the distinction beween the two remains important for understanding how these systems work.
+Some database systems offer *hybrid transactional/analytic processing* (HTAP), which aims to enable OLTP and analytics in a single system without requiring ETL from one system into another [[7](ch01.html#Ozcan2017), [8](ch01.html#Prout2022)]. However, many HTAP systems internally consist of an OLTP system coupled with a separate analytical system, hidden behind a common interface—so the distinction beween the two remains important for understanding how these systems work.
 
-Moreover, even though HTAP exists, it is common to have a separation between transactional and analytic systems due to their different goals and requirements. In particular, it is considered good practice for each operational system to have its own database (see [“Microservices and Serverless”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_microservices)), leading to hundreds of separate operational databases; on the other hand, an enterprise usually has a single data warehouse, so that business analysts can combine data from several operational systems in a single query.
+Moreover, even though HTAP exists, it is common to have a separation between transactional and analytic systems due to their different goals and requirements. In particular, it is considered good practice for each operational system to have its own database (see [“Microservices and Serverless”](ch01.html#sec_introduction_microservices)), leading to hundreds of separate operational databases; on the other hand, an enterprise usually has a single data warehouse, so that business analysts can combine data from several operational systems in a single query.
 
-The separation between operational and analytical systems is part of a wider trend: as workloads have become more demanding, systems have become more specialized and optimized for particular workloads. General-purpose systems can handle small data volumes comfortably, but the greater the scale, the more specialized systems tend to become [[9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Stonebraker2005fitsall)].
+The separation between operational and analytical systems is part of a wider trend: as workloads have become more demanding, systems have become more specialized and optimized for particular workloads. General-purpose systems can handle small data volumes comfortably, but the greater the scale, the more specialized systems tend to become [[9](ch01.html#Stonebraker2005fitsall)].
 
 #### 从数据仓库到数据湖
 
-数据仓库通常使用*关系*数据模型，通过SQL查询（见[第3章](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#ch_datamodels)），可能使用专业的商业智能软件。这种模型很适合业务分析师需要进行的类型的查询，但它不太适合数据科学家的需求，他们可能需要执行的任务如下：
+数据仓库通常使用*关系*数据模型，通过SQL查询（见[第3章](ch03.html#ch_datamodels)），可能使用专业的商业智能软件。这种模型很适合业务分析师需要进行的类型的查询，但它不太适合数据科学家的需求，他们可能需要执行的任务如下：
 
 - 将数据转换成适合训练机器学习模型的形式；这通常需要将数据库表的行和列转换为称为*特征*的数字值向量或矩阵。以一种最大化训练模型性能的方式执行这种转换的过程称为*特征工程*，它通常需要使用SQL难以表达的自定义代码。
 - 获取文本数据（例如，产品评论）并使用自然语言处理技术尝试从中提取结构化信息（例如，作者的情感或他们提到的主题）。类似地，他们可能需要使用计算机视觉技术从照片中提取结构化信息。
 
-尽管已经努力在SQL数据模型中添加机器学习操作符 [[10](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Cohen2009)] 并在关系基础上构建高效的机器学习系统 [[11](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Olteanu2020)]，许多数据科学家更喜欢不在数据仓库这类关系数据库中工作。相反，许多人更喜欢使用如pandas和scikit-learn这样的Python数据分析库，统计分析语言如R，以及分布式分析框架如Spark [[12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Bornstein2020)]。我们在[“数据框架、矩阵和数组”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_dataframes)中进一步讨论这些内容。
+尽管已经努力在SQL数据模型中添加机器学习操作符 [[10](ch01.html#Cohen2009)] 并在关系基础上构建高效的机器学习系统 [[11](ch01.html#Olteanu2020)]，许多数据科学家更喜欢不在数据仓库这类关系数据库中工作。相反，许多人更喜欢使用如pandas和scikit-learn这样的Python数据分析库，统计分析语言如R，以及分布式分析框架如Spark [[12](ch01.html#Bornstein2020)]。我们在[“数据框架、矩阵和数组”](ch03.html#sec_datamodels_dataframes)中进一步讨论这些内容。
 
-因此，组织面临着使数据以适合数据科学家使用的形式可用的需求。答案是*数据湖*：一个集中的数据存储库，存放可能对分析有用的任何数据，通过ETL过程从业务系统获取。与数据仓库的不同之处在于，数据湖只包含文件，不强加任何特定的文件格式或数据模型。数据湖中的文件可能是使用如Avro或Parquet等文件格式编码的数据库记录集合（见[链接即将到来]），但它们同样可能包含文本、图像、视频、传感器读数、稀疏矩阵、特征向量、基因序列或任何其他类型的数据 [[13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Fowler2015)]。
+因此，组织面临着使数据以适合数据科学家使用的形式可用的需求。答案是*数据湖*：一个集中的数据存储库，存放可能对分析有用的任何数据，通过ETL过程从业务系统获取。与数据仓库的不同之处在于，数据湖只包含文件，不强加任何特定的文件格式或数据模型。数据湖中的文件可能是使用如Avro或Parquet等文件格式编码的数据库记录集合（见[链接即将到来]），但它们同样可能包含文本、图像、视频、传感器读数、稀疏矩阵、特征向量、基因序列或任何其他类型的数据 [[13](ch01.html#Fowler2015)]。
 
-ETL过程已经概括为*数据管道*，在某些情况下，数据湖已成为从业务系统到数据仓库的中间停靠点。数据湖包含由业务系统产生的“原始”形式的数据，而不是转换成关系数据仓库架构的数据。这种方法的优点是，每个数据的消费者都可以将原始数据转换成最适合其需要的形式。这被称为*寿司原则*：“原始数据更好” [[14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Johnson2015)]。
+ETL过程已经概括为*数据管道*，在某些情况下，数据湖已成为从业务系统到数据仓库的中间停靠点。数据湖包含由业务系统产生的“原始”形式的数据，而不是转换成关系数据仓库架构的数据。这种方法的优点是，每个数据的消费者都可以将原始数据转换成最适合其需要的形式。这被称为*寿司原则*：“原始数据更好” [[14](ch01.html#Johnson2015)]。
 
-除了从数据湖加载数据到单独的数据仓库外，还可以直接在数据湖中的文件上运行典型的数据仓库工作负载（SQL查询和商业分析），以及数据科学/机器学习工作负载。这种架构被称为*数据湖仓*，它需要一个查询执行引擎和一个元数据（例如，模式管理）层来扩展数据湖的文件存储 [[15](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Armbrust2021)]。Apache Hive、Spark SQL、Presto和Trino是这种方法的例子。
+除了从数据湖加载数据到单独的数据仓库外，还可以直接在数据湖中的文件上运行典型的数据仓库工作负载（SQL查询和商业分析），以及数据科学/机器学习工作负载。这种架构被称为*数据湖仓*，它需要一个查询执行引擎和一个元数据（例如，模式管理）层来扩展数据湖的文件存储 [[15](ch01.html#Armbrust2021)]。Apache Hive、Spark SQL、Presto和Trino是这种方法的例子。
 
-A data warehouse often uses a *relational* data model that is queried through SQL (see [Chapter 3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#ch_datamodels)), perhaps using specialized business intelligence software. This model works well for the types of queries that business analysts need to make, but it is less well suited to the needs of data scientists, who might need to perform tasks such as:
+A data warehouse often uses a *relational* data model that is queried through SQL (see [Chapter 3](ch03.html#ch_datamodels)), perhaps using specialized business intelligence software. This model works well for the types of queries that business analysts need to make, but it is less well suited to the needs of data scientists, who might need to perform tasks such as:
 
 - Transform data into a form that is suitable for training a machine learning model; often this requires turning the rows and columns of a database table into a vector or matrix of numerical values called *features*. The process of performing this transformation in a way that maximizes the performance of the trained model is called *feature engineering*, and it often requires custom code that is difficult to express using SQL.
 - Take textual data (e.g., reviews of a product) and use natural language processing techniques to try to extract structured information from it (e.g., the sentiment of the author, or which topics they mention). Similarly, they might need to extract structured information from photos using computer vision techniques.
 
-Although there have been efforts to add machine learning operators to a SQL data model [[10](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Cohen2009)] and to build efficient machine learning systems on top of a relational foundation [[11](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Olteanu2020)], many data scientists prefer not to work in a relational database such as a data warehouse. Instead, many prefer to use Python data analysis libraries such as pandas and scikit-learn, statistical analysis languages such as R, and distributed analytics frameworks such as Spark [[12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Bornstein2020)]. We discuss these further in [“Dataframes, Matrices, and Arrays”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_dataframes).
+Although there have been efforts to add machine learning operators to a SQL data model [[10](ch01.html#Cohen2009)] and to build efficient machine learning systems on top of a relational foundation [[11](ch01.html#Olteanu2020)], many data scientists prefer not to work in a relational database such as a data warehouse. Instead, many prefer to use Python data analysis libraries such as pandas and scikit-learn, statistical analysis languages such as R, and distributed analytics frameworks such as Spark [[12](ch01.html#Bornstein2020)]. We discuss these further in [“Dataframes, Matrices, and Arrays”](ch03.html#sec_datamodels_dataframes).
 
-Consequently, organizations face a need to make data available in a form that is suitable for use by data scientists. The answer is a *data lake*: a centralized data repository that holds a copy of any data that might be useful for analysis, obtained from operational systems via ETL processes. The difference from a data warehouse is that a data lake simply contains files, without imposing any particular file format or data model. Files in a data lake might be collections of database records, encoded using a file format such as Avro or Parquet (see [Link to Come]), but they can equally well contain text, images, videos, sensor readings, sparse matrices, feature vectors, genome sequences, or any other kind of data [[13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Fowler2015)].
+Consequently, organizations face a need to make data available in a form that is suitable for use by data scientists. The answer is a *data lake*: a centralized data repository that holds a copy of any data that might be useful for analysis, obtained from operational systems via ETL processes. The difference from a data warehouse is that a data lake simply contains files, without imposing any particular file format or data model. Files in a data lake might be collections of database records, encoded using a file format such as Avro or Parquet (see [Link to Come]), but they can equally well contain text, images, videos, sensor readings, sparse matrices, feature vectors, genome sequences, or any other kind of data [[13](ch01.html#Fowler2015)].
 
-ETL processes have been generalized to *data pipelines*, and in some cases the data lake has become an intermediate stop on the path from the operational systems to the data warehouse. The data lake contains data in a “raw” form produced by the operational systems, without the transformation into a relational data warehouse schema. This approach has the advantage that each consumer of the data can transform the raw data into a form that best suits their needs. It has been dubbed the *sushi principle*: “raw data is better” [[14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Johnson2015)].
+ETL processes have been generalized to *data pipelines*, and in some cases the data lake has become an intermediate stop on the path from the operational systems to the data warehouse. The data lake contains data in a “raw” form produced by the operational systems, without the transformation into a relational data warehouse schema. This approach has the advantage that each consumer of the data can transform the raw data into a form that best suits their needs. It has been dubbed the *sushi principle*: “raw data is better” [[14](ch01.html#Johnson2015)].
 
-Besides loading data from a data lake into a separate data warehouse, it is also possible to run typical data warehousing workloads (SQL queries and business analytics) directly on the files in the data lake, alongside data science/machine learning workloads. This architecture is known as a *data lakehouse*, and it requires a query execution engine and a metadata (e.g., schema management) layer that extend the data lake’s file storage [[15](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Armbrust2021)]. Apache Hive, Spark SQL, Presto, and Trino are examples of this approach.
+Besides loading data from a data lake into a separate data warehouse, it is also possible to run typical data warehousing workloads (SQL queries and business analytics) directly on the files in the data lake, alongside data science/machine learning workloads. This architecture is known as a *data lakehouse*, and it requires a query execution engine and a metadata (e.g., schema management) layer that extend the data lake’s file storage [[15](ch01.html#Armbrust2021)]. Apache Hive, Spark SQL, Presto, and Trino are examples of this approach.
 
 
 #### 数据湖之外
 
-随着分析实践的成熟，组织越来越关注分析系统和数据管道的管理和运营，例如在DataOps宣言中捕捉到的内容 [[16](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#DataOps)]。其中包括治理、隐私和遵守像GDPR和CCPA这样的法规问题，我们将在[“数据系统、法律与社会”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_compliance)和[即将到来的链接]中讨论。
+随着分析实践的成熟，组织越来越关注分析系统和数据管道的管理和运营，例如在DataOps宣言中捕捉到的内容 [[16](ch01.html#DataOps)]。其中包括治理、隐私和遵守像GDPR和CCPA这样的法规问题，我们将在[“数据系统、法律与社会”](ch01.html#sec_introduction_compliance)和[即将到来的链接]中讨论。
 
 此外，分析数据越来越多地不仅以文件和关系表的形式提供，还以事件流的形式提供（见[即将到来的链接]）。使用基于文件的数据分析，你可以定期（例如，每天）重新运行分析，以响应数据的变化，但流处理允许分析系统更快地响应事件，大约在几秒钟的数量级。根据应用程序和时间敏感性，流处理方法可以很有价值，例如识别并阻止潜在的欺诈或滥用行为。
 
-在某些情况下，分析系统的输出会提供给业务系统（有时被称为*反向ETL* [[17](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Manohar2021)]）。例如，一个在分析系统中训练的机器学习模型可能被部署到生产中，以便它可以为终端用户生成推荐，如“购买X的人也买了Y”。这些部署的分析系统输出也被称为*数据产品* [[18](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#ORegan2018)]。机器学习模型可以使用TFX、Kubeflow或MLflow等专门工具部署到业务系统中。
+在某些情况下，分析系统的输出会提供给业务系统（有时被称为*反向ETL* [[17](ch01.html#Manohar2021)]）。例如，一个在分析系统中训练的机器学习模型可能被部署到生产中，以便它可以为终端用户生成推荐，如“购买X的人也买了Y”。这些部署的分析系统输出也被称为*数据产品* [[18](ch01.html#ORegan2018)]。机器学习模型可以使用TFX、Kubeflow或MLflow等专门工具部署到业务系统中。
 
-As analytics practices have matured, organizations have been increasingly paying attention to the management and operations of analytics systems and data pipelines, as captured for example in the DataOps manifesto [[16](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#DataOps)]. Part of this are issues of governance, privacy, and compliance with regulation such as GDPR and CCPA, which we discuss in [“Data Systems, Law, and Society”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_compliance) and [Link to Come].
+As analytics practices have matured, organizations have been increasingly paying attention to the management and operations of analytics systems and data pipelines, as captured for example in the DataOps manifesto [[16](ch01.html#DataOps)]. Part of this are issues of governance, privacy, and compliance with regulation such as GDPR and CCPA, which we discuss in [“Data Systems, Law, and Society”](ch01.html#sec_introduction_compliance) and [Link to Come].
 
 Moreover, analytical data is increasingly made available not only as files and relational tables, but also as streams of events (see [Link to Come]). With file-based data analysis you can re-run the analysis periodically (e.g., daily) in order to respond to changes in the data, but stream processing allows analytics systems to respond to events much faster, on the order of seconds. Depending on the application and how time-sensitive it is, a stream processing approach can be valuable, for example to identify and block potentially fraudulent or abusive activity.
 
-In some cases the outputs of analytics systems are made available to operational systems (a process sometimes known as *reverse ETL* [[17](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Manohar2021)]). For example, a machine-learning model that was trained on data in an analytics system may be deployed to production, so that it can generate recommendations for end-users, such as “people who bought X also bought Y”. Such deployed outputs of analytics systems are also known as *data products* [[18](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#ORegan2018)]. Machine learning models can be deployed to operational systems using specialized tools such as TFX, Kubeflow, or MLflow.
+In some cases the outputs of analytics systems are made available to operational systems (a process sometimes known as *reverse ETL* [[17](ch01.html#Manohar2021)]). For example, a machine-learning model that was trained on data in an analytics system may be deployed to production, so that it can generate recommendations for end-users, such as “people who bought X also bought Y”. Such deployed outputs of analytics systems are also known as *data products* [[18](ch01.html#ORegan2018)]. Machine learning models can be deployed to operational systems using specialized tools such as TFX, Kubeflow, or MLflow.
 
 
 ### 记录系统与衍生数据系统
@@ -283,7 +283,7 @@ In some cases the outputs of analytics systems are made available to operational
 
 - 记录系统
 
-  记录系统，也称为*真实来源*，持有某些数据的权威或*规范*版本。当新数据进入时，例如作为用户输入，首先在此处写入。每个事实只表示一次（通常是*规范化*的；见[“规范化、反规范化和连接”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_normalization)）。如果另一个系统与记录系统之间存在任何差异，则记录系统中的值（按定义）是正确的。
+  记录系统，也称为*真实来源*，持有某些数据的权威或*规范*版本。当新数据进入时，例如作为用户输入，首先在此处写入。每个事实只表示一次（通常是*规范化*的；见[“规范化、反规范化和连接”](ch03.html#sec_datamodels_normalization)）。如果另一个系统与记录系统之间存在任何差异，则记录系统中的值（按定义）是正确的。
 
 - 衍生数据系统
 
@@ -303,7 +303,7 @@ Related to the distinction between operational and analytical systems, this book
 
 - Systems of record
 
-  A system of record, also known as *source of truth*, holds the authoritative or *canonical* version of some data. When new data comes in, e.g., as user input, it is first written here. Each fact is represented exactly once (the representation is typically *normalized*; see [“Normalization, Denormalization, and Joins”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_normalization)). If there is any discrepancy between another system and the system of record, then the value in the system of record is (by definition) the correct one.
+  A system of record, also known as *source of truth*, holds the authoritative or *canonical* version of some data. When new data comes in, e.g., as user input, it is first written here. Each fact is represented exactly once (the representation is typically *normalized*; see [“Normalization, Denormalization, and Joins”](ch03.html#sec_datamodels_normalization)). If there is any discrepancy between another system and the system of record, then the value in the system of record is (by definition) the correct one.
 
 - Derived data systems
 
@@ -328,15 +328,15 @@ That brings us to the end of our comparison of analytics and transaction process
 
 对于组织需要执行的任何事务，首先要问的问题之一是：应该在内部完成还是外包？您应该自行构建还是购买？
 
-这最终是一个关于业务优先级的问题。管理学的普遍观点是，作为组织的核心能力或竞争优势的事物应该在内部完成，而非核心、常规或普通的事务则应交给供应商处理 [[19](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Fournier2021)]。举一个极端的例子，大多数公司不会自己发电（除非它们是能源公司，且不考虑紧急备用电力），因为从电网购买电力更便宜。
+这最终是一个关于业务优先级的问题。管理学的普遍观点是，作为组织的核心能力或竞争优势的事物应该在内部完成，而非核心、常规或普通的事务则应交给供应商处理 [[19](ch01.html#Fournier2021)]。举一个极端的例子，大多数公司不会自己发电（除非它们是能源公司，且不考虑紧急备用电力），因为从电网购买电力更便宜。
 
-在软件方面，需要做出的两个重要决策是谁来构建软件以及谁来部署它。有一个将每个决策外包出去的可能性的范围，如[图 1-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#fig_cloud_spectrum)所示。一个极端是你编写并在内部运行的定制软件；另一个极端是广泛使用的云服务或软件即服务（SaaS）产品，由外部供应商实施和操作，你只能通过Web界面或API访问。
+在软件方面，需要做出的两个重要决策是谁来构建软件以及谁来部署它。有一个将每个决策外包出去的可能性的范围，如[图 1-2](ch01.html#fig_cloud_spectrum)所示。一个极端是你编写并在内部运行的定制软件；另一个极端是广泛使用的云服务或软件即服务（SaaS）产品，由外部供应商实施和操作，你只能通过Web界面或API访问。
 
 With anything that an organization needs to do, one of the first questions is: should it be done in-house, or should it be outsourced? Should you build or should you buy?
 
-Ultimately, this is a question about business priorities. The received management wisdom is that things that are a core competency or a competitive advantage of your organization should be done in-house, whereas things that are non-core, routine, or commonplace should be left to a vendor [[19](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Fournier2021)]. To give an extreme example, most companies do not generate their own electricity (unless they are an energy company, and leaving aside emergency backup power), since it is cheaper to buy electricity from the grid.
+Ultimately, this is a question about business priorities. The received management wisdom is that things that are a core competency or a competitive advantage of your organization should be done in-house, whereas things that are non-core, routine, or commonplace should be left to a vendor [[19](ch01.html#Fournier2021)]. To give an extreme example, most companies do not generate their own electricity (unless they are an energy company, and leaving aside emergency backup power), since it is cheaper to buy electricity from the grid.
 
-With software, two important decisions to be made are who builds the software and who deploys it. There is a spectrum of possibilities that outsource each decision to various degrees, as illustrated in [Figure 1-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#fig_cloud_spectrum). At one extreme is bespoke software that you write and run in-house; at the other extreme are widely-used cloud services or Software as a Service (SaaS) products that are implemented and operated by an external vendor, and which you only access through a web interface or API.
+With software, two important decisions to be made are who builds the software and who deploys it. There is a spectrum of possibilities that outsource each decision to various degrees, as illustrated in [Figure 1-2](ch01.html#fig_cloud_spectrum). At one extreme is bespoke software that you write and run in-house; at the other extreme are widely-used cloud services or Software as a Service (SaaS) products that are implemented and operated by an external vendor, and which you only access through a web interface or API.
 
 ![ddia 0101](img/ddia_0101.png)
 
@@ -355,9 +355,9 @@ Seperately from this spectrum there is also the question of *how* you deploy ser
 
 使用云服务，而不是自己运行可比软件，本质上是将该软件的运营外包给云提供商。支持和反对使用云服务的理由都很充分。云提供商声称使用他们的服务可以节省时间和金钱，并允许你比建立自己的基础设施更快地行动。
 
-云服务是否实际上比自托管更便宜和更容易，很大程度上取决于你的技能和系统的工作负载。如果你已经有设置和操作所需系统的经验，并且你的负载相当可预测（即，你需要的机器数量不会剧烈波动），那么通常购买自己的机器并自己运行软件会更便宜 [[20](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#HeinemeierHansson2022), [21](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Badizadegan2022)]。
+云服务是否实际上比自托管更便宜和更容易，很大程度上取决于你的技能和系统的工作负载。如果你已经有设置和操作所需系统的经验，并且你的负载相当可预测（即，你需要的机器数量不会剧烈波动），那么通常购买自己的机器并自己运行软件会更便宜 [[20](ch01.html#HeinemeierHansson2022), [21](ch01.html#Badizadegan2022)]。
 
-另一方面，如果你需要一个你不知道如何部署和操作的系统，那么采用云服务通常比自己学习管理系统更容易且更快。如果你必须雇佣并培训专门的员工来维护和业务系统，这可能非常昂贵。当你使用云时，仍然需要一个运营团队（见[“云时代的运营”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_operations)），但将基本的系统管理外包可以释放你的团队，专注于更高层次的问题。
+另一方面，如果你需要一个你不知道如何部署和操作的系统，那么采用云服务通常比自己学习管理系统更容易且更快。如果你必须雇佣并培训专门的员工来维护和业务系统，这可能非常昂贵。当你使用云时，仍然需要一个运营团队（见[“云时代的运营”](ch01.html#sec_introduction_operations)），但将基本的系统管理外包可以释放你的团队，专注于更高层次的问题。
 
 当你将系统的运营外包给专门运营该服务的公司时，这可能会带来更好的服务，因为提供商从为许多客户提供服务中获得运营专长。另一方面，如果你自己运行服务，你可以配置并调整它以在你特定的工作负载上表现良好；云服务不太可能愿意代表你进行此类定制。
 
@@ -370,15 +370,15 @@ Seperately from this spectrum there is also the question of *how* you deploy ser
 - 如果它缺少你需要的功能，你唯一能做的就是礼貌地询问供应商是否会添加它；你通常无法自己实现它。
 - 如果服务出现故障，你只能等待它恢复。
 - 如果你以某种方式使用服务，触发了一个错误或导致性能问题，你很难诊断问题。对于你自己运行的软件，你可以从业务系统获取性能指标和调试信息来帮助你了解其行为，你可以查看服务器日志，但使用供应商托管的服务时，你通常无法访问这些内部信息。
-- 此外，如果服务关闭或变得无法接受地昂贵，或者如果供应商决定以你不喜欢的方式更改其产品，你将受制于他们——继续运行软件的旧版本通常不是一个选项，因此你将被迫迁移到另一个服务 [[22](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Yegge2020)]。如果有提供兼容API的替代服务，这种风险可以缓解，但对于许多云服务，没有标准的API，这增加了切换的成本，使供应商锁定成为一个问题。
+- 此外，如果服务关闭或变得无法接受地昂贵，或者如果供应商决定以你不喜欢的方式更改其产品，你将受制于他们——继续运行软件的旧版本通常不是一个选项，因此你将被迫迁移到另一个服务 [[22](ch01.html#Yegge2020)]。如果有提供兼容API的替代服务，这种风险可以缓解，但对于许多云服务，没有标准的API，这增加了切换的成本，使供应商锁定成为一个问题。
 
 尽管存在这些风险，组织构建基于云服务的新应用变得越来越流行。然而，云服务并不能取代所有的内部数据系统：许多旧系统早于云技术，且对于那些现有云服务无法满足的特殊需求，内部系统仍然是必需的。例如，像高频交易这样对延迟极其敏感的应用需要完全控制硬件。
 
 Using a cloud service, rather than running comparable software yourself, essentially outsources the operation of that software to the cloud provider. There are good arguments for and against cloud services. Cloud providers claim that using their services saves you time and money, and allows you to move faster compared to setting up your own infrastructure.
 
-Whether a cloud service is actually cheaper and easier than self-hosting depends very much on your skills and the workload on your systems. If you already have experience setting up and operating the systems you need, and if your load is quite predictable (i.e., the number of machines you need does not fluctuate wildly), then it’s often cheaper to buy your own machines and run the software on them yourself [[20](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#HeinemeierHansson2022), [21](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Badizadegan2022)].
+Whether a cloud service is actually cheaper and easier than self-hosting depends very much on your skills and the workload on your systems. If you already have experience setting up and operating the systems you need, and if your load is quite predictable (i.e., the number of machines you need does not fluctuate wildly), then it’s often cheaper to buy your own machines and run the software on them yourself [[20](ch01.html#HeinemeierHansson2022), [21](ch01.html#Badizadegan2022)].
 
-On the other hand, if you need a system that you don’t already know how to deploy and operate, then adopting a cloud service is often easier and quicker than learning to manage the system yourself. If you have to hire and train staff specifically to maintain and operate the system, that can get very expensive. You still need an operations team when you’re using the cloud (see [“Operations in the Cloud Era”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_operations)), but outsourcing the basic system administration can free up your team to focus on higher-level concerns.
+On the other hand, if you need a system that you don’t already know how to deploy and operate, then adopting a cloud service is often easier and quicker than learning to manage the system yourself. If you have to hire and train staff specifically to maintain and operate the system, that can get very expensive. You still need an operations team when you’re using the cloud (see [“Operations in the Cloud Era”](ch01.html#sec_introduction_operations)), but outsourcing the basic system administration can free up your team to focus on higher-level concerns.
 
 When you outsource the operation of a system to a company that specializes in running that service, that can potentially result in a better service, since the provider gains operational expertise from providing the service to many customers. On the other hand, if you run the service yourself, you can configure and tune it to perform well on your particular workload; it is unlikely that a cloud service would be willing to make such customizations on your behalf.
 
@@ -391,7 +391,7 @@ The biggest downside of a cloud service is that you have no control over it:
 - If it is lacking a feature you need, all you can do is to politely ask the vendor whether they will add it; you generally cannot implement it yourself.
 - If the service goes down, all you can do is to wait for it to recover.
 - If you are using the service in a way that triggers a bug or causes performance problems, it will be difficult for you to diagnose the issue. With software that you run yourself, you can get performance metrics and debugging information from the operating system to help you understand its behavior, and you can look at the server logs, but with a service hosted by a vendor you usually do not have access to these internals.
-- Moreover, if the service shuts down or becomes unacceptably expensive, or if the vendor decides to change their product in a way you don’t like, you are at their mercy—continuing to run an old version of the software is usually not an option, so you will be forced to migrate to an alternative service [[22](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Yegge2020)]. This risk is mitigated if there are alternative services that expose a compatible API, but for many cloud services there are no standard APIs, which raises the cost of switching, making vendor lock-in a problem.
+- Moreover, if the service shuts down or becomes unacceptably expensive, or if the vendor decides to change their product in a way you don’t like, you are at their mercy—continuing to run an old version of the software is usually not an option, so you will be forced to migrate to an alternative service [[22](ch01.html#Yegge2020)]. This risk is mitigated if there are alternative services that expose a compatible API, but for many cloud services there are no standard APIs, which raises the cost of switching, making vendor lock-in a problem.
 
 Despite all these risks, it has become more and more popular for organizations to build new applications on top of cloud services. However, cloud services will not subsume all in-house data systems: many older systems predate the cloud, and for any services that have specialist requirements that existing cloud services cannot meet, in-house systems remain necessary. For example, very latency-sensitive applications such as high-frequency trading require full control of the hardware.
 
@@ -402,21 +402,16 @@ Despite all these risks, it has become more and more popular for organizations t
 
 除了经济模式的不同（订阅服务而非购买硬件并在其上运行许可软件），云计算的兴起还在技术层面深刻影响了数据系统的实施方式。*云原生* 一词用来描述一种旨在利用云服务优势的架构。
 
-原则上，几乎任何你可以自行托管的软件也可以作为云服务提供，实际上，许多流行的数据系统现在已经有了这样的托管服务。然而，从底层设计为云原生的系统显示出多项优势：在相同硬件上有更好的性能，从失败中更快恢复，能迅速扩展计算资源以匹配负载，并支持更大的数据集[[23](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Verbitski2017), [24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Antonopoulos2019_ch1), [25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vuppalapati2020)]。[表 1-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#tab_cloud_native_dbs)列出了这两类系统的一些例子。
+原则上，几乎任何你可以自行托管的软件也可以作为云服务提供，实际上，许多流行的数据系统现在已经有了这样的托管服务。然而，从底层设计为云原生的系统显示出多项优势：在相同硬件上有更好的性能，从失败中更快恢复，能迅速扩展计算资源以匹配负载，并支持更大的数据集[[23](ch01.html#Verbitski2017), [24](ch01.html#Antonopoulos2019_ch1), [25](ch01.html#Vuppalapati2020)]。[表 1-2](ch01.html#tab_cloud_native_dbs)列出了这两类系统的一些例子。
 
 | 类别       | 自托管系统                       | 云原生系统                                                               |
 |----------|-----------------------------|---------------------------------------------------------------------|
-| 操作型/OLTP | MySQL, PostgreSQL, MongoDB  | AWS Aurora 【23】, Azure SQL DB Hyperscale 【24】, Google Cloud Spanner |
+| 事务型/OLTP | MySQL, PostgreSQL, MongoDB  | AWS Aurora 【23】, Azure SQL DB Hyperscale 【24】, Google Cloud Spanner |
 | 分析型/OLAP | Teradata, ClickHouse, Spark | Snowflake 【25】, Google BigQuery, Azure Synapse Analytics            |
 
 Besides having a different economic model (subscribing to a service instead of buying hardware and licensing software to run on it), the rise of the cloud has also had a profound effect on how data systems are implemented on a technical level. The term *cloud-native* is used to describe an architecture that is designed to take advantage of cloud services.
 
-In principle, almost any software that you can self-host could also be provided as a cloud service, and indeed such managed services are now available for many popular data systems. However, systems that have been designed from the ground up to be cloud-native have been shown to have several advantages: better performance on the same hardware, faster recovery from failures, being able to quickly scale computing resources to match the load, and supporting larger datasets [[23](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Verbitski2017), [24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Antonopoulos2019_ch1), [25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vuppalapati2020)]. [Table 1-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#tab_cloud_native_dbs) lists some examples of both types of systems.
-
-| Category         | Self-hosted systems         | Cloud-native systems                                                |
-|:-----------------|:----------------------------|:--------------------------------------------------------------------|
-| Operational/OLTP | MySQL, PostgreSQL, MongoDB  | AWS Aurora 【23】, Azure SQL DB Hyperscale 【24】, Google Cloud Spanner |
-| Analytical/OLAP  | Teradata, ClickHouse, Spark | Snowflake 【25】, Google BigQuery, Azure Synapse Analytics            |
+In principle, almost any software that you can self-host could also be provided as a cloud service, and indeed such managed services are now available for many popular data systems. However, systems that have been designed from the ground up to be cloud-native have been shown to have several advantages: better performance on the same hardware, faster recovery from failures, being able to quickly scale computing resources to match the load, and supporting larger datasets [[23](ch01.html#Verbitski2017), [24](ch01.html#Antonopoulos2019_ch1), [25](ch01.html#Vuppalapati2020)]. [Table 1-2](ch01.html#tab_cloud_native_dbs) lists some examples of both types of systems.
 
 
 
@@ -429,7 +424,7 @@ In principle, almost any software that you can self-host could also be provided
 相比之下，云原生服务的关键思想是不仅使用由业务系统管理的计算资源，还要构建在更低层级的云服务之上，创建更高层级的服务。例如：
 
 - *对象存储*服务，如亚马逊 S3、Azure Blob 存储和 Cloudflare R2 存储大文件。它们提供的 API 比典型文件系统的 API 更有限（基本的文件读写），但它们的优势在于隐藏了底层的物理机器：服务自动将数据分布在许多机器上，因此你无需担心任何一台机器上的磁盘空间耗尽。即使某些机器或其磁盘完全失败，也不会丢失数据。
-- 许多其他服务又是建立在对象存储和其他云服务之上的：例如，Snowflake 是一种基于云的分析数据库（数据仓库），依赖于 S3 进行数据存储 [[25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vuppalapati2020)]，还有一些服务又建立在 Snowflake 之上。
+- 许多其他服务又是建立在对象存储和其他云服务之上的：例如，Snowflake 是一种基于云的分析数据库（数据仓库），依赖于 S3 进行数据存储 [[25](ch01.html#Vuppalapati2020)]，还有一些服务又建立在 Snowflake 之上。
 
 正如计算中的抽象总是一样，关于你应该使用什么，没有一个正确的答案。一般规则是，更高层次的抽象往往更针对特定用例。如果你的需求与更高层系统设计的情况匹配，使用现有的更高层系统可能会比从更低层系统自行构建省去许多麻烦。另一方面，如果没有高层系统满足你的需求，那么自己从更低层组件构建是唯一的选择。
 
@@ -440,7 +435,7 @@ In a cloud, this type of software can be run on an Infrastructure-as-a-Service e
 In contrast, the key idea of cloud-native services is to use not only the computing resources managed by your operating system, but also to build upon lower-level cloud services to create higher-level services. For example:
 
 - *Object storage* services such as Amazon S3, Azure Blob Storage, and Cloudflare R2 store large files. They provide more limited APIs than a typical filesystem (basic file reads and writes), but they have the advantage that they hide the underlying physical machines: the service automatically distributes the data across many machines, so that you don’t have to worry about running out of disk space on any one machine. Even if some machines or their disks fail entirely, no data is lost.
-- Many other services are in turn built upon object storage and other cloud services: for example, Snowflake is a cloud-based analytic database (data warehouse) that relies on S3 for data storage [[25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vuppalapati2020)], and some other services in turn build upon Snowflake.
+- Many other services are in turn built upon object storage and other cloud services: for example, Snowflake is a cloud-based analytic database (data warehouse) that relies on S3 for data storage [[25](ch01.html#Vuppalapati2020)], and some other services in turn build upon Snowflake.
 
 As always with abstractions in computing, there is no one right answer to what you should use. As a general rule, higher-level abstractions tend to be more oriented towards particular use cases. If your needs match the situations for which a higher-level system is designed, using the existing higher-level system will probably provide what you need with much less hassle than building it yourself from lower-level systems. On the other hand, if there is no high-level system that meets your needs, then building it yourself from lower-level components is the only option.
 
@@ -450,34 +445,34 @@ As always with abstractions in computing, there is no one right answer to what y
 
 在传统计算中，磁盘存储被视为持久的（我们假设一旦某些内容被写入磁盘，它就不会丢失）；为了容忍单个硬盘的失败，经常使用 RAID 来在几个磁盘上维护数据的副本。在云中，计算实例（虚拟机）也可能有本地磁盘附加，但云原生系统通常将这些磁盘更像是临时缓存，而不是长期存储。这是因为如果关联实例失败，或者为了适应负载变化而用更大或更小的实例替换实例（在不同的物理机上），本地磁盘将变得无法访问。
 
-作为本地磁盘的替代，云服务还提供了可以从一个实例分离并连接到另一个实例的虚拟磁盘存储（Amazon EBS、Azure 管理磁盘和 Google Cloud 中的持久磁盘）。这种虚拟磁盘实际上不是物理磁盘，而是由一组独立机器提供的云服务，模拟磁盘（块设备）的行为（每个块通常为 4 KiB 大小）。这项技术使得在云中运行传统基于磁盘的软件成为可能，但它通常表现出较差的性能和可扩展性 [[23](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Verbitski2017)]。
+作为本地磁盘的替代，云服务还提供了可以从一个实例分离并连接到另一个实例的虚拟磁盘存储（Amazon EBS、Azure 管理磁盘和 Google Cloud 中的持久磁盘）。这种虚拟磁盘实际上不是物理磁盘，而是由一组独立机器提供的云服务，模拟磁盘（块设备）的行为（每个块通常为 4 KiB 大小）。这项技术使得在云中运行传统基于磁盘的软件成为可能，但它通常表现出较差的性能和可扩展性 [[23](ch01.html#Verbitski2017)]。
 
-为解决这个问题，云原生服务通常避免使用虚拟磁盘，而是建立在专门为特定工作负载优化的专用存储服务之上。如 S3 等对象存储服务旨在长期存储相对较大的文件，大小从数百千字节到几个千兆字节不等。存储在数据库中的单独行或值通常比这小得多；因此云数据库通常在单独的服务中管理更小的值，并在对象存储中存储更大的数据块（包含许多单独的值） [[24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Antonopoulos2019_ch1)]。
+为解决这个问题，云原生服务通常避免使用虚拟磁盘，而是建立在专门为特定工作负载优化的专用存储服务之上。如 S3 等对象存储服务旨在长期存储相对较大的文件，大小从数百千字节到几个千兆字节不等。存储在数据库中的单独行或值通常比这小得多；因此云数据库通常在单独的服务中管理更小的值，并在对象存储中存储更大的数据块（包含许多单独的值） [[24](ch01.html#Antonopoulos2019_ch1)]。
 
-在传统的系统架构中，同一台计算机负责存储（磁盘）和计算（CPU 和 RAM），但在云原生系统中，这两种责任已经有所分离或*解耦* [[8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Prout2022), [25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vuppalapati2020), [26](https://learning.oreilly.com/library/view/designing-data-intensive-applications/例如，S3仅存储文件，如果你想分析那些数据，你将不得不在 S3 外部的某处运行分析代码。这意味着需要通过网络传输数据，我们将在[“分布式与单节点系统”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_distributed)中进一步讨论这一点。
+在传统的系统架构中，同一台计算机负责存储（磁盘）和计算（CPU 和 RAM），但在云原生系统中，这两种责任已经有所分离或*解耦* [[8](ch01.html#Prout2022), [25](ch01.html#Vuppalapati2020), [26](https://learning.oreilly.com/library/view/designing-data-intensive-applications/例如，S3仅存储文件，如果你想分析那些数据，你将不得不在 S3 外部的某处运行分析代码。这意味着需要通过网络传输数据，我们将在[“分布式与单节点系统”](ch01.html#sec_introduction_distributed)中进一步讨论这一点。
 
-此外，云原生系统通常是*多租户*的，这意味着它们不是为每个客户配置单独的机器，而是在同一共享硬件上由同一服务处理来自几个不同客户的数据和计算 [[28](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vanlightly2023)]。多租户可以实现更好的硬件利用率、更容易的可扩展性和云提供商更容易的管理，但它也需要精心的工程设计，以确保一个客户的活动不影响系统对其他客户的性能或安全性 [[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Jonas2019)]。
+此外，云原生系统通常是*多租户*的，这意味着它们不是为每个客户配置单独的机器，而是在同一共享硬件上由同一服务处理来自几个不同客户的数据和计算 [[28](ch01.html#Vanlightly2023)]。多租户可以实现更好的硬件利用率、更容易的可扩展性和云提供商更容易的管理，但它也需要精心的工程设计，以确保一个客户的活动不影响系统对其他客户的性能或安全性 [[29](ch01.html#Jonas2019)]。
 
 In traditional computing, disk storage is regarded as durable (we assume that once something is written to disk, it will not be lost); to tolerate the failure of an individual hard disk, RAID is often used to maintain copies of the data on several disks. In the cloud, compute instances (virtual machines) may also have local disks attached, but cloud-native systems typically treat these disks more like an ephemeral cache, and less like long-term storage. This is because the local disk becomes inaccessible if the associated instance fails, or if the instance is replaced with a bigger or a smaller one (on a different physical machine) in order to adapt to changes in load.
 
-As an alternative to local disks, cloud services also offer virtual disk storage that can be detached from one instance and attached to a different one (Amazon EBS, Azure managed disks, and persistent disks in Google Cloud). Such a virtual disk is not actually a physical disk, but rather a cloud service provided by a separate set of machines, which emulates the behavior of a disk (a *block device*, where each block is typically 4 KiB in size). This technology makes it possible to run traditional disk-based software in the cloud, but it often suffers from poor performance and poor scalability [[23](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Verbitski2017)].
+As an alternative to local disks, cloud services also offer virtual disk storage that can be detached from one instance and attached to a different one (Amazon EBS, Azure managed disks, and persistent disks in Google Cloud). Such a virtual disk is not actually a physical disk, but rather a cloud service provided by a separate set of machines, which emulates the behavior of a disk (a *block device*, where each block is typically 4 KiB in size). This technology makes it possible to run traditional disk-based software in the cloud, but it often suffers from poor performance and poor scalability [[23](ch01.html#Verbitski2017)].
 
-To address this problem, cloud-native services generally avoid using virtual disks, and instead build on dedicated storage services that are optimized for particular workloads. Object storage services such as S3 are designed for long-term storage of fairly large files, ranging from hundreds of kilobytes to several gigabytes in size. The individual rows or values stored in a database are typically much smaller than this; cloud databases therefore typically manage smaller values in a separate service, and store larger data blocks (containing many individual values) in an object store [[24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Antonopoulos2019_ch1)].
+To address this problem, cloud-native services generally avoid using virtual disks, and instead build on dedicated storage services that are optimized for particular workloads. Object storage services such as S3 are designed for long-term storage of fairly large files, ranging from hundreds of kilobytes to several gigabytes in size. The individual rows or values stored in a database are typically much smaller than this; cloud databases therefore typically manage smaller values in a separate service, and store larger data blocks (containing many individual values) in an object store [[24](ch01.html#Antonopoulos2019_ch1)].
 
-In a traditional systems architecture, the same computer is responsible for both storage (disk) and computation (CPU and RAM), but in cloud-native systems, these two responsibilities have become somewhat separated or *disaggregated* [[8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Prout2022), [25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vuppalapati2020), [26](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Shapira2023), [27](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Murthy2022)]: for example, S3 only stores files, and if you want to analyze that data, you will have to run the analysis code somewhere outside of S3. This implies transferring the data over the network, which we will discuss further in [“Distributed versus Single-Node Systems”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_distributed).
+In a traditional systems architecture, the same computer is responsible for both storage (disk) and computation (CPU and RAM), but in cloud-native systems, these two responsibilities have become somewhat separated or *disaggregated* [[8](ch01.html#Prout2022), [25](ch01.html#Vuppalapati2020), [26](ch01.html#Shapira2023), [27](ch01.html#Murthy2022)]: for example, S3 only stores files, and if you want to analyze that data, you will have to run the analysis code somewhere outside of S3. This implies transferring the data over the network, which we will discuss further in [“Distributed versus Single-Node Systems”](ch01.html#sec_introduction_distributed).
 
-Moreover, cloud-native systems are often *multitenant*, which means that rather than having a separate machine for each customer, data and computation from several different customers are handled on the same shared hardware by the same service [[28](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vanlightly2023)]. Multitenancy can enable better hardware utilization, easier scalability, and easier management by the cloud provider, but it also requires careful engineering to ensure that one customer’s activity does not affect the performance or security of the system for other customers [[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Jonas2019)].
+Moreover, cloud-native systems are often *multitenant*, which means that rather than having a separate machine for each customer, data and computation from several different customers are handled on the same shared hardware by the same service [[28](ch01.html#Vanlightly2023)]. Multitenancy can enable better hardware utilization, easier scalability, and easier management by the cloud provider, but it also requires careful engineering to ensure that one customer’s activity does not affect the performance or security of the system for other customers [[29](ch01.html#Jonas2019)].
 
 
 --------
 
 ### 在云时代的运营
 
-传统上，管理组织服务器端数据基础设施的人被称为*数据库管理员*（DBAs）或*系统管理员*（sysadmins）。近年来，许多组织试图将软件开发和运营的角色整合到一个团队中，共同负责后端服务和数据基础设施；*DevOps*哲学指导了这一趋势。*站点可靠性工程师*（SREs）是谷歌实施这一理念的方式 [[30](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Beyer2016)]。
+传统上，管理组织服务器端数据基础设施的人被称为*数据库管理员*（DBAs）或*系统管理员*（sysadmins）。近年来，许多组织试图将软件开发和运营的角色整合到一个团队中，共同负责后端服务和数据基础设施；*DevOps*哲学指导了这一趋势。*站点可靠性工程师*（SREs）是谷歌实施这一理念的方式 [[30](ch01.html#Beyer2016)]。
 
 运营的角色是确保服务可靠地交付给用户（包括配置基础设施和部署应用程序），并确保稳定的生产环境（包括监控和诊断可能影响可靠性的问题）。对于自托管系统，运营传统上涉及大量单机层面的工作，如容量规划（例如，监控可用磁盘空间并在空间用尽前添加更多磁盘）、配置新机器、将服务从一台机器移至另一台以及安装业务系统补丁。
 
-许多云服务提供了一个API，隐藏了实际实现服务的单个机器。例如，云存储用*计量计费*取代了固定大小的磁盘，您可以在不提前规划容量需求的情况下存储数据，并根据实际使用的空间收费。此外，许多云服务即使单个机器失败也能保持高可用性（见[“可靠性和容错”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_reliability)）。
+许多云服务提供了一个API，隐藏了实际实现服务的单个机器。例如，云存储用*计量计费*取代了固定大小的磁盘，您可以在不提前规划容量需求的情况下存储数据，并根据实际使用的空间收费。此外，许多云服务即使单个机器失败也能保持高可用性（见[“可靠性和容错”](ch02.html#sec_introduction_reliability)）。
 
 从单个机器到服务的这种重点转变伴随着运营角色的变化。提供可靠服务的高级目标仍然相同，但过程和工具已经演变。DevOps/SRE哲学更加强调：
 
@@ -485,22 +480,22 @@ Moreover, cloud-native systems are often *multitenant*, which means that rather
 - 偏好短暂的虚拟机和服务而不是长时间运行的服务器，
 - 促进频繁的应用更新，
 - 从事件中学习，
-- 即使个别人员来去，也要保留组织对系统的知识 [[31](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Limoncelli2020)]。
+- 即使个别人员来去，也要保留组织对系统的知识 [[31](ch01.html#Limoncelli2020)]。
 
-随着云服务的兴起，角色出现了分化：基础设施公司的运营团队专注于向大量客户提供可靠服务的细节，而服务的客户尽可能少地花时间和精力在基础设施上 [[32](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Majors2020)]。
+随着云服务的兴起，角色出现了分化：基础设施公司的运营团队专注于向大量客户提供可靠服务的细节，而服务的客户尽可能少地花时间和精力在基础设施上 [[32](ch01.html#Majors2020)]。
 
-云服务的客户仍然需要运营，但他们关注的方面不同，如选择最适合特定任务的服务、将不同服务相互集成以及从一个服务迁移到另一个服务。尽管计量计费消除了传统意义上的容量规划的需要，但仍然重要的是了解您正在使用哪些资源以及用途，以免在不需要的云资源上浪费金钱：容量规划变成了财务规划，性能优化变成了成本优化 [[33](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Cherkasky2021)]。此外，云服务确实有资源限制或*配额*（如您可以同时运行的最大进程数），您需要了解并计划这些限制，以免遇到问题 [[34](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Kushchi2023)]。
+云服务的客户仍然需要运营，但他们关注的方面不同，如选择最适合特定任务的服务、将不同服务相互集成以及从一个服务迁移到另一个服务。尽管计量计费消除了传统意义上的容量规划的需要，但仍然重要的是了解您正在使用哪些资源以及用途，以免在不需要的云资源上浪费金钱：容量规划变成了财务规划，性能优化变成了成本优化 [[33](ch01.html#Cherkasky2021)]。此外，云服务确实有资源限制或*配额*（如您可以同时运行的最大进程数），您需要了解并计划这些限制，以免遇到问题 [[34](ch01.html#Kushchi2023)]。
 
-采用云服务可能比运行自己的基础设施更容易且更快，尽管即使在这里，学习如何使用它和可能绕过其限制也有成本。随着越来越多的供应商提供针对不同用例的更广泛的云服务，不同服务之间的集成成为特别的挑战 [[35](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Bernhardsson2021), [36](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Stancil2021)]。ETL（见[“数据仓库”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_dwh)）只是故事的一部分；运营云服务也需要相互集成。目前缺乏促进此类集成的标准，因此它通常涉及大量的手动努力。
+采用云服务可能比运行自己的基础设施更容易且更快，尽管即使在这里，学习如何使用它和可能绕过其限制也有成本。随着越来越多的供应商提供针对不同用例的更广泛的云服务，不同服务之间的集成成为特别的挑战 [[35](ch01.html#Bernhardsson2021), [36](ch01.html#Stancil2021)]。ETL（见[“数据仓库”](ch01.html#sec_introduction_dwh)）只是故事的一部分；运营云服务也需要相互集成。目前缺乏促进此类集成的标准，因此它通常涉及大量的手动努力。
 
 其他不能完全外包给云服务的运营方面包括维护应用程序及其使用的库的安全性、管理自己的服务之间的互动、监控服务的负载以及追踪性能下降或中断等问题的原因。虽然云正在改变运营的角色，但运营的需求依旧迫切。
 
 
-Traditionally, the people managing an organization’s server-side data infrastructure were known as *database administrators* (DBAs) or *system administrators* (sysadmins). More recently, many organizations have tried to integrate the roles of software development and operations into teams with a shared responsibility for both backend services and data infrastructure; the *DevOps* philosophy has guided this trend. *Site Reliability Engineers* (SREs) are Google’s implementation of this idea [[30](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Beyer2016)].
+Traditionally, the people managing an organization’s server-side data infrastructure were known as *database administrators* (DBAs) or *system administrators* (sysadmins). More recently, many organizations have tried to integrate the roles of software development and operations into teams with a shared responsibility for both backend services and data infrastructure; the *DevOps* philosophy has guided this trend. *Site Reliability Engineers* (SREs) are Google’s implementation of this idea [[30](ch01.html#Beyer2016)].
 
 The role of operations is to ensure services are reliably delivered to users (including configuring infrastructure and deploying applications), and to ensure a stable production environment (including monitoring and diagnosing any problems that may affect reliability). For self-hosted systems, operations traditionally involves a significant amount of work at the level of individual machines, such as capacity planning (e.g., monitoring available disk space and adding more disks before you run out of space), provisioning new machines, moving services from one machine to another, and installing operating system patches.
 
-Many cloud services present an API that hides the individual machines that actually implement the service. For example, cloud storage replaces fixed-size disks with *metered billing*, where you can store data without planning your capacity needs in advance, and you are then charged based on the space actually used. Moreover, many cloud services remain highly available, even when individual machines have failed (see [“Reliability and Fault Tolerance”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_reliability)).
+Many cloud services present an API that hides the individual machines that actually implement the service. For example, cloud storage replaces fixed-size disks with *metered billing*, where you can store data without planning your capacity needs in advance, and you are then charged based on the space actually used. Moreover, many cloud services remain highly available, even when individual machines have failed (see [“Reliability and Fault Tolerance”](ch02.html#sec_introduction_reliability)).
 
 This shift in emphasis from individual machines to services has been accompanied by a change in the role of operations. The high-level goal of providing a reliable service remains the same, but the processes and tools have evolved. The DevOps/SRE philosophy places greater emphasis on:
 
@@ -508,13 +503,13 @@ This shift in emphasis from individual machines to services has been accompanied
 - preferring ephemeral virtual machines and services over long running servers,
 - enabling frequent application updates,
 - learning from incidents, and
-- preserving the organization’s knowledge about the system, even as individual people come and go [[31](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Limoncelli2020)].
+- preserving the organization’s knowledge about the system, even as individual people come and go [[31](ch01.html#Limoncelli2020)].
 
-With the rise of cloud services, there has been a bifurcation of roles: operations teams at infrastructure companies specialize in the details of providing a reliable service to a large number of customers, while the customers of the service spend as little time and effort as possible on infrastructure [[32](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Majors2020)].
+With the rise of cloud services, there has been a bifurcation of roles: operations teams at infrastructure companies specialize in the details of providing a reliable service to a large number of customers, while the customers of the service spend as little time and effort as possible on infrastructure [[32](ch01.html#Majors2020)].
 
-Customers of cloud services still require operations, but they focus on different aspects, such as choosing the most appropriate service for a given task, integrating different services with each other, and migrating from one service to another. Even though metered billing removes the need for capacity planning in the traditional sense, it’s still important to know what resources you are using for which purpose, so that you don’t waste money on cloud resources that are not needed: capacity planning becomes financial planning, and performance optimization becomes cost optimization [[33](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Cherkasky2021)]. Moreover, cloud services do have resource limits or *quotas* (such as the maximum number of processes you can run concurrently), which you need to know about and plan for before you run into them [[34](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Kushchi2023)].
+Customers of cloud services still require operations, but they focus on different aspects, such as choosing the most appropriate service for a given task, integrating different services with each other, and migrating from one service to another. Even though metered billing removes the need for capacity planning in the traditional sense, it’s still important to know what resources you are using for which purpose, so that you don’t waste money on cloud resources that are not needed: capacity planning becomes financial planning, and performance optimization becomes cost optimization [[33](ch01.html#Cherkasky2021)]. Moreover, cloud services do have resource limits or *quotas* (such as the maximum number of processes you can run concurrently), which you need to know about and plan for before you run into them [[34](ch01.html#Kushchi2023)].
 
-Adopting a cloud service can be easier and quicker than running your own infrastructure, although even here there is a cost in learning how to use it, and perhaps working around its limitations. Integration between different services becomes a particular challenge as a growing number of vendors offers an ever broader range of cloud services targeting different use cases [[35](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Bernhardsson2021), [36](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Stancil2021)]. ETL (see [“Data Warehousing”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_dwh)) is only part of the story; operational cloud services also need to be integrated with each other. At present, there is a lack of standards that would facilitate this sort of integration, so it often involves significant manual effort.
+Adopting a cloud service can be easier and quicker than running your own infrastructure, although even here there is a cost in learning how to use it, and perhaps working around its limitations. Integration between different services becomes a particular challenge as a growing number of vendors offers an ever broader range of cloud services targeting different use cases [[35](ch01.html#Bernhardsson2021), [36](ch01.html#Stancil2021)]. ETL (see [“Data Warehousing”](ch01.html#sec_introduction_dwh)) is only part of the story; operational cloud services also need to be integrated with each other. At present, there is a lack of standards that would facilitate this sort of integration, so it often involves significant manual effort.
 
 Other operational aspects that cannot fully be outsourced to cloud services include maintaining the security of an application and the libraries it uses, managing the interactions between your own services, monitoring the load on your services, and tracking down the cause of problems such as performance degradations or outages. While the cloud is changing the role of operations, the need for operations is as great as ever.
 
@@ -537,15 +532,15 @@ Other operational aspects that cannot fully be outsourced to cloud services incl
 
 - 容错/高可用性
 
-  如果您的应用程序需要在一台机器（或多台机器、网络或整个数据中心）宕机时仍然继续工作，您可以使用多台机器来提供冗余。当一台机器失败时，另一台可以接管。见[“可靠性和容错”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_reliability)。
+  如果您的应用程序需要在一台机器（或多台机器、网络或整个数据中心）宕机时仍然继续工作，您可以使用多台机器来提供冗余。当一台机器失败时，另一台可以接管。见[“可靠性和容错”](ch02.html#sec_introduction_reliability)。
 
 - 可扩展性
 
-  如果您的数据量或计算需求超过单台机器的处理能力，您可以将负载分散到多台机器上。见[“可扩展性”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_scalability)。
+  如果您的数据量或计算需求超过单台机器的处理能力，您可以将负载分散到多台机器上。见[“可扩展性”](ch02.html#sec_introduction_scalability)。
 
 - 延迟
 
-  如果您的用户遍布全球，您可能希望在全球各地设置服务器，以便每个用户都可以从地理位置靠近他们的数据中心获得服务。这避免了用户必须等待网络包绕地球半圈来响应他们的请求。见[“描述性能”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_percentiles)。
+  如果您的用户遍布全球，您可能希望在全球各地设置服务器，以便每个用户都可以从地理位置靠近他们的数据中心获得服务。这避免了用户必须等待网络包绕地球半圈来响应他们的请求。见[“描述性能”](ch02.html#sec_introduction_percentiles)。
 
 - 弹性
 
@@ -557,7 +552,7 @@ Other operational aspects that cannot fully be outsourced to cloud services incl
 
 - 法律合规
 
-  一些国家有数据居留法律，要求在其管辖区内的人的数据必须在该国地理范围内存储和处理 [[37](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Korolov2022)]。这些规则的范围各不相同——例如，在某些情况下，它仅适用于医疗或财务数据，而其他情况则更广泛。因此，一个在几个这样的司法管辖区有用户的服务将不得不将其数据分布在几个位置的服务器上。
+  一些国家有数据居留法律，要求在其管辖区内的人的数据必须在该国地理范围内存储和处理 [[37](ch01.html#Korolov2022)]。这些规则的范围各不相同——例如，在某些情况下，它仅适用于医疗或财务数据，而其他情况则更广泛。因此，一个在几个这样的司法管辖区有用户的服务将不得不将其数据分布在几个位置的服务器上。
 
 这些原因适用于您自己编写的服务（应用程序代码）和由现成软件组成的服务（例如数据库）。
 
@@ -574,15 +569,15 @@ A system that involves several machines communicating via a network is called a
 
 - Fault tolerance/high availability
 
-  If your application needs to continue working even if one machine (or several machines, or the network, or an entire datacenter) goes down, you can use multiple machines to give you redundancy. When one fails, another one can take over. See [“Reliability and Fault Tolerance”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_reliability).
+  If your application needs to continue working even if one machine (or several machines, or the network, or an entire datacenter) goes down, you can use multiple machines to give you redundancy. When one fails, another one can take over. See [“Reliability and Fault Tolerance”](ch02.html#sec_introduction_reliability).
 
 - Scalability
 
-  If your data volume or computing requirements grow bigger than a single machine can handle, you can potentially spread the load across multiple machines. See [“Scalability”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_scalability).
+  If your data volume or computing requirements grow bigger than a single machine can handle, you can potentially spread the load across multiple machines. See [“Scalability”](ch02.html#sec_introduction_scalability).
 
 - Latency
 
-  If you have users around the world, you might want to have servers at various locations worldwide so that each user can be served from a datacenter that is geographically close to them. That avoids the users having to wait for network packets to travel halfway around the world to answer their requests. See [“Describing Performance”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_percentiles).
+  If you have users around the world, you might want to have servers at various locations worldwide so that each user can be served from a datacenter that is geographically close to them. That avoids the users having to wait for network packets to travel halfway around the world to answer their requests. See [“Describing Performance”](ch02.html#sec_introduction_percentiles).
 
 - Elasticity
 
@@ -594,7 +589,7 @@ A system that involves several machines communicating via a network is called a
 
 - Legal compliance
 
-  Some countries have data residency laws that require data about people in their jurisdiction to be stored and processed geographically within that country [[37](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Korolov2022)]. The scope of these rules varies—for example, in some cases it applies only to medical or financial data, while other cases are broader. A service with users in several such jurisdictions will therefore have to distribute their data across servers in several locations.
+  Some countries have data residency laws that require data about people in their jurisdiction to be stored and processed geographically within that country [[37](ch01.html#Korolov2022)]. The scope of these rules varies—for example, in some cases it applies only to medical or financial data, while other cases are broader. A service with users in several such jurisdictions will therefore have to distribute their data across servers in several locations.
 
 These reasons apply both to services that you write yourself (application code) and services consisting of off-the-shelf software (such as databases).
 
@@ -622,7 +617,7 @@ These reasons apply both to services that you write yourself (application code)
 
 分布式系统通常将系统分布在多台机器上，最常见的方式是将它们分为客户端和服务器，并让客户端向服务器发出请求。如我们将在[链接待补充]中讨论的，这种通信最常使用HTTP。同一个过程可能既是服务器（处理传入请求）也是客户端（向其他服务发出传出请求）。
 
-这种构建应用程序的方式传统上被称为*面向服务的架构*（SOA）；最近这个想法被细化为*微服务*架构 [[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Newman2021_ch1), [46](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Richardson2014)]。在这种架构中，每个服务都有一个明确定义的目的（例如，在S3的情况下，这将是文件存储）；每个服务都暴露一个可以通过网络由客户端调用的API，并且每个服务都有一个负责其维护的团队。因此，一个复杂的应用程序可以被分解为多个互动的服务，每个服务由一个单独的团队管理。
+这种构建应用程序的方式传统上被称为*面向服务的架构*（SOA）；最近这个想法被细化为*微服务*架构 [[45](ch01.html#Newman2021_ch1), [46](ch01.html#Richardson2014)]。在这种架构中，每个服务都有一个明确定义的目的（例如，在S3的情况下，这将是文件存储）；每个服务都暴露一个可以通过网络由客户端调用的API，并且每个服务都有一个负责其维护的团队。因此，一个复杂的应用程序可以被分解为多个互动的服务，每个服务由一个单独的团队管理。
 
 将复杂的软件分解为多个服务有几个优点：每个服务都可以独立更新，减少团队间的协调工作；每个服务可以被分配其所需的硬件资源；通过在API后面隐藏实现细节，服务所有者可以自由更改实现，而不影响客户端。在数据存储方面，通常每个服务都有自己的数据库，并且服务之间不共享数据库：共享数据库将有效地使整个数据库结构成为服务API的一部分，然后更改该结构将会很困难。共享的数据库还可能导致一个服务的查询负面影响其他服务的性能。
 
@@ -630,15 +625,15 @@ These reasons apply both to services that you write yourself (application code)
 
 微服务API的演进可能具有挑战性。调用API的客户端希望API具有某些字段。开发人员可能希望根据业务需求的变化添加或删除API中的字段，但这样做可能导致客户端失败。更糟糕的是，这种失败通常直到开发周期后期，当更新的服务API部署到暂存或生产环境时才被发现。API描述标准如OpenAPI和gRPC有助于管理客户端和服务器API之间的关系；我们将在[链接待补充]中进一步讨论这些内容。
 
-微服务主要是对人的问题的技术解决方案：允许不同团队独立进展，无需彼此协调。这在大公司中很有价值，但在小公司中，如果没有许多团队，使用微服务可能是不必要的开销，更倾向于以最简单的方式实现应用程序 [[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Newman2021_ch1)]。
+微服务主要是对人的问题的技术解决方案：允许不同团队独立进展，无需彼此协调。这在大公司中很有价值，但在小公司中，如果没有许多团队，使用微服务可能是不必要的开销，更倾向于以最简单的方式实现应用程序 [[45](ch01.html#Newman2021_ch1)]。
 
-*无服务器*，或*功能即服务*（FaaS），是部署服务的另一种方法，其中基础设施的管理被外包给云供应商 [[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Jonas2019)]。使用虚拟机时，您必须明确选择何时启动或关闭实例；相比之下，在无服务器模型中，云提供商根据对您服务的传入请求，自动分配和释放硬件资源 [[47](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Shahrad2020)]。“无服务器”的术语可能会产生误导：每个无服务器功能执行仍然在服务器上运行，但后续执行可能在不同的服务器上进行。
+*无服务器*，或*功能即服务*（FaaS），是部署服务的另一种方法，其中基础设施的管理被外包给云供应商 [[29](ch01.html#Jonas2019)]。使用虚拟机时，您必须明确选择何时启动或关闭实例；相比之下，在无服务器模型中，云提供商根据对您服务的传入请求，自动分配和释放硬件资源 [[47](ch01.html#Shahrad2020)]。“无服务器”的术语可能会产生误导：每个无服务器功能执行仍然在服务器上运行，但后续执行可能在不同的服务器上进行。
 
 就像云存储用计量计费模式取代了容量规划（提前决定购买多少硬盘）一样，无服务器方法正在将计量计费带到代码执行：您只需为应用程序代码实际运行的时间付费，而不必提前预配资源。
 
 The most common way of distributing a system across multiple machines is to divide them into clients and servers, and let the clients make requests to the servers. Most commonly HTTP is used for this communication, as we will discuss in [Link to Come]. The same process may be both a server (handling incoming requests) and a client (making outbound requests to other services).
 
-This way of building applications has traditionally been called a *service-oriented architecture* (SOA); more recently the idea has been refined into a *microservices* architecture [[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Newman2021_ch1), [46](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Richardson2014)]. In this architecture, a service has one well-defined purpose (for example, in the case of S3, this would be file storage); each service exposes an API that can be called by clients via the network, and each service has one team that is responsible for its maintenance. A complex application can thus be decomposed into multiple interacting services, each managed by a separate team.
+This way of building applications has traditionally been called a *service-oriented architecture* (SOA); more recently the idea has been refined into a *microservices* architecture [[45](ch01.html#Newman2021_ch1), [46](ch01.html#Richardson2014)]. In this architecture, a service has one well-defined purpose (for example, in the case of S3, this would be file storage); each service exposes an API that can be called by clients via the network, and each service has one team that is responsible for its maintenance. A complex application can thus be decomposed into multiple interacting services, each managed by a separate team.
 
 There are several advantages to breaking down a complex piece of software into multiple services: each service can be updated independently, reducing coordination effort among teams; each service can be assigned the hardware resources it needs; and by hiding the implementation details behind an API, the service owners are free to change the implementation without affecting clients. In terms of data storage, it is common for each service to have its own databases, and not to share databases between services: sharing a database would effectively make the entire database structure a part of the service’s API, and then that structure would be difficult to change. Shared databases could also cause one service’s queries to negatively impact the performance of other services.
 
@@ -646,9 +641,9 @@ On the other hand, having many services can itself breed complexity: each servic
 
 Microservice APIs can be challenging to evolve. Clients that call an API expect the API to have certain fields. Developers might wish to add or remove fields to an API as business needs change, but doing so can cause clients to fail. Worse still, such failures are often not discovered until late in the development cycle when the updated service API is deployed to a staging or production environment. API description standards such as OpenAPI and gRPC help manage the relationship between client and server APIs; we discuss these further in [Link to Come].
 
-Microservices are primarily a technical solution to a people problem: allowing different teams to make progress independently without having to coordinate with each other. This is valuable in a large company, but in a small company where there are not many teams, using microservices is likely to be unnecessary overhead, and it is preferable to implement the application in the simplest way possible [[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Newman2021_ch1)].
+Microservices are primarily a technical solution to a people problem: allowing different teams to make progress independently without having to coordinate with each other. This is valuable in a large company, but in a small company where there are not many teams, using microservices is likely to be unnecessary overhead, and it is preferable to implement the application in the simplest way possible [[45](ch01.html#Newman2021_ch1)].
 
-*Serverless*, or *function-as-a-service* (FaaS), is another approach to deploying services, in which the management of the infrastructure is outsourced to a cloud vendor [[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Jonas2019)]. When using virtual machines, you have to explicitly choose when to start up or shut down an instance; in contrast, with the serverless model, the cloud provider automatically allocates and frees hardware resources as needed, based on the incoming requests to your service [[47](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Shahrad2020)]. The term “serverless” can misleading: each serverless function execution still runs on a server, but subsequent executions might run on a different one.
+*Serverless*, or *function-as-a-service* (FaaS), is another approach to deploying services, in which the management of the infrastructure is outsourced to a cloud vendor [[29](ch01.html#Jonas2019)]. When using virtual machines, you have to explicitly choose when to start up or shut down an instance; in contrast, with the serverless model, the cloud provider automatically allocates and frees hardware resources as needed, based on the incoming requests to your service [[47](ch01.html#Shahrad2020)]. The term “serverless” can misleading: each serverless function execution still runs on a server, but subsequent executions might run on a different one.
 
 Just like cloud storage replaced capacity planning (deciding in advance how many disks to buy) with a metered billing model, the serverless approach is bringing metered billing to code execution: you only pay for the time that your application code is actually running, rather than having to provision resources in advance.
 
@@ -660,24 +655,24 @@ Just like cloud storage replaced capacity planning (deciding in advance how many
 云计算并非构建大规模计算系统的唯一方式；另一种选择是*高性能计算*（HPC），也称为*超级计算*。虽然有一些重叠，但HPC通常有不同的优先级并采用与云计算和企业数据中心系统不同的技术。其中一些差异包括：
 
 - 超级计算机通常用于计算密集型的科学计算任务，如天气预报、分子动力学（模拟原子和分子的运动）、复杂的优化问题和求解偏微分方程。另一方面，云计算倾向于用于在线服务、商业数据系统和需要高可用性服务用户请求的类似系统。
-- 超级计算机通常运行大型批处理作业，这些作业会不时地将计算状态检查点保存到磁盘。如果节点失败，一个常见的解决方案是简单地停止整个集群工作，修复故障节点，然后从最后一个检查点重新开始计算 [[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Barroso2018), [49](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Fiala2012)]。在云服务中，通常不希望停止整个集群，因为服务需要持续地以最小的中断服务于用户。
-- 超级计算机通常由专用硬件构建，每个节点都相当可靠。云服务中的节点通常由商品机构建，这些商品机由于规模经济可以以较低成本提供等效性能，但也具有更高的故障率（见[“硬件和软件故障”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_hardware_faults)）。
-- 超级计算机节点通常通过共享内存和远程直接内存访问（RDMA）进行通信，这支持高带宽和低延迟，但假设系统用户之间有高度的信任 [[50](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#KornfeldSimpson2020)]。在云计算中，网络和机器经常由互不信任的组织共享，需要更强的安全机制，如资源隔离（例如，虚拟机）、加密和认证。
-- 云数据中心网络通常基于IP和以太网，按Clos拓扑排列，以提供高切面带宽——这是衡量网络整体性能的常用指标 [[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Barroso2018), [51](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Singh2015)]。超级计算机通常使用专用的网络拓扑，如多维网格和环面 [[52](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Lockwood2014)]，这为具有已知通信模式的HPC工作负载提供了更好的性能。
+- 超级计算机通常运行大型批处理作业，这些作业会不时地将计算状态检查点保存到磁盘。如果节点失败，一个常见的解决方案是简单地停止整个集群工作，修复故障节点，然后从最后一个检查点重新开始计算 [[48](ch01.html#Barroso2018), [49](ch01.html#Fiala2012)]。在云服务中，通常不希望停止整个集群，因为服务需要持续地以最小的中断服务于用户。
+- 超级计算机通常由专用硬件构建，每个节点都相当可靠。云服务中的节点通常由商品机构建，这些商品机由于规模经济可以以较低成本提供等效性能，但也具有更高的故障率（见[“硬件和软件故障”](ch02.html#sec_introduction_hardware_faults)）。
+- 超级计算机节点通常通过共享内存和远程直接内存访问（RDMA）进行通信，这支持高带宽和低延迟，但假设系统用户之间有高度的信任 [[50](ch01.html#KornfeldSimpson2020)]。在云计算中，网络和机器经常由互不信任的组织共享，需要更强的安全机制，如资源隔离（例如，虚拟机）、加密和认证。
+- 云数据中心网络通常基于IP和以太网，按Clos拓扑排列，以提供高切面带宽——这是衡量网络整体性能的常用指标 [[48](ch01.html#Barroso2018), [51](ch01.html#Singh2015)]。超级计算机通常使用专用的网络拓扑，如多维网格和环面 [[52](ch01.html#Lockwood2014)]，这为具有已知通信模式的HPC工作负载提供了更好的性能。
 - 云计算允许节点分布在多个地理位置，而超级计算机通常假设其所有节点都靠近在一起。
 
-大规模分析系统有时与超级计算共享一些特征，这就是为什么如果您在这一领域工作，了解这些技术可能是值得的。然而，本书主要关注需要持续可用的服务，如[“可靠性和容错”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_reliability)中所讨论的。
+大规模分析系统有时与超级计算共享一些特征，这就是为什么如果您在这一领域工作，了解这些技术可能是值得的。然而，本书主要关注需要持续可用的服务，如[“可靠性和容错”](ch02.html#sec_introduction_reliability)中所讨论的。
 
 Cloud computing is not the only way of building large-scale computing systems; an alternative is *high-performance computing* (HPC), also known as *supercomputing*. Although there are overlaps, HPC often has different priorities and uses different techniques compared to cloud computing and enterprise datacenter systems. Some of those differences are:
 
 - Supercomputers are typically used for computationally intensive scientific computing tasks, such as weather forecasting, molecular dynamics (simulating the movement of atoms and molecules), complex optimization problems, and solving partial differential equations. On the other hand, cloud computing tends to be used for online services, business data systems, and similar systems that need to serve user requests with high availability.
-- A supercomputer typically runs large batch jobs that checkpoint the state of their computation to disk from time to time. If a node fails, a common solution is to simply stop the entire cluster workload, repair the faulty node, and then restart the computation from the last checkpoint [[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Barroso2018), [49](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Fiala2012)]. With cloud services, it is usually not desirable to stop the entire cluster, since the services need to continually serve users with minimal interruptions.
-- Supercomputers are typically built from specialized hardware, where each node is quite reliable. Nodes in cloud services are usually built from commodity machines, which can provide equivalent performance at lower cost due to economies of scale, but which also have higher failure rates (see [“Hardware and Software Faults”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_hardware_faults)).
-- Supercomputer nodes typically communicate through shared memory and remote direct memory access (RDMA), which support high bandwidth and low latency, but assume a high level of trust among the users of the system [[50](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#KornfeldSimpson2020)]. In cloud computing, the network and the machines are often shared by mutually untrusting organizations, requiring stronger security mechanisms such as resource isolation (e.g., virtual machines), encryption and authentication.
-- Cloud datacenter networks are often based on IP and Ethernet, arranged in Clos topologies to provide high bisection bandwidth—a commonly used measure of a network’s overall performance [[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Barroso2018), [51](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Singh2015)]. Supercomputers often use specialized network topologies, such as multi-dimensional meshes and toruses [[52](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Lockwood2014)], which yield better performance for HPC workloads with known communication patterns.
+- A supercomputer typically runs large batch jobs that checkpoint the state of their computation to disk from time to time. If a node fails, a common solution is to simply stop the entire cluster workload, repair the faulty node, and then restart the computation from the last checkpoint [[48](ch01.html#Barroso2018), [49](ch01.html#Fiala2012)]. With cloud services, it is usually not desirable to stop the entire cluster, since the services need to continually serve users with minimal interruptions.
+- Supercomputers are typically built from specialized hardware, where each node is quite reliable. Nodes in cloud services are usually built from commodity machines, which can provide equivalent performance at lower cost due to economies of scale, but which also have higher failure rates (see [“Hardware and Software Faults”](ch02.html#sec_introduction_hardware_faults)).
+- Supercomputer nodes typically communicate through shared memory and remote direct memory access (RDMA), which support high bandwidth and low latency, but assume a high level of trust among the users of the system [[50](ch01.html#KornfeldSimpson2020)]. In cloud computing, the network and the machines are often shared by mutually untrusting organizations, requiring stronger security mechanisms such as resource isolation (e.g., virtual machines), encryption and authentication.
+- Cloud datacenter networks are often based on IP and Ethernet, arranged in Clos topologies to provide high bisection bandwidth—a commonly used measure of a network’s overall performance [[48](ch01.html#Barroso2018), [51](ch01.html#Singh2015)]. Supercomputers often use specialized network topologies, such as multi-dimensional meshes and toruses [[52](ch01.html#Lockwood2014)], which yield better performance for HPC workloads with known communication patterns.
 - Cloud computing allows nodes to be distributed across multiple geographic locations, whereas supercomputers generally assume that all of their nodes are close together.
 
-Large-scale analytics systems sometimes share some characteristics with supercomputing, which is why it can be worth knowing about these techniques if you are working in this area. However, this book is mostly concerned with services that need to be continually available, as discussed in [“Reliability and Fault Tolerance”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_reliability).
+Large-scale analytics systems sometimes share some characteristics with supercomputing, which is why it can be worth knowing about these techniques if you are working in this area. However, this book is mostly concerned with services that need to be continually available, as discussed in [“Reliability and Fault Tolerance”](ch02.html#sec_introduction_reliability).
 
 
 
@@ -690,11 +685,11 @@ Large-scale analytics systems sometimes share some characteristics with supercom
 
 特别需要关注的是存储关于人们及其行为的数据的系统。自2018年以来，*通用数据保护条例*（GDPR）为许多欧洲国家的居民提供了更大的控制权和法律权利，用以管理他们的个人数据，类似的隐私法规也在世界各地的不同国家和地区得到采纳，例如加利福尼亚消费者隐私法案（CCPA）。围绕人工智能的法规，如*欧盟人工智能法案*，对个人数据的使用施加了进一步的限制。
 
-此外，即使在不直接受法规约束的领域，也越来越多地认识到计算机系统对人和社会的影响。社交媒体改变了个人获取新闻的方式，这影响了他们的政治观点，从而可能影响选举结果。自动化系统越来越多地做出对个人有深远影响的决定，例如决定谁应获得贷款或保险，谁应被邀请参加工作面试，或者谁应被怀疑犯有罪行 [[53](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#ONeil2016_ch1)]。
+此外，即使在不直接受法规约束的领域，也越来越多地认识到计算机系统对人和社会的影响。社交媒体改变了个人获取新闻的方式，这影响了他们的政治观点，从而可能影响选举结果。自动化系统越来越多地做出对个人有深远影响的决定，例如决定谁应获得贷款或保险，谁应被邀请参加工作面试，或者谁应被怀疑犯有罪行 [[53](ch01.html#ONeil2016_ch1)]。
 
 从事这些系统的每个人都负有考虑其伦理影响并确保遵守相关法律的责任。并不是每个人都必须成为法律和伦理的专家，但基本的法律和伦理原则意识与分布式系统的一些基础知识同样重要。
 
-法律考量正在影响数据系统设计的基础 [[54](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Shastri2020)]。例如，GDPR授予个人在请求时删除其数据的权利（有时称为*被遗忘权*）。然而，正如我们在本书中将看到的，许多数据系统依赖于不可变构造，如作为设计一部分的仅追加日志；我们如何确保在一个本应不可变的文件中删除某些数据？我们如何处理已并入派生数据集的数据的删除问题（见[“记录系统与派生数据”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_derived)），如机器学习模型的训练数据？回答这些问题创造了新的工程挑战。
+法律考量正在影响数据系统设计的基础 [[54](ch01.html#Shastri2020)]。例如，GDPR授予个人在请求时删除其数据的权利（有时称为*被遗忘权*）。然而，正如我们在本书中将看到的，许多数据系统依赖于不可变构造，如作为设计一部分的仅追加日志；我们如何确保在一个本应不可变的文件中删除某些数据？我们如何处理已并入派生数据集的数据的删除问题（见[“记录系统与派生数据”](ch01.html#sec_introduction_derived)），如机器学习模型的训练数据？回答这些问题创造了新的工程挑战。
 
 目前我们没有明确的指南来判断哪些特定技术或系统架构应被视为“符合GDPR”的。法规故意没有规定特定的技术，因为这些可能随着技术的进步而迅速变化。相反，法律文本提出了需要解释的高级原则。这意味着关于如何遵守隐私法规的问题没有简单的答案，但我们将通过这个视角审视本书中的一些技术。
 
@@ -702,7 +697,7 @@ Large-scale analytics systems sometimes share some characteristics with supercom
 
 政府或警察部门也可能强制公司交出数据。当存在数据可能揭示被刑事化行为的风险时（例如，在几个中东和非洲国家的同性恋行为，或在几个美国州寻求堕胎），存储该数据为用户创造了真正的安全风险。例如，通过位置数据很容易揭露到堕胎诊所的旅行，甚至可能通过一段时间内用户 IP 地址的日志（表明大致位置）揭露。
 
-一旦考虑到所有风险，可能会合理地决定某些数据根本不值得存储，因此应该将其删除。*数据最小化*原则（有时称为德语术语*Datensparsamkeit*）与存储大量数据的“大数据”哲学相悖，以防它在未来证明有用 [[55](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Datensparsamkeit)]。但这与 GDPR 相符，后者规定只能为特定的、明确的目的收集个人数据，这些数据以后不能用于任何其他目的，且为了收集目的，保存的数据不得超过必要的时间 [[56](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#GDPR)]。
+一旦考虑到所有风险，可能会合理地决定某些数据根本不值得存储，因此应该将其删除。*数据最小化*原则（有时称为德语术语*Datensparsamkeit*）与存储大量数据的“大数据”哲学相悖，以防它在未来证明有用 [[55](ch01.html#Datensparsamkeit)]。但这与 GDPR 相符，后者规定只能为特定的、明确的目的收集个人数据，这些数据以后不能用于任何其他目的，且为了收集目的，保存的数据不得超过必要的时间 [[56](ch01.html#GDPR)]。
 
 企业也注意到了隐私和安全问题。信用卡公司要求支付处理业务遵守严格的支付卡行业（PCI）标准。处理者经常接受独立审计师的评估，以验证持续合规。软件供应商也看到了增加的审查。现在许多买家要求其供应商符合服务组织控制（SOC）类型 2 标准。与 PCI 合规一样，供应商接受第三方审计以验证遵守情况。
 
@@ -713,11 +708,11 @@ So far you’ve seen in this chapter that the architecture of data systems is in
 
 One particular concern are systems that store data about people and their behavior. Since 2018 the *General Data Protection Regulation* (GDPR) has given residents of many European countries greater control and legal rights over their personal data, and similar privacy regulation has been adopted in various other countries and states around the world, including for example the California Consumer Privacy Act (CCPA). Regulations around AI, such as the *EU AI Act*, place further restrictions on how personal data can be used.
 
-Moreover, even in areas that are not directly subject to regulation, there is increasing recognition of the effects that computer systems have on people and society. Social media has changed how individuals consume news, which influences their political opinions and hence may affect the outcome of elections. Automated systems increasingly make decisions that have profound consequences for individuals, such as deciding who should be given a loan or insurance coverage, who should be invited to a job interview, or who should be suspected of a crime [[53](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#ONeil2016_ch1)].
+Moreover, even in areas that are not directly subject to regulation, there is increasing recognition of the effects that computer systems have on people and society. Social media has changed how individuals consume news, which influences their political opinions and hence may affect the outcome of elections. Automated systems increasingly make decisions that have profound consequences for individuals, such as deciding who should be given a loan or insurance coverage, who should be invited to a job interview, or who should be suspected of a crime [[53](ch01.html#ONeil2016_ch1)].
 
 Everyone who works on such systems shares a responsibility for considering the ethical impact and ensuring that they comply with relevant law. It is not necessary for everybody to become an expert in law and ethics, but a basic awareness of legal and ethical principles is just as important as, say, some foundational knowledge in distributed systems.
 
-Legal considerations are influencing the very foundations of how data systems are being designed [[54](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Shastri2020)]. For example, the GDPR grants individuals the right to have their data erased on request (sometimes known as the *right to be forgotten*). However, as we shall see in this book, many data systems rely on immutable constructs such as append-only logs as part of their design; how can we ensure deletion of some data in the middle of a file that is supposed to be immutable? How do we handle deletion of data that has been incorporated into derived datasets (see [“Systems of Record and Derived Data”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_derived)), such as training data for machine learning models? Answering these questions creates new engineering challenges.
+Legal considerations are influencing the very foundations of how data systems are being designed [[54](ch01.html#Shastri2020)]. For example, the GDPR grants individuals the right to have their data erased on request (sometimes known as the *right to be forgotten*). However, as we shall see in this book, many data systems rely on immutable constructs such as append-only logs as part of their design; how can we ensure deletion of some data in the middle of a file that is supposed to be immutable? How do we handle deletion of data that has been incorporated into derived datasets (see [“Systems of Record and Derived Data”](ch01.html#sec_introduction_derived)), such as training data for machine learning models? Answering these questions creates new engineering challenges.
 
 At present we don’t have clear guidelines on which particular technologies or system architectures should be considered “GDPR-compliant” or not. The regulation deliberately does not mandate particular technologies, because these may quickly change as technology progresses. Instead, the legal texts set out high-level principles that are subject to interpretation. This means that there are no simple answers to the question of how to comply with privacy regulation, but we will look at some of the technologies in this book through this lens.
 
@@ -725,7 +720,7 @@ In general, we store data because we think that its value is greater than the co
 
 Governments or police forces might also compel companies to hand over data. When there is a risk that the data may reveal criminalized behaviors (for example, homosexuality in several Middle Eastern and African countries, or seeking an abortion in several US states), storing that data creates real safety risks for users. Travel to an abortion clinic, for example, could easily be revealed by location data, perhaps even by a log of the user’s IP addresses over time (which indicate approximate location).
 
-Once all the risks are taken into account, it might be reasonable to decide that some data is simply not worth storing, and that it should therefore be deleted. This principle of *data minimization* (sometimes known by the German term *Datensparsamkeit*) runs counter to the “big data” philosophy of storing lots of data speculatively in case it turns out to be useful in the future [[55](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Datensparsamkeit)]. But it fits with the GDPR, which mandates that personal data many only be collected for a specified, explicit purpose, that this data may not later be used for any other purpose, and that the data must not be kept for longer than necessary for the purposes for which it was collected [[56](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#GDPR)].
+Once all the risks are taken into account, it might be reasonable to decide that some data is simply not worth storing, and that it should therefore be deleted. This principle of *data minimization* (sometimes known by the German term *Datensparsamkeit*) runs counter to the “big data” philosophy of storing lots of data speculatively in case it turns out to be useful in the future [[55](ch01.html#Datensparsamkeit)]. But it fits with the GDPR, which mandates that personal data many only be collected for a specified, explicit purpose, that this data may not later be used for any other purpose, and that the data must not be kept for longer than necessary for the purposes for which it was collected [[56](ch01.html#GDPR)].
 
 Businesses have also taken notice of privacy and safety concerns. Credit card companies require payment processing businesses to adhere to strict payment card industry (PCI) standards. Processors undergo frequent evaluations from independent auditors to verify continued compliance. Software vendors have also seen increased scrutiny. Many buyers now require their vendors to comply with Service Organization Control (SOC) Type 2 standards. As with PCI compliance, vendors undergo third party audits to verify adherence.
 
@@ -762,114 +757,114 @@ Finally, we saw that data systems architecture is determined not only by the nee
 
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-[[24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Antonopoulos2019_ch1-marker)] Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, Vijendra Purohit, Hugh Qu, Chaitanya Sreenivas Ravella, Krystyna Reisteter, Sheetal Shrotri, Dixin Tang, and Vikram Wakade. [Socrates: The New SQL Server in the Cloud](https://www.microsoft.com/en-us/research/uploads/prod/2019/05/socrates.pdf). At *ACM International Conference on Management of Data* (SIGMOD), pages 1743–1756, June 2019. [doi:10.1145/3299869.3314047](https://doi.org/10.1145/3299869.3314047)
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-[[25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Vuppalapati2020-marker)] Midhul Vuppalapati, Justin Miron, Rachit Agarwal, Dan Truong, Ashish Motivala, and Thierry Cruanes. [Building An Elastic Query Engine on Disaggregated Storage](https://www.usenix.org/system/files/nsdi20-paper-vuppalapati.pdf). At *17th USENIX Symposium on Networked Systems Design and Implementation* (NSDI), February 2020.
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-[[27](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Murthy2022-marker)] Ravi Murthy and Gurmeet Goindi. [AlloyDB for PostgreSQL under the hood: Intelligent, database-aware storage](https://cloud.google.com/blog/products/databases/alloydb-for-postgresql-intelligent-scalable-storage). *cloud.google.com*, May 2022. Archived at [archive.org](https://web.archive.org/web/20220514021120/https://cloud.google.com/blog/products/databases/alloydb-for-postgresql-intelligent-scalable-storage)
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-[[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Jonas2019-marker)] Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, Joseph E Gonzalez, Raluca Ada Popa, Ion Stoica, David A Patterson. [Cloud Programming Simplified: A Berkeley View on Serverless Computing](https://arxiv.org/abs/1902.03383). *arxiv.org*, February 2019.
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-[[36](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Stancil2021-marker)] Benn Stancil. [The data OS](https://benn.substack.com/p/the-data-os). *benn.substack.com*, September 2021. Archived at [perma.cc/WQ43-FHS6](https://perma.cc/WQ43-FHS6)
+[[36](ch01.html#Stancil2021-marker)] Benn Stancil. [The data OS](https://benn.substack.com/p/the-data-os). *benn.substack.com*, September 2021. Archived at [perma.cc/WQ43-FHS6](https://perma.cc/WQ43-FHS6)
 
-[[37](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Korolov2022-marker)] Maria Korolov. [Data residency laws pushing companies toward residency as a service](https://www.csoonline.com/article/3647761/data-residency-laws-pushing-companies-toward-residency-as-a-service.html). *csoonline.com*, January 2022. Archived at [perma.cc/CHE4-XZZ2](https://perma.cc/CHE4-XZZ2)
+[[37](ch01.html#Korolov2022-marker)] Maria Korolov. [Data residency laws pushing companies toward residency as a service](https://www.csoonline.com/article/3647761/data-residency-laws-pushing-companies-toward-residency-as-a-service.html). *csoonline.com*, January 2022. Archived at [perma.cc/CHE4-XZZ2](https://perma.cc/CHE4-XZZ2)
 
-[[38](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Nath2019-marker)] Kousik Nath. [These are the numbers every computer engineer should know](https://www.freecodecamp.org/news/must-know-numbers-for-every-computer-engineer/). *freecodecamp.org*, September 2019. Archived at [perma.cc/RW73-36RL](https://perma.cc/RW73-36RL)
+[[38](ch01.html#Nath2019-marker)] Kousik Nath. [These are the numbers every computer engineer should know](https://www.freecodecamp.org/news/must-know-numbers-for-every-computer-engineer/). *freecodecamp.org*, September 2019. Archived at [perma.cc/RW73-36RL](https://perma.cc/RW73-36RL)
 
-[[39](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Hellerstein2019-marker)] Joseph M Hellerstein, Jose Faleiro, Joseph E Gonzalez, Johann Schleier-Smith, Vikram Sreekanti, Alexey Tumanov, and Chenggang Wu. [Serverless Computing: One Step Forward, Two Steps Back](https://arxiv.org/abs/1812.03651). At *Conference on Innovative Data Systems Research* (CIDR), January 2019.
+[[39](ch01.html#Hellerstein2019-marker)] Joseph M Hellerstein, Jose Faleiro, Joseph E Gonzalez, Johann Schleier-Smith, Vikram Sreekanti, Alexey Tumanov, and Chenggang Wu. [Serverless Computing: One Step Forward, Two Steps Back](https://arxiv.org/abs/1812.03651). At *Conference on Innovative Data Systems Research* (CIDR), January 2019.
 
-[[40](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#McSherry2015_ch1-marker)] Frank McSherry, Michael Isard, and Derek G. Murray. [Scalability! But at What COST?](https://www.usenix.org/system/files/conference/hotos15/hotos15-paper-mcsherry.pdf) At *15th USENIX Workshop on Hot Topics in Operating Systems* (HotOS), May 2015.
+[[40](ch01.html#McSherry2015_ch1-marker)] Frank McSherry, Michael Isard, and Derek G. Murray. [Scalability! But at What COST?](https://www.usenix.org/system/files/conference/hotos15/hotos15-paper-mcsherry.pdf) At *15th USENIX Workshop on Hot Topics in Operating Systems* (HotOS), May 2015.
 
-[[41](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Sridharan2018-marker)] Cindy Sridharan. *[Distributed Systems Observability: A Guide to Building Robust Systems](https://unlimited.humio.com/rs/756-LMY-106/images/Distributed-Systems-Observability-eBook.pdf)*. Report, O’Reilly Media, May 2018. Archived at [perma.cc/M6JL-XKCM](https://perma.cc/M6JL-XKCM)
+[[41](ch01.html#Sridharan2018-marker)] Cindy Sridharan. *[Distributed Systems Observability: A Guide to Building Robust Systems](https://unlimited.humio.com/rs/756-LMY-106/images/Distributed-Systems-Observability-eBook.pdf)*. Report, O’Reilly Media, May 2018. Archived at [perma.cc/M6JL-XKCM](https://perma.cc/M6JL-XKCM)
 
-[[42](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Majors2019-marker)] Charity Majors. [Observability — A 3-Year Retrospective](https://thenewstack.io/observability-a-3-year-retrospective/). *thenewstack.io*, August 2019. Archived at [perma.cc/CG62-TJWL](https://perma.cc/CG62-TJWL)
+[[42](ch01.html#Majors2019-marker)] Charity Majors. [Observability — A 3-Year Retrospective](https://thenewstack.io/observability-a-3-year-retrospective/). *thenewstack.io*, August 2019. Archived at [perma.cc/CG62-TJWL](https://perma.cc/CG62-TJWL)
 
-[[43](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Sigelman2010-marker)] Benjamin H. Sigelman, Luiz André Barroso, Mike Burrows, Pat Stephenson, Manoj Plakal, Donald Beaver, Saul Jaspan, and Chandan Shanbhag. [Dapper, a Large-Scale Distributed Systems Tracing Infrastructure](https://research.google/pubs/pub36356/). Google Technical Report dapper-2010-1, April 2010. Archived at [perma.cc/K7KU-2TMH](https://perma.cc/K7KU-2TMH)
+[[43](ch01.html#Sigelman2010-marker)] Benjamin H. Sigelman, Luiz André Barroso, Mike Burrows, Pat Stephenson, Manoj Plakal, Donald Beaver, Saul Jaspan, and Chandan Shanbhag. [Dapper, a Large-Scale Distributed Systems Tracing Infrastructure](https://research.google/pubs/pub36356/). Google Technical Report dapper-2010-1, April 2010. Archived at [perma.cc/K7KU-2TMH](https://perma.cc/K7KU-2TMH)
 
-[[44](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Laigner2021-marker)] Rodrigo Laigner, Yongluan Zhou, Marcos Antonio Vaz Salles, Yijian Liu, and Marcos Kalinowski. [Data management in microservices: State of the practice, challenges, and research directions](http://www.vldb.org/pvldb/vol14/p3348-laigner.pdf). *Proceedings of the VLDB Endowment*, volume 14, issue 13, pages 3348–3361, September 2021. [doi:10.14778/3484224.3484232](https://doi.org/10.14778/3484224.3484232)
+[[44](ch01.html#Laigner2021-marker)] Rodrigo Laigner, Yongluan Zhou, Marcos Antonio Vaz Salles, Yijian Liu, and Marcos Kalinowski. [Data management in microservices: State of the practice, challenges, and research directions](http://www.vldb.org/pvldb/vol14/p3348-laigner.pdf). *Proceedings of the VLDB Endowment*, volume 14, issue 13, pages 3348–3361, September 2021. [doi:10.14778/3484224.3484232](https://doi.org/10.14778/3484224.3484232)
 
-[[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Newman2021_ch1-marker)] Sam Newman. [*Building Microservices*, second edition](https://www.oreilly.com/library/view/building-microservices-2nd/9781492034018/). O’Reilly Media, 2021. ISBN: 9781492034025
+[[45](ch01.html#Newman2021_ch1-marker)] Sam Newman. [*Building Microservices*, second edition](https://www.oreilly.com/library/view/building-microservices-2nd/9781492034018/). O’Reilly Media, 2021. ISBN: 9781492034025
 
-[[46](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Richardson2014-marker)] Chris Richardson. [Microservices: Decomposing Applications for Deployability and Scalability](http://www.infoq.com/articles/microservices-intro). *infoq.com*, May 2014. Archived at [perma.cc/CKN4-YEQ2](https://perma.cc/CKN4-YEQ2)
+[[46](ch01.html#Richardson2014-marker)] Chris Richardson. [Microservices: Decomposing Applications for Deployability and Scalability](http://www.infoq.com/articles/microservices-intro). *infoq.com*, May 2014. Archived at [perma.cc/CKN4-YEQ2](https://perma.cc/CKN4-YEQ2)
 
-[[47](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Shahrad2020-marker)] Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, Ricardo Bianchini. [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://www.usenix.org/system/files/atc20-shahrad.pdf). At *USENIX Annual Technical Conference* (ATC), July 2020.
+[[47](ch01.html#Shahrad2020-marker)] Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, Ricardo Bianchini. [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://www.usenix.org/system/files/atc20-shahrad.pdf). At *USENIX Annual Technical Conference* (ATC), July 2020.
 
-[[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Barroso2018-marker)] Luiz André Barroso, Urs Hölzle, and Parthasarathy Ranganathan. [The Datacenter as a Computer: Designing Warehouse-Scale Machines](https://www.morganclaypool.com/doi/10.2200/S00874ED3V01Y201809CAC046), third edition. Morgan & Claypool Synthesis Lectures on Computer Architecture, October 2018. [doi:10.2200/S00874ED3V01Y201809CAC046](https://doi.org/10.2200/S00874ED3V01Y201809CAC046)
+[[48](ch01.html#Barroso2018-marker)] Luiz André Barroso, Urs Hölzle, and Parthasarathy Ranganathan. [The Datacenter as a Computer: Designing Warehouse-Scale Machines](https://www.morganclaypool.com/doi/10.2200/S00874ED3V01Y201809CAC046), third edition. Morgan & Claypool Synthesis Lectures on Computer Architecture, October 2018. [doi:10.2200/S00874ED3V01Y201809CAC046](https://doi.org/10.2200/S00874ED3V01Y201809CAC046)
 
-[[49](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Fiala2012-marker)] David Fiala, Frank Mueller, Christian Engelmann, Rolf Riesen, Kurt Ferreira, and Ron Brightwell. [Detection and Correction of Silent Data Corruption for Large-Scale High-Performance Computing](http://moss.csc.ncsu.edu/~mueller/ftp/pub/mueller/papers/sc12.pdf),” at *International Conference for High Performance Computing, Networking, Storage and Analysis* (SC), November 2012. [doi:10.1109/SC.2012.49](https://doi.org/10.1109/SC.2012.49)
+[[49](ch01.html#Fiala2012-marker)] David Fiala, Frank Mueller, Christian Engelmann, Rolf Riesen, Kurt Ferreira, and Ron Brightwell. [Detection and Correction of Silent Data Corruption for Large-Scale High-Performance Computing](http://moss.csc.ncsu.edu/~mueller/ftp/pub/mueller/papers/sc12.pdf),” at *International Conference for High Performance Computing, Networking, Storage and Analysis* (SC), November 2012. [doi:10.1109/SC.2012.49](https://doi.org/10.1109/SC.2012.49)
 
-[[50](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#KornfeldSimpson2020-marker)] Anna Kornfeld Simpson, Adriana Szekeres, Jacob Nelson, and Irene Zhang. [Securing RDMA for High-Performance Datacenter Storage Systems](https://www.usenix.org/conference/hotcloud20/presentation/kornfeld-simpson). At *12th USENIX Workshop on Hot Topics in Cloud Computing* (HotCloud), July 2020.
+[[50](ch01.html#KornfeldSimpson2020-marker)] Anna Kornfeld Simpson, Adriana Szekeres, Jacob Nelson, and Irene Zhang. [Securing RDMA for High-Performance Datacenter Storage Systems](https://www.usenix.org/conference/hotcloud20/presentation/kornfeld-simpson). At *12th USENIX Workshop on Hot Topics in Cloud Computing* (HotCloud), July 2020.
 
-[[51](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Singh2015-marker)] Arjun Singh, Joon Ong, Amit Agarwal, Glen Anderson, Ashby Armistead, Roy Bannon, Seb Boving, Gaurav Desai, Bob Felderman, Paulie Germano, Anand Kanagala, Jeff Provost, Jason Simmons, Eiichi Tanda, Jim Wanderer, Urs Hölzle, Stephen Stuart, and Amin Vahdat. [Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network](http://conferences.sigcomm.org/sigcomm/2015/pdf/papers/p183.pdf). At *Annual Conference of the ACM Special Interest Group on Data Communication* (SIGCOMM), August 2015. [doi:10.1145/2785956.2787508](https://doi.org/10.1145/2785956.2787508)
+[[51](ch01.html#Singh2015-marker)] Arjun Singh, Joon Ong, Amit Agarwal, Glen Anderson, Ashby Armistead, Roy Bannon, Seb Boving, Gaurav Desai, Bob Felderman, Paulie Germano, Anand Kanagala, Jeff Provost, Jason Simmons, Eiichi Tanda, Jim Wanderer, Urs Hölzle, Stephen Stuart, and Amin Vahdat. [Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network](http://conferences.sigcomm.org/sigcomm/2015/pdf/papers/p183.pdf). At *Annual Conference of the ACM Special Interest Group on Data Communication* (SIGCOMM), August 2015. [doi:10.1145/2785956.2787508](https://doi.org/10.1145/2785956.2787508)
 
-[[52](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Lockwood2014-marker)] Glenn K. Lockwood. [Hadoop’s Uncomfortable Fit in HPC](http://glennklockwood.blogspot.co.uk/2014/05/hadoops-uncomfortable-fit-in-hpc.html). *glennklockwood.blogspot.co.uk*, May 2014. Archived at [perma.cc/S8XX-Y67B](https://perma.cc/S8XX-Y67B)
+[[52](ch01.html#Lockwood2014-marker)] Glenn K. Lockwood. [Hadoop’s Uncomfortable Fit in HPC](http://glennklockwood.blogspot.co.uk/2014/05/hadoops-uncomfortable-fit-in-hpc.html). *glennklockwood.blogspot.co.uk*, May 2014. Archived at [perma.cc/S8XX-Y67B](https://perma.cc/S8XX-Y67B)
 
-[[53](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#ONeil2016_ch1-marker)] Cathy O’Neil: *Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy*. Crown Publishing, 2016. ISBN: 9780553418811
+[[53](ch01.html#ONeil2016_ch1-marker)] Cathy O’Neil: *Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy*. Crown Publishing, 2016. ISBN: 9780553418811
 
-[[54](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Shastri2020-marker)] Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, and Vijay Chidambaram. [Understanding and Benchmarking the Impact of GDPR on Database Systems](http://www.vldb.org/pvldb/vol13/p1064-shastri.pdf). *Proceedings of the VLDB Endowment*, volume 13, issue 7, pages 1064–1077, March 2020. [doi:10.14778/3384345.3384354](https://doi.org/10.14778/3384345.3384354)
+[[54](ch01.html#Shastri2020-marker)] Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, and Vijay Chidambaram. [Understanding and Benchmarking the Impact of GDPR on Database Systems](http://www.vldb.org/pvldb/vol13/p1064-shastri.pdf). *Proceedings of the VLDB Endowment*, volume 13, issue 7, pages 1064–1077, March 2020. [doi:10.14778/3384345.3384354](https://doi.org/10.14778/3384345.3384354)
 
-[[55](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#Datensparsamkeit-marker)] Martin Fowler. [Datensparsamkeit](https://www.martinfowler.com/bliki/Datensparsamkeit.html). *martinfowler.com*, December 2013. Archived at [perma.cc/R9QX-CME6](https://perma.cc/R9QX-CME6)
+[[55](ch01.html#Datensparsamkeit-marker)] Martin Fowler. [Datensparsamkeit](https://www.martinfowler.com/bliki/Datensparsamkeit.html). *martinfowler.com*, December 2013. Archived at [perma.cc/R9QX-CME6](https://perma.cc/R9QX-CME6)
 
-[[56](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#GDPR-marker)] [Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation)](https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&from=EN). *Official Journal of the European Union* L 119/1, May 2016.
+[[56](ch01.html#GDPR-marker)] [Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation)](https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&from=EN). *Official Journal of the European Union* L 119/1, May 2016.

ch2.md

@@ -25,10 +25,10 @@ In addition, you probably also have some *nonfunctional requirements*: for examp
 
 Not all nonfunctional requirements fall within the scope of this book, but several do. In this chapter we will introduce several technical concepts that will help you articulate the nonfunctional requirements for your own systems:
 
-- How to define and measure the *performance* of a system (see [“Describing Performance”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_percentiles));
-- What it means for a service to be *reliable*—namely, continuing to work correctly, even when things go wrong (see [“Reliability and Fault Tolerance”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_reliability));
-- Allowing a system to be *scalable* by having efficient ways of adding computing capacity as the load on the system grows (see [“Scalability”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_scalability)); and
-- Making it easier to maintain a system in the long term (see [“Maintainability”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_maintainability)).
+- How to define and measure the *performance* of a system (see [“Describing Performance”](ch02.html#sec_introduction_percentiles));
+- What it means for a service to be *reliable*—namely, continuing to work correctly, even when things go wrong (see [“Reliability and Fault Tolerance”](ch02.html#sec_introduction_reliability));
+- Allowing a system to be *scalable* by having efficient ways of adding computing capacity as the load on the system grows (see [“Scalability”](ch02.html#sec_introduction_scalability)); and
+- Making it easier to maintain a system in the long term (see [“Maintainability”](ch02.html#sec_introduction_maintainability)).
 
 The terminology introduced in this chapter will also be useful in the following chapters, when we go into the details of how data-intensive systems are implemented. However, abstract definitions can be quite dry; to make the ideas more concrete, we will start this chapter with a case study of how a social networking service might work, which will provide practical examples of performance and scalability.
 
@@ -37,21 +37,21 @@ The terminology introduced in this chapter will also be useful in the following
 
 ## 案例学习：社交网络主页时间线
 
-假设你被分配了一个任务，要实现一个类似X（前身为Twitter）的社交网络，在这个网络中，用户可以发布消息并关注其他用户。这将是对这种服务实际工作方式的极大简化 [[1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cvet2016), [2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Krikorian2012_ch2), [3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Twitter2023)]，但它将有助于说明大规模系统中出现的一些问题。
+假设你被分配了一个任务，要实现一个类似X（前身为Twitter）的社交网络，在这个网络中，用户可以发布消息并关注其他用户。这将是对这种服务实际工作方式的极大简化 [[1](ch02.html#Cvet2016), [2](ch02.html#Krikorian2012_ch2), [3](ch02.html#Twitter2023)]，但它将有助于说明大规模系统中出现的一些问题。
 
-假设用户每天发布 5 亿条消息，平均每秒 5700 条消息。偶尔，这个速率可能会激增至每秒 150,000 条消息 [[4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Krikorian2013)]。我们还假设平均每个用户关注 200 人，拥有 200 名粉丝（尽管这个范围非常广泛：大多数人只有少数几个粉丝，而像巴拉克·奥巴马这样的名人粉丝超过 1 亿）。
+假设用户每天发布 5 亿条消息，平均每秒 5700 条消息。偶尔，这个速率可能会激增至每秒 150,000 条消息 [[4](ch02.html#Krikorian2013)]。我们还假设平均每个用户关注 200 人，拥有 200 名粉丝（尽管这个范围非常广泛：大多数人只有少数几个粉丝，而像巴拉克·奥巴马这样的名人粉丝超过 1 亿）。
 
 
-Imagine you are given the task of implementing a social network in the style of X (formerly Twitter), in which users can post messages and follow other users. This will be a huge simplification of how such a service actually works [[1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cvet2016), [2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Krikorian2012_ch2), [3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Twitter2023)], but it will help illustrate some of the issues that arise in large-scale systems.
+Imagine you are given the task of implementing a social network in the style of X (formerly Twitter), in which users can post messages and follow other users. This will be a huge simplification of how such a service actually works [[1](ch02.html#Cvet2016), [2](ch02.html#Krikorian2012_ch2), [3](ch02.html#Twitter2023)], but it will help illustrate some of the issues that arise in large-scale systems.
 
-Let’s assume that users make 500 million posts per day, or 5,700 posts per second on average. Occasionally, the rate can spike as high as 150,000 posts/second [[4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Krikorian2013)]. Let’s also assume that the average user follows 200 people and has 200 followers (although there is a very wide range: most people have only a handful of followers, and a few celebrities such as Barack Obama have over 100 million followers).
+Let’s assume that users make 500 million posts per day, or 5,700 posts per second on average. Occasionally, the rate can spike as high as 150,000 posts/second [[4](ch02.html#Krikorian2013)]. Let’s also assume that the average user follows 200 people and has 200 followers (although there is a very wide range: most people have only a handful of followers, and a few celebrities such as Barack Obama have over 100 million followers).
 
 ### 用户、帖子和关注关系的表示
 
 
-设想我们将所有数据保存在关系数据库中，如 [图 2-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_twitter_relational) 所示。我们有一个用户表、一个帖子表和一个关注关系表。
+设想我们将所有数据保存在关系数据库中，如 [图 2-1](ch02.html#fig_twitter_relational) 所示。我们有一个用户表、一个帖子表和一个关注关系表。
 
-Imagine we keep all of the data in a relational database as shown in [Figure 2-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_twitter_relational). We have one table for users, one table for posts, and one table for follow relationships.
+Imagine we keep all of the data in a relational database as shown in [Figure 2-1](ch02.html#fig_twitter_relational). We have one table for users, one table for posts, and one table for follow relationships.
 
 ![ddia 0102](img/ddia_0102.png)
 
@@ -90,13 +90,13 @@ Moreover, the query above is quite expensive: if you are following 200 people, i
 
 想象一下，对于每个用户，我们存储一个包含他们首页时间线的数据结构，即他们所关注的人的最近帖子。每当用户发表帖子时，我们查找他们所有的关注者，并将该帖子插入到每个关注者的首页时间线中——就像将信息送达邮箱一样。现在，当用户登录时，我们可以简单地提供我们预计算的这个首页时间线。此外，为了接收其时间线上任何新帖子的通知，用户的客户端只需订阅被添加到他们首页时间线的帖子流。
 
-这种方法的缺点是，每当用户发帖时，我们都需要做更多的工作，因为首页时间线是派生数据，需要更新。这一过程在 [图 2-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_twitter_timelines) 中有所示。当一个初始请求导致执行多个下游请求时，我们使用*扩散*一词来描述请求数量的增加因素。
+这种方法的缺点是，每当用户发帖时，我们都需要做更多的工作，因为首页时间线是派生数据，需要更新。这一过程在 [图 2-2](ch02.html#fig_twitter_timelines) 中有所示。当一个初始请求导致执行多个下游请求时，我们使用*扩散*一词来描述请求数量的增加因素。
 
 How can we do better? Firstly, instead of polling, it would be better if the server actively pushed new posts to any followers who are currently online. Secondly, we should precompute the results of the query above so that a user’s request for their home timeline can be served from a cache.
 
 Imagine that for each user we store a data structure containing their home timeline, i.e., the recent posts by people they are following. Every time a user makes a post, we look up all of their followers, and insert that post into the home timeline of each follower—like delivering a message to a mailbox. Now when a user logs in, we can simply give them this home timeline that we precomputed. Moreover, to receive a notification about any new posts on their timeline, the user’s client simply needs to subscribe to the stream of posts being added to their home timeline.
 
-The downside of this approach is that we now need to do more work every time a user makes a post, because the home timelines are derived data that needs to be updated. The process is illustrated in [Figure 2-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_twitter_timelines). When one initial request results in several downstream requests being carried out, we use the term *fan-out* to describe the factor by which the number of requests increases.
+The downside of this approach is that we now need to do more work every time a user makes a post, because the home timelines are derived data that needs to be updated. The process is illustrated in [Figure 2-2](ch02.html#fig_twitter_timelines). When one initial request results in several downstream requests being carried out, we use the term *fan-out* to describe the factor by which the number of requests increases.
 
 ![ddia 0103](img/ddia_0103.png)
 
@@ -108,7 +108,7 @@ The downside of this approach is that we now need to do more work every time a u
 
 这种预计算和更新查询结果的过程被称为*实体化*，而时间线缓存则是一个*实体化视图*的例子（这是我们将进一步讨论的一个概念）。实体化的缺点是，每当一位名人发帖时，我们现在必须做大量的工作，将那篇帖子插入他们数百万关注者的首页时间线中。
 
-解决这个问题的一种方法是将名人的帖子与其他人的帖子分开处理：我们可以通过将名人的帖子单独存储并在读取时与实体化时间线合并，从而避免将它们添加到数百万时间线上的努力。尽管有此类优化，处理社交网络上的名人可能需要大量的基础设施 [[5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Axon2010_ch2)]。
+解决这个问题的一种方法是将名人的帖子与其他人的帖子分开处理：我们可以通过将名人的帖子单独存储并在读取时与实体化时间线合并，从而避免将它们添加到数百万时间线上的努力。尽管有此类优化，处理社交网络上的名人可能需要大量的基础设施 [[5](ch02.html#Axon2010_ch2)]。
 
 At a rate of 5,700 posts posted per second, if the average post reaches 200 followers (i.e., a fan-out factor of 200), we will need to do just over 1 million home timeline writes per second. This is a lot, but it’s still a significant saving compared to the 400 million per-sender post lookups per second that we would otherwise have to do.
 
@@ -116,7 +116,7 @@ If the rate of posts spikes due to some special event, we don’t have to do the
 
 This process of precomputing and updating the results of a query is called *materialization*, and the timeline cache is an example of a *materialized view* (a concept we will discuss further in [Link to Come]). The downside of materialization is that every time a celebrity makes a post, we now have to do a large amount of work to insert that post into the home timelines of each of their millions of followers.
 
-One way of solving this problem is to handle celebrity posts separately from everyone else’s posts: we can save ourselves the effort of adding them to millions of timelines by storing the celebrity posts separately and merging them with the materialized timeline when it is read. Despite such optimizations, handling celebrities on a social network can require a lot of infrastructure [[5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Axon2010_ch2)].
+One way of solving this problem is to handle celebrity posts separately from everyone else’s posts: we can save ourselves the effort of adding them to millions of timelines by storing the celebrity posts separately and merging them with the materialized timeline when it is read. Despite such optimizations, handling celebrities on a social network can require a lot of infrastructure [[5](ch02.html#Axon2010_ch2)].
 
 
 
@@ -130,37 +130,69 @@ One way of solving this problem is to handle celebrity posts separately from eve
 
 ## 描述性能
 
-Most discussions of software performance consider two main types of metric:
+在软件性能的讨论中，通常考虑两种主要的度量指标：
 
 - **响应时间**（Response Time）
 
-  The elapsed time from the moment when a user makes a request until they receive the requested answer. The unit of measurement is seconds.
+  从用户发出请求的那一刻到他们接收到请求的答案所经历的时间。测量单位是秒。
 
 - **吞吐量**（Throughput）
 
+  系统每秒处理的请求数量或每秒处理的数据量。对于给定的硬件资源配置，存在一个*最大吞吐量*。测量单位是“每秒某事物数”。
+
+在社交网络案例研究中，“每秒帖子数”和“每秒时间线写入数”是吞吐量指标，而“加载首页时间线所需的时间”或“帖子传递给关注者的时间”是响应时间指标。
+
+吞吐量与响应时间之间通常存在联系；在线服务中这种关系的一个示例在 [图 2-3](ch02.html#fig_throughput) 中进行了描述。当请求吞吐量低时，服务具有低响应时间，但随着负载增加，响应时间会增长。这是因为*排队*：当请求到达一个负载较高的系统时，很可能 CPU 正在处理先前的请求，因此新来的请求需要等待直到先前的请求完成。当吞吐量接近硬件能够处理的最大值时，排队延迟会急剧增加。
+
+
+Most discussions of software performance consider two main types of metric:
+
+- Response Time
+
+  The elapsed time from the moment when a user makes a request until they receive the requested answer. The unit of measurement is seconds.
+
+- Throughput
+
   The number of requests per second, or the data volume per second, that the system is processing. For a given a particular allocation of hardware resources, there is a *maximum throughput* that can be handled. The unit of measurement is “somethings per second”.
 
 In the social network case study, “posts per second” and “timeline writes per second” are throughput metrics, whereas the “time it takes to load the home timeline” or the “time until a post is delivered to followers” are response time metrics.
 
-There is often a connection between throughput and response time; an example of such a relationship for an online service is sketched in [Figure 2-3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_throughput). The service has a low response time when request throughput is low, but response time increases as load increases. This is because of *queueing*: when a request arrives on a highly loaded system, it’s likely that the CPU is already in the process of handling an earlier request, and therefore the incoming request needs to wait until the earlier request has been completed. As throughput approaches the maximum that the hardware can handle, queueing delays increase sharply.
+There is often a connection between throughput and response time; an example of such a relationship for an online service is sketched in [Figure 2-3](ch02.html#fig_throughput). The service has a low response time when request throughput is low, but response time increases as load increases. This is because of *queueing*: when a request arrives on a highly loaded system, it’s likely that the CPU is already in the process of handling an earlier request, and therefore the incoming request needs to wait until the earlier request has been completed. As throughput approaches the maximum that the hardware can handle, queueing delays increase sharply.
 
 ![ddia 0104b](img/ddia_0104b.png)
 
-###### Figure 2-3. As the throughput of a service approaches its capacity, the response time increases dramatically due to queueing.
+> 图2-3. 当服务吞吐量接近容量时，响应时间会由于排队而急剧增加
 
-#### When an overloaded system won’t recover
 
-If a system is close to overload, with throughput pushed close to the limit, it can sometimes enter a vicious cycle where it becomes less efficient and hence even more overloaded. For example, if there is a long queue of requests waiting to be handled, response times may increase so much that clients time out and resend their request. This causes the rate of requests to increase even further, making the problem worse—a *retry storm*. Even when the load is reduced again, such a system may remain in an overloaded state until it is rebooted or otherwise reset. This phenomenon is called a *metastable failure*, and it can cause serious outages in production systems [[6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bronson2021), [7](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2021)].
+#### 当过载系统无法恢复时
 
-To avoid retries overloading a service, you can increase and randomize the time between successive retries on the client side (*exponential backoff* [[8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2015), [9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2022backoff)]), and temporarily stop sending requests to a service that has returned errors or timed out recently (using a *circuit breaker* [[10](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Nygard2018)] or *token bucket* algorithm [[11](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2022retries)]). The server can also detect when it is approaching overload and start proactively rejecting requests (*load shedding* [[12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#YanacekLoadShedding)]), and send back responses asking clients to slow down (*backpressure* [[1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cvet2016), [13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Sackman2016_ch2)]). The choice of queueing and load-balancing algorithms can also make a difference [[14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kopytkov2018)].
+如果系统接近过载，吞吐量接近极限，有时会进入一个恶性循环，使得系统变得效率更低，从而更加过载。例如，如果有大量请求在排队等待处理，响应时间可能会增加到客户端超时并重新发送请求的程度。这会导致请求率进一步增加，使问题更加严重——这就是所谓的*重试风暴*。即使负载再次减少，这样的系统也可能仍处于过载状态，直到重新启动或以其他方式重置。这种现象称为*亚稳定故障*，可能会导致生产系统中严重的中断[[6](ch02.html#Bronson2021), [7](ch02.html#Brooker2021)]。
+
+为了避免重试过度加载服务，你可以增加并随机化客户端连续重试之间的时间（*指数退避*[[8](ch02.html#Brooker2015), [9](ch02.html#Brooker2022backoff)]），并暂时停止向最近返回错误或超时的服务发送请求（使用*断路器*[[10](ch02.html#Nygard2018)]或*令牌桶*算法[[11](ch02.html#Brooker2022retries)]）。服务器也可以检测到它即将过载，并开始主动拒绝请求（*减载*[[12](ch02.html#YanacekLoadShedding)]），并发送回响应要求客户端减慢速度（*反压力*[[1](ch02.html#Cvet2016), [13](ch02.html#Sackman2016_ch2)]）。队列和负载平衡算法的选择也可以有所不同[[14](ch02.html#Kopytkov2018)]。
+
+在性能指标方面，响应时间通常是用户最关心的，而吞吐量决定了所需的计算资源（例如，你需要多少服务器），从而决定了服务特定工作负载的成本。如果吞吐量可能超过当前硬件能够处理的范围，就需要扩展容量；如果一个系统能够通过增加计算资源显著提高其最大吞吐量，则称该系统具有*可扩展性*。
+
+在本节中，我们将主要关注响应时间，并将在[“可扩展性”](ch02.html#sec_introduction_scalability)一节中回归讨论吞吐量和可扩展性。
+
+
+If a system is close to overload, with throughput pushed close to the limit, it can sometimes enter a vicious cycle where it becomes less efficient and hence even more overloaded. For example, if there is a long queue of requests waiting to be handled, response times may increase so much that clients time out and resend their request. This causes the rate of requests to increase even further, making the problem worse—a *retry storm*. Even when the load is reduced again, such a system may remain in an overloaded state until it is rebooted or otherwise reset. This phenomenon is called a *metastable failure*, and it can cause serious outages in production systems [[6](ch02.html#Bronson2021), [7](ch02.html#Brooker2021)].
+
+To avoid retries overloading a service, you can increase and randomize the time between successive retries on the client side (*exponential backoff* [[8](ch02.html#Brooker2015), [9](ch02.html#Brooker2022backoff)]), and temporarily stop sending requests to a service that has returned errors or timed out recently (using a *circuit breaker* [[10](ch02.html#Nygard2018)] or *token bucket* algorithm [[11](ch02.html#Brooker2022retries)]). The server can also detect when it is approaching overload and start proactively rejecting requests (*load shedding* [[12](ch02.html#YanacekLoadShedding)]), and send back responses asking clients to slow down (*backpressure* [[1](ch02.html#Cvet2016), [13](ch02.html#Sackman2016_ch2)]). The choice of queueing and load-balancing algorithms can also make a difference [[14](ch02.html#Kopytkov2018)].
 
 In terms of performance metrics, the response time is usually what users care about the most, whereas the throughput determines the required computing resources (e.g., how many servers you need), and hence the cost of serving a particular workload. If throughput is likely to increase beyond what the current hardware can handle, the capacity needs to be expanded; a system is said to be *scalable* if its maximum throughput can be significantly increased by adding computing resources.
 
-In this section we will focus primarily on response times, and we will return to throughput and scalability in [“Scalability”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_scalability).
+In this section we will focus primarily on response times, and we will return to throughput and scalability in [“Scalability”](ch02.html#sec_introduction_scalability).
 
 ### 延迟与响应时间
 
-“Latency” and “response time” are sometimes used interchangeably, but in this book we will use the terms in a specific way (illustrated in [Figure 2-4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_response_time)):
+“Latency”和“response time”有时被交替使用，但在本书中，我们将以特定的方式使用这些术语（如[图2-4](ch02.html#fig_response_time)所示）：
+
+- *响应时间*是客户端所看到的；它包括系统中任何地方产生的所有延迟。
+- *服务时间*是服务实际处理用户请求的持续时间。
+- *排队延迟*可以在流程的几个点出现：例如，接收到请求后
+- *延迟* 是一个包罗万象的术语，用于描述请求未被积极处理的时间，即处于 *潜伏状态* 的时间。特别是，*网络延迟* 或 *网络延迟* 指的是请求和响应在网络中传输的时间。
+
+“Latency” and “response time” are sometimes used interchangeably, but in this book we will use the terms in a specific way (illustrated in [Figure 2-4](ch02.html#fig_response_time)):
 
 - The *response time* is what the client sees; it includes all delays incurred anywhere in the system.
 - The *service time* is the duration for which the service is actively processing the user request.
@@ -169,57 +201,94 @@ In this section we will focus primarily on response times, and we will return to
 
 ![ddia 0104a](img/ddia_0104a.png)
 
-###### Figure 2-4. Response time, service time, network latency, and queueing delay.
+> 图2-4. 响应时间、服务时间、网络延迟和排队延迟
 
-The response time can vary significantly from one request to the next, even if you keep making the same request over and over again. Many factors can add random delays: for example, a context switch to a background process, the loss of a network packet and TCP retransmission, a garbage collection pause, a page fault forcing a read from disk, mechanical vibrations in the server rack [[15](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gunawi2018)], or many other causes. We will discuss this topic in more detail in [Link to Come].
+即使反复发出同一请求，响应时间也可能因请求而异，差异显著。许多因素可能会导致随机延迟：例如，切换到后台进程的上下文切换，网络数据包丢失和 TCP 重传，垃圾收集暂停，页面错误强制从磁盘读取，服务器架的机械振动[[15](ch02.html#Gunawi2018)]，或许多其他原因。我们将在 [未来链接] 中更详细地讨论这个话题。
+
+排队延迟通常是响应时间变化性的一个重要部分。由于服务器同时只能处理少量事务（例如，受其 CPU 核心数量的限制），只需少数几个慢请求就足以阻塞后续请求的处理——这种效应被称为 *队首阻塞*。即使那些后续请求的服务时间很快，客户端也会因为等待先前请求完成而感觉到整体响应时间的缓慢。排队延迟不属于服务时间的一部分，因此在客户端测量响应时间十分重要。
+
+The response time can vary significantly from one request to the next, even if you keep making the same request over and over again. Many factors can add random delays: for example, a context switch to a background process, the loss of a network packet and TCP retransmission, a garbage collection pause, a page fault forcing a read from disk, mechanical vibrations in the server rack [[15](ch02.html#Gunawi2018)], or many other causes. We will discuss this topic in more detail in [Link to Come].
 
 Queueing delays often account for a large part of the variability in response times. As a server can only process a small number of things in parallel (limited, for example, by its number of CPU cores), it only takes a small number of slow requests to hold up the processing of subsequent requests—an effect known as *head-of-line blocking*. Even if those subsequent requests have fast service times, the client will see a slow overall response time due to the time waiting for the prior request to complete. The queueing delay is not part of the service time, and for this reason it is important to measure response times on the client side.
 
 ### 平均数，中位数与百分位点
 
-Because the response time varies from one request to the next, we need to think of it not as a single number, but as a *distribution* of values that you can measure. In [Figure 2-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_lognormal), each gray bar represents a request to a service, and its height shows how long that request took. Most requests are reasonably fast, but there are occasional *outliers* that take much longer. Variation in network delay is also known as *jitter*.
+因为响应时间从一个请求到另一个请求都在变化，我们需要把它视为一个你可以测量的值的 *分布*，而不是一个单一的数字。在 [图 2-5](ch02.html#fig_lognormal)，每个灰色条代表对一个服务的请求，其高度显示了该请求所需的时间。大多数请求相当快，但偶尔也有 *异常值* 花费的时间要长得多。网络延迟的变化也被称为 *抖动*。
+
+Because the response time varies from one request to the next, we need to think of it not as a single number, but as a *distribution* of values that you can measure. In [Figure 2-5](ch02.html#fig_lognormal), each gray bar represents a request to a service, and its height shows how long that request took. Most requests are reasonably fast, but there are occasional *outliers* that take much longer. Variation in network delay is also known as *jitter*.
 
 ![ddia 0104](img/ddia_0104.png)
 
-###### Figure 2-5. Illustrating mean and percentiles: response times for a sample of 100 requests to a service.
+> 图 2-5. 描述平均值和百分位数：对某服务100次请求的响应时间。
+>
+> Figure 2-5. Illustrating mean and percentiles: response times for a sample of 100 requests to a service.
+
+通常我们会报告服务的*平均*响应时间（技术上说是*算术平均值*：即总和所有的响应时间，然后除以请求的数量）。然而，如果你想了解你的“典型”响应时间，平均值并不是一个很好的度量，因为它不能告诉你有多少用户实际经历了那种延迟。
+
+通常使用*百分位数*会更好。如果你将响应时间列表从最快到最慢排序，那么*中位数*是中间点：例如，如果你的中位响应时间是200毫秒，这意味着你一半的请求在200毫秒内返回，另一半请求需要超过这个时间。这使得中位数成为一个好的度量，如果你想知道用户通常需要等待多久。中位数也被称为*第50百分位*，有时缩写为*p50*。
+
+为了弄清楚你的异常值有多严重，你可以查看更高的百分位数：*第95、第99和第99.9百分位*是常见的（缩写为*p95、p99和p999*）。它们是响应时间的阈值，即95%、99%或99.9%的请求比该特定阈值快。例如，如果第95百分位的响应时间是1.5秒，这意味着100次请求中有95次不到1.5秒，有5次需要1.5秒或更多时间。这在[图 2-5](ch02.html#fig_lognormal)中有所示。
+
+响应时间的高百分位数，也称为*尾部延迟*，很重要，因为它们直接影响用户对服务的体验。例如，亚马逊描述其内部服务的响应时间要求是以第99.9百分位来衡量，尽管它只影响1/1000的请求。这是因为请求最慢的客户往往是那些在他们的账户上有最多数据的客户，因为他们进行了许多购买——即，他们是最有价值的客户[[16](ch02.html#DeCandia2007_ch1)]。保证网站对他们来说快速是很重要的，以保持这些客户的满意。
+
+另一方面，优化第99.99百分位（最慢的1/10,000的请求）被认为过于昂贵且对亚马逊的目的来说收益不足。在非常高的百分位数上减少响应时间是困难的，因为它们容易受到你无法控制的随机事件的影响，而且收益递减。
+
 
 It’s common to report the *average* response time of a service (technically, the *arithmetic mean*: that is, sum all the response times, and divide by the number of requests). However, the mean is not a very good metric if you want to know your “typical” response time, because it doesn’t tell you how many users actually experienced that delay.
 
 Usually it is better to use *percentiles*. If you take your list of response times and sort it from fastest to slowest, then the *median* is the halfway point: for example, if your median response time is 200 ms, that means half your requests return in less than 200 ms, and half your requests take longer than that. This makes the median a good metric if you want to know how long users typically have to wait. The median is also known as the *50th percentile*, and sometimes abbreviated as *p50*.
 
-In order to figure out how bad your outliers are, you can look at higher percentiles: the *95th*, *99th*, and *99.9th* percentiles are common (abbreviated *p95*, *p99*, and *p999*). They are the response time thresholds at which 95%, 99%, or 99.9% of requests are faster than that particular threshold. For example, if the 95th percentile response time is 1.5 seconds, that means 95 out of 100 requests take less than 1.5 seconds, and 5 out of 100 requests take 1.5 seconds or more. This is illustrated in [Figure 2-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_lognormal).
+In order to figure out how bad your outliers are, you can look at higher percentiles: the *95th*, *99th*, and *99.9th* percentiles are common (abbreviated *p95*, *p99*, and *p999*). They are the response time thresholds at which 95%, 99%, or 99.9% of requests are faster than that particular threshold. For example, if the 95th percentile response time is 1.5 seconds, that means 95 out of 100 requests take less than 1.5 seconds, and 5 out of 100 requests take 1.5 seconds or more. This is illustrated in [Figure 2-5](ch02.html#fig_lognormal).
 
-High percentiles of response times, also known as *tail latencies*, are important because they directly affect users’ experience of the service. For example, Amazon describes response time requirements for internal services in terms of the 99.9th percentile, even though it only affects 1 in 1,000 requests. This is because the customers with the slowest requests are often those who have the most data on their accounts because they have made many purchases—that is, they’re the most valuable customers [[16](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#DeCandia2007_ch1)]. It’s important to keep those customers happy by ensuring the website is fast for them.
+High percentiles of response times, also known as *tail latencies*, are important because they directly affect users’ experience of the service. For example, Amazon describes response time requirements for internal services in terms of the 99.9th percentile, even though it only affects 1 in 1,000 requests. This is because the customers with the slowest requests are often those who have the most data on their accounts because they have made many purchases—that is, they’re the most valuable customers [[16](ch02.html#DeCandia2007_ch1)]. It’s important to keep those customers happy by ensuring the website is fast for them.
 
 On the other hand, optimizing the 99.99th percentile (the slowest 1 in 10,000 requests) was deemed too expensive and to not yield enough benefit for Amazon’s purposes. Reducing response times at very high percentiles is difficult because they are easily affected by random events outside of your control, and the benefits are diminishing.
 
-### The user impact of response times
+### 响应时间对用户的影响
 
-It seems intuitively obvious that a fast service is better for users than a slow service [[17](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Whitenton2020)]. However, it is surprisingly difficult to get hold of reliable data to quantify the effect that latency has on user behavior.
+直觉上看，快速服务比慢服务更有利于用户似乎是显而易见的[[17](ch02.html#Whitenton2020)]。然而，要获取可靠数据来量化延迟对用户行为的影响却出奇地困难。
 
-Some often-cited statistics are unreliable. In 2006 Google reported that a slowdown in search results from 400 ms to 900 ms was associated with a 20% drop in traffic and revenue [[18](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Linden2006)]. However, another Google study from 2009 reported that a 400 ms increase in latency resulted in only 0.6% fewer searches per day [[19](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brutlag2009)], and in the same year Bing found that a two-second increase in load time reduced ad revenue by 4.3% [[20](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schurman2009)]. Newer data from these companies appears not to be publicly available.
+一些经常被引用的统计数据是不可靠的。2006年谷歌报告称，搜索结果从400毫秒减慢到900毫秒，导致流量和收入下降20%[[18](ch02.html#Linden2006)]。然而，谷歌在2009年的另一项研究报告称，延迟增加400毫秒仅导致每天的搜索量减少0.6%[[19](ch02.html#Brutlag2009)]，同年必应发现加载时间增加两秒钟，广告收入减少了4.3%[[20](ch02.html#Schurman2009)]。这些公司的更新数据似乎没有公开。
 
-A more recent Akamai study [[21](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Akamai2017)] claims that a 100 ms increase in response time reduced the conversion rate of e-commerce sites by up to 7%; however, on closer inspection, the same study reveals that very *fast* page load times are also correlated with lower conversion rates! This seemingly paradoxical result is explained by the fact that the pages that load fastest are often those that have no useful content (e.g., 404 error pages). However, since the study makes no effort to separate the effects of page content from the effects of load time, its results are probably not meaningful.
+Akamai的一项较新研究[[21](ch02.html#Akamai2017)]声称响应时间增加100毫秒，会使电子商务网站的转化率降低多达7%；然而，仔细检查同一研究发现，非常*快*的页面加载时间也与较低的转化率相关！这种看似矛盾的结果是由于最快加载的页面往往是那些没有有用内容的页面（例如，404错误页面）。然而，由于该研究没有努力区分页面内容和加载时间的影响，其结果可能没有意义。
 
-A study by Yahoo [[22](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bai2017)] compares click-through rates on fast-loading versus slow-loading search results, controlling for quality of search results. It finds 20–30% more clicks on fast searches when the difference between fast and slow responses is 1.25 seconds or more.
+雅虎的一项研究[[22](ch02.html#Bai2017)]比较了快速加载与慢速加载搜索结果的点击率，控制搜索结果的质量。研究发现，当快速和慢速响应之间的差异在1.25秒或更多时，快速搜索的点击率增加了20-30%。
+
+It seems intuitively obvious that a fast service is better for users than a slow service [[17](ch02.html#Whitenton2020)]. However, it is surprisingly difficult to get hold of reliable data to quantify the effect that latency has on user behavior.
+
+Some often-cited statistics are unreliable. In 2006 Google reported that a slowdown in search results from 400 ms to 900 ms was associated with a 20% drop in traffic and revenue [[18](ch02.html#Linden2006)]. However, another Google study from 2009 reported that a 400 ms increase in latency resulted in only 0.6% fewer searches per day [[19](ch02.html#Brutlag2009)], and in the same year Bing found that a two-second increase in load time reduced ad revenue by 4.3% [[20](ch02.html#Schurman2009)]. Newer data from these companies appears not to be publicly available.
+
+A more recent Akamai study [[21](ch02.html#Akamai2017)] claims that a 100 ms increase in response time reduced the conversion rate of e-commerce sites by up to 7%; however, on closer inspection, the same study reveals that very *fast* page load times are also correlated with lower conversion rates! This seemingly paradoxical result is explained by the fact that the pages that load fastest are often those that have no useful content (e.g., 404 error pages). However, since the study makes no effort to separate the effects of page content from the effects of load time, its results are probably not meaningful.
+
+A study by Yahoo [[22](ch02.html#Bai2017)] compares click-through rates on fast-loading versus slow-loading search results, controlling for quality of search results. It finds 20–30% more clicks on fast searches when the difference between fast and slow responses is 1.25 seconds or more.
 
 #### 使用响应时间指标
 
-High percentiles are especially important in backend services that are called multiple times as part of serving a single end-user request. Even if you make the calls in parallel, the end-user request still needs to wait for the slowest of the parallel calls to complete. It takes just one slow call to make the entire end-user request slow, as illustrated in [Figure 2-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_tail_amplification). Even if only a small percentage of backend calls are slow, the chance of getting a slow call increases if an end-user request requires multiple backend calls, and so a higher proportion of end-user requests end up being slow (an effect known as *tail latency amplification* [[23](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dean2013)]).
+高百分位数在后端服务中尤其重要，这些服务在处理单个最终用户请求时会被多次调用。即使你并行进行调用，最终用户请求仍然需要等待并行调用中最慢的一个完成。正如[图 2-6](ch02.html#fig_tail_amplification)所示，只需一个慢调用就能使整个最终用户请求变慢。即使只有少数后端调用较慢，如果最终用户请求需要多次后端调用，获得慢调用的几率就会增加，因此更高比例的最终用户请求最终变慢（这种效应被称为*尾延迟放大*[[23](ch02.html#Dean2013)]）。
+
+High percentiles are especially important in backend services that are called multiple times as part of serving a single end-user request. Even if you make the calls in parallel, the end-user request still needs to wait for the slowest of the parallel calls to complete. It takes just one slow call to make the entire end-user request slow, as illustrated in [Figure 2-6](ch02.html#fig_tail_amplification). Even if only a small percentage of backend calls are slow, the chance of getting a slow call increases if an end-user request requires multiple backend calls, and so a higher proportion of end-user requests end up being slow (an effect known as *tail latency amplification* [[23](ch02.html#Dean2013)]).
 
 ![ddia 0105](img/ddia_0105.png)
 
-###### Figure 2-6. When several backend calls are needed to serve a request, it takes just a single slow backend request to slow down the entire end-user request.
+> 图 2-6. 当一个请求需要多次后端调用时，只需要一个缓慢的后端请求，就能拖慢整个终端用户的请求
 
-Percentiles are often used in *service level objectives* (SLOs) and *service level agreements* (SLAs) as ways of defining the expected performance and availability of a service [[24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hidalgo2020)]. For example, an SLO may set a target for a service to have a median response time of less than 200 ms and a 99th percentile under 1 s, and a target that at least 99.9% of valid requests result in non-error responses. An SLA is a contract that specifies what happens if the SLO is not met (for example, customers may be entitled to a refund). That is the basic idea, at least; in practice, defining good availability metrics for SLOs and SLAs is not straightforward [[25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Mogul2019), [26](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hauer2020)].
+百分位数通常用于*服务级别目标*（SLOs）和*服务级别协议*（SLAs），作为定义服务预期性能和可用性的方式[[24](ch02.html#Hidalgo2020)]。例如，SLO可能设定一个目标，要求服务的中位响应时间少于200毫秒，第99百分位在1秒以下，并且至少99.9%的有效请求结果为非错误响应。SLA是一份合同，规定如果未达到SLO将发生什么（例如，客户可能有权获得退款）。至少基本思想是这样的；实际上，为SLOs和SLAs定义良好的可用性指标并不简单[[25](ch02.html#Mogul2019), 26]。
+
+Percentiles are often used in *service level objectives* (SLOs) and *service level agreements* (SLAs) as ways of defining the expected performance and availability of a service [[24](ch02.html#Hidalgo2020)]. For example, an SLO may set a target for a service to have a median response time of less than 200 ms and a 99th percentile under 1 s, and a target that at least 99.9% of valid requests result in non-error responses. An SLA is a contract that specifies what happens if the SLO is not met (for example, customers may be entitled to a refund). That is the basic idea, at least; in practice, defining good availability metrics for SLOs and SLAs is not straightforward [[25](ch02.html#Mogul2019), [26](ch02.html#Hauer2020)].
 
 #### 计算百分位点
 
+如果你想在服务的监控仪表板上添加响应时间百分位数，你需要持续有效地计算它们。例如，你可能希望保持一个最近10分钟内请求响应时间的滚动窗口。每分钟，你都会计算该窗口中的中位数和各种百分位数，并将这些指标绘制在图表上。
+
+最简单的实现方式是保留时间窗口内所有请求的响应时间列表，并每分钟对该列表进行排序。如果这对你来说效率太低，有些算法可以以最小的CPU和内存成本计算出百分位数的良好近似值。开源的百分位数估计库包括 HdrHistogram、t-digest [[27](ch02.html#Dunning2021), [28](ch02.html#Kohn2021)]、OpenHistogram [[29](ch02.html#Hartmann2020)] 和 DDSketch [[30](ch02.html#Masson2019)]。
+
+注意，对百分位数进行平均化，例如为了降低时间分辨率或将来自几台机器的数据结合在一起，从数学上讲是没有意义的——聚合响应时间数据的正确方法是添加直方图[[31](ch02.html#Schwartz2015)]。
+
 If you want to add response time percentiles to the monitoring dashboards for your services, you need to efficiently calculate them on an ongoing basis. For example, you may want to keep a rolling window of response times of requests in the last 10 minutes. Every minute, you calculate the median and various percentiles over the values in that window and plot those metrics on a graph.
 
-The simplest implementation is to keep a list of response times for all requests within the time window and to sort that list every minute. If that is too inefficient for you, there are algorithms that can calculate a good approximation of percentiles at minimal CPU and memory cost. Open source percentile estimation libraries include HdrHistogram, t-digest [[27](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dunning2021), [28](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kohn2021)], OpenHistogram [[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hartmann2020)], and DDSketch [[30](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Masson2019)].
+The simplest implementation is to keep a list of response times for all requests within the time window and to sort that list every minute. If that is too inefficient for you, there are algorithms that can calculate a good approximation of percentiles at minimal CPU and memory cost. Open source percentile estimation libraries include HdrHistogram, t-digest [[27](ch02.html#Dunning2021), [28](ch02.html#Kohn2021)], OpenHistogram [[29](ch02.html#Hartmann2020)], and DDSketch [[30](ch02.html#Masson2019)].
 
-Beware that averaging percentiles, e.g., to reduce the time resolution or to combine data from several machines, is mathematically meaningless—the right way of aggregating response time data is to add the histograms [[31](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schwartz2015)].
+Beware that averaging percentiles, e.g., to reduce the time resolution or to combine data from several machines, is mathematically meaningless—the right way of aggregating response time data is to add the histograms [[31](ch02.html#Schwartz2015)].
 
 
 
@@ -227,47 +296,73 @@ Beware that averaging percentiles, e.g., to reduce the time resolution or to com
 
 ## 可靠性与容错
 
-Everybody has an intuitive idea of what it means for something to be reliable or unreliable. For software, typical expectations include:
+每个人对于一个东西可靠不可靠都有自己的直观想法。对于软件来说，典型的期望包括：
 
-- The application performs the function that the user expected.
-- It can tolerate the user making mistakes or using the software in unexpected ways.
-- Its performance is good enough for the required use case, under the expected load and data volume.
-- The system prevents any unauthorized access and abuse.
+* 应用程序表现出用户所期望的功能。
+* 软件允许用户犯错，或以意料之外的方式来使用软件。
+* 在预期的负载和数据量下，性能可以满足要求。
+* 系统能够阻止未经授权的访问和滥用。
 
-If all those things together mean “working correctly,” then we can understand *reliability* as meaning, roughly, “continuing to work correctly, even when things go wrong.” To be more precise about things going wrong, we will distinguish between *faults* and *failures* [[32](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Heimerdinger1992), [33](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gaertner1999)]:
+如果把所有这些要求放一块儿意味着 “正确工作”，那么我们可以把 *可靠性* 粗略理解为：“即使出现问题，也能继续正常工作”。为了更准确地描述问题的发生，我们将区分*故障*和*失败*[[32](ch02.html#Heimerdinger1992), [33](ch02.html#Gaertner1999)]：
 
-- Fault
+- **故障**（fault）
 
+  故障是指系统的某个部分停止正常工作：例如，单个硬盘故障，或者单台机器崩溃，或者系统依赖的外部服务出现中断。
   A fault is when a particular *part* of a system stops working correctly: for example, if a single hard drive malfunctions, or a single machine crashes, or an external service (that the system depends on) has an outage.
 
-- Failure
+- **失效**（Failure）
 
+  失效是指系统整体停止向用户提供所需服务；换句话说，就是未达到服务级别目标（SLO）。
   A failure is when the system *as a whole* stops providing the required service to the user; in other words, when it does not meet the service level objective (SLO).
 
+故障与失败之间的区别可能会引起混淆，因为它们是同一件事，只是在不同的层级上。例如，如果一个硬盘停止工作，我们说硬盘发生了失败：如果系统只由那一个硬盘组成，它就停止提供所需的服务。然而，如果你所说的系统包含多个硬盘，那么单个硬盘的失败只是从更大系统的角度看是一个故障，并且更大的系统可能能够通过在另一个硬盘上有数据的副本来容忍这个故障。
+
 The distinction between fault and failure can be confusing because they are the same thing, just at different levels. For example, if a hard drive stops working, we say that the hard drive has failed: if the system consists only of that one hard drive, it has stopped providing the required service. However, if the system you’re talking about contains many hard drives, then the failure of a single hard drive is only a fault from the point of view of the bigger system, and the bigger system might be able to tolerate that fault by having a copy of the data on another hard drive.
 
-### 容灾
+
+### 容错
+
+如果系统在某些故障发生时仍继续向用户提供所需服务，我们称该系统为*容错*系统。如果系统不能容忍某部分出现故障，我们称该部分为*单点故障*（SPOF），因为该部分的故障会升级为导致整个系统的失败。
+
+例如，在社交网络案例研究中，可能发生的故障是在广播过程中，参与更新物化时间线的机器崩溃或变得不可用。为了使这个过程具有容错性，我们需要确保另一台机器能够接管这个任务，不遗漏任何本应传送的帖子，也不重复任何帖子。（这个概念被称为*精确一次语义*，我们将在[未来链接]中详细讨论）
 
 We call a system *fault-tolerant* if it continues providing the required service to the user in spite of certain faults occurring. If a system cannot tolerate a certain part becoming faulty, we call that part a *single point of failure* (SPOF), because a fault in that part escalates to cause the failure of the whole system.
 
 For example, in the social network case study, a fault that might happen is that during the fan-out process, a machine involved in updating the materialized timelines crashes or become unavailable. To make this process fault-tolerant, we would need to ensure that another machine can take over this task without missing any posts that should have been delivered, and without duplicating any posts. (This idea is known as *exactly-once semantics*, and we will examine it in detail in [Link to Come].)
 
+容错性始终仅限于一定数量的特定类型的故障。例如，一个系统可能能够同时容忍最多两个硬盘故障，或者三个节点中最多有一个崩溃。容忍任意数量的故障是没有意义的：如果所有节点都崩溃了，那就无计可施。如果整个地球（及其上的所有服务器）被黑洞吞噬，那么要容忍这种故障就需要在太空中进行网络托管——祝你好运，让这个预算项目获批。
+
+违反直觉的是，在这样的容错系统中，通过故意触发故障来*增加*故障率是有意义的——例如，随机无预警地终止个别进程。许多关键性的错误实际上是由于错误处理不当引起的[[34](ch02.html#Yuan2014)]；通过故意诱发故障，你确保了容错机制不断地得到运用和测试，这可以增强你的信心，相信在自然发生故障时能够得到正确处理。*混沌工程*是一门旨在通过诸如故意注入故障的实验来提高对容错机制信心的学科[[35](ch02.html#Rosenthal2020)]。
+
+虽然我们通常倾向于容忍故障而非预防故障，但在某些情况下，预防比治疗更好（例如，因为没有治疗方法）。在安全问题上就是这样，例如：如果攻击者已经侵入系统并获取了敏感数据，那个事件是无法撤销的。然而，本书主要讨论的是可以治愈的故障类型，如下文所述。
+
 Fault tolerance is always limited to a certain number of certain types of faults. For example, a system might be able to tolerate a maximum of two hard drives failing at the same time, or a maximum of one out of three nodes crashing. It would not make sense to tolerate any number of faults: if all nodes crash, there is nothing that can be done. If the entire planet Earth (and all servers on it) were swallowed by a black hole, tolerance of that fault would require web hosting in space—good luck getting that budget item approved.
 
-Counter-intuitively, in such fault-tolerant systems, it can make sense to *increase* the rate of faults by triggering them deliberately—for example, by randomly killing individual processes without warning. Many critical bugs are actually due to poor error handling [[34](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Yuan2014)]; by deliberately inducing faults, you ensure that the fault-tolerance machinery is continually exercised and tested, which can increase your confidence that faults will be handled correctly when they occur naturally. *Chaos engineering* is a discipline that aims to improve confidence in fault-tolerance mechanisms through experiments such as deliberately injecting faults [[35](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Rosenthal2020)].
+Counter-intuitively, in such fault-tolerant systems, it can make sense to *increase* the rate of faults by triggering them deliberately—for example, by randomly killing individual processes without warning. Many critical bugs are actually due to poor error handling [[34](ch02.html#Yuan2014)]; by deliberately inducing faults, you ensure that the fault-tolerance machinery is continually exercised and tested, which can increase your confidence that faults will be handled correctly when they occur naturally. *Chaos engineering* is a discipline that aims to improve confidence in fault-tolerance mechanisms through experiments such as deliberately injecting faults [[35](ch02.html#Rosenthal2020)].
 
 Although we generally prefer tolerating faults over preventing faults, there are cases where prevention is better than cure (e.g., because no cure exists). This is the case with security matters, for example: if an attacker has compromised a system and gained access to sensitive data, that event cannot be undone. However, this book mostly deals with the kinds of faults that can be cured, as described in the following sections.
 
 ### 硬件与软件缺陷
 
+当我们思考系统故障的原因时，硬件故障很快浮现脑海：
+
+- 每年大约有 2-5% 的磁盘硬盘出现故障[[36](ch02.html#Pinheiro2007), [37](ch02.html#Schroeder2007)]；在一个拥有 10,000 块硬盘的存储集群中，我们因此可以预计平均每天会有一块硬盘故障。最近的数据表明硬盘越来越可靠，但故障率仍然显著[[38](ch02.html#Klein2021)]。
+- 每年大约有 0.5-1% 的固态硬盘（SSD）故障[[39](ch02.html#Narayanan2016)]。少量的位错误可以自动纠正[[40](ch02.html#Alibaba2019_ch2)]，但不可纠正的错误大约每年每块硬盘发生一次，即使是相当新的硬盘（即，磨损较少的硬盘）；这种错误率高于磁盘硬盘[[41](ch02.html#Schroeder2016), [42](ch02.html#Alter2019)]。
+- 其他硬件组件如电源供应器、RAID 控制器和内存模块也会发生故障，尽管频率低于硬盘[[43](ch02.html#Ford2010), [44](ch02.html#Vishwanath2010)]。
+- 大约每 1,000 台机器中就有一台的 CPU 核心偶尔计算出错误的结果，这很可能是由制造缺陷引起的[[45](ch02.html#Hochschild2021), [46](ch02.html#Dixit2021), [47](ch02.html#Behrens2015)]。在某些情况下，错误的计算会导致崩溃，但在其他情况下，它会导致程序简单地返回错误的结果。
+- RAM 中的数据也可能被破坏，原因可能是宇宙射线等随机事件，或是永久性物理缺陷。即使使用了具有纠错码（ECC）的内存，超过 1% 的机器在给定年份遇到不可纠正的错误，这通常会导致机器和受影响的内存模块崩溃并需要更换[[48](ch02.html#Schroeder2009)]。此外，某些病态的内存访问模式可以高概率地翻转位[[49](ch02.html#Kim2014)]。
+- 整个数据中心可能变得不可用（例如，由于停电或网络配置错误）或甚至被永久性破坏（例如火灾或洪水）。尽管这种大规模故障很少见，但如果一项服务不能容忍数据中心的丢失，其影响可能是灾难性的[[50](ch02.html#Cockcroft2019)]。
+
+这些事件足够罕见，以至于在处理小型系统时你通常不需要担心它们，只要你可以轻松替换变得有故障的硬件。然而，在大规模系统中，硬件故障发生得足够频繁，以至于它们成为正常系统运作的一部分。
+
 When we think of causes of system failure, hardware faults quickly come to mind:
 
-- Approximately 2–5% of magnetic hard drives fail per year [[36](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Pinheiro2007), [37](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schroeder2007)]; in a storage cluster with 10,000 disks, we should therefore expect on average one disk failure per day. Recent data suggests that disks are getting more reliable, but failure rates remain significant [[38](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Klein2021)].
-- Approximately 0.5–1% of solid state drives (SSDs) fail per year [[39](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Narayanan2016)]. Small numbers of bit errors are corrected automatically [[40](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Alibaba2019_ch2)], but uncorrectable errors occur approximately once per year per drive, even in drives that are fairly new (i.e., that have experienced little wear); this error rate is higher than that of magnetic hard drives [[41](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schroeder2016), [42](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Alter2019)].
-- Other hardware components such as power supplies, RAID controllers, and memory modules also fail, although less frequently than hard drives [[43](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Ford2010), [44](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Vishwanath2010)].
-- Approximately one in 1,000 machines has a CPU core that occasionally computes the wrong result, likely due to manufacturing defects [[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hochschild2021), [46](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dixit2021), [47](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Behrens2015)]. In some cases, an erroneous computation leads to a crash, but in other cases it leads to a program simply returning the wrong result.
-- Data in RAM can also be corrupted, either due to random events such as cosmic rays, or due to permanent physical defects. Even when memory with error-correcting codes (ECC) is used, more than 1% of machines encounter an uncorrectable error in a given year, which typically leads to a crash of the machine and the affected memory module needing to be replaced [[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schroeder2009)]. Moreover, certain pathological memory access patterns can flip bits with high probability [[49](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kim2014)].
-- An entire datacenter might become unavailable (for example, due to power outage or network misconfiguration) or even be permanently destroyed (for example by fire or flood). Although such large-scale failures are rare, their impact can be catastrophic if a service cannot tolerate the loss of a datacenter [[50](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cockcroft2019)].
+- Approximately 2–5% of magnetic hard drives fail per year [[36](ch02.html#Pinheiro2007), [37](ch02.html#Schroeder2007)]; in a storage cluster with 10,000 disks, we should therefore expect on average one disk failure per day. Recent data suggests that disks are getting more reliable, but failure rates remain significant [[38](ch02.html#Klein2021)].
+- Approximately 0.5–1% of solid state drives (SSDs) fail per year [[39](ch02.html#Narayanan2016)]. Small numbers of bit errors are corrected automatically [[40](ch02.html#Alibaba2019_ch2)], but uncorrectable errors occur approximately once per year per drive, even in drives that are fairly new (i.e., that have experienced little wear); this error rate is higher than that of magnetic hard drives [[41](ch02.html#Schroeder2016), [42](ch02.html#Alter2019)].
+- Other hardware components such as power supplies, RAID controllers, and memory modules also fail, although less frequently than hard drives [[43](ch02.html#Ford2010), [44](ch02.html#Vishwanath2010)].
+- Approximately one in 1,000 machines has a CPU core that occasionally computes the wrong result, likely due to manufacturing defects [[45](ch02.html#Hochschild2021), [46](ch02.html#Dixit2021), [47](ch02.html#Behrens2015)]. In some cases, an erroneous computation leads to a crash, but in other cases it leads to a program simply returning the wrong result.
+- Data in RAM can also be corrupted, either due to random events such as cosmic rays, or due to permanent physical defects. Even when memory with error-correcting codes (ECC) is used, more than 1% of machines encounter an uncorrectable error in a given year, which typically leads to a crash of the machine and the affected memory module needing to be replaced [[48](ch02.html#Schroeder2009)]. Moreover, certain pathological memory access patterns can flip bits with high probability [[49](ch02.html#Kim2014)].
+- An entire datacenter might become unavailable (for example, due to power outage or network misconfiguration) or even be permanently destroyed (for example by fire or flood). Although such large-scale failures are rare, their impact can be catastrophic if a service cannot tolerate the loss of a datacenter [[50](ch02.html#Cockcroft2019)].
 
 These events are rare enough that you often don’t need to worry about them when working on a small system, as long as you can easily replace hardware that becomes faulty. However, in a large-scale system, hardware faults happen often enough that they become part of the normal system operation.
 
@@ -275,9 +370,9 @@ These events are rare enough that you often don’t need to worry about them whe
 
 Our first response to unreliable hardware is usually to add redundancy to the individual hardware components in order to reduce the failure rate of the system. Disks may be set up in a RAID configuration (spreading data across multiple disks in the same machine so that a failed disk does not cause data loss), servers may have dual power supplies and hot-swappable CPUs, and datacenters may have batteries and diesel generators for backup power. Such redundancy can often keep a machine running uninterrupted for years.
 
-Redundancy is most effective when component faults are independent, that is, the occurrence of one fault does not change how likely it is that another fault will occur. However, experience has shown that there are often significant correlations between component failures [[37](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schroeder2007), [51](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Han2021), [52](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Nightingale2011)]; unavailability of an entire server rack or an entire datacenter still happens more often than we would like.
+Redundancy is most effective when component faults are independent, that is, the occurrence of one fault does not change how likely it is that another fault will occur. However, experience has shown that there are often significant correlations between component failures [[37](ch02.html#Schroeder2007), [51](ch02.html#Han2021), [52](ch02.html#Nightingale2011)]; unavailability of an entire server rack or an entire datacenter still happens more often than we would like.
 
-Hardware redundancy increases the uptime of a single machine; however, as discussed in [“Distributed versus Single-Node Systems”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_distributed), there are advantages to using a distributed system, such as being able to tolerate a complete outage of one datacenter. For this reason, cloud systems tend to focus less on the reliability of individual machines, and instead aim to make services highly available by tolerating faulty nodes at the software level. Cloud providers use *availability zones* to identify which resources are physically co-located; resources in the same place are more likely to fail at the same time than geographically separated resources.
+Hardware redundancy increases the uptime of a single machine; however, as discussed in [“Distributed versus Single-Node Systems”](ch01.html#sec_introduction_distributed), there are advantages to using a distributed system, such as being able to tolerate a complete outage of one datacenter. For this reason, cloud systems tend to focus less on the reliability of individual machines, and instead aim to make services highly available by tolerating faulty nodes at the software level. Cloud providers use *availability zones* to identify which resources are physically co-located; resources in the same place are more likely to fail at the same time than geographically separated resources.
 
 The fault-tolerance techniques we discuss in this book are designed to tolerate the loss of entire machines, racks, or availability zones. They generally work by allowing a machine in one datacenter to take over when a machine in another datacenter fails or becomes unreachable. We will discuss such techniques for fault tolerance in [Link to Come], [Link to Come], and at various other points in this book.
 
@@ -285,39 +380,72 @@ Systems that can tolerate the loss of entire machines also have operational adva
 
 #### 软件缺陷
 
-Although hardware failures can be weakly correlated, they are still mostly independent: for example, if one disk fails, it’s likely that other disks in the same machine will be fine for another while. On the other hand, software faults are often very highly correlated, because it is common for many nodes to run the same software and thus have the same bugs [[53](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gunawi2014), [54](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kreps2012_ch1)]. Such faults are harder to anticipate, and they tend to cause many more system failures than uncorrelated hardware faults [[43](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Ford2010)]. For example:
+我们通常认为硬件故障是随机的、相互独立的：一台机器的磁盘失效并不意味着另一台机器的磁盘也会失效。虽然大量硬件组件之间可能存在微弱的相关性（例如服务器机架的温度等共同的原因），但同时发生故障也是极为罕见的。
 
-- A software bug that causes every node to fail at the same time in particular circumstances. For example, on June 30, 2012, a leap second caused many Java applications to hang simultaneously due to a bug in the Linux kernel, bringing down many Internet services [[55](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Minar2012_ch1)]. Due to a firmware bug, all SSDs of certain models suddenly fail after precisely 32,768 hours of operation (less than 4 years), rendering the data on them unrecoverable [[56](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#HPE2019)].
-- A runaway process that uses up some shared, limited resource, such as CPU time, memory, disk space, network bandwidth, or threads [[57](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hochstein2020)]. For example, a process that consumes too much memory while processing a large request may be killed by the operating system.
+另一类错误是内部的 **系统性错误（systematic error）**【8】。这类错误难以预料，而且因为是跨节点相关的，所以比起不相关的硬件故障往往可能造成更多的 **系统失效**【5】。例子包括：
+
+* 接受特定的错误输入，便导致所有应用服务器实例崩溃的 BUG。例如 2012 年 6 月 30 日的闰秒，由于 Linux 内核中的一个错误【9】，许多应用同时挂掉了。
+* 失控进程会用尽一些共享资源，包括 CPU 时间、内存、磁盘空间或网络带宽。
+* 系统依赖的服务变慢，没有响应，或者开始返回错误的响应。
+* 级联故障，一个组件中的小故障触发另一个组件中的故障，进而触发更多的故障【10】。
+
+导致这类软件故障的 BUG 通常会潜伏很长时间，直到被异常情况触发为止。这种情况意味着软件对其环境做出了某种假设 —— 虽然这种假设通常来说是正确的，但由于某种原因最后不再成立了【11】。
+
+虽然软件中的系统性故障没有速效药，但我们还是有很多小办法，例如：仔细考虑系统中的假设和交互；彻底的测试；进程隔离；允许进程崩溃并重启；测量、监控并分析生产环境中的系统行为。如果系统能够提供一些保证（例如在一个消息队列中，进入与发出的消息数量相等），那么系统就可以在运行时不断自检，并在出现 **差异（discrepancy）** 时报警【12】。
+
+
+Although hardware failures can be weakly correlated, they are still mostly independent: for example, if one disk fails, it’s likely that other disks in the same machine will be fine for another while. On the other hand, software faults are often very highly correlated, because it is common for many nodes to run the same software and thus have the same bugs [[53](ch02.html#Gunawi2014), [54](ch02.html#Kreps2012_ch1)]. Such faults are harder to anticipate, and they tend to cause many more system failures than uncorrelated hardware faults [[43](ch02.html#Ford2010)]. For example:
+
+- A software bug that causes every node to fail at the same time in particular circumstances. For example, on June 30, 2012, a leap second caused many Java applications to hang simultaneously due to a bug in the Linux kernel, bringing down many Internet services [[55](ch02.html#Minar2012_ch1)]. Due to a firmware bug, all SSDs of certain models suddenly fail after precisely 32,768 hours of operation (less than 4 years), rendering the data on them unrecoverable [[56](ch02.html#HPE2019)].
+- A runaway process that uses up some shared, limited resource, such as CPU time, memory, disk space, network bandwidth, or threads [[57](ch02.html#Hochstein2020)]. For example, a process that consumes too much memory while processing a large request may be killed by the operating system.
 - A service that the system depends on slows down, becomes unresponsive, or starts returning corrupted responses.
-- An interaction between different systems results in emergent behavior that does not occur when each system was tested in isolation [[58](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Tang2023)].
-- Cascading failures, where a problem in one component causes another component to become overloaded and slow down, which in turn brings down another component [[59](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Ulrich2016), [60](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Fassbender2022)].
+- An interaction between different systems results in emergent behavior that does not occur when each system was tested in isolation [[58](ch02.html#Tang2023)].
+- Cascading failures, where a problem in one component causes another component to become overloaded and slow down, which in turn brings down another component [[59](ch02.html#Ulrich2016), [60](ch02.html#Fassbender2022)].
 
-The bugs that cause these kinds of software faults often lie dormant for a long time until they are triggered by an unusual set of circumstances. In those circumstances, it is revealed that the software is making some kind of assumption about its environment—and while that assumption is usually true, it eventually stops being true for some reason [[61](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cook2000), [62](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Woods2017)].
+The bugs that cause these kinds of software faults often lie dormant for a long time until they are triggered by an unusual set of circumstances. In those circumstances, it is revealed that the software is making some kind of assumption about its environment—and while that assumption is usually true, it eventually stops being true for some reason [[61](ch02.html#Cook2000), [62](ch02.html#Woods2017)].
 
-There is no quick solution to the problem of systematic faults in software. Lots of small things can help: carefully thinking about assumptions and interactions in the system; thorough testing; process isolation; allowing processes to crash and restart; avoiding feedback loops such as retry storms (see [“When an overloaded system won’t recover”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sidebar_metastable)); measuring, monitoring, and analyzing system behavior in production.
+There is no quick solution to the problem of systematic faults in software. Lots of small things can help: carefully thinking about assumptions and interactions in the system; thorough testing; process isolation; allowing processes to crash and restart; avoiding feedback loops such as retry storms (see [“When an overloaded system won’t recover”](ch02.html#sidebar_metastable)); measuring, monitoring, and analyzing system behavior in production.
 
 ### 人类与可靠性
 
-Humans design and build software systems, and the operators who keep the systems running are also human. Unlike machines, humans don’t just follow rules; their strength is being creative and adaptive in getting their job done. However, this characteristic also leads to unpredictability, and sometimes mistakes that can lead to failures, despite best intentions. For example, one study of large internet services found that configuration changes by operators were the leading cause of outages, whereas hardware faults (servers or network) played a role in only 10–25% of outages [[63](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Oppenheimer2003)].
+设计并构建了软件系统的工程师是人类，维持系统运行的运维也是人类。即使他们怀有最大的善意，人类也是不可靠的。举个例子，一项关于大型互联网服务的研究发现，运维配置错误是导致服务中断的首要原因，而硬件故障（服务器或网络）仅导致了 10-25% 的服务中断【13】。
 
-It is tempting to label such problems as “human error” and to wish that they could be solved by better controlling human behavior through tighter procedures and compliance with rules. However, blaming people for mistakes is counterproductive. What we call “human error” is not really the cause of an incident, but rather a symptom of a problem with the sociotechnical system in which people are trying their best to do their jobs [[64](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dekker2017)].
+尽管人类不可靠，但怎么做才能让系统变得可靠？最好的系统会组合使用以下几种办法：
 
-Various technical measures can help minimize the impact of human mistakes, including thorough testing [[34](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Yuan2014)], rollback mechanisms for quickly reverting configuration changes, gradual roll-outs of new code, detailed and clear monitoring, observability tools for diagnosing production issues (see [“Problems with Distributed Systems”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_dist_sys_problems)), and well-designed interfaces that encourage “the right thing” and discourage “the wrong thing”.
+* 以最小化犯错机会的方式设计系统。例如，精心设计的抽象、API 和管理后台使做对事情更容易，搞砸事情更困难。但如果接口限制太多，人们就会忽略它们的好处而想办法绕开。很难正确把握这种微妙的平衡。
+* 将人们最容易犯错的地方与可能导致失效的地方 **解耦（decouple）**。特别是提供一个功能齐全的非生产环境 **沙箱（sandbox）**，使人们可以在不影响真实用户的情况下，使用真实数据安全地探索和实验。
+* 在各个层次进行彻底的测试【3】，从单元测试、全系统集成测试到手动测试。自动化测试易于理解，已经被广泛使用，特别适合用来覆盖正常情况中少见的 **边缘场景（corner case）**。
+* 允许从人为错误中简单快速地恢复，以最大限度地减少失效情况带来的影响。例如，快速回滚配置变更，分批发布新代码（以便任何意外错误只影响一小部分用户），并提供数据重算工具（以备旧的计算出错）。
+* 配置详细和明确的监控，比如性能指标和错误率。在其他工程学科中这指的是 **遥测（telemetry）**（一旦火箭离开了地面，遥测技术对于跟踪发生的事情和理解失败是至关重要的）。监控可以向我们发出预警信号，并允许我们检查是否有任何地方违反了假设和约束。当出现问题时，指标数据对于问题诊断是非常宝贵的。
+* 良好的管理实践与充分的培训 —— 一个复杂而重要的方面，但超出了本书的范围。
+
+
+Humans design and build software systems, and the operators who keep the systems running are also human. Unlike machines, humans don’t just follow rules; their strength is being creative and adaptive in getting their job done. However, this characteristic also leads to unpredictability, and sometimes mistakes that can lead to failures, despite best intentions. For example, one study of large internet services found that configuration changes by operators were the leading cause of outages, whereas hardware faults (servers or network) played a role in only 10–25% of outages [[63](ch02.html#Oppenheimer2003)].
+
+It is tempting to label such problems as “human error” and to wish that they could be solved by better controlling human behavior through tighter procedures and compliance with rules. However, blaming people for mistakes is counterproductive. What we call “human error” is not really the cause of an incident, but rather a symptom of a problem with the sociotechnical system in which people are trying their best to do their jobs [[64](ch02.html#Dekker2017)].
+
+Various technical measures can help minimize the impact of human mistakes, including thorough testing [[34](ch02.html#Yuan2014)], rollback mechanisms for quickly reverting configuration changes, gradual roll-outs of new code, detailed and clear monitoring, observability tools for diagnosing production issues (see [“Problems with Distributed Systems”](ch01.html#sec_introduction_dist_sys_problems)), and well-designed interfaces that encourage “the right thing” and discourage “the wrong thing”.
 
 However, these things require an investment of time and money, and in the pragmatic reality of everyday business, organizations often prioritize revenue-generating activities over measures that increase their resilience against mistakes. If there is a choice between more features and more testing, many organizations understandably choose features. Given this choice, when a preventable mistake inevitably occurs, it does not make sense to blame the person who made the mistake—the problem is the organization’s priorities.
 
-Increasingly, organizations are adopting a culture of *blameless postmortems*: after an incident, the people involved are encouraged to share full details about what happened, without fear of punishment, since this allows others in the organization to learn how to prevent similar problems in the future [[65](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Allspaw2012)]. This process may uncover a need to change business priorities, a need to invest in areas that have been neglected, a need to change the incentives for the people involved, or some other systemic issue that needs to be brought to the management’s attention.
+Increasingly, organizations are adopting a culture of *blameless postmortems*: after an incident, the people involved are encouraged to share full details about what happened, without fear of punishment, since this allows others in the organization to learn how to prevent similar problems in the future [[65](ch02.html#Allspaw2012)]. This process may uncover a need to change business priorities, a need to invest in areas that have been neglected, a need to change the incentives for the people involved, or some other systemic issue that needs to be brought to the management’s attention.
 
-As a general principle, when investigating an incident, you should be suspicious of simplistic answers. “Bob should have been more careful when deploying that change” is not productive, but neither is “We must rewrite the backend in Haskell.” Instead, management should take the opportunity to learn the details of how the sociotechnical system works from the point of view of the people who work with it every day, and take steps to improve it based on this feedback [[64](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dekker2017)].
+As a general principle, when investigating an incident, you should be suspicious of simplistic answers. “Bob should have been more careful when deploying that change” is not productive, but neither is “We must rewrite the backend in Haskell.” Instead, management should take the opportunity to learn the details of how the sociotechnical system works from the point of view of the people who work with it every day, and take steps to improve it based on this feedback [[64](ch02.html#Dekker2017)].
 
 ### 可靠性到底有多重要？
 
+可靠性不仅仅是针对核电站和空中交通管制软件而言，我们也期望更多平凡的应用能可靠地运行。商务应用中的错误会导致生产力损失（也许数据报告不完整还会有法律风险），而电商网站的中断则可能会导致收入和声誉的巨大损失。
+
+即使在 “非关键” 应用中，我们也对用户负有责任。试想一位家长把所有的照片和孩子的视频储存在你的照片应用里【15】。如果数据库突然损坏，他们会感觉如何？他们可能会知道如何从备份恢复吗？
+
+在某些情况下，我们可能会选择牺牲可靠性来降低开发成本（例如为未经证实的市场开发产品原型）或运营成本（例如利润率极低的服务），但我们偷工减料时，应该清楚意识到自己在做什么。
+
+
 Reliability is not just for nuclear power stations and air traffic control—more mundane applications are also expected to work reliably. Bugs in business applications cause lost productivity (and legal risks if figures are reported incorrectly), and outages of e-commerce sites can have huge costs in terms of lost revenue and damage to reputation.
 
-In many applications, a temporary outage of a few minutes or even a few hours is tolerable [[66](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Sabo2023)], but permanent data loss or corruption would be catastrophic. Consider a parent who stores all their pictures and videos of their children in your photo application [[67](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Jurewitz2013)]. How would they feel if that database was suddenly corrupted? Would they know how to restore it from a backup?
+In many applications, a temporary outage of a few minutes or even a few hours is tolerable [[66](ch02.html#Sabo2023)], but permanent data loss or corruption would be catastrophic. Consider a parent who stores all their pictures and videos of their children in your photo application [[67](ch02.html#Jurewitz2013)]. How would they feel if that database was suddenly corrupted? Would they know how to restore it from a backup?
 
-As another example of how unreliable software can harm people, consider the Post Office Horizon scandal. Between 1999 and 2019, hundreds of people managing Post Office branches in Britain were convicted of theft or fraud because the accounting software showed a shortfall in their accounts. Eventually it became clear that many of these shortfalls were due to bugs in the software, and many convictions have since been overturned [[68](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Siddique2021)]. What led to this, probably the largest miscarriage of justice in British history, is the fact that English law assumes that computers operate correctly (and hence, evidence produced by computers is reliable) unless there is evidence to the contrary [[69](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bohm2022)]. Software engineers may laugh at the idea that software could ever be bug-free, but this is little solace to the people who were wrongfully imprisoned, declared bankrupt, or even committed suicide as a result of a wrongful conviction due to an unreliable computer system.
+As another example of how unreliable software can harm people, consider the Post Office Horizon scandal. Between 1999 and 2019, hundreds of people managing Post Office branches in Britain were convicted of theft or fraud because the accounting software showed a shortfall in their accounts. Eventually it became clear that many of these shortfalls were due to bugs in the software, and many convictions have since been overturned [[68](ch02.html#Siddique2021)]. What led to this, probably the largest miscarriage of justice in British history, is the fact that English law assumes that computers operate correctly (and hence, evidence produced by computers is reliable) unless there is evidence to the contrary [[69](ch02.html#Bohm2022)]. Software engineers may laugh at the idea that software could ever be bug-free, but this is little solace to the people who were wrongfully imprisoned, declared bankrupt, or even committed suicide as a result of a wrongful conviction due to an unreliable computer system.
 
 There are situations in which we may choose to sacrifice reliability in order to reduce development cost (e.g., when developing a prototype product for an unproven market)—but we should be very conscious of when we are cutting corners and keep in mind the potential consequences.
 
@@ -346,7 +474,7 @@ Even if a system is working reliably today, that doesn’t mean it will necessar
 
 *Scalability* is the term we use to describe a system’s ability to cope with increased load. Sometimes, when discussing scalability, people make comments along the lines of, “You’re not Google or Amazon. Stop worrying about scale and just use a relational database.” Whether this maxim applies to you depends on the type of application you are building.
 
-If you are building a new product that currently only has a small number of users, perhaps at a startup, the overriding engineering goal is usually to keep the system as simple and flexible as possible, so that you can easily modify and adapt the features of your product as you learn more about customers’ needs [[70](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#McKinley2015)]. In such an environment, it is counterproductive to worry about hypothetical scale that might be needed in the future: in the best case, investments in scalability are wasted effort and premature optimization; in the worst case, they lock you into an inflexible design and make it harder to evolve your application.
+If you are building a new product that currently only has a small number of users, perhaps at a startup, the overriding engineering goal is usually to keep the system as simple and flexible as possible, so that you can easily modify and adapt the features of your product as you learn more about customers’ needs [[70](ch02.html#McKinley2015)]. In such an environment, it is counterproductive to worry about hypothetical scale that might be needed in the future: in the best case, investments in scalability are wasted effort and premature optimization; in the worst case, they lock you into an inflexible design and make it harder to evolve your application.
 
 The reason is that scalability is not a one-dimensional label: it is meaningless to say “X is scalable” or “Y doesn’t scale.” Rather, discussing scalability means considering questions like:
 
@@ -358,7 +486,7 @@ If you succeed in making your application popular, and therefore handling a grow
 
 ### 描述负载
 
-首先，我们需要简洁地描述系统当前的负载；只有这样，我们才能讨论增长问题（如果我们的负载翻倍会发生什么？）。这通常是通过吞吐量来衡量的：例如，每秒向服务的请求数量、每天新增多少吉字节的数据，或者每小时有多少购物车结账。有时你关心某些变量的峰值，比如同时在线用户的数量，如[“案例研究：社交网络首页时间线”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_twitter)中所述。
+首先，我们需要简洁地描述系统当前的负载；只有这样，我们才能讨论增长问题（如果我们的负载翻倍会发生什么？）。这通常是通过吞吐量来衡量的：例如，每秒向服务的请求数量、每天新增多少吉字节的数据，或者每小时有多少购物车结账。有时你关心某些变量的峰值，比如同时在线用户的数量，如[“案例研究：社交网络首页时间线”](ch02.html#sec_introduction_twitter)中所述。
 
 负载的其他统计特性也可能影响访问模式，从而影响可扩展性需求。例如，你可能需要知道数据库中读写的比例、缓存的命中率，或每个用户的数据项数量（例如，社交网络案例研究中的关注者数量）。也许平均情况是你关心的，或许你的瓶颈由少数极端情况主导。这一切都取决于你特定应用的细节。
 
@@ -367,11 +495,11 @@ If you succeed in making your application popular, and therefore handling a grow
 - 当你以某种方式增加负载并保持系统资源（CPU、内存、网络带宽等）不变时，你的系统性能会受到什么影响？
 - 当你以某种方式增加负载时，如果你想保持性能不变，你需要增加多少资源？
 
-通常我们的目标是在最小化运行系统的成本的同时，保持系统性能符合SLA的要求（见[“响应时间指标的使用”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_slo_sla)）。所需的计算资源越多，成本就越高。可能某些类型的硬件比其他类型更具成本效益，随着新型硬件的出现，这些因素可能会随时间而变化。
+通常我们的目标是在最小化运行系统的成本的同时，保持系统性能符合SLA的要求（见[“响应时间指标的使用”](ch02.html#sec_introduction_slo_sla)）。所需的计算资源越多，成本就越高。可能某些类型的硬件比其他类型更具成本效益，随着新型硬件的出现，这些因素可能会随时间而变化。
 
-如果你可以通过加倍资源来处理双倍的负载，同时保持性能不变，我们就说你实现了*线性可扩展性*，这被认为是一件好事。偶尔也可能通过不到双倍的资源来处理双倍的负载，这得益于规模经济或更好的高峰负载分配[[71](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Warfield2023)，[72](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2023)]。更常见的情况是，成本增长超过线性，可能有许多原因导致这种低效。例如，如果你有大量数据，那么处理单个写请求可能涉及的工作量比你的数据量小的时候要多，即使请求的大小相同。
+如果你可以通过加倍资源来处理双倍的负载，同时保持性能不变，我们就说你实现了*线性可扩展性*，这被认为是一件好事。偶尔也可能通过不到双倍的资源来处理双倍的负载，这得益于规模经济或更好的高峰负载分配[[71](ch02.html#Warfield2023)，[72](ch02.html#Brooker2023)]。更常见的情况是，成本增长超过线性，可能有许多原因导致这种低效。例如，如果你有大量数据，那么处理单个写请求可能涉及的工作量比你的数据量小的时候要多，即使请求的大小相同。
 
-First, we need to succinctly describe the current load on the system; only then can we discuss growth questions (what happens if our load doubles?). Often this will be a measure of throughput: for example, the number of requests per second to a service, how many gigabytes of new data arrive per day, or the number of shopping cart checkouts per hour. Sometimes you care about the peak of some variable quantity, such as the number of simultaneously online users in [“Case Study: Social Network Home Timelines”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_twitter).
+First, we need to succinctly describe the current load on the system; only then can we discuss growth questions (what happens if our load doubles?). Often this will be a measure of throughput: for example, the number of requests per second to a service, how many gigabytes of new data arrive per day, or the number of shopping cart checkouts per hour. Sometimes you care about the peak of some variable quantity, such as the number of simultaneously online users in [“Case Study: Social Network Home Timelines”](ch02.html#sec_introduction_twitter).
 
 Often there are other statistical characteristics of the load that also affect the access patterns and hence the scalability requirements. For example, you may need to know the ratio of reads to writes in a database, the hit rate on a cache, or the number of data items per user (for example, the number of followers in the social network case study). Perhaps the average case is what matters for you, or perhaps your bottleneck is dominated by a small number of extreme cases. It all depends on the details of your particular application.
 
@@ -380,9 +508,9 @@ Once you have described the load on your system, you can investigate what happen
 - When you increase the load in a certain way and keep the system resources (CPUs, memory, network bandwidth, etc.) unchanged, how is the performance of your system affected?
 - When you increase the load in a certain way, how much do you need to increase the resources if you want to keep performance unchanged?
 
-Usually our goal is to keep the performance of the system within the requirements of the SLA (see [“Use of Response Time Metrics”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_slo_sla)) while also minimizing the cost of running the system. The greater the required computing resources, the higher the cost. It might be that some types of hardware are more cost-effective than others, and these factors may change over time as new types of hardware become available.
+Usually our goal is to keep the performance of the system within the requirements of the SLA (see [“Use of Response Time Metrics”](ch02.html#sec_introduction_slo_sla)) while also minimizing the cost of running the system. The greater the required computing resources, the higher the cost. It might be that some types of hardware are more cost-effective than others, and these factors may change over time as new types of hardware become available.
 
-If you can double the resources in order to handle twice the load, while keeping performance the same, we say that you have *linear scalability*, and this is considered a good thing. Occasionally it is possible to handle twice the load with less than double the resources, due to economies of scale or a better distribution of peak load [[71](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Warfield2023), [72](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2023)]. Much more likely is that the cost grows faster than linearly, and there may be many reasons for the inefficiency. For example, if you have a lot of data, then processing a single write request may involve more work than if you have a small amount of data, even if the size of the request is the same.
+If you can double the resources in order to handle twice the load, while keeping performance the same, we say that you have *linear scalability*, and this is considered a good thing. Occasionally it is possible to handle twice the load with less than double the resources, due to economies of scale or a better distribution of peak load [[71](ch02.html#Warfield2023), [72](ch02.html#Brooker2023)]. Much more likely is that the cost grows faster than linearly, and there may be many reasons for the inefficiency. For example, if you have a lot of data, then processing a single write request may involve more work than if you have a small amount of data, even if the size of the request is the same.
 
 ### 共享内存，共享磁盘，无共享架构
 
@@ -390,25 +518,25 @@ If you can double the resources in order to handle twice the load, while keeping
 
 在单台机器上，您可以通过使用多个进程或线程来实现并行性。属于同一进程的所有线程可以访问同一RAM，因此这种方法也被称为*共享内存架构*。共享内存方法的问题在于成本增长超过线性：拥有双倍硬件资源的高端机器通常的成本显著高于两倍。而且由于瓶颈，一台规模加倍的机器往往处理的负载不到两倍。
 
-另一种方法是*共享磁盘架构*，它使用多台拥有独立CPU和RAM的机器，但将数据存储在一个磁盘阵列上，这些磁盘阵列在机器之间通过快速网络共享：*网络附加存储*（NAS）或*存储区域网络*（SAN）。这种架构传统上用于本地数据仓库工作负载，但争用和锁定开销限制了共享磁盘方法的可扩展性[[73](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Stopford2009)]。
+另一种方法是*共享磁盘架构*，它使用多台拥有独立CPU和RAM的机器，但将数据存储在一个磁盘阵列上，这些磁盘阵列在机器之间通过快速网络共享：*网络附加存储*（NAS）或*存储区域网络*（SAN）。这种架构传统上用于本地数据仓库工作负载，但争用和锁定开销限制了共享磁盘方法的可扩展性[[73](ch02.html#Stopford2009)]。
 
-相比之下，*无共享架构* [[74](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Stonebraker1986)]（也称为*水平扩展*或*向外扩展*）获得了很大的流行。在这种方法中，我们使用一个具有多个节点的分布式系统，每个节点都拥有自己的CPU、RAM和磁盘。节点之间的任何协调都在软件层面通过常规网络完成。
+相比之下，*无共享架构* [[74](ch02.html#Stonebraker1986)]（也称为*水平扩展*或*向外扩展*）获得了很大的流行。在这种方法中，我们使用一个具有多个节点的分布式系统，每个节点都拥有自己的CPU、RAM和磁盘。节点之间的任何协调都在软件层面通过常规网络完成。
 
 无共享的优势在于它有潜力线性扩展，它可以使用提供最佳价格/性能比的任何硬件（特别是在云中），它可以随着负载的增减更容易地调整其硬件资源，并且通过在多个数据中心和地区分布系统，它可以实现更大的容错性。缺点是它需要显式的数据分区（见[链接即将到来]），并且带来了分布式系统的所有复杂性（见[链接即将到来]）。
 
-一些云原生数据库系统使用独立的服务来执行存储和事务处理（见[“存储与计算的分离”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_storage_compute)），多个计算节点共享访问同一个存储服务。这种模型与共享磁盘架构有些相似，但它避免了旧系统的可扩展性问题：存储服务不提供文件系统（NAS）或块设备（SAN）抽象，而是提供了专门为数据库需求设计的专用API[[75](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Antonopoulos2019_ch2)]。
+一些云原生数据库系统使用独立的服务来执行存储和事务处理（见[“存储与计算的分离”](ch01.html#sec_introduction_storage_compute)），多个计算节点共享访问同一个存储服务。这种模型与共享磁盘架构有些相似，但它避免了旧系统的可扩展性问题：存储服务不提供文件系统（NAS）或块设备（SAN）抽象，而是提供了专门为数据库需求设计的专用API[[75](ch02.html#Antonopoulos2019_ch2)]。
 
 The simplest way of increasing the hardware resources of a service is to move it to a more powerful machine. Individual CPU cores are no longer getting significantly faster, but you can buy a machine (or rent a cloud instance) with more CPU cores, more RAM, and more disk space. This approach is called *vertical scaling* or *scaling up*.
 
 You can get parallelism on a single machine by using multiple processes or threads. All the threads belonging to the same process can access the same RAM, and hence this approach is also called a *shared-memory architecture*. The problem with a shared-memory approach is that the cost grows faster than linearly: a high-end machine with twice the hardware resources typically costs significantly more than twice as much. And due to bottlenecks, a machine twice the size can often handle less than twice the load.
 
-Another approach is the *shared-disk architecture*, which uses several machines with independent CPUs and RAM, but which stores data on an array of disks that is shared between the machines, which are connected via a fast network: *Network-Attached Storage* (NAS) or *Storage Area Network* (SAN). This architecture has traditionally been used for on-premises data warehousing workloads, but contention and the overhead of locking limit the scalability of the shared-disk approach [[73](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Stopford2009)].
+Another approach is the *shared-disk architecture*, which uses several machines with independent CPUs and RAM, but which stores data on an array of disks that is shared between the machines, which are connected via a fast network: *Network-Attached Storage* (NAS) or *Storage Area Network* (SAN). This architecture has traditionally been used for on-premises data warehousing workloads, but contention and the overhead of locking limit the scalability of the shared-disk approach [[73](ch02.html#Stopford2009)].
 
-By contrast, the *shared-nothing architecture* [[74](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Stonebraker1986)] (also called *horizontal scaling* or *scaling out*) has gained a lot of popularity. In this approach, we use a distributed system with multiple nodes, each of which has its own CPUs, RAM, and disks. Any coordination between nodes is done at the software level, via a conventional network.
+By contrast, the *shared-nothing architecture* [[74](ch02.html#Stonebraker1986)] (also called *horizontal scaling* or *scaling out*) has gained a lot of popularity. In this approach, we use a distributed system with multiple nodes, each of which has its own CPUs, RAM, and disks. Any coordination between nodes is done at the software level, via a conventional network.
 
 The advantages of shared-nothing are that it has the potential to scale linearly, it can use whatever hardware offers the best price/performance ratio (especially in the cloud), it can more easily adjust its hardware resources as load increases or decreases, and it can achieve greater fault tolerance by distributing the system across multiple data centers and regions. The downsides are that it requires explicit data partitioning (see [Link to Come]), and it incurs all the complexity of distributed systems ([Link to Come]).
 
-Some cloud-native database systems use separate services for storage and transaction execution (see [“Separation of storage and compute”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_storage_compute)), with multiple compute nodes sharing access to the same storage service. This model has some similarity to a shared-disk architecture, but it avoids the scalability problems of older systems: instead of providing a filesystem (NAS) or block device (SAN) abstraction, the storage service offers a specialized API that is designed for the specific needs of the database [[75](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Antonopoulos2019_ch2)].
+Some cloud-native database systems use separate services for storage and transaction execution (see [“Separation of storage and compute”](ch01.html#sec_introduction_storage_compute)), with multiple compute nodes sharing access to the same storage service. This model has some similarity to a shared-disk architecture, but it avoids the scalability problems of older systems: instead of providing a filesystem (NAS) or block device (SAN) abstraction, the storage service offers a specialized API that is designed for the specific needs of the database [[75](ch02.html#Antonopoulos2019_ch2)].
 
 
 
@@ -418,17 +546,16 @@ Some cloud-native database systems use separate services for storage and transac
 
 此外，适用于某一负载水平的架构不太可能应对10倍的负载。因此，如果您正在处理一个快速增长的服务，很可能您需要在每个数量级负载增加时重新思考您的架构。由于应用的需求可能会发展变化，通常不值得提前超过一个数量级来规划未来的扩展需求。
 
-一个关于可扩展性的好的一般原则是将系统分解成可以相对独立运行的小组件。这是微服务背后的基本原则（见[“微服务与无服务器”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_microservices)）、分区（[链接即将到来]）、流处理（[链接即将到来]）和无共享架构。然而，挑战在于知道在应该在一起的事物和应该分开的事物之间划线的位置。关于微服务的设计指南可以在其他书籍中找到[[76](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Newman2021_ch2)]，我们将在[链接即将到来]中讨论无共享系统的分区。
+一个关于可扩展性的好的一般原则是将系统分解成可以相对独立运行的小组件。这是微服务背后的基本原则（见[“微服务与无服务器”](ch01.html#sec_introduction_microservices)）、分区（[链接即将到来]）、流处理（[链接即将到来]）和无共享架构。然而，挑战在于知道在应该在一起的事物和应该分开的事物之间划线的位置。关于微服务的设计指南可以在其他书籍中找到[[76](ch02.html#Newman2021_ch2)]，我们将在[链接即将到来]中讨论无共享系统的分区。
 
 另一个好的原则是不要让事情变得比必要的更复杂。如果单机数据库可以完成工作，它可能比复杂的分布式设置更可取。自动扩展系统（根据需求自动增加或减少资源）很酷，但如果您的负载相当可预测，手动扩展的系统可能会有更少的运营惊喜（见[链接即将到来]）。一个拥有五个服务的系统比拥有五十个服务的系统简单。好的架构通常涉及到方法的实用混合。
 
 
-
 The architecture of systems that operate at large scale is usually highly specific to the application—there is no such thing as a generic, one-size-fits-all scalable architecture (informally known as *magic scaling sauce*). For example, a system that is designed to handle 100,000 requests per second, each 1 kB in size, looks very different from a system that is designed for 3 requests per minute, each 2 GB in size—even though the two systems have the same data throughput (100 MB/sec).
 
 Moreover, an architecture that is appropriate for one level of load is unlikely to cope with 10 times that load. If you are working on a fast-growing service, it is therefore likely that you will need to rethink your architecture on every order of magnitude load increase. As the needs of the application are likely to evolve, it is usually not worth planning future scaling needs more than one order of magnitude in advance.
 
-A good general principle for scalability is to break a system down into smaller components that can operate largely independently from each other. This is the underlying principle behind microservices (see [“Microservices and Serverless”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_microservices)), partitioning ([Link to Come]), stream processing ([Link to Come]), and shared-nothing architectures. However, the challenge is in knowing where to draw the line between things that should be together, and things that should be apart. Design guidelines for microservices can be found in other books [[76](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Newman2021_ch2)], and we discuss partitioning of shared-nothing systems in [Link to Come].
+A good general principle for scalability is to break a system down into smaller components that can operate largely independently from each other. This is the underlying principle behind microservices (see [“Microservices and Serverless”](ch01.html#sec_introduction_microservices)), partitioning ([Link to Come]), stream processing ([Link to Come]), and shared-nothing architectures. However, the challenge is in knowing where to draw the line between things that should be together, and things that should be apart. Design guidelines for microservices can be found in other books [[76](ch02.html#Newman2021_ch2)], and we discuss partitioning of shared-nothing systems in [Link to Come].
 
 Another good principle is not to make things more complicated than necessary. If a single-machine database will do the job, it’s probably preferable to a complicated distributed setup. Auto-scaling systems (which automatically add or remove resources in response to demand) are cool, but if your load is fairly predictable, a manually scaled system may have fewer operational surprises (see [Link to Come]). A system with five services is simpler than one with fifty. Good architectures usually involve a pragmatic mixture of approaches.
 
@@ -451,9 +578,9 @@ Another good principle is not to make things more complicated than necessary. If
 
 Software does not wear out or suffer material fatigue, so it does not break in the same ways as mechanical objects do. But the requirements for an application frequently change, the environment that the software runs in changes (such as its dependencies and the underlying platform), and it has bugs that need fixing.
 
-It is widely recognized that the majority of the cost of software is not in its initial development, but in its ongoing maintenance—fixing bugs, keeping its systems operational, investigating failures, adapting it to new platforms, modifying it for new use cases, repaying technical debt, and adding new features [[77](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Ensmenger2016), [78](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Glass2002)].
+It is widely recognized that the majority of the cost of software is not in its initial development, but in its ongoing maintenance—fixing bugs, keeping its systems operational, investigating failures, adapting it to new platforms, modifying it for new use cases, repaying technical debt, and adding new features [[77](ch02.html#Ensmenger2016), [78](ch02.html#Glass2002)].
 
-However, maintenance is also difficult. If a system has been successfully running for a long time, it may well use outdated technologies that not many engineers understand today (such as mainframes and COBOL code); institutional knowledge of how and why a system was designed in a certain way may have been lost as people have left the organization; it might be necessary to fix other people’s mistakes. Moreover, the computer system is often intertwined with the human organization that it supports, which means that maintenance of such *legacy* systems is as much a people problem as a technical one [[79](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bellotti2021)].
+However, maintenance is also difficult. If a system has been successfully running for a long time, it may well use outdated technologies that not many engineers understand today (such as mainframes and COBOL code); institutional knowledge of how and why a system was designed in a certain way may have been lost as people have left the organization; it might be necessary to fix other people’s mistakes. Moreover, the computer system is often intertwined with the human organization that it supports, which means that maintenance of such *legacy* systems is as much a people problem as a technical one [[79](ch02.html#Bellotti2021)].
 
 Every system we create today will one day become a legacy system if it is valuable enough to survive for a long time. In order to minimize the pain for future generations who need to maintain our software, we should design it with maintenance concerns in mind. Although we cannot always predict which decisions might create maintenance headaches in the future, in this book we will pay attention to several principles that are widely applicable:
 
@@ -473,7 +600,7 @@ Every system we create today will one day become a legacy system if it is valuab
 
 ### 可操作性：人生苦短，关爱运维
 
-有人认为，“良好的运维经常可以绕开垃圾（或不完整）软件的局限性，而再好的软件摊上垃圾运维也没法可靠运行”。尽管运维的某些方面可以，而且应该是自动化的，但在最初建立正确运作的自动化机制仍然取决于人。
+我们先前在[云时代的运营](ch1.md#在云时代的运营)中讨论过运维的角色，不难发现在这个过程中人类扮演的角色至少也是与工具一样重要的。 实际上有人认为，“良好的运维经常可以绕开垃圾（或不完整）软件的局限性，而再好的软件摊上垃圾运维也没法可靠运行”。尽管运维的某些方面可以，而且应该是自动化的，但在最初建立正确运作的自动化机制仍然取决于人。
 
 运维团队对于保持软件系统顺利运行至关重要。一个优秀运维团队的典型职责如下（或者更多）【29】：
 
@@ -498,15 +625,15 @@ Every system we create today will one day become a legacy system if it is valuab
 * 有条件时进行自我修复，但需要时也允许管理员手动控制系统状态。
 * 行为可预测，最大限度减少意外。
 
-We previously discussed the role of operations in [“Operations in the Cloud Era”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_operations), and we saw that human processes are at least as important for reliable operations as software tools. In fact, it has been suggested that “good operations can often work around the limitations of bad (or incomplete) software, but good software cannot run reliably with bad operations” [[54](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kreps2012_ch1)].
+We previously discussed the role of operations in [“Operations in the Cloud Era”](ch01.html#sec_introduction_operations), and we saw that human processes are at least as important for reliable operations as software tools. In fact, it has been suggested that “good operations can often work around the limitations of bad (or incomplete) software, but good software cannot run reliably with bad operations” [[54](ch02.html#Kreps2012_ch1)].
 
-In large-scale systems consisting of many thousands of machines, manual maintenance would be unreasonably expensive, and automation is essential. However, automation can be a two-edged sword: there will always be edge cases (such as rare failure scenarios) that require manual intervention from the operations team. Since the cases that cannot be handled automatically are the most complex issues, greater automation requires a *more* skilled operations team that can resolve those issues [[80](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bainbridge1983)].
+In large-scale systems consisting of many thousands of machines, manual maintenance would be unreasonably expensive, and automation is essential. However, automation can be a two-edged sword: there will always be edge cases (such as rare failure scenarios) that require manual intervention from the operations team. Since the cases that cannot be handled automatically are the most complex issues, greater automation requires a *more* skilled operations team that can resolve those issues [[80](ch02.html#Bainbridge1983)].
 
 Moreover, if an automated system goes wrong, it is often harder to troubleshoot than a system that relies on an operator to perform some actions manually. For that reason, it is not the case that more automation is always better for operability. However, some amount of automation is important, and the sweet spot will depend on the specifics of your particular application and organization.
 
-Good operability means making routine tasks easy, allowing the operations team to focus their efforts on high-value activities. Data systems can do various things to make routine tasks easy, including [[81](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hamilton2007)]:
+Good operability means making routine tasks easy, allowing the operations team to focus their efforts on high-value activities. Data systems can do various things to make routine tasks easy, including [[81](ch02.html#Hamilton2007)]:
 
-- Allowing monitoring tools to check the system’s key metrics, and supporting observability tools (see [“Problems with Distributed Systems”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_dist_sys_problems)) to give insights into the system’s runtime behavior. A variety of commercial and open source tools can help here [[82](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Horovits2021)].
+- Allowing monitoring tools to check the system’s key metrics, and supporting observability tools (see [“Problems with Distributed Systems”](ch01.html#sec_introduction_dist_sys_problems)) to give insights into the system’s runtime behavior. A variety of commercial and open source tools can help here [[82](ch02.html#Horovits2021)].
 - Avoiding dependency on individual machines (allowing machines to be taken down for maintenance while the system as a whole continues running uninterrupted)
 - Providing good documentation and an easy-to-understand operational model (“If I do X, Y will happen”)
 - Providing good default behavior, but also giving administrators the freedom to override defaults when needed
@@ -535,19 +662,19 @@ Good operability means making routine tasks easy, allowing the operations team t
 
 本书将紧盯那些允许我们将大型系统的部分提取为定义明确的、可重用的组件的优秀抽象。
 
-Small software projects can have delightfully simple and expressive code, but as projects get larger, they often become very complex and difficult to understand. This complexity slows down everyone who needs to work on the system, further increasing the cost of maintenance. A software project mired in complexity is sometimes described as a *big ball of mud* [[83](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Foote1997)].
+Small software projects can have delightfully simple and expressive code, but as projects get larger, they often become very complex and difficult to understand. This complexity slows down everyone who needs to work on the system, further increasing the cost of maintenance. A software project mired in complexity is sometimes described as a *big ball of mud* [[83](ch02.html#Foote1997)].
 
-When complexity makes maintenance hard, budgets and schedules are often overrun. In complex software, there is also a greater risk of introducing bugs when making a change: when the system is harder for developers to understand and reason about, hidden assumptions, unintended consequences, and unexpected interactions are more easily overlooked [[62](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Woods2017)]. Conversely, reducing complexity greatly improves the maintainability of software, and thus simplicity should be a key goal for the systems we build.
+When complexity makes maintenance hard, budgets and schedules are often overrun. In complex software, there is also a greater risk of introducing bugs when making a change: when the system is harder for developers to understand and reason about, hidden assumptions, unintended consequences, and unexpected interactions are more easily overlooked [[62](ch02.html#Woods2017)]. Conversely, reducing complexity greatly improves the maintainability of software, and thus simplicity should be a key goal for the systems we build.
 
-Simple systems are easier to understand, and therefore we should try to solve a given problem in the simplest way possible. Unfortunately, this is easier said than done. Whether something is simple or not is often a subjective matter of taste, as there is no objective standard of simplicity [[84](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2022)]. For example, one system may hide a complex implementation behind a simple interface, whereas another may have a simple implementation that exposes more internal detail to its users—which one is simpler?
+Simple systems are easier to understand, and therefore we should try to solve a given problem in the simplest way possible. Unfortunately, this is easier said than done. Whether something is simple or not is often a subjective matter of taste, as there is no objective standard of simplicity [[84](ch02.html#Brooker2022)]. For example, one system may hide a complex implementation behind a simple interface, whereas another may have a simple implementation that exposes more internal detail to its users—which one is simpler?
 
-One attempt at reasoning about complexity has been to break it down into two categories, *essential* and *accidental* complexity [[85](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooks1995)]. The idea is that essential complexity is inherent in the problem domain of the application, while accidental complexity arises only because of limitations of our tooling. Unfortunately, this distinction is also flawed, because boundaries between the essential and the accidental shift as our tooling evolves [[86](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Luu2020)].
+One attempt at reasoning about complexity has been to break it down into two categories, *essential* and *accidental* complexity [[85](ch02.html#Brooks1995)]. The idea is that essential complexity is inherent in the problem domain of the application, while accidental complexity arises only because of limitations of our tooling. Unfortunately, this distinction is also flawed, because boundaries between the essential and the accidental shift as our tooling evolves [[86](ch02.html#Luu2020)].
 
 One of the best tools we have for managing complexity is *abstraction*. A good abstraction can hide a great deal of implementation detail behind a clean, simple-to-understand façade. A good abstraction can also be used for a wide range of different applications. Not only is this reuse more efficient than reimplementing a similar thing multiple times, but it also leads to higher-quality software, as quality improvements in the abstracted component benefit all applications that use it.
 
 For example, high-level programming languages are abstractions that hide machine code, CPU registers, and syscalls. SQL is an abstraction that hides complex on-disk and in-memory data structures, concurrent requests from other clients, and inconsistencies after crashes. Of course, when programming in a high-level language, we are still using machine code; we are just not using it *directly*, because the programming language abstraction saves us from having to think about it.
 
-Abstractions for application code, which aim to reduce its complexity, can be created using methodologies such as *design patterns* [[87](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gamma1994)] and *domain-driven design* (DDD) [[88](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Evans2003)]. This book is not about such application-specific abstractions, but rather about general-purpose abstractions on top of which you can build your applications, such as database transactions, indexes, and event logs. If you want to use techniques such as DDD, you can implement them on top of the foundations described in this book.
+Abstractions for application code, which aim to reduce its complexity, can be created using methodologies such as *design patterns* [[87](ch02.html#Gamma1994)] and *domain-driven design* (DDD) [[88](ch02.html#Evans2003)]. This book is not about such application-specific abstractions, but rather about general-purpose abstractions on top of which you can build your applications, such as database transactions, indexes, and event logs. If you want to use techniques such as DDD, you can implement them on top of the foundations described in this book.
 
 ### 可演化性：让变更更容易
 
@@ -564,9 +691,9 @@ It’s extremely unlikely that your system’s requirements will remain unchange
 
 In terms of organizational processes, *Agile* working patterns provide a framework for adapting to change. The Agile community has also developed technical tools and processes that are helpful when developing software in a frequently changing environment, such as test-driven development (TDD) and refactoring. In this book, we search for ways of increasing agility at the level of a system consisting of several different applications or services with different characteristics.
 
-The ease with which you can modify a data system, and adapt it to changing requirements, is closely linked to its simplicity and its abstractions: simple and easy-to-understand systems are usually easier to modify than complex ones. Since this is such an important idea, we will use a different word to refer to agility on a data system level: *evolvability* [[89](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Breivold2008)].
+The ease with which you can modify a data system, and adapt it to changing requirements, is closely linked to its simplicity and its abstractions: simple and easy-to-understand systems are usually easier to modify than complex ones. Since this is such an important idea, we will use a different word to refer to agility on a data system level: *evolvability* [[89](ch02.html#Breivold2008)].
 
-One major factor that makes change difficult in large systems is when some action is irreversible, and therefore that action needs to be taken very carefully [[90](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Zaninotto2002)]. For example, say you are migrating from one database to another: if you cannot switch back to the old system in case of problems wth the new one, the stakes are much higher than if you can easily go back. Minimizing irreversibility improves flexibility.
+One major factor that makes change difficult in large systems is when some action is irreversible, and therefore that action needs to be taken very carefully [[90](ch02.html#Zaninotto2002)]. For example, say you are migrating from one database to another: if you cannot switch back to the old system in case of problems wth the new one, the stakes are much higher than if you can easily go back. Minimizing irreversibility improves flexibility.
 
 
 
@@ -598,185 +725,185 @@ Finally, we examined several facets of maintainability, including supporting the
 
 ## 参考文献
 
-[[1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cvet2016-marker)] Mike Cvet. [How We Learned to Stop Worrying and Love Fan-In at Twitter](https://www.youtube.com/watch?v=WEgCjwyXvwc). At *QCon San Francisco*, December 2016.
+[[1](ch02.html#Cvet2016-marker)] Mike Cvet. [How We Learned to Stop Worrying and Love Fan-In at Twitter](https://www.youtube.com/watch?v=WEgCjwyXvwc). At *QCon San Francisco*, December 2016.
 
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+[[2](ch02.html#Krikorian2012_ch2-marker)] Raffi Krikorian. [Timelines at Scale](http://www.infoq.com/presentations/Twitter-Timeline-Scalability). At *QCon San Francisco*, November 2012. Archived at [perma.cc/V9G5-KLYK](https://perma.cc/V9G5-KLYK)
 
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+[[3](ch02.html#Twitter2023-marker)] Twitter. [Twitter’s Recommendation Algorithm](https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm). *blog.twitter.com*, March 2023. Archived at [perma.cc/L5GT-229T](https://perma.cc/L5GT-229T)
 
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+[[4](ch02.html#Krikorian2013-marker)] Raffi Krikorian. [New Tweets per second record, and how!](https://blog.twitter.com/engineering/en_us/a/2013/new-tweets-per-second-record-and-how) *blog.twitter.com*, August 2013. Archived at [perma.cc/6JZN-XJYN](https://perma.cc/6JZN-XJYN)
 
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+[[5](ch02.html#Axon2010_ch2-marker)] Samuel Axon. [3% of Twitter’s Servers Dedicated to Justin Bieber](http://mashable.com/2010/09/07/justin-bieber-twitter/). *mashable.com*, September 2010. Archived at [perma.cc/F35N-CGVX](https://perma.cc/F35N-CGVX)
 
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+[[6](ch02.html#Bronson2021-marker)] Nathan Bronson, Abutalib Aghayev, Aleksey Charapko, and Timothy Zhu. [Metastable Failures in Distributed Systems](https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s11-bronson.pdf). At *Workshop on Hot Topics in Operating Systems* (HotOS), May 2021. [doi:10.1145/3458336.3465286](https://doi.org/10.1145/3458336.3465286)
 
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+[[7](ch02.html#Brooker2021-marker)] Marc Brooker. [Metastability and Distributed Systems](https://brooker.co.za/blog/2021/05/24/metastable.html). *brooker.co.za*, May 2021. Archived at [archive.org](https://web.archive.org/web/20230324043015/https://brooker.co.za/blog/2021/05/24/metastable.html)
 
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+[[8](ch02.html#Brooker2015-marker)] Marc Brooker. [Exponential Backoff And Jitter](https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/). *aws.amazon.com*, March 2015. Archived at [perma.cc/R6MS-AZKH](https://perma.cc/R6MS-AZKH)
 
-[[9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2022backoff-marker)] Marc Brooker. [What is Backoff For?](https://brooker.co.za/blog/2022/08/11/backoff.html) *brooker.co.za*, August 2022. Archived at [archive.org](https://web.archive.org/web/20230331022111/https://brooker.co.za/blog/2022/08/11/backoff.html)
+[[9](ch02.html#Brooker2022backoff-marker)] Marc Brooker. [What is Backoff For?](https://brooker.co.za/blog/2022/08/11/backoff.html) *brooker.co.za*, August 2022. Archived at [archive.org](https://web.archive.org/web/20230331022111/https://brooker.co.za/blog/2022/08/11/backoff.html)
 
-[[10](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Nygard2018-marker)] Michael T. Nygard. [*Release It!*](https://learning.oreilly.com/library/view/release-it-2nd/9781680504552/), 2nd Edition. Pragmatic Bookshelf, January 2018. ISBN: 9781680502398
+[[10](ch02.html#Nygard2018-marker)] Michael T. Nygard. [*Release It!*](https://learning.oreilly.com/library/view/release-it-2nd/9781680504552/), 2nd Edition. Pragmatic Bookshelf, January 2018. ISBN: 9781680502398
 
-[[11](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2022retries-marker)] Marc Brooker. [Fixing retries with token buckets and circuit breakers](https://brooker.co.za/blog/2022/02/28/retries.html). *brooker.co.za*, February 2022. Archived at [archive.org](https://web.archive.org/web/20230325195445/https://brooker.co.za/blog/2022/02/28/retries.html)
+[[11](ch02.html#Brooker2022retries-marker)] Marc Brooker. [Fixing retries with token buckets and circuit breakers](https://brooker.co.za/blog/2022/02/28/retries.html). *brooker.co.za*, February 2022. Archived at [archive.org](https://web.archive.org/web/20230325195445/https://brooker.co.za/blog/2022/02/28/retries.html)
 
-[[12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#YanacekLoadShedding-marker)] David Yanacek. [Using load shedding to avoid overload](https://aws.amazon.com/builders-library/using-load-shedding-to-avoid-overload/). Amazon Builders’ Library, *aws.amazon.com*. Archived at [perma.cc/9SAW-68MP](https://perma.cc/9SAW-68MP)
+[[12](ch02.html#YanacekLoadShedding-marker)] David Yanacek. [Using load shedding to avoid overload](https://aws.amazon.com/builders-library/using-load-shedding-to-avoid-overload/). Amazon Builders’ Library, *aws.amazon.com*. Archived at [perma.cc/9SAW-68MP](https://perma.cc/9SAW-68MP)
 
-[[13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Sackman2016_ch2-marker)] Matthew Sackman. [Pushing Back](https://wellquite.org/posts/lshift/pushing_back/). *wellquite.org*, May 2016. Archived at [perma.cc/3KCZ-RUFY](https://perma.cc/3KCZ-RUFY)
+[[13](ch02.html#Sackman2016_ch2-marker)] Matthew Sackman. [Pushing Back](https://wellquite.org/posts/lshift/pushing_back/). *wellquite.org*, May 2016. Archived at [perma.cc/3KCZ-RUFY](https://perma.cc/3KCZ-RUFY)
 
-[[14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kopytkov2018-marker)] Dmitry Kopytkov and Patrick Lee. [Meet Bandaid, the Dropbox service proxy](https://dropbox.tech/infrastructure/meet-bandaid-the-dropbox-service-proxy). *dropbox.tech*, March 2018. Archived at [perma.cc/KUU6-YG4S](https://perma.cc/KUU6-YG4S)
+[[14](ch02.html#Kopytkov2018-marker)] Dmitry Kopytkov and Patrick Lee. [Meet Bandaid, the Dropbox service proxy](https://dropbox.tech/infrastructure/meet-bandaid-the-dropbox-service-proxy). *dropbox.tech*, March 2018. Archived at [perma.cc/KUU6-YG4S](https://perma.cc/KUU6-YG4S)
 
-[[15](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gunawi2018-marker)] Haryadi S. Gunawi, Riza O. Suminto, Russell Sears, Casey Golliher, Swaminathan Sundararaman, Xing Lin, Tim Emami, Weiguang Sheng, Nematollah Bidokhti, Caitie McCaffrey, Gary Grider, Parks M. Fields, Kevin Harms, Robert B. Ross, Andree Jacobson, Robert Ricci, Kirk Webb, Peter Alvaro, H. Birali Runesha, Mingzhe Hao, and Huaicheng Li. [Fail-Slow at Scale: Evidence of Hardware Performance Faults in Large Production Systems](https://www.usenix.org/system/files/conference/fast18/fast18-gunawi.pdf). At *16th USENIX Conference on File and Storage Technologies*, February 2018.
+[[15](ch02.html#Gunawi2018-marker)] Haryadi S. Gunawi, Riza O. Suminto, Russell Sears, Casey Golliher, Swaminathan Sundararaman, Xing Lin, Tim Emami, Weiguang Sheng, Nematollah Bidokhti, Caitie McCaffrey, Gary Grider, Parks M. Fields, Kevin Harms, Robert B. Ross, Andree Jacobson, Robert Ricci, Kirk Webb, Peter Alvaro, H. Birali Runesha, Mingzhe Hao, and Huaicheng Li. [Fail-Slow at Scale: Evidence of Hardware Performance Faults in Large Production Systems](https://www.usenix.org/system/files/conference/fast18/fast18-gunawi.pdf). At *16th USENIX Conference on File and Storage Technologies*, February 2018.
 
-[[16](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#DeCandia2007_ch1-marker)] Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall, and Werner Vogels. [Dynamo: Amazon’s Highly Available Key-Value Store](http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf). At *21st ACM Symposium on Operating Systems Principles* (SOSP), October 2007. [doi:10.1145/1294261.1294281](https://doi.org/10.1145/1294261.1294281)
+[[16](ch02.html#DeCandia2007_ch1-marker)] Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall, and Werner Vogels. [Dynamo: Amazon’s Highly Available Key-Value Store](http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf). At *21st ACM Symposium on Operating Systems Principles* (SOSP), October 2007. [doi:10.1145/1294261.1294281](https://doi.org/10.1145/1294261.1294281)
 
-[[17](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Whitenton2020-marker)] Kathryn Whitenton. [The Need for Speed, 23 Years Later](https://www.nngroup.com/articles/the-need-for-speed/). *nngroup.com*, May 2020. Archived at [perma.cc/C4ER-LZYA](https://perma.cc/C4ER-LZYA)
+[[17](ch02.html#Whitenton2020-marker)] Kathryn Whitenton. [The Need for Speed, 23 Years Later](https://www.nngroup.com/articles/the-need-for-speed/). *nngroup.com*, May 2020. Archived at [perma.cc/C4ER-LZYA](https://perma.cc/C4ER-LZYA)
 
-[[18](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Linden2006-marker)] Greg Linden. [Marissa Mayer at Web 2.0](https://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html). *glinden.blogspot.com*, November 2005. Archived at [perma.cc/V7EA-3VXB](https://perma.cc/V7EA-3VXB)
+[[18](ch02.html#Linden2006-marker)] Greg Linden. [Marissa Mayer at Web 2.0](https://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html). *glinden.blogspot.com*, November 2005. Archived at [perma.cc/V7EA-3VXB](https://perma.cc/V7EA-3VXB)
 
-[[19](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brutlag2009-marker)] Jake Brutlag. [Speed Matters for Google Web Search](https://services.google.com/fh/files/blogs/google_delayexp.pdf). *services.google.com*, June 2009. Archived at [perma.cc/BK7R-X7M2](https://perma.cc/BK7R-X7M2)
+[[19](ch02.html#Brutlag2009-marker)] Jake Brutlag. [Speed Matters for Google Web Search](https://services.google.com/fh/files/blogs/google_delayexp.pdf). *services.google.com*, June 2009. Archived at [perma.cc/BK7R-X7M2](https://perma.cc/BK7R-X7M2)
 
-[[20](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schurman2009-marker)] Eric Schurman and Jake Brutlag. [Performance Related Changes and their User Impact](https://www.youtube.com/watch?v=bQSE51-gr2s). Talk at *Velocity 2009*.
+[[20](ch02.html#Schurman2009-marker)] Eric Schurman and Jake Brutlag. [Performance Related Changes and their User Impact](https://www.youtube.com/watch?v=bQSE51-gr2s). Talk at *Velocity 2009*.
 
-[[21](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Akamai2017-marker)] Akamai Technologies, Inc. [The State of Online Retail Performance](https://web.archive.org/web/20210729180749/https://www.akamai.com/us/en/multimedia/documents/report/akamai-state-of-online-retail-performance-spring-2017.pdf). *akamai.com*, April 2017. Archived at [perma.cc/UEK2-HYCS](https://perma.cc/UEK2-HYCS)
+[[21](ch02.html#Akamai2017-marker)] Akamai Technologies, Inc. [The State of Online Retail Performance](https://web.archive.org/web/20210729180749/https://www.akamai.com/us/en/multimedia/documents/report/akamai-state-of-online-retail-performance-spring-2017.pdf). *akamai.com*, April 2017. Archived at [perma.cc/UEK2-HYCS](https://perma.cc/UEK2-HYCS)
 
-[[22](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bai2017-marker)] Xiao Bai, Ioannis Arapakis, B. Barla Cambazoglu, and Ana Freire. [Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search](https://iarapakis.github.io/papers/TOIS17.pdf). *ACM Transactions on Information Systems*, volume 36, issue 2, article 21, April 2018. [doi:10.1145/3106372](https://doi.org/10.1145/3106372)
+[[22](ch02.html#Bai2017-marker)] Xiao Bai, Ioannis Arapakis, B. Barla Cambazoglu, and Ana Freire. [Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search](https://iarapakis.github.io/papers/TOIS17.pdf). *ACM Transactions on Information Systems*, volume 36, issue 2, article 21, April 2018. [doi:10.1145/3106372](https://doi.org/10.1145/3106372)
 
-[[23](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dean2013-marker)] Jeffrey Dean and Luiz André Barroso. [The Tail at Scale](http://cacm.acm.org/magazines/2013/2/160173-the-tail-at-scale/fulltext). *Communications of the ACM*, volume 56, issue 2, pages 74–80, February 2013. [doi:10.1145/2408776.2408794](https://doi.org/10.1145/2408776.2408794)
+[[23](ch02.html#Dean2013-marker)] Jeffrey Dean and Luiz André Barroso. [The Tail at Scale](http://cacm.acm.org/magazines/2013/2/160173-the-tail-at-scale/fulltext). *Communications of the ACM*, volume 56, issue 2, pages 74–80, February 2013. [doi:10.1145/2408776.2408794](https://doi.org/10.1145/2408776.2408794)
 
-[[24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hidalgo2020-marker)] Alex Hidalgo. [*Implementing Service Level Objectives: A Practical Guide to SLIs, SLOs, and Error Budgets*](https://www.oreilly.com/library/view/implementing-service-level/9781492076803/). O’Reilly Media, September 2020. ISBN: 1492076813
+[[24](ch02.html#Hidalgo2020-marker)] Alex Hidalgo. [*Implementing Service Level Objectives: A Practical Guide to SLIs, SLOs, and Error Budgets*](https://www.oreilly.com/library/view/implementing-service-level/9781492076803/). O’Reilly Media, September 2020. ISBN: 1492076813
 
-[[25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Mogul2019-marker)] Jeffrey C. Mogul and John Wilkes. [Nines are Not Enough: Meaningful Metrics for Clouds](https://research.google/pubs/pub48033/). At *17th Workshop on Hot Topics in Operating Systems* (HotOS), May 2019. [doi:10.1145/3317550.3321432](https://doi.org/10.1145/3317550.3321432)
+[[25](ch02.html#Mogul2019-marker)] Jeffrey C. Mogul and John Wilkes. [Nines are Not Enough: Meaningful Metrics for Clouds](https://research.google/pubs/pub48033/). At *17th Workshop on Hot Topics in Operating Systems* (HotOS), May 2019. [doi:10.1145/3317550.3321432](https://doi.org/10.1145/3317550.3321432)
 
-[[26](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hauer2020-marker)] Tamás Hauer, Philipp Hoffmann, John Lunney, Dan Ardelean, and Amer Diwan. [Meaningful Availability](https://www.usenix.org/conference/nsdi20/presentation/hauer). At *17th USENIX Symposium on Networked Systems Design and Implementation* (NSDI), February 2020.
+[[26](ch02.html#Hauer2020-marker)] Tamás Hauer, Philipp Hoffmann, John Lunney, Dan Ardelean, and Amer Diwan. [Meaningful Availability](https://www.usenix.org/conference/nsdi20/presentation/hauer). At *17th USENIX Symposium on Networked Systems Design and Implementation* (NSDI), February 2020.
 
-[[27](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dunning2021-marker)] Ted Dunning. [The t-digest: Efficient estimates of distributions](https://www.sciencedirect.com/science/article/pii/S2665963820300403). *Software Impacts*, volume 7, article 100049, February 2021. [doi:10.1016/j.simpa.2020.100049](https://doi.org/10.1016/j.simpa.2020.100049)
+[[27](ch02.html#Dunning2021-marker)] Ted Dunning. [The t-digest: Efficient estimates of distributions](https://www.sciencedirect.com/science/article/pii/S2665963820300403). *Software Impacts*, volume 7, article 100049, February 2021. [doi:10.1016/j.simpa.2020.100049](https://doi.org/10.1016/j.simpa.2020.100049)
 
-[[28](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kohn2021-marker)] David Kohn. [How percentile approximation works (and why it’s more useful than averages)](https://www.timescale.com/blog/how-percentile-approximation-works-and-why-its-more-useful-than-averages/). *timescale.com*, September 2021. Archived at [perma.cc/3PDP-NR8B](https://perma.cc/3PDP-NR8B)
+[[28](ch02.html#Kohn2021-marker)] David Kohn. [How percentile approximation works (and why it’s more useful than averages)](https://www.timescale.com/blog/how-percentile-approximation-works-and-why-its-more-useful-than-averages/). *timescale.com*, September 2021. Archived at [perma.cc/3PDP-NR8B](https://perma.cc/3PDP-NR8B)
 
-[[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hartmann2020-marker)] Heinrich Hartmann and Theo Schlossnagle. [Circllhist — A Log-Linear Histogram Data Structure for IT Infrastructure Monitoring](https://arxiv.org/pdf/2001.06561.pdf). *arxiv.org*, January 2020.
+[[29](ch02.html#Hartmann2020-marker)] Heinrich Hartmann and Theo Schlossnagle. [Circllhist — A Log-Linear Histogram Data Structure for IT Infrastructure Monitoring](https://arxiv.org/pdf/2001.06561.pdf). *arxiv.org*, January 2020.
 
-[[30](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Masson2019-marker)] Charles Masson, Jee E. Rim, and Homin K. Lee. [DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees](http://www.vldb.org/pvldb/vol12/p2195-masson.pdf). *Proceedings of the VLDB Endowment*, volume 12, issue 12, pages 2195–2205, August 2019. [doi:10.14778/3352063.3352135](https://doi.org/10.14778/3352063.3352135)
+[[30](ch02.html#Masson2019-marker)] Charles Masson, Jee E. Rim, and Homin K. Lee. [DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees](http://www.vldb.org/pvldb/vol12/p2195-masson.pdf). *Proceedings of the VLDB Endowment*, volume 12, issue 12, pages 2195–2205, August 2019. [doi:10.14778/3352063.3352135](https://doi.org/10.14778/3352063.3352135)
 
-[[31](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schwartz2015-marker)] Baron Schwartz. [Why Percentiles Don’t Work the Way You Think](https://orangematter.solarwinds.com/2016/11/18/why-percentiles-dont-work-the-way-you-think/). *solarwinds.com*, November 2016. Archived at [perma.cc/469T-6UGB](https://perma.cc/469T-6UGB)
+[[31](ch02.html#Schwartz2015-marker)] Baron Schwartz. [Why Percentiles Don’t Work the Way You Think](https://orangematter.solarwinds.com/2016/11/18/why-percentiles-dont-work-the-way-you-think/). *solarwinds.com*, November 2016. Archived at [perma.cc/469T-6UGB](https://perma.cc/469T-6UGB)
 
-[[32](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Heimerdinger1992-marker)] Walter L. Heimerdinger and Charles B. Weinstock. [A Conceptual Framework for System Fault Tolerance](https://resources.sei.cmu.edu/asset_files/TechnicalReport/1992_005_001_16112.pdf). Technical Report CMU/SEI-92-TR-033, Software Engineering Institute, Carnegie Mellon University, October 1992. Archived at [perma.cc/GD2V-DMJW](https://perma.cc/GD2V-DMJW)
+[[32](ch02.html#Heimerdinger1992-marker)] Walter L. Heimerdinger and Charles B. Weinstock. [A Conceptual Framework for System Fault Tolerance](https://resources.sei.cmu.edu/asset_files/TechnicalReport/1992_005_001_16112.pdf). Technical Report CMU/SEI-92-TR-033, Software Engineering Institute, Carnegie Mellon University, October 1992. Archived at [perma.cc/GD2V-DMJW](https://perma.cc/GD2V-DMJW)
 
-[[33](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gaertner1999-marker)] Felix C. Gärtner. [Fundamentals of fault-tolerant distributed computing in asynchronous environments](https://dl.acm.org/doi/pdf/10.1145/311531.311532). *ACM Computing Surveys*, volume 31, issue 1, pages 1–26, March 1999. [doi:10.1145/311531.311532](https://doi.org/10.1145/311531.311532)
+[[33](ch02.html#Gaertner1999-marker)] Felix C. Gärtner. [Fundamentals of fault-tolerant distributed computing in asynchronous environments](https://dl.acm.org/doi/pdf/10.1145/311531.311532). *ACM Computing Surveys*, volume 31, issue 1, pages 1–26, March 1999. [doi:10.1145/311531.311532](https://doi.org/10.1145/311531.311532)
 
-[[34](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Yuan2014-marker)] Ding Yuan, Yu Luo, Xin Zhuang, Guilherme Renna Rodrigues, Xu Zhao, Yongle Zhang, Pranay U. Jain, and Michael Stumm. [Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf). At *11th USENIX Symposium on Operating Systems Design and Implementation* (OSDI), October 2014.
+[[34](ch02.html#Yuan2014-marker)] Ding Yuan, Yu Luo, Xin Zhuang, Guilherme Renna Rodrigues, Xu Zhao, Yongle Zhang, Pranay U. Jain, and Michael Stumm. [Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf). At *11th USENIX Symposium on Operating Systems Design and Implementation* (OSDI), October 2014.
 
-[[35](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Rosenthal2020-marker)] Casey Rosenthal and Nora Jones. [*Chaos Engineering*](https://learning.oreilly.com/library/view/chaos-engineering/9781492043850/). O’Reilly Media, April 2020. ISBN: 9781492043867
+[[35](ch02.html#Rosenthal2020-marker)] Casey Rosenthal and Nora Jones. [*Chaos Engineering*](https://learning.oreilly.com/library/view/chaos-engineering/9781492043850/). O’Reilly Media, April 2020. ISBN: 9781492043867
 
-[[36](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Pinheiro2007-marker)] Eduardo Pinheiro, Wolf-Dietrich Weber, and Luiz Andre Barroso. [Failure Trends in a Large Disk Drive Population](https://www.usenix.org/legacy/events/fast07/tech/full_papers/pinheiro/pinheiro_old.pdf). At *5th USENIX Conference on File and Storage Technologies* (FAST), February 2007.
+[[36](ch02.html#Pinheiro2007-marker)] Eduardo Pinheiro, Wolf-Dietrich Weber, and Luiz Andre Barroso. [Failure Trends in a Large Disk Drive Population](https://www.usenix.org/legacy/events/fast07/tech/full_papers/pinheiro/pinheiro_old.pdf). At *5th USENIX Conference on File and Storage Technologies* (FAST), February 2007.
 
-[[37](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schroeder2007-marker)] Bianca Schroeder and Garth A. Gibson. [Disk failures in the real world: What does an MTTF of 1,000,000 hours mean to you?](https://www.usenix.org/legacy/events/fast07/tech/schroeder/schroeder.pdf) At *5th USENIX Conference on File and Storage Technologies* (FAST), February 2007.
+[[37](ch02.html#Schroeder2007-marker)] Bianca Schroeder and Garth A. Gibson. [Disk failures in the real world: What does an MTTF of 1,000,000 hours mean to you?](https://www.usenix.org/legacy/events/fast07/tech/schroeder/schroeder.pdf) At *5th USENIX Conference on File and Storage Technologies* (FAST), February 2007.
 
-[[38](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Klein2021-marker)] Andy Klein. [Backblaze Drive Stats for Q2 2021](https://www.backblaze.com/blog/backblaze-drive-stats-for-q2-2021/). *backblaze.com*, August 2021. Archived at [perma.cc/2943-UD5E](https://perma.cc/2943-UD5E)
+[[38](ch02.html#Klein2021-marker)] Andy Klein. [Backblaze Drive Stats for Q2 2021](https://www.backblaze.com/blog/backblaze-drive-stats-for-q2-2021/). *backblaze.com*, August 2021. Archived at [perma.cc/2943-UD5E](https://perma.cc/2943-UD5E)
 
-[[39](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Narayanan2016-marker)] Iyswarya Narayanan, Di Wang, Myeongjae Jeon, Bikash Sharma, Laura Caulfield, Anand Sivasubramaniam, Ben Cutler, Jie Liu, Badriddine Khessib, and Kushagra Vaid. [SSD Failures in Datacenters: What? When? and Why?](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/a7-narayanan.pdf) At *9th ACM International on Systems and Storage Conference* (SYSTOR), June 2016. [doi:10.1145/2928275.2928278](https://doi.org/10.1145/2928275.2928278)
+[[39](ch02.html#Narayanan2016-marker)] Iyswarya Narayanan, Di Wang, Myeongjae Jeon, Bikash Sharma, Laura Caulfield, Anand Sivasubramaniam, Ben Cutler, Jie Liu, Badriddine Khessib, and Kushagra Vaid. [SSD Failures in Datacenters: What? When? and Why?](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/a7-narayanan.pdf) At *9th ACM International on Systems and Storage Conference* (SYSTOR), June 2016. [doi:10.1145/2928275.2928278](https://doi.org/10.1145/2928275.2928278)
 
-[[40](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Alibaba2019_ch2-marker)] Alibaba Cloud Storage Team. [Storage System Design Analysis: Factors Affecting NVMe SSD Performance (1)](https://www.alibabacloud.com/blog/594375). *alibabacloud.com*, January 2019. Archived at [archive.org](https://web.archive.org/web/20230522005034/https://www.alibabacloud.com/blog/594375)
+[[40](ch02.html#Alibaba2019_ch2-marker)] Alibaba Cloud Storage Team. [Storage System Design Analysis: Factors Affecting NVMe SSD Performance (1)](https://www.alibabacloud.com/blog/594375). *alibabacloud.com*, January 2019. Archived at [archive.org](https://web.archive.org/web/20230522005034/https://www.alibabacloud.com/blog/594375)
 
-[[41](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schroeder2016-marker)] Bianca Schroeder, Raghav Lagisetty, and Arif Merchant. [Flash Reliability in Production: The Expected and the Unexpected](https://www.usenix.org/system/files/conference/fast16/fast16-papers-schroeder.pdf). At *14th USENIX Conference on File and Storage Technologies* (FAST), February 2016.
+[[41](ch02.html#Schroeder2016-marker)] Bianca Schroeder, Raghav Lagisetty, and Arif Merchant. [Flash Reliability in Production: The Expected and the Unexpected](https://www.usenix.org/system/files/conference/fast16/fast16-papers-schroeder.pdf). At *14th USENIX Conference on File and Storage Technologies* (FAST), February 2016.
 
-[[42](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Alter2019-marker)] Jacob Alter, Ji Xue, Alma Dimnaku, and Evgenia Smirni. [SSD failures in the field: symptoms, causes, and prediction models](https://dl.acm.org/doi/pdf/10.1145/3295500.3356172). At *International Conference for High Performance Computing, Networking, Storage and Analysis* (SC), November 2019. [doi:10.1145/3295500.3356172](https://doi.org/10.1145/3295500.3356172)
+[[42](ch02.html#Alter2019-marker)] Jacob Alter, Ji Xue, Alma Dimnaku, and Evgenia Smirni. [SSD failures in the field: symptoms, causes, and prediction models](https://dl.acm.org/doi/pdf/10.1145/3295500.3356172). At *International Conference for High Performance Computing, Networking, Storage and Analysis* (SC), November 2019. [doi:10.1145/3295500.3356172](https://doi.org/10.1145/3295500.3356172)
 
-[[43](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Ford2010-marker)] Daniel Ford, François Labelle, Florentina I. Popovici, Murray Stokely, Van-Anh Truong, Luiz Barroso, Carrie Grimes, and Sean Quinlan. [Availability in Globally Distributed Storage Systems](https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Ford.pdf). At *9th USENIX Symposium on Operating Systems Design and Implementation* (OSDI), October 2010.
+[[43](ch02.html#Ford2010-marker)] Daniel Ford, François Labelle, Florentina I. Popovici, Murray Stokely, Van-Anh Truong, Luiz Barroso, Carrie Grimes, and Sean Quinlan. [Availability in Globally Distributed Storage Systems](https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Ford.pdf). At *9th USENIX Symposium on Operating Systems Design and Implementation* (OSDI), October 2010.
 
-[[44](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Vishwanath2010-marker)] Kashi Venkatesh Vishwanath and Nachiappan Nagappan. [Characterizing Cloud Computing Hardware Reliability](https://www.microsoft.com/en-us/research/wp-content/uploads/2010/06/socc088-vishwanath.pdf). At *1st ACM Symposium on Cloud Computing* (SoCC), June 2010. [doi:10.1145/1807128.1807161](https://doi.org/10.1145/1807128.1807161)
+[[44](ch02.html#Vishwanath2010-marker)] Kashi Venkatesh Vishwanath and Nachiappan Nagappan. [Characterizing Cloud Computing Hardware Reliability](https://www.microsoft.com/en-us/research/wp-content/uploads/2010/06/socc088-vishwanath.pdf). At *1st ACM Symposium on Cloud Computing* (SoCC), June 2010. [doi:10.1145/1807128.1807161](https://doi.org/10.1145/1807128.1807161)
 
-[[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hochschild2021-marker)] Peter H. Hochschild, Paul Turner, Jeffrey C. Mogul, Rama Govindaraju, Parthasarathy Ranganathan, David E. Culler, and Amin Vahdat. [Cores that don’t count](https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s01-hochschild.pdf). At *Workshop on Hot Topics in Operating Systems* (HotOS), June 2021. [doi:10.1145/3458336.3465297](https://doi.org/10.1145/3458336.3465297)
+[[45](ch02.html#Hochschild2021-marker)] Peter H. Hochschild, Paul Turner, Jeffrey C. Mogul, Rama Govindaraju, Parthasarathy Ranganathan, David E. Culler, and Amin Vahdat. [Cores that don’t count](https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s01-hochschild.pdf). At *Workshop on Hot Topics in Operating Systems* (HotOS), June 2021. [doi:10.1145/3458336.3465297](https://doi.org/10.1145/3458336.3465297)
 
-[[46](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dixit2021-marker)] Harish Dattatraya Dixit, Sneha Pendharkar, Matt Beadon, Chris Mason, Tejasvi Chakravarthy, Bharath Muthiah, and Sriram Sankar. [Silent Data Corruptions at Scale](https://arxiv.org/abs/2102.11245). *arXiv:2102.11245*, February 2021.
+[[46](ch02.html#Dixit2021-marker)] Harish Dattatraya Dixit, Sneha Pendharkar, Matt Beadon, Chris Mason, Tejasvi Chakravarthy, Bharath Muthiah, and Sriram Sankar. [Silent Data Corruptions at Scale](https://arxiv.org/abs/2102.11245). *arXiv:2102.11245*, February 2021.
 
-[[47](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Behrens2015-marker)] Diogo Behrens, Marco Serafini, Sergei Arnautov, Flavio P. Junqueira, and Christof Fetzer. [Scalable Error Isolation for Distributed Systems](https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/behrens). At *12th USENIX Symposium on Networked Systems Design and Implementation* (NSDI), May 2015.
+[[47](ch02.html#Behrens2015-marker)] Diogo Behrens, Marco Serafini, Sergei Arnautov, Flavio P. Junqueira, and Christof Fetzer. [Scalable Error Isolation for Distributed Systems](https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/behrens). At *12th USENIX Symposium on Networked Systems Design and Implementation* (NSDI), May 2015.
 
-[[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Schroeder2009-marker)] Bianca Schroeder, Eduardo Pinheiro, and Wolf-Dietrich Weber. [DRAM Errors in the Wild: A Large-Scale Field Study](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35162.pdf). At *11th International Joint Conference on Measurement and Modeling of Computer Systems* (SIGMETRICS), June 2009. [doi:10.1145/1555349.1555372](https://doi.org/10.1145/1555349.1555372)
+[[48](ch02.html#Schroeder2009-marker)] Bianca Schroeder, Eduardo Pinheiro, and Wolf-Dietrich Weber. [DRAM Errors in the Wild: A Large-Scale Field Study](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35162.pdf). At *11th International Joint Conference on Measurement and Modeling of Computer Systems* (SIGMETRICS), June 2009. [doi:10.1145/1555349.1555372](https://doi.org/10.1145/1555349.1555372)
 
-[[49](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kim2014-marker)] Yoongu Kim, Ross Daly, Jeremie Kim, Chris Fallin, Ji Hye Lee, Donghyuk Lee, Chris Wilkerson, Konrad Lai, and Onur Mutlu. [Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors](https://users.ece.cmu.edu/~yoonguk/papers/kim-isca14.pdf). At *41st Annual International Symposium on Computer Architecture* (ISCA), June 2014. [doi:10.5555/2665671.2665726](https://doi.org/10.5555/2665671.2665726)
+[[49](ch02.html#Kim2014-marker)] Yoongu Kim, Ross Daly, Jeremie Kim, Chris Fallin, Ji Hye Lee, Donghyuk Lee, Chris Wilkerson, Konrad Lai, and Onur Mutlu. [Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors](https://users.ece.cmu.edu/~yoonguk/papers/kim-isca14.pdf). At *41st Annual International Symposium on Computer Architecture* (ISCA), June 2014. [doi:10.5555/2665671.2665726](https://doi.org/10.5555/2665671.2665726)
 
-[[50](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cockcroft2019-marker)] Adrian Cockcroft. [Failure Modes and Continuous Resilience](https://adrianco.medium.com/failure-modes-and-continuous-resilience-6553078caad5). *adrianco.medium.com*, November 2019. Archived at [perma.cc/7SYS-BVJP](https://perma.cc/7SYS-BVJP)
+[[50](ch02.html#Cockcroft2019-marker)] Adrian Cockcroft. [Failure Modes and Continuous Resilience](https://adrianco.medium.com/failure-modes-and-continuous-resilience-6553078caad5). *adrianco.medium.com*, November 2019. Archived at [perma.cc/7SYS-BVJP](https://perma.cc/7SYS-BVJP)
 
-[[51](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Han2021-marker)] Shujie Han, Patrick P. C. Lee, Fan Xu, Yi Liu, Cheng He, and Jiongzhou Liu. [An In-Depth Study of Correlated Failures in Production SSD-Based Data Centers](https://www.usenix.org/conference/fast21/presentation/han). At *19th USENIX Conference on File and Storage Technologies* (FAST), February 2021.
+[[51](ch02.html#Han2021-marker)] Shujie Han, Patrick P. C. Lee, Fan Xu, Yi Liu, Cheng He, and Jiongzhou Liu. [An In-Depth Study of Correlated Failures in Production SSD-Based Data Centers](https://www.usenix.org/conference/fast21/presentation/han). At *19th USENIX Conference on File and Storage Technologies* (FAST), February 2021.
 
-[[52](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Nightingale2011-marker)] Edmund B. Nightingale, John R. Douceur, and Vince Orgovan. [Cycles, Cells and Platters: An Empirical Analysis of Hardware Failures on a Million Consumer PCs](https://eurosys2011.cs.uni-salzburg.at/pdf/eurosys2011-nightingale.pdf). At *6th European Conference on Computer Systems* (EuroSys), April 2011. [doi:10.1145/1966445.1966477](https://doi.org/10.1145/1966445.1966477)
+[[52](ch02.html#Nightingale2011-marker)] Edmund B. Nightingale, John R. Douceur, and Vince Orgovan. [Cycles, Cells and Platters: An Empirical Analysis of Hardware Failures on a Million Consumer PCs](https://eurosys2011.cs.uni-salzburg.at/pdf/eurosys2011-nightingale.pdf). At *6th European Conference on Computer Systems* (EuroSys), April 2011. [doi:10.1145/1966445.1966477](https://doi.org/10.1145/1966445.1966477)
 
-[[53](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gunawi2014-marker)] Haryadi S. Gunawi, Mingzhe Hao, Tanakorn Leesatapornwongsa, Tiratat Patana-anake, Thanh Do, Jeffry Adityatama, Kurnia J. Eliazar, Agung Laksono, Jeffrey F. Lukman, Vincentius Martin, and Anang D. Satria. [What Bugs Live in the Cloud?](http://ucare.cs.uchicago.edu/pdf/socc14-cbs.pdf) At *5th ACM Symposium on Cloud Computing* (SoCC), November 2014. [doi:10.1145/2670979.2670986](https://doi.org/10.1145/2670979.2670986)
+[[53](ch02.html#Gunawi2014-marker)] Haryadi S. Gunawi, Mingzhe Hao, Tanakorn Leesatapornwongsa, Tiratat Patana-anake, Thanh Do, Jeffry Adityatama, Kurnia J. Eliazar, Agung Laksono, Jeffrey F. Lukman, Vincentius Martin, and Anang D. Satria. [What Bugs Live in the Cloud?](http://ucare.cs.uchicago.edu/pdf/socc14-cbs.pdf) At *5th ACM Symposium on Cloud Computing* (SoCC), November 2014. [doi:10.1145/2670979.2670986](https://doi.org/10.1145/2670979.2670986)
 
-[[54](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Kreps2012_ch1-marker)] Jay Kreps. [Getting Real About Distributed System Reliability](http://blog.empathybox.com/post/19574936361/getting-real-about-distributed-system-reliability). *blog.empathybox.com*, March 2012. Archived at [perma.cc/9B5Q-AEBW](https://perma.cc/9B5Q-AEBW)
+[[54](ch02.html#Kreps2012_ch1-marker)] Jay Kreps. [Getting Real About Distributed System Reliability](http://blog.empathybox.com/post/19574936361/getting-real-about-distributed-system-reliability). *blog.empathybox.com*, March 2012. Archived at [perma.cc/9B5Q-AEBW](https://perma.cc/9B5Q-AEBW)
 
-[[55](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Minar2012_ch1-marker)] Nelson Minar. [Leap Second Crashes Half the Internet](http://www.somebits.com/weblog/tech/bad/leap-second-2012.html). *somebits.com*, July 2012. Archived at [perma.cc/2WB8-D6EU](https://perma.cc/2WB8-D6EU)
+[[55](ch02.html#Minar2012_ch1-marker)] Nelson Minar. [Leap Second Crashes Half the Internet](http://www.somebits.com/weblog/tech/bad/leap-second-2012.html). *somebits.com*, July 2012. Archived at [perma.cc/2WB8-D6EU](https://perma.cc/2WB8-D6EU)
 
-[[56](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#HPE2019-marker)] Hewlett Packard Enterprise. [Support Alerts – Customer Bulletin a00092491en_us](https://support.hpe.com/hpesc/public/docDisplay?docId=emr_na-a00092491en_us). *support.hpe.com*, November 2019. Archived at [perma.cc/S5F6-7ZAC](https://perma.cc/S5F6-7ZAC)
+[[56](ch02.html#HPE2019-marker)] Hewlett Packard Enterprise. [Support Alerts – Customer Bulletin a00092491en_us](https://support.hpe.com/hpesc/public/docDisplay?docId=emr_na-a00092491en_us). *support.hpe.com*, November 2019. Archived at [perma.cc/S5F6-7ZAC](https://perma.cc/S5F6-7ZAC)
 
-[[57](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hochstein2020-marker)] Lorin Hochstein. [awesome limits](https://github.com/lorin/awesome-limits). *github.com*, November 2020. Archived at [perma.cc/3R5M-E5Q4](https://perma.cc/3R5M-E5Q4)
+[[57](ch02.html#Hochstein2020-marker)] Lorin Hochstein. [awesome limits](https://github.com/lorin/awesome-limits). *github.com*, November 2020. Archived at [perma.cc/3R5M-E5Q4](https://perma.cc/3R5M-E5Q4)
 
-[[58](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Tang2023-marker)] Lilia Tang, Chaitanya Bhandari, Yongle Zhang, Anna Karanika, Shuyang Ji, Indranil Gupta, and Tianyin Xu. [Fail through the Cracks: Cross-System Interaction Failures in Modern Cloud Systems](https://tianyin.github.io/pub/csi-failures.pdf). At *18th European Conference on Computer Systems* (EuroSys), May 2023. [doi:10.1145/3552326.3587448](https://doi.org/10.1145/3552326.3587448)
+[[58](ch02.html#Tang2023-marker)] Lilia Tang, Chaitanya Bhandari, Yongle Zhang, Anna Karanika, Shuyang Ji, Indranil Gupta, and Tianyin Xu. [Fail through the Cracks: Cross-System Interaction Failures in Modern Cloud Systems](https://tianyin.github.io/pub/csi-failures.pdf). At *18th European Conference on Computer Systems* (EuroSys), May 2023. [doi:10.1145/3552326.3587448](https://doi.org/10.1145/3552326.3587448)
 
-[[59](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Ulrich2016-marker)] Mike Ulrich. [Addressing Cascading Failures](https://sre.google/sre-book/addressing-cascading-failures/). In Betsy Beyer, Jennifer Petoff, Chris Jones, and Niall Richard Murphy (ed). [*Site Reliability Engineering: How Google Runs Production Systems*](https://www.oreilly.com/library/view/site-reliability-engineering/9781491929117/). O’Reilly Media, 2016. ISBN: 9781491929124
+[[59](ch02.html#Ulrich2016-marker)] Mike Ulrich. [Addressing Cascading Failures](https://sre.google/sre-book/addressing-cascading-failures/). In Betsy Beyer, Jennifer Petoff, Chris Jones, and Niall Richard Murphy (ed). [*Site Reliability Engineering: How Google Runs Production Systems*](https://www.oreilly.com/library/view/site-reliability-engineering/9781491929117/). O’Reilly Media, 2016. ISBN: 9781491929124
 
-[[60](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Fassbender2022-marker)] Harri Faßbender. [Cascading failures in large-scale distributed systems](https://blog.mi.hdm-stuttgart.de/index.php/2022/03/03/cascading-failures-in-large-scale-distributed-systems/). *blog.mi.hdm-stuttgart.de*, March 2022. Archived at [perma.cc/K7VY-YJRX](https://perma.cc/K7VY-YJRX)
+[[60](ch02.html#Fassbender2022-marker)] Harri Faßbender. [Cascading failures in large-scale distributed systems](https://blog.mi.hdm-stuttgart.de/index.php/2022/03/03/cascading-failures-in-large-scale-distributed-systems/). *blog.mi.hdm-stuttgart.de*, March 2022. Archived at [perma.cc/K7VY-YJRX](https://perma.cc/K7VY-YJRX)
 
-[[61](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Cook2000-marker)] Richard I. Cook. [How Complex Systems Fail](https://www.adaptivecapacitylabs.com/HowComplexSystemsFail.pdf). Cognitive Technologies Laboratory, April 2000. Archived at [perma.cc/RDS6-2YVA](https://perma.cc/RDS6-2YVA)
+[[61](ch02.html#Cook2000-marker)] Richard I. Cook. [How Complex Systems Fail](https://www.adaptivecapacitylabs.com/HowComplexSystemsFail.pdf). Cognitive Technologies Laboratory, April 2000. Archived at [perma.cc/RDS6-2YVA](https://perma.cc/RDS6-2YVA)
 
-[[62](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Woods2017-marker)] David D Woods. [STELLA: Report from the SNAFUcatchers Workshop on Coping With Complexity](https://snafucatchers.github.io/). *snafucatchers.github.io*, March 2017. Archived at [archive.org](https://web.archive.org/web/20230306130131/https://snafucatchers.github.io/)
+[[62](ch02.html#Woods2017-marker)] David D Woods. [STELLA: Report from the SNAFUcatchers Workshop on Coping With Complexity](https://snafucatchers.github.io/). *snafucatchers.github.io*, March 2017. Archived at [archive.org](https://web.archive.org/web/20230306130131/https://snafucatchers.github.io/)
 
-[[63](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Oppenheimer2003-marker)] David Oppenheimer, Archana Ganapathi, and David A. Patterson. [Why Do Internet Services Fail, and What Can Be Done About It?](http://static.usenix.org/legacy/events/usits03/tech/full_papers/oppenheimer/oppenheimer.pdf) At *4th USENIX Symposium on Internet Technologies and Systems* (USITS), March 2003.
+[[63](ch02.html#Oppenheimer2003-marker)] David Oppenheimer, Archana Ganapathi, and David A. Patterson. [Why Do Internet Services Fail, and What Can Be Done About It?](http://static.usenix.org/legacy/events/usits03/tech/full_papers/oppenheimer/oppenheimer.pdf) At *4th USENIX Symposium on Internet Technologies and Systems* (USITS), March 2003.
 
-[[64](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Dekker2017-marker)] Sidney Dekker. [*The Field Guide to Understanding ‘Human Error’, 3rd Edition*](https://learning.oreilly.com/library/view/the-field-guide/9781317031833/). CRC Press, November 2017. ISBN: 9781472439055
+[[64](ch02.html#Dekker2017-marker)] Sidney Dekker. [*The Field Guide to Understanding ‘Human Error’, 3rd Edition*](https://learning.oreilly.com/library/view/the-field-guide/9781317031833/). CRC Press, November 2017. ISBN: 9781472439055
 
-[[65](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Allspaw2012-marker)] John Allspaw. [Blameless PostMortems and a Just Culture](https://www.etsy.com/codeascraft/blameless-postmortems/). *etsy.com*, May 2012. Archived at [perma.cc/YMJ7-NTAP](https://perma.cc/YMJ7-NTAP)
+[[65](ch02.html#Allspaw2012-marker)] John Allspaw. [Blameless PostMortems and a Just Culture](https://www.etsy.com/codeascraft/blameless-postmortems/). *etsy.com*, May 2012. Archived at [perma.cc/YMJ7-NTAP](https://perma.cc/YMJ7-NTAP)
 
-[[66](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Sabo2023-marker)] Itzy Sabo. [Uptime Guarantees — A Pragmatic Perspective](https://world.hey.com/itzy/uptime-guarantees-a-pragmatic-perspective-736d7ea4). *world.hey.com*, March 2023. Archived at [perma.cc/F7TU-78JB](https://perma.cc/F7TU-78JB)
+[[66](ch02.html#Sabo2023-marker)] Itzy Sabo. [Uptime Guarantees — A Pragmatic Perspective](https://world.hey.com/itzy/uptime-guarantees-a-pragmatic-perspective-736d7ea4). *world.hey.com*, March 2023. Archived at [perma.cc/F7TU-78JB](https://perma.cc/F7TU-78JB)
 
-[[67](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Jurewitz2013-marker)] Michael Jurewitz. [The Human Impact of Bugs](http://jury.me/blog/2013/3/14/the-human-impact-of-bugs). *jury.me*, March 2013. Archived at [perma.cc/5KQ4-VDYL](https://perma.cc/5KQ4-VDYL)
+[[67](ch02.html#Jurewitz2013-marker)] Michael Jurewitz. [The Human Impact of Bugs](http://jury.me/blog/2013/3/14/the-human-impact-of-bugs). *jury.me*, March 2013. Archived at [perma.cc/5KQ4-VDYL](https://perma.cc/5KQ4-VDYL)
 
-[[68](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Siddique2021-marker)] Haroon Siddique and Ben Quinn. [Court clears 39 post office operators convicted due to ‘corrupt data’](https://www.theguardian.com/uk-news/2021/apr/23/court-clears-39-post-office-staff-convicted-due-to-corrupt-data). *theguardian.com*, April 2021. Archived at [archive.org](https://web.archive.org/web/20220630124107/https://www.theguardian.com/uk-news/2021/apr/23/court-clears-39-post-office-staff-convicted-due-to-corrupt-data)
+[[68](ch02.html#Siddique2021-marker)] Haroon Siddique and Ben Quinn. [Court clears 39 post office operators convicted due to ‘corrupt data’](https://www.theguardian.com/uk-news/2021/apr/23/court-clears-39-post-office-staff-convicted-due-to-corrupt-data). *theguardian.com*, April 2021. Archived at [archive.org](https://web.archive.org/web/20220630124107/https://www.theguardian.com/uk-news/2021/apr/23/court-clears-39-post-office-staff-convicted-due-to-corrupt-data)
 
-[[69](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bohm2022-marker)] Nicholas Bohm, James Christie, Peter Bernard Ladkin, Bev Littlewood, Paul Marshall, Stephen Mason, Martin Newby, Steven J. Murdoch, Harold Thimbleby, and Martyn Thomas. [The legal rule that computers are presumed to be operating correctly – unforeseen and unjust consequences](https://www.benthamsgaze.org/wp-content/uploads/2022/06/briefing-presumption-that-computers-are-reliable.pdf). Briefing note, *benthamsgaze.org*, June 2022. Archived at [perma.cc/WQ6X-TMW4](https://perma.cc/WQ6X-TMW4)
+[[69](ch02.html#Bohm2022-marker)] Nicholas Bohm, James Christie, Peter Bernard Ladkin, Bev Littlewood, Paul Marshall, Stephen Mason, Martin Newby, Steven J. Murdoch, Harold Thimbleby, and Martyn Thomas. [The legal rule that computers are presumed to be operating correctly – unforeseen and unjust consequences](https://www.benthamsgaze.org/wp-content/uploads/2022/06/briefing-presumption-that-computers-are-reliable.pdf). Briefing note, *benthamsgaze.org*, June 2022. Archived at [perma.cc/WQ6X-TMW4](https://perma.cc/WQ6X-TMW4)
 
-[[70](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#McKinley2015-marker)] Dan McKinley. [Choose Boring Technology](https://mcfunley.com/choose-boring-technology). *mcfunley.com*, March 2015. Archived at [perma.cc/7QW7-J4YP](https://perma.cc/7QW7-J4YP)
+[[70](ch02.html#McKinley2015-marker)] Dan McKinley. [Choose Boring Technology](https://mcfunley.com/choose-boring-technology). *mcfunley.com*, March 2015. Archived at [perma.cc/7QW7-J4YP](https://perma.cc/7QW7-J4YP)
 
-[[71](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Warfield2023-marker)] Andy Warfield. [Building and operating a pretty big storage system called S3](https://www.allthingsdistributed.com/2023/07/building-and-operating-a-pretty-big-storage-system.html). *allthingsdistributed.com*, July 2023. Archived at [perma.cc/7LPK-TP7V](https://perma.cc/7LPK-TP7V)
+[[71](ch02.html#Warfield2023-marker)] Andy Warfield. [Building and operating a pretty big storage system called S3](https://www.allthingsdistributed.com/2023/07/building-and-operating-a-pretty-big-storage-system.html). *allthingsdistributed.com*, July 2023. Archived at [perma.cc/7LPK-TP7V](https://perma.cc/7LPK-TP7V)
 
-[[72](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2023-marker)] Marc Brooker. [Surprising Scalability of Multitenancy](https://brooker.co.za/blog/2023/03/23/economics.html). *brooker.co.za*, March 2023. Archived at [archive.org](https://web.archive.org/web/20230404065818/https://brooker.co.za/blog/2023/03/23/economics.html)
+[[72](ch02.html#Brooker2023-marker)] Marc Brooker. [Surprising Scalability of Multitenancy](https://brooker.co.za/blog/2023/03/23/economics.html). *brooker.co.za*, March 2023. Archived at [archive.org](https://web.archive.org/web/20230404065818/https://brooker.co.za/blog/2023/03/23/economics.html)
 
-[[73](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Stopford2009-marker)] Ben Stopford. [Shared Nothing vs. Shared Disk Architectures: An Independent View](http://www.benstopford.com/2009/11/24/understanding-the-shared-nothing-architecture/). *benstopford.com*, November 2009. Archived at [perma.cc/7BXH-EDUR](https://perma.cc/7BXH-EDUR)
+[[73](ch02.html#Stopford2009-marker)] Ben Stopford. [Shared Nothing vs. Shared Disk Architectures: An Independent View](http://www.benstopford.com/2009/11/24/understanding-the-shared-nothing-architecture/). *benstopford.com*, November 2009. Archived at [perma.cc/7BXH-EDUR](https://perma.cc/7BXH-EDUR)
 
-[[74](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Stonebraker1986-marker)] Michael Stonebraker. [The Case for Shared Nothing](http://db.cs.berkeley.edu/papers/hpts85-nothing.pdf). *IEEE Database Engineering Bulletin*, volume 9, issue 1, pages 4–9, March 1986.
+[[74](ch02.html#Stonebraker1986-marker)] Michael Stonebraker. [The Case for Shared Nothing](http://db.cs.berkeley.edu/papers/hpts85-nothing.pdf). *IEEE Database Engineering Bulletin*, volume 9, issue 1, pages 4–9, March 1986.
 
-[[75](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Antonopoulos2019_ch2-marker)] Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, Vijendra Purohit, Hugh Qu, Chaitanya Sreenivas Ravella, Krystyna Reisteter, Sheetal Shrotri, Dixin Tang, and Vikram Wakade. [Socrates: The New SQL Server in the Cloud](https://www.microsoft.com/en-us/research/uploads/prod/2019/05/socrates.pdf). At *ACM International Conference on Management of Data* (SIGMOD), pages 1743–1756, June 2019. [doi:10.1145/3299869.3314047](https://doi.org/10.1145/3299869.3314047)
+[[75](ch02.html#Antonopoulos2019_ch2-marker)] Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, Vijendra Purohit, Hugh Qu, Chaitanya Sreenivas Ravella, Krystyna Reisteter, Sheetal Shrotri, Dixin Tang, and Vikram Wakade. [Socrates: The New SQL Server in the Cloud](https://www.microsoft.com/en-us/research/uploads/prod/2019/05/socrates.pdf). At *ACM International Conference on Management of Data* (SIGMOD), pages 1743–1756, June 2019. [doi:10.1145/3299869.3314047](https://doi.org/10.1145/3299869.3314047)
 
-[[76](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Newman2021_ch2-marker)] Sam Newman. [*Building Microservices*, second edition](https://www.oreilly.com/library/view/building-microservices-2nd/9781492034018/). O’Reilly Media, 2021. ISBN: 9781492034025
+[[76](ch02.html#Newman2021_ch2-marker)] Sam Newman. [*Building Microservices*, second edition](https://www.oreilly.com/library/view/building-microservices-2nd/9781492034018/). O’Reilly Media, 2021. ISBN: 9781492034025
 
-[[77](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Ensmenger2016-marker)] Nathan Ensmenger. [When Good Software Goes Bad: The Surprising Durability of an Ephemeral Technology](https://themaintainers.wpengine.com/wp-content/uploads/2021/04/ensmenger-maintainers-v2.pdf). At *The Maintainers Conference*, April 2016. Archived at [perma.cc/ZXT4-HGZB](https://perma.cc/ZXT4-HGZB)
+[[77](ch02.html#Ensmenger2016-marker)] Nathan Ensmenger. [When Good Software Goes Bad: The Surprising Durability of an Ephemeral Technology](https://themaintainers.wpengine.com/wp-content/uploads/2021/04/ensmenger-maintainers-v2.pdf). At *The Maintainers Conference*, April 2016. Archived at [perma.cc/ZXT4-HGZB](https://perma.cc/ZXT4-HGZB)
 
-[[78](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Glass2002-marker)] Robert L. Glass. [*Facts and Fallacies of Software Engineering*](https://learning.oreilly.com/library/view/facts-and-fallacies/0321117425/). Addison-Wesley Professional, October 2002. ISBN: 9780321117427
+[[78](ch02.html#Glass2002-marker)] Robert L. Glass. [*Facts and Fallacies of Software Engineering*](https://learning.oreilly.com/library/view/facts-and-fallacies/0321117425/). Addison-Wesley Professional, October 2002. ISBN: 9780321117427
 
-[[79](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bellotti2021-marker)] Marianne Bellotti. [*Kill It with Fire*](https://learning.oreilly.com/library/view/kill-it-with/9781098128883/). No Starch Press, April 2021. ISBN: 9781718501188
+[[79](ch02.html#Bellotti2021-marker)] Marianne Bellotti. [*Kill It with Fire*](https://learning.oreilly.com/library/view/kill-it-with/9781098128883/). No Starch Press, April 2021. ISBN: 9781718501188
 
-[[80](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Bainbridge1983-marker)] Lisanne Bainbridge. [Ironies of automation](https://www.adaptivecapacitylabs.com/IroniesOfAutomation-Bainbridge83.pdf). *Automatica*, volume 19, issue 6, pages 775–779, November 1983. [doi:10.1016/0005-1098(83)90046-8](https://doi.org/10.1016/0005-1098(83)90046-8)
+[[80](ch02.html#Bainbridge1983-marker)] Lisanne Bainbridge. [Ironies of automation](https://www.adaptivecapacitylabs.com/IroniesOfAutomation-Bainbridge83.pdf). *Automatica*, volume 19, issue 6, pages 775–779, November 1983. [doi:10.1016/0005-1098(83)90046-8](https://doi.org/10.1016/0005-1098(83)90046-8)
 
-[[81](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Hamilton2007-marker)] James Hamilton. [On Designing and Deploying Internet-Scale Services](https://www.usenix.org/legacy/events/lisa07/tech/full_papers/hamilton/hamilton.pdf). At *21st Large Installation System Administration Conference* (LISA), November 2007.
+[[81](ch02.html#Hamilton2007-marker)] James Hamilton. [On Designing and Deploying Internet-Scale Services](https://www.usenix.org/legacy/events/lisa07/tech/full_papers/hamilton/hamilton.pdf). At *21st Large Installation System Administration Conference* (LISA), November 2007.
 
-[[82](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Horovits2021-marker)] Dotan Horovits. [Open Source for Better Observability](https://horovits.medium.com/open-source-for-better-observability-8c65b5630561). *horovits.medium.com*, October 2021. Archived at [perma.cc/R2HD-U2ZT](https://perma.cc/R2HD-U2ZT)
+[[82](ch02.html#Horovits2021-marker)] Dotan Horovits. [Open Source for Better Observability](https://horovits.medium.com/open-source-for-better-observability-8c65b5630561). *horovits.medium.com*, October 2021. Archived at [perma.cc/R2HD-U2ZT](https://perma.cc/R2HD-U2ZT)
 
-[[83](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Foote1997-marker)] Brian Foote and Joseph Yoder. [Big Ball of Mud](http://www.laputan.org/pub/foote/mud.pdf). At *4th Conference on Pattern Languages of Programs* (PLoP), September 1997. Archived at [perma.cc/4GUP-2PBV](https://perma.cc/4GUP-2PBV)
+[[83](ch02.html#Foote1997-marker)] Brian Foote and Joseph Yoder. [Big Ball of Mud](http://www.laputan.org/pub/foote/mud.pdf). At *4th Conference on Pattern Languages of Programs* (PLoP), September 1997. Archived at [perma.cc/4GUP-2PBV](https://perma.cc/4GUP-2PBV)
 
-[[84](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooker2022-marker)] Marc Brooker. [What is a simple system?](https://brooker.co.za/blog/2022/05/03/simplicity.html) *brooker.co.za*, May 2022. Archived at [archive.org](https://web.archive.org/web/20220602141902/https://brooker.co.za/blog/2022/05/03/simplicity.html)
+[[84](ch02.html#Brooker2022-marker)] Marc Brooker. [What is a simple system?](https://brooker.co.za/blog/2022/05/03/simplicity.html) *brooker.co.za*, May 2022. Archived at [archive.org](https://web.archive.org/web/20220602141902/https://brooker.co.za/blog/2022/05/03/simplicity.html)
 
-[[85](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Brooks1995-marker)] Frederick P Brooks. [No Silver Bullet – Essence and Accident in Software Engineering](http://worrydream.com/refs/Brooks-NoSilverBullet.pdf). In [*The Mythical Man-Month*](https://www.oreilly.com/library/view/mythical-man-month-the/0201835959/), Anniversary edition, Addison-Wesley, 1995. ISBN: 9780201835953
+[[85](ch02.html#Brooks1995-marker)] Frederick P Brooks. [No Silver Bullet – Essence and Accident in Software Engineering](http://worrydream.com/refs/Brooks-NoSilverBullet.pdf). In [*The Mythical Man-Month*](https://www.oreilly.com/library/view/mythical-man-month-the/0201835959/), Anniversary edition, Addison-Wesley, 1995. ISBN: 9780201835953
 
-[[86](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Luu2020-marker)] Dan Luu. [Against essential and accidental complexity](https://danluu.com/essential-complexity/). *danluu.com*, December 2020. Archived at [perma.cc/H5ES-69KC](https://perma.cc/H5ES-69KC)
+[[86](ch02.html#Luu2020-marker)] Dan Luu. [Against essential and accidental complexity](https://danluu.com/essential-complexity/). *danluu.com*, December 2020. Archived at [perma.cc/H5ES-69KC](https://perma.cc/H5ES-69KC)
 
-[[87](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Gamma1994-marker)] Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. [*Design Patterns: Elements of Reusable Object-Oriented Software*](https://learning.oreilly.com/library/view/design-patterns-elements/0201633612/). Addison-Wesley Professional, October 1994. ISBN: 9780201633610
+[[87](ch02.html#Gamma1994-marker)] Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. [*Design Patterns: Elements of Reusable Object-Oriented Software*](https://learning.oreilly.com/library/view/design-patterns-elements/0201633612/). Addison-Wesley Professional, October 1994. ISBN: 9780201633610
 
-[[88](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Evans2003-marker)] Eric Evans. [*Domain-Driven Design: Tackling Complexity in the Heart of Software*](https://learning.oreilly.com/library/view/domain-driven-design-tackling/0321125215/). Addison-Wesley Professional, August 2003. ISBN: 9780321125217
+[[88](ch02.html#Evans2003-marker)] Eric Evans. [*Domain-Driven Design: Tackling Complexity in the Heart of Software*](https://learning.oreilly.com/library/view/domain-driven-design-tackling/0321125215/). Addison-Wesley Professional, August 2003. ISBN: 9780321125217
 
-[[89](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Breivold2008-marker)] Hongyu Pei Breivold, Ivica Crnkovic, and Peter J. Eriksson. [Analyzing Software Evolvability](http://www.es.mdh.se/pdf_publications/1251.pdf). at *32nd Annual IEEE International Computer Software and Applications Conference* (COMPSAC), July 2008. [doi:10.1109/COMPSAC.2008.50](https://doi.org/10.1109/COMPSAC.2008.50)
+[[89](ch02.html#Breivold2008-marker)] Hongyu Pei Breivold, Ivica Crnkovic, and Peter J. Eriksson. [Analyzing Software Evolvability](http://www.es.mdh.se/pdf_publications/1251.pdf). at *32nd Annual IEEE International Computer Software and Applications Conference* (COMPSAC), July 2008. [doi:10.1109/COMPSAC.2008.50](https://doi.org/10.1109/COMPSAC.2008.50)
 
-[[90](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#Zaninotto2002-marker)] Enrico Zaninotto. [From X programming to the X organisation](https://martinfowler.com/articles/zaninotto.pdf). At *XP Conference*, May 2002. Archived at [perma.cc/R9AR-QCKZ](https://perma.cc/R9AR-QCKZ)
+[[90](ch02.html#Zaninotto2002-marker)] Enrico Zaninotto. [From X programming to the X organisation](https://martinfowler.com/articles/zaninotto.pdf). At *XP Conference*, May 2002. Archived at [perma.cc/R9AR-QCKZ](https://perma.cc/R9AR-QCKZ)
 
 
 ------

ch3.md

@@ -27,9 +27,9 @@ Several different data models are widely used in practice, often for different p
 
 Many of the query languages in this chapter (such as SQL, Cypher, SPARQL, or Datalog) are *declarative*, which means that you specify the pattern of the data you want—what conditions the results must meet, and how you want the data to be transformed (e.g., sorted, grouped, and aggregated)—but not *how* to achieve that goal. The database system’s query optimizer can decide which indexes and which join algorithms to use, and in which order to execute various parts of the query.
 
-In contrast, with most programming languages you would have to write an *algorithm*—i.e., telling the computer which operations to perform in which order. A declarative query language is attractive because it is typically more concise and easier to write than an explicit algorithm. But more importantly, it also hides implementation details of the query engine, which makes it possible for the database system to introduce performance improvements without requiring any changes to queries. [[1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Brandon2024)].
+In contrast, with most programming languages you would have to write an *algorithm*—i.e., telling the computer which operations to perform in which order. A declarative query language is attractive because it is typically more concise and easier to write than an explicit algorithm. But more importantly, it also hides implementation details of the query engine, which makes it possible for the database system to introduce performance improvements without requiring any changes to queries. [[1](ch03.html#Brandon2024)].
 
-For example, a database might be able to execute a declarative query in parallel across multiple CPU cores and machines, without you having to worry about how to implement that parallelism [[2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Hellerstein2010)]. In a hand-coded algorithm it would be a lot of work to implement such parallel execution yourself.
+For example, a database might be able to execute a declarative query in parallel across multiple CPU cores and machines, without you having to worry about how to implement that parallelism [[2](ch03.html#Hellerstein2010)]. In a hand-coded algorithm it would be a lot of work to implement such parallel execution yourself.
 
 
 
@@ -39,11 +39,11 @@ For example, a database might be able to execute a declarative query in parallel
 
 ## 关系模型与文档模型
 
-The best-known data model today is probably that of SQL, based on the relational model proposed by Edgar Codd in 1970 [[3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Codd1970)]: data is organized into *relations* (called *tables* in SQL), where each relation is an unordered collection of *tuples* (*rows* in SQL).
+The best-known data model today is probably that of SQL, based on the relational model proposed by Edgar Codd in 1970 [[3](ch03.html#Codd1970)]: data is organized into *relations* (called *tables* in SQL), where each relation is an unordered collection of *tuples* (*rows* in SQL).
 
-The relational model was originally a theoretical proposal, and many people at the time doubted whether it could be implemented efficiently. However, by the mid-1980s, relational database management systems (RDBMS) and SQL had become the tools of choice for most people who needed to store and query data with some kind of regular structure. Many data management use cases are still dominated by relational data decades later—for example, business analytics (see [“Stars and Snowflakes: Schemas for Analytics”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_analytics)).
+The relational model was originally a theoretical proposal, and many people at the time doubted whether it could be implemented efficiently. However, by the mid-1980s, relational database management systems (RDBMS) and SQL had become the tools of choice for most people who needed to store and query data with some kind of regular structure. Many data management use cases are still dominated by relational data decades later—for example, business analytics (see [“Stars and Snowflakes: Schemas for Analytics”](ch03.html#sec_datamodels_analytics)).
 
-Over the years, there have been many competing approaches to data storage and querying. In the 1970s and early 1980s, the *network model* and the *hierarchical model* were the main alternatives, but the relational model came to dominate them. Object databases came and went again in the late 1980s and early 1990s. XML databases appeared in the early 2000s, but have only seen niche adoption. Each competitor to the relational model generated a lot of hype in its time, but it never lasted [[4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Stonebraker2005around)]. Instead, SQL has grown to incorporate other data types besides its relational core—for example, adding support for XML, JSON, and graph data [[5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Winand2015)].
+Over the years, there have been many competing approaches to data storage and querying. In the 1970s and early 1980s, the *network model* and the *hierarchical model* were the main alternatives, but the relational model came to dominate them. Object databases came and went again in the late 1980s and early 1990s. XML databases appeared in the early 2000s, but have only seen niche adoption. Each competitor to the relational model generated a lot of hype in its time, but it never lasted [[4](ch03.html#Stonebraker2005around)]. Instead, SQL has grown to incorporate other data types besides its relational core—for example, adding support for XML, JSON, and graph data [[5](ch03.html#Winand2015)].
 
 In the 2010s, *NoSQL* was the latest buzzword that tried to overthrow the dominance of relational databases. NoSQL refers not to a single technology, but a loose set of ideas around new data models, schema flexibility, scalability, and a move towards open source licensing models. Some databases branded themselves as *NewSQL*, as they aim to provide the scalability of NoSQL systems along with the data model and transactional guarantees of traditional relational databases. The NoSQL and NewSQL ideas have been very influential in the design of data systems, but as the principles have become widely adopted, use of those terms has faded.
 
@@ -61,14 +61,14 @@ Much application development today is done in object-oriented programming langua
 
 #### 对象关系映射（ORM）
 
-Object-relational mapping (ORM) frameworks like ActiveRecord and Hibernate reduce the amount of boilerplate code required for this translation layer, but they are often criticized [[6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Fowler2012)]. Some commonly cited problems are:
+Object-relational mapping (ORM) frameworks like ActiveRecord and Hibernate reduce the amount of boilerplate code required for this translation layer, but they are often criticized [[6](ch03.html#Fowler2012)]. Some commonly cited problems are:
 
 - ORMs are complex and can’t completely hide the differences between the two models, so developers still end up having to think about both the relational and the object representations of the data.
-- ORMs are generally only used for OLTP app development (see [“Characterizing Analytical and Operational Systems”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_oltp)); data engineers making the data available for analytics purposes still need to work with the underlying relational representation, so the design of the relational schema still matters when using an ORM.
+- ORMs are generally only used for OLTP app development (see [“Characterizing Analytical and Operational Systems”](ch01.html#sec_introduction_oltp)); data engineers making the data available for analytics purposes still need to work with the underlying relational representation, so the design of the relational schema still matters when using an ORM.
 - Many ORMs work only with relational OLTP databases. Organizations with diverse data systems such as search engines, graph databases, and NoSQL systems might find ORM support lacking.
 - Some ORMs generate relational schemas automatically, but these might be awkward for the users who are accessing the relational data directly, and they might be inefficient on the underlying database. Customizing the ORM’s schema and query generation can be complex and negate the benefit of using the ORM in the first place.
-- ORMs often come with schema migration tools that update database schemas as model definitions change. Such tools are handy, but should be used with caution. Migrations on large or high-traffic tables can lock the entire table for an extended amount of time, resulting in downtime. Many operations teams prefer to run schema migrations manually, incrementally, during off peak hours, or with specialized tools. Safe schema migrations are discussed further in [“Schema flexibility in the document model”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_schema_flexibility).
-- ORMs make it easy to accidentally write inefficient queries, such as the *N+1 query problem* [[7](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Mihalcea2023)]. For example, say you want to display a list of user comments on a page, so you perform one query that returns *N* comments, each containing the ID of its author. To show the name of the comment author you need to look up the ID in the users table. In hand-written SQL you would probably perform this join in the query and return the author name along with each comment, but with an ORM you might end up making a separate query on the users table for each of the *N* comments to look up its author, resulting in *N*+1 database queries in total, which is slower than performing the join in the database. To avoid this problem, you may need to tell the ORM to fetch the author information at the same time as fetching the comments.
+- ORMs often come with schema migration tools that update database schemas as model definitions change. Such tools are handy, but should be used with caution. Migrations on large or high-traffic tables can lock the entire table for an extended amount of time, resulting in downtime. Many operations teams prefer to run schema migrations manually, incrementally, during off peak hours, or with specialized tools. Safe schema migrations are discussed further in [“Schema flexibility in the document model”](ch03.html#sec_datamodels_schema_flexibility).
+- ORMs make it easy to accidentally write inefficient queries, such as the *N+1 query problem* [[7](ch03.html#Mihalcea2023)]. For example, say you want to display a list of user comments on a page, so you perform one query that returns *N* comments, each containing the ID of its author. To show the name of the comment author you need to look up the ID in the users table. In hand-written SQL you would probably perform this join in the query and return the author name along with each comment, but with an ORM you might end up making a separate query on the users table for each of the *N* comments to look up its author, resulting in *N*+1 database queries in total, which is slower than performing the join in the database. To avoid this problem, you may need to tell the ORM to fetch the author information at the same time as fetching the comments.
 
 Nevertheless, ORMs also have advantages:
 
@@ -78,15 +78,15 @@ Nevertheless, ORMs also have advantages:
 
 #### The document data model for one-to-many relationships
 
-Not all data lends itself well to a relational representation; let’s look at an example to explore a limitation of the relational model. [Figure 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_relational) illustrates how a résumé (a LinkedIn profile) could be expressed in a relational schema. The profile as a whole can be identified by a unique identifier, `user_id`. Fields like `first_name` and `last_name` appear exactly once per user, so they can be modeled as columns on the `users` table.
+Not all data lends itself well to a relational representation; let’s look at an example to explore a limitation of the relational model. [Figure 3-1](ch03.html#fig_obama_relational) illustrates how a résumé (a LinkedIn profile) could be expressed in a relational schema. The profile as a whole can be identified by a unique identifier, `user_id`. Fields like `first_name` and `last_name` appear exactly once per user, so they can be modeled as columns on the `users` table.
 
-Most people have had more than one job in their career (positions), and people may have varying numbers of periods of education and any number of pieces of contact information. One way of representing such *one-to-many relationships* is to put positions, education, and contact information in separate tables, with a foreign key reference to the `users` table, as in [Figure 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_relational).
+Most people have had more than one job in their career (positions), and people may have varying numbers of periods of education and any number of pieces of contact information. One way of representing such *one-to-many relationships* is to put positions, education, and contact information in separate tables, with a foreign key reference to the `users` table, as in [Figure 3-1](ch03.html#fig_obama_relational).
 
 ![ddia 0201](img/ddia_0201.png)
 
 > Figure 3-1. Representing a LinkedIn profile using a relational schema.
 
-Another way of representing the same information, which is perhaps more natural and maps more closely to an object structure in application code, is as a JSON document as shown in [Example 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_json).
+Another way of representing the same information, which is perhaps more natural and maps more closely to an object structure in application code, is as a JSON document as shown in [Example 3-1](ch03.html#fig_obama_json).
 
 > Example 3-1. Representing a LinkedIn profile as a JSON document
 
@@ -113,11 +113,11 @@ Another way of representing the same information, which is perhaps more natural
 }
 ```
 
-Some developers feel that the JSON model reduces the impedance mismatch between the application code and the storage layer. However, as we shall see in [Link to Come], there are also problems with JSON as a data encoding format. The lack of a schema is often cited as an advantage; we will discuss this in [“Schema flexibility in the document model”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_schema_flexibility).
+Some developers feel that the JSON model reduces the impedance mismatch between the application code and the storage layer. However, as we shall see in [Link to Come], there are also problems with JSON as a data encoding format. The lack of a schema is often cited as an advantage; we will discuss this in [“Schema flexibility in the document model”](ch03.html#sec_datamodels_schema_flexibility).
 
-The JSON representation has better *locality* than the multi-table schema in [Figure 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_relational) (see [“Data locality for reads and writes”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_document_locality)). If you want to fetch a profile in the relational example, you need to either perform multiple queries (query each table by `user_id`) or perform a messy multi-way join between the `users` table and its subordinate tables [[8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Schauder2023)]. In the JSON representation, all the relevant information is in one place, making the query both faster and simpler.
+The JSON representation has better *locality* than the multi-table schema in [Figure 3-1](ch03.html#fig_obama_relational) (see [“Data locality for reads and writes”](ch03.html#sec_datamodels_document_locality)). If you want to fetch a profile in the relational example, you need to either perform multiple queries (query each table by `user_id`) or perform a messy multi-way join between the `users` table and its subordinate tables [[8](ch03.html#Schauder2023)]. In the JSON representation, all the relevant information is in one place, making the query both faster and simpler.
 
-The one-to-many relationships from the user profile to the user’s positions, educational history, and contact information imply a tree structure in the data, and the JSON representation makes this tree structure explicit (see [Figure 3-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_json_tree)).
+The one-to-many relationships from the user profile to the user’s positions, educational history, and contact information imply a tree structure in the data, and the JSON representation makes this tree structure explicit (see [Figure 3-2](ch03.html#fig_json_tree)).
 
 ![ddia 0202](img/ddia_0202.png)
 
@@ -125,7 +125,7 @@ The one-to-many relationships from the user profile to the user’s positions, e
 
 > **注意**
 >
-> This type of relationship is sometimes called *one-to-few* rather than *one-to-many*, since a résumé typically has a small number of positions [[9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Zola2014), [10](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Andrews2023)]. In sitations where there may be a genuinely large number of related items—say, comments on a celebrity’s social media post, of which there could be many thousands—embedding them all in the same document may be too unwieldy, so the relational approach in [Figure 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_relational) is preferable.
+> This type of relationship is sometimes called *one-to-few* rather than *one-to-many*, since a résumé typically has a small number of positions [[9](ch03.html#Zola2014), [10](ch03.html#Andrews2023)]. In sitations where there may be a genuinely large number of related items—say, comments on a celebrity’s social media post, of which there could be many thousands—embedding them all in the same document may be too unwieldy, so the relational approach in [Figure 3-1](ch03.html#fig_obama_relational) is preferable.
 
 
 
@@ -134,7 +134,7 @@ The one-to-many relationships from the user profile to the user’s positions, e
 
 ### 范式化，反范式化，连接
 
-In [Example 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_json) in the preceding section, `region_id` is given as an ID, not as the plain-text string `"Washington, DC, United States"`. Why?
+In [Example 3-1](ch03.html#fig_obama_json) in the preceding section, `region_id` is given as an ID, not as the plain-text string `"Washington, DC, United States"`. Why?
 
 If the user interface has a free-text field for entering the region, it makes sense to store it as a plain-text string. But there are advantages to having standardized lists of geographic regions, and letting users choose from a drop-down list or autocompleter:
 
@@ -177,10 +177,10 @@ In the résumé example, while the `region_id` field is a reference into a stand
 
 Perhaps the organization and school should be entities instead, and the profile should reference their IDs instead of their names? The same arguments for referencing the ID of a region also apply here. For example, say we wanted to include the logo of the school or company in addition to their name:
 
-- In a denormalized representation, we would include the image URL of the logo on every individual person’s profile; this makes the JSON document self-contained, but it creates a headache if we ever need to change the logo, because we now need to find all of the occurrences of the old URL and update them [[9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Zola2014)].
+- In a denormalized representation, we would include the image URL of the logo on every individual person’s profile; this makes the JSON document self-contained, but it creates a headache if we ever need to change the logo, because we now need to find all of the occurrences of the old URL and update them [[9](ch03.html#Zola2014)].
 - In a normalized representation, we would create an entity representing an organization or school, and store its name, logo URL, and perhaps other attributes (description, news feed, etc.) once on that entity. Every résumé that mentions the organization would then simply reference its ID, and updating the logo is easy.
 
-As a general principle, normalized data is usually faster to write (since there is only one copy), but slower to query (since it requires joins); denormalized data is usually faster to read (fewer joins), but more expensive to write (more copies to update). You might find it helpful to view denormalization as a form of derived data ([“Systems of Record and Derived Data”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_derived)), since you need to set up a process for updating the redundant copies of the data.
+As a general principle, normalized data is usually faster to write (since there is only one copy), but slower to query (since it requires joins); denormalized data is usually faster to read (fewer joins), but more expensive to write (more copies to update). You might find it helpful to view denormalization as a form of derived data ([“Systems of Record and Derived Data”](ch01.html#sec_introduction_derived)), since you need to set up a process for updating the redundant copies of the data.
 
 Besides the cost of performing all these updates, you also need to consider the consistency of the database if a process crashes halfway through making its updates. Databases that offer atomic transactions (see [Link to Come]) make it easier to remain consistent, but not all databases offer atomicity across multiple documents. It is also possible to ensure consistency through stream processing, which we discuss in [Link to Come].
 
@@ -188,9 +188,9 @@ Normalization tends to be better for OLTP systems, where both reads and updates
 
 #### Denormalization in the social networking case study
 
-In [“Case Study: Social Network Home Timelines”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_twitter) we compared a normalized representation ([Figure 2-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#fig_twitter_relational)) and a denormalized one (precomputed, materialized timelines): here, the join between `posts` and `follows` was too expensive, and the materialized timeline is a cache of the result of that join. The fan-out process that inserts a new post into followers’ timelines was our way of keeping the denormalized representation consistent.
+In [“Case Study: Social Network Home Timelines”](ch02.html#sec_introduction_twitter) we compared a normalized representation ([Figure 2-1](ch02.html#fig_twitter_relational)) and a denormalized one (precomputed, materialized timelines): here, the join between `posts` and `follows` was too expensive, and the materialized timeline is a cache of the result of that join. The fan-out process that inserts a new post into followers’ timelines was our way of keeping the denormalized representation consistent.
 
-However, the implementation of materialized timelines at X (formerly Twitter) does not store the actual text of each post: each entry actually only stores the post ID, the ID of the user who posted it, and a little bit of extra information to identify reposts and replies [[11](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Krikorian2012_ch3)]. In other words, it is a precomputed result of (approximately) the following query:
+However, the implementation of materialized timelines at X (formerly Twitter) does not store the actual text of each post: each entry actually only stores the post ID, the ID of the user who posted it, and a little bit of extra information to identify reposts and replies [[11](ch03.html#Krikorian2012_ch3)]. In other words, it is a precomputed result of (approximately) the following query:
 
 ```
 SELECT posts.id, posts.sender_id FROM posts
@@ -200,7 +200,7 @@ SELECT posts.id, posts.sender_id FROM posts
   LIMIT 1000
 ```
 
-This means that whenever the timeline is read, the service still needs to perform two joins: look up the post ID to fetch the actual post content (as well as statistics such as the number of likes and replies), and look up the sender’s profile by ID (to get their username, profile picture, and other details). This process of looking up the human-readable information by ID is called *hydrating* the IDs, and it is essentially a join performed in application code [[11](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Krikorian2012_ch3)].
+This means that whenever the timeline is read, the service still needs to perform two joins: look up the post ID to fetch the actual post content (as well as statistics such as the number of likes and replies), and look up the sender’s profile by ID (to get their username, profile picture, and other details). This process of looking up the human-readable information by ID is called *hydrating* the IDs, and it is essentially a join performed in application code [[11](ch03.html#Krikorian2012_ch3)].
 
 The reason for storing only IDs in the precomputed timeline is that the data they refer to is fast-changing: the number of likes and replies may change multiple times per second on a popular post, and some users regularly change their username or profile photo. Since the timeline should show the latest like count and profile picture when it is viewed, it would not make sense to denormalize this information into the materialized timeline. Moreover, the storage cost would be increased significantly by such denormalization.
 
@@ -216,15 +216,15 @@ If you need to decide whether to denormalize something in your application, the
 
 ### 多对一与多对多关系
 
-While `positions` and `education` in [Figure 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_relational) are examples of one-to-many or one-to-few relationships (one résumé has several positions, but each position belongs only to one résumé), the `region_id` field is an example of a *many-to-one* relationship (many people live in the same region, but we assume that each person lives in only one region at any one time).
+While `positions` and `education` in [Figure 3-1](ch03.html#fig_obama_relational) are examples of one-to-many or one-to-few relationships (one résumé has several positions, but each position belongs only to one résumé), the `region_id` field is an example of a *many-to-one* relationship (many people live in the same region, but we assume that each person lives in only one region at any one time).
 
-If we introduce entities for organizations and schools, and reference them by ID from the résumé, then we also have *many-to-many* relationships (one person has worked for several organizations, and an organization has several past or present employees). In a relational model, such a relationship is usually represented as an *associative table* or *join table*, as shown in [Figure 3-3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_m2m_rel): each position associates one user ID with one organization ID.
+If we introduce entities for organizations and schools, and reference them by ID from the résumé, then we also have *many-to-many* relationships (one person has worked for several organizations, and an organization has several past or present employees). In a relational model, such a relationship is usually represented as an *associative table* or *join table*, as shown in [Figure 3-3](ch03.html#fig_datamodels_m2m_rel): each position associates one user ID with one organization ID.
 
 ![ddia 0203](img/ddia_0203.png)
 
 > Figure 3-3. Many-to-many relationships in the relational model.
 
-Many-to-one and many-to-many relationships do not easily fit within one self-contained JSON document; they lend themselves more to a normalized representation. In a document model, one possible representation is given in [Example 3-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_m2m_json) and illustrated in [Figure 3-4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_many_to_many): the data within each dotted rectangle can be grouped into one document, but the links to organizations and schools are best represented as references to other documents.
+Many-to-one and many-to-many relationships do not easily fit within one self-contained JSON document; they lend themselves more to a normalized representation. In a document model, one possible representation is given in [Example 3-2](ch03.html#fig_datamodels_m2m_json) and illustrated in [Figure 3-4](ch03.html#fig_datamodels_many_to_many): the data within each dotted rectangle can be grouped into one document, but the links to organizations and schools are best represented as references to other documents.
 
 > Example 3-2. A résumé that references organizations by ID.
 
@@ -247,15 +247,15 @@ Many-to-one and many-to-many relationships do not easily fit within one self-con
 
 Many-to-many relationships often need to be queried in “both directions”: for example, finding all of the organizations that a particular person has worked for, and finding all of the people who have worked at a particular organization. One way of enabling such queries is to store ID references on both sides, i.e., a résumé includes the ID of each organization where the person has worked, and the organization document includes the IDs of the résumés that mention that organization. This representation is denormalized, since the relationship is stored in two places, which could become inconsistent with each other.
 
-A normalized representation stores the relationship in only one place, and relies on *secondary indexes* (which we discuss in [Link to Come]) to allow the relationship to be efficiently queried in both directions. In the relational schema of [Figure 3-3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_m2m_rel), we would tell the database to create indexes on both the `user_id` and the `org_id` columns of the `positions` table.
+A normalized representation stores the relationship in only one place, and relies on *secondary indexes* (which we discuss in [Link to Come]) to allow the relationship to be efficiently queried in both directions. In the relational schema of [Figure 3-3](ch03.html#fig_datamodels_m2m_rel), we would tell the database to create indexes on both the `user_id` and the `org_id` columns of the `positions` table.
 
-In the document model of [Example 3-2](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_m2m_json), the database needs to index the `org_id` field of objects inside the `positions` array. Many document databases and relational databases with JSON support are able to create such indexes on values inside a document.
+In the document model of [Example 3-2](ch03.html#fig_datamodels_m2m_json), the database needs to index the `org_id` field of objects inside the `positions` array. Many document databases and relational databases with JSON support are able to create such indexes on values inside a document.
 
 #### Stars and Snowflakes: Schemas for Analytics
 
-Data warehouses (see [“Data Warehousing”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_dwh)) are usually relational, and there are a few widely-used conventions for the structure of tables in a data warehouse: a *star schema*, *snowflake schema*, *dimensional modeling* [[12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Kimball2013_ch3)], and *one big table* (OBT). These structures are optimized for the needs of business analysts. ETL processes translate data from operational systems into this schema.
+Data warehouses (see [“Data Warehousing”](ch01.html#sec_introduction_dwh)) are usually relational, and there are a few widely-used conventions for the structure of tables in a data warehouse: a *star schema*, *snowflake schema*, *dimensional modeling* [[12](ch03.html#Kimball2013_ch3)], and *one big table* (OBT). These structures are optimized for the needs of business analysts. ETL processes translate data from operational systems into this schema.
 
-The example schema in [Figure 3-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dwh_schema) shows a data warehouse that might be found at a grocery retailer. At the center of the schema is a so-called *fact table* (in this example, it is called `fact_sales`). Each row of the fact table represents an event that occurred at a particular time (here, each row represents a customer’s purchase of a product). If we were analyzing website traffic rather than retail sales, each row might represent a page view or a click by a user.
+The example schema in [Figure 3-5](ch03.html#fig_dwh_schema) shows a data warehouse that might be found at a grocery retailer. At the center of the schema is a so-called *fact table* (in this example, it is called `fact_sales`). Each row of the fact table represents an event that occurred at a particular time (here, each row represents a customer’s purchase of a product). If we were analyzing website traffic rather than retail sales, each row might represent a page view or a click by a user.
 
 ![ddia 0309](img/ddia_0309.png)
 
@@ -265,19 +265,19 @@ Usually, facts are captured as individual events, because this allows maximum fl
 
 Some of the columns in the fact table are attributes, such as the price at which the product was sold and the cost of buying it from the supplier (allowing the profit margin to be calculated). Other columns in the fact table are foreign key references to other tables, called *dimension tables*. As each row in the fact table represents an event, the dimensions represent the *who*, *what*, *where*, *when*, *how*, and *why* of the event.
 
-For example, in [Figure 3-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dwh_schema), one of the dimensions is the product that was sold. Each row in the `dim_product` table represents one type of product that is for sale, including its stock-keeping unit (SKU), description, brand name, category, fat content, package size, etc. Each row in the `fact_sales` table uses a foreign key to indicate which product was sold in that particular transaction. Queries often involve multiple joins to multiple dimension tables.
+For example, in [Figure 3-5](ch03.html#fig_dwh_schema), one of the dimensions is the product that was sold. Each row in the `dim_product` table represents one type of product that is for sale, including its stock-keeping unit (SKU), description, brand name, category, fat content, package size, etc. Each row in the `fact_sales` table uses a foreign key to indicate which product was sold in that particular transaction. Queries often involve multiple joins to multiple dimension tables.
 
 Even date and time are often represented using dimension tables, because this allows additional information about dates (such as public holidays) to be encoded, allowing queries to differentiate between sales on holidays and non-holidays.
 
-[Figure 3-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dwh_schema) is an example of a star schema. The name comes from the fact that when the table relationships are visualized, the fact table is in the middle, surrounded by its dimension tables; the connections to these tables are like the rays of a star.
+[Figure 3-5](ch03.html#fig_dwh_schema) is an example of a star schema. The name comes from the fact that when the table relationships are visualized, the fact table is in the middle, surrounded by its dimension tables; the connections to these tables are like the rays of a star.
 
-A variation of this template is known as the *snowflake schema*, where dimensions are further broken down into subdimensions. For example, there could be separate tables for brands and product categories, and each row in the `dim_product` table could reference the brand and category as foreign keys, rather than storing them as strings in the `dim_product` table. Snowflake schemas are more normalized than star schemas, but star schemas are often preferred because they are simpler for analysts to work with [[12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Kimball2013_ch3)].
+A variation of this template is known as the *snowflake schema*, where dimensions are further broken down into subdimensions. For example, there could be separate tables for brands and product categories, and each row in the `dim_product` table could reference the brand and category as foreign keys, rather than storing them as strings in the `dim_product` table. Snowflake schemas are more normalized than star schemas, but star schemas are often preferred because they are simpler for analysts to work with [[12](ch03.html#Kimball2013_ch3)].
 
 In a typical data warehouse, tables are often quite wide: fact tables often have over 100 columns, sometimes several hundred. Dimension tables can also be wide, as they include all the metadata that may be relevant for analysis—for example, the `dim_store` table may include details of which services are offered at each store, whether it has an in-store bakery, the square footage, the date when the store was first opened, when it was last remodeled, how far it is from the nearest highway, etc.
 
 A star or snowflake schema consists mostly of many-to-one relationships (e.g., many sales occur for one particular product, in one particular store), represented as the fact table having foreign keys into dimension tables, or dimensions into sub-dimensions. In principle, other types of relationship could exist, but they are often denormalized in order to simplify queries. For example, if a customer buys several different products at once, that multi-item transaction is not represented explicitly; instead, there is a separate row in the fact table for each product purchased, and those facts all just happen to have the same customer ID, store ID, and timestamp.
 
-Some data warehouse schemas take denormalization even further and leave out the dimension tables entirely, folding the information in the dimensions into denormalized columns on the fact table instead (essentially, precomputing the join between the fact table and the dimension tables). This approach is known as *one big table* (OBT), and while it requires more storage space, it sometimes enables faster queries [[13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Kaminsky2022)].
+Some data warehouse schemas take denormalization even further and leave out the dimension tables entirely, folding the information in the dimensions into denormalized columns on the fact table instead (essentially, precomputing the join between the fact table and the dimension tables). This approach is known as *one big table* (OBT), and while it requires more storage space, it sometimes enables faster queries [[13](ch03.html#Kaminsky2022)].
 
 In the context of analytics, such denormalization is unproblematic, since the data typically represents a log of historical data that is not going to change (except maybe for occasionally correcting an error). The issues of data consistency and write overheads that occur with denormalization in OLTP systems are not as pressing in analytics.
 
@@ -285,21 +285,21 @@ In the context of analytics, such denormalization is unproblematic, since the da
 
 The main arguments in favor of the document data model are schema flexibility, better performance due to locality, and that for some applications it is closer to the object model used by the application. The relational model counters by providing better support for joins, many-to-one, and many-to-many relationships. Let’s examine these arguments in more detail.
 
-If the data in your application has a document-like structure (i.e., a tree of one-to-many relationships, where typically the entire tree is loaded at once), then it’s probably a good idea to use a document model. The relational technique of *shredding*—splitting a document-like structure into multiple tables (like `positions`, `education`, and `contact_info` in [Figure 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_relational))—can lead to cumbersome schemas and unnecessarily complicated application code.
+If the data in your application has a document-like structure (i.e., a tree of one-to-many relationships, where typically the entire tree is loaded at once), then it’s probably a good idea to use a document model. The relational technique of *shredding*—splitting a document-like structure into multiple tables (like `positions`, `education`, and `contact_info` in [Figure 3-1](ch03.html#fig_obama_relational))—can lead to cumbersome schemas and unnecessarily complicated application code.
 
 The document model has limitations: for example, you cannot refer directly to a nested item within a document, but instead you need to say something like “the second item in the list of positions for user 251”. If you do need to reference nested items, a relational approach works better, since you can refer to any item directly by its ID.
 
-Some applications allow the user to choose the order of items: for example, imagine a to-do list or issue tracker where the user can drag and drop tasks to reorder them. The document model supports such applications well, because the items (or their IDs) can simply be stored in a JSON array to determine their order. In relational databases there isn’t a standard way of representing such reorderable lists, and various tricks are used: sorting by an integer column (requiring renumbering when you insert into the middle), a linked list of IDs, or fractional indexing [[14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Nelson2018), [15](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Wallace2017), [16](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Greenspan2020)].
+Some applications allow the user to choose the order of items: for example, imagine a to-do list or issue tracker where the user can drag and drop tasks to reorder them. The document model supports such applications well, because the items (or their IDs) can simply be stored in a JSON array to determine their order. In relational databases there isn’t a standard way of representing such reorderable lists, and various tricks are used: sorting by an integer column (requiring renumbering when you insert into the middle), a linked list of IDs, or fractional indexing [[14](ch03.html#Nelson2018), [15](ch03.html#Wallace2017), [16](ch03.html#Greenspan2020)].
 
 #### Schema flexibility in the document model
 
 Most document databases, and the JSON support in relational databases, do not enforce any schema on the data in documents. XML support in relational databases usually comes with optional schema validation. No schema means that arbitrary keys and values can be added to a document, and when reading, clients have no guarantees as to what fields the documents may contain.
 
-Document databases are sometimes called *schemaless*, but that’s misleading, as the code that reads the data usually assumes some kind of structure—i.e., there is an implicit schema, but it is not enforced by the database [[17](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Schemaless)]. A more accurate term is *schema-on-read* (the structure of the data is implicit, and only interpreted when the data is read), in contrast with *schema-on-write* (the traditional approach of relational databases, where the schema is explicit and the database ensures all data conforms to it when the data is written) [[18](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Awadallah2009)].
+Document databases are sometimes called *schemaless*, but that’s misleading, as the code that reads the data usually assumes some kind of structure—i.e., there is an implicit schema, but it is not enforced by the database [[17](ch03.html#Schemaless)]. A more accurate term is *schema-on-read* (the structure of the data is implicit, and only interpreted when the data is read), in contrast with *schema-on-write* (the traditional approach of relational databases, where the schema is explicit and the database ensures all data conforms to it when the data is written) [[18](ch03.html#Awadallah2009)].
 
-Schema-on-read is similar to dynamic (runtime) type checking in programming languages, whereas schema-on-write is similar to static (compile-time) type checking. Just as the advocates of static and dynamic type checking have big debates about their relative merits [[19](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Odersky2013)], enforcement of schemas in database is a contentious topic, and in general there’s no right or wrong answer.
+Schema-on-read is similar to dynamic (runtime) type checking in programming languages, whereas schema-on-write is similar to static (compile-time) type checking. Just as the advocates of static and dynamic type checking have big debates about their relative merits [[19](ch03.html#Odersky2013)], enforcement of schemas in database is a contentious topic, and in general there’s no right or wrong answer.
 
-The difference between the approaches is particularly noticeable in situations where an application wants to change the format of its data. For example, say you are currently storing each user’s full name in one field, and you instead want to store the first name and last name separately [[20](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Irwin2013)]. In a document database, you would just start writing new documents with the new fields and have code in the application that handles the case when old documents are read. For example:
+The difference between the approaches is particularly noticeable in situations where an application wants to change the format of its data. For example, say you are currently storing each user’s full name in one field, and you instead want to store the first name and last name separately [[20](ch03.html#Irwin2013)]. In a document database, you would just start writing new documents with the new fields and have code in the application that handles the case when old documents are read. For example:
 
 ```
 if (user && user.name && !user.first_name) {
@@ -318,7 +318,7 @@ UPDATE users SET first_name = substring_index(name, ' ', 1);      -- MySQL
 
 In most relational databases, adding a column with a default value is fast and unproblematic, even on large tables. However, running the `UPDATE` statement is likely to be slow on a large table, since every row needs to be rewritten, and other schema operations (such as changing the data type of a column) also typically require the entire table to be copied.
 
-Various tools exist to allow this type of schema changes to be performed in the background without downtime [[21](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Percona2023), [22](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Noach2016), [23](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Mukherjee2022), [24](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#PerezAradros2023)], but performing such migrations on large databases remains operationally challenging. Complicated migrations can be avoided by only adding the `first_name` column with a default value of `NULL` (which is fast), and filling it in at read time, like you would with a document database.
+Various tools exist to allow this type of schema changes to be performed in the background without downtime [[21](ch03.html#Percona2023), [22](ch03.html#Noach2016), [23](ch03.html#Mukherjee2022), [24](ch03.html#PerezAradros2023)], but performing such migrations on large databases remains operationally challenging. Complicated migrations can be avoided by only adding the `first_name` column with a default value of `NULL` (which is fast), and filling it in at read time, like you would with a document database.
 
 The schema-on-read approach is advantageous if the items in the collection don’t all have the same structure for some reason (i.e., the data is heterogeneous)—for example, because:
 
@@ -329,17 +329,17 @@ In situations like these, a schema may hurt more than it helps, and schemaless d
 
 #### Data locality for reads and writes
 
-A document is usually stored as a single continuous string, encoded as JSON, XML, or a binary variant thereof (such as MongoDB’s BSON). If your application often needs to access the entire document (for example, to render it on a web page), there is a performance advantage to this *storage locality*. If data is split across multiple tables, like in [Figure 3-1](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_obama_relational), multiple index lookups are required to retrieve it all, which may require more disk seeks and take more time.
+A document is usually stored as a single continuous string, encoded as JSON, XML, or a binary variant thereof (such as MongoDB’s BSON). If your application often needs to access the entire document (for example, to render it on a web page), there is a performance advantage to this *storage locality*. If data is split across multiple tables, like in [Figure 3-1](ch03.html#fig_obama_relational), multiple index lookups are required to retrieve it all, which may require more disk seeks and take more time.
 
 The locality advantage only applies if you need large parts of the document at the same time. The database typically needs to load the entire document, which can be wasteful if you only need to access a small part of a large document. On updates to a document, the entire document usually needs to be rewritten. For these reasons, it is generally recommended that you keep documents fairly small and avoid frequent small updates to a document.
 
-However, the idea of storing related data together for locality is not limited to the document model. For example, Google’s Spanner database offers the same locality properties in a relational data model, by allowing the schema to declare that a table’s rows should be interleaved (nested) within a parent table [[25](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Corbett2012_ch2)]. Oracle allows the same, using a feature called *multi-table index cluster tables* [[26](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#BurlesonCluster)]. The *column-family* concept in the Bigtable data model (used in Cassandra, HBase, and ScyllaDB), also known as a *wide-column* model, has a similar purpose of managing locality [[27](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Chang2006_ch2)].
+However, the idea of storing related data together for locality is not limited to the document model. For example, Google’s Spanner database offers the same locality properties in a relational data model, by allowing the schema to declare that a table’s rows should be interleaved (nested) within a parent table [[25](ch03.html#Corbett2012_ch2)]. Oracle allows the same, using a feature called *multi-table index cluster tables* [[26](ch03.html#BurlesonCluster)]. The *column-family* concept in the Bigtable data model (used in Cassandra, HBase, and ScyllaDB), also known as a *wide-column* model, has a similar purpose of managing locality [[27](ch03.html#Chang2006_ch2)].
 
 #### Query languages for documents
 
 Another difference between a relational and a document database is the language or API that you use to query it. Most relational databases are queried using SQL, but document databases are more varied. Some allow only key-value access by primary key, while others also offer secondary indexes to query for values inside documents, and some provide rich query languages.
 
-XML databases are often queried using XQuery and XPath, which are designed to allow complex queries, including joins across multiple documents, and also format their results as XML [[28](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Walmsley2015)]. JSON Pointer [[29](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Bryan2013)] and JSONPath [[30](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Goessner2024)] provide an equivalent to XPath for JSON. MongoDB’s aggregation pipeline, whose `$lookup` operator for joins we saw in [“Normalization, Denormalization, and Joins”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_normalization), is an example of a query language for collections of JSON documents.
+XML databases are often queried using XQuery and XPath, which are designed to allow complex queries, including joins across multiple documents, and also format their results as XML [[28](ch03.html#Walmsley2015)]. JSON Pointer [[29](ch03.html#Bryan2013)] and JSONPath [[30](ch03.html#Goessner2024)] provide an equivalent to XPath for JSON. MongoDB’s aggregation pipeline, whose `$lookup` operator for joins we saw in [“Normalization, Denormalization, and Joins”](ch03.html#sec_datamodels_normalization), is an example of a query language for collections of JSON documents.
 
 Let’s look at another example to get a feel for this language—this time an aggregation, which is especially needed for analytics. Imagine you are a marine biologist, and you add an observation record to your database every time you see animals in the ocean. Now you want to generate a report saying how many sharks you have sighted per month. In PostgreSQL you might express that query like this:
 
@@ -351,7 +351,7 @@ WHERE family = 'Sharks'
 GROUP BY observation_month;
 ```
 
-- [![1](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/1.png)](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#co_data_models_and_query_languages_CO1-1)
+- [![1](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/1.png)](ch03.html#co_data_models_and_query_languages_CO1-1)
 
   The `date_trunc('month', timestamp)` function determines the calendar month containing `timestamp`, and returns another timestamp representing the beginning of that month. In other words, it rounds a timestamp down to the nearest month.
 
@@ -380,7 +380,7 @@ This convergence of the models is good news for application developers, because
 
 > **注意**
 >
-> Codd’s original description of the relational model [[3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Codd1970)] actually allowed something similar to JSON within a relational schema. He called it *nonsimple domains*. The idea was that a value in a row doesn’t have to just be a primitive datatype like a number or a string, but it could also be a nested relation (table)—so you can have an arbitrarily nested tree structure as a value, much like the JSON or XML support that was added to SQL over 30 years later.
+> Codd’s original description of the relational model [[3](ch03.html#Codd1970)] actually allowed something similar to JSON within a relational schema. He called it *nonsimple domains*. The idea was that a value in a row doesn’t have to just be a primitive datatype like a number or a string, but it could also be a nested relation (table)—so you can have an arbitrarily nested tree structure as a value, much like the JSON or XML support that was added to SQL over 30 years later.
 
 
 
@@ -415,20 +415,20 @@ A graph consists of two kinds of objects: *vertices* (also known as *nodes* or *
 
   Vertices are junctions, and edges represent the roads or railway lines between them.
 
-Well-known algorithms can operate on these graphs: for example, map navigation apps search for the shortest path between two points in a road network, and PageRank can be used on the web graph to determine the popularity of a web page and thus its ranking in search results [[31](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Page1999)].
+Well-known algorithms can operate on these graphs: for example, map navigation apps search for the shortest path between two points in a road network, and PageRank can be used on the web graph to determine the popularity of a web page and thus its ranking in search results [[31](ch03.html#Page1999)].
 
-Graphs can be represented in several different ways. In the *adjacency list* model, each vertex stores the IDs of its neighbor vertices that are one edge away. Alternatively, you can use an *adjacency matrix*, a two-dimensional array where each row and each column corresponds to a vertex, where the value is zero when there is no edge between the row vertex and the column vertex, and where the value is one if there is an edge. The adjacency list is good for graph traversals, and the matrix is good for machine learning (see [“Dataframes, Matrices, and Arrays”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_dataframes)).
+Graphs can be represented in several different ways. In the *adjacency list* model, each vertex stores the IDs of its neighbor vertices that are one edge away. Alternatively, you can use an *adjacency matrix*, a two-dimensional array where each row and each column corresponds to a vertex, where the value is zero when there is no edge between the row vertex and the column vertex, and where the value is one if there is an edge. The adjacency list is good for graph traversals, and the matrix is good for machine learning (see [“Dataframes, Matrices, and Arrays”](ch03.html#sec_datamodels_dataframes)).
 
 In the examples just given, all the vertices in a graph represent the same kind of thing (people, web pages, or road junctions, respectively). However, graphs are not limited to such *homogeneous* data: an equally powerful use of graphs is to provide a consistent way of storing completely different types of objects in a single database. For example:
 
-- Facebook maintains a single graph with many different types of vertices and edges: vertices represent people, locations, events, checkins, and comments made by users; edges indicate which people are friends with each other, which checkin happened in which location, who commented on which post, who attended which event, and so on [[32](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Bronson2013)].
-- Knowledge graphs are used by search engines to record facts about entities that often occur in search queries, such as organizations, people, and places [[33](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Noy2019)]. This information is obtained by crawling and analyzing the text on websites; some websites, such as Wikidata, also publish graph data in a structured form.
+- Facebook maintains a single graph with many different types of vertices and edges: vertices represent people, locations, events, checkins, and comments made by users; edges indicate which people are friends with each other, which checkin happened in which location, who commented on which post, who attended which event, and so on [[32](ch03.html#Bronson2013)].
+- Knowledge graphs are used by search engines to record facts about entities that often occur in search queries, such as organizations, people, and places [[33](ch03.html#Noy2019)]. This information is obtained by crawling and analyzing the text on websites; some websites, such as Wikidata, also publish graph data in a structured form.
 
-There are several different, but related, ways of structuring and querying data in graphs. In this section we will discuss the *property graph* model (implemented by Neo4j, Memgraph, KùzuDB [[34](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Feng2023)], and others [[35](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Besta2019)]) and the *triple-store* model (implemented by Datomic, AllegroGraph, Blazegraph, and others). These models are fairly similar in what they can express, and some graph databases (such as Amazon Neptune) support both models.
+There are several different, but related, ways of structuring and querying data in graphs. In this section we will discuss the *property graph* model (implemented by Neo4j, Memgraph, KùzuDB [[34](ch03.html#Feng2023)], and others [[35](ch03.html#Besta2019)]) and the *triple-store* model (implemented by Datomic, AllegroGraph, Blazegraph, and others). These models are fairly similar in what they can express, and some graph databases (such as Amazon Neptune) support both models.
 
-We will also look at four query languages for graphs (Cypher, SPARQL, Datalog, and GraphQL), as well as SQL support for querying graphs. Other graph query languages exist, such as Gremlin [[36](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#TinkerPop2023)], but these will give us a representative overview.
+We will also look at four query languages for graphs (Cypher, SPARQL, Datalog, and GraphQL), as well as SQL support for querying graphs. Other graph query languages exist, such as Gremlin [[36](ch03.html#TinkerPop2023)], but these will give us a representative overview.
 
-To illustrate these different languages and models, this section uses the graph shown in [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph) as running example. It could be taken from a social network or a genealogical database: it shows two people, Lucy from Idaho and Alain from Saint-Lô, France. They are married and living in London. Each person and each location is represented as a vertex, and the relationships between them as edges. This example will help demonstrate some queries that are easy in graph databases, but difficult in other models.
+To illustrate these different languages and models, this section uses the graph shown in [Figure 3-6](ch03.html#fig_datamodels_graph) as running example. It could be taken from a social network or a genealogical database: it shows two people, Lucy from Idaho and Alain from Saint-Lô, France. They are married and living in London. Each person and each location is represented as a vertex, and the relationships between them as edges. This example will help demonstrate some queries that are easy in graph databases, but difficult in other models.
 
 ![ddia 0205](img/ddia_0205.png)
 
@@ -455,7 +455,7 @@ Each edge consists of:
 - A label to describe the kind of relationship between the two vertices
 - A collection of properties (key-value pairs)
 
-You can think of a graph store as consisting of two relational tables, one for vertices and one for edges, as shown in [Example 3-3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_sql_schema) (this schema uses the PostgreSQL `jsonb` datatype to store the properties of each vertex or edge). The head and tail vertex are stored for each edge; if you want the set of incoming or outgoing edges for a vertex, you can query the `edges` table by `head_vertex` or `tail_vertex`, respectively.
+You can think of a graph store as consisting of two relational tables, one for vertices and one for edges, as shown in [Example 3-3](ch03.html#fig_graph_sql_schema) (this schema uses the PostgreSQL `jsonb` datatype to store the properties of each vertex or edge). The head and tail vertex are stored for each edge; if you want the set of incoming or outgoing edges for a vertex, you can query the `edges` table by `head_vertex` or `tail_vertex`, respectively.
 
 ##### Example 3-3. Representing a property graph using a relational schema
 
@@ -481,17 +481,17 @@ CREATE INDEX edges_heads ON edges (head_vertex);
 Some important aspects of this model are:
 
 1. Any vertex can have an edge connecting it with any other vertex. There is no schema that restricts which kinds of things can or cannot be associated.
-2. Given any vertex, you can efficiently find both its incoming and its outgoing edges, and thus *traverse* the graph—i.e., follow a path through a chain of vertices—both forward and backward. (That’s why [Example 3-3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_sql_schema) has indexes on both the `tail_vertex` and `head_vertex` columns.)
+2. Given any vertex, you can efficiently find both its incoming and its outgoing edges, and thus *traverse* the graph—i.e., follow a path through a chain of vertices—both forward and backward. (That’s why [Example 3-3](ch03.html#fig_graph_sql_schema) has indexes on both the `tail_vertex` and `head_vertex` columns.)
 3. By using different labels for different kinds of vertices and relationships, you can store several different kinds of information in a single graph, while still maintaining a clean data model.
 
-The edges table is like the many-to-many associative table/join table we saw in [“Many-to-One and Many-to-Many Relationships”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_many_to_many), generalized to allow many different types of relationship to be stored in the same table. There may also be indexes on the labels and the properties, allowing vertices or edges with certain properties to be found efficiently.
+The edges table is like the many-to-many associative table/join table we saw in [“Many-to-One and Many-to-Many Relationships”](ch03.html#sec_datamodels_many_to_many), generalized to allow many different types of relationship to be stored in the same table. There may also be indexes on the labels and the properties, allowing vertices or edges with certain properties to be found efficiently.
 
 > **Note**
 
 > A limitation of graph models is that an edge can only associate two vertices with each other, whereas a relational join table can represent three-way or even higher-degree relationships by having multiple foreign key references on a single row. Such relationships can be represented in a graph by creating an additional vertex corresponding to each row of the join table, and edges to/from that vertex, or by using a *hypergraph*.
 
 
-Those features give graphs a great deal of flexibility for data modeling, as illustrated in [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph). The figure shows a few things that would be difficult to express in a traditional relational schema, such as different kinds of regional structures in different countries (France has *départements* and *régions*, whereas the US has *counties* and *states*), quirks of history such as a country within a country (ignoring for now the intricacies of sovereign states and nations), and varying granularity of data (Lucy’s current residence is specified as a city, whereas her place of birth is specified only at the level of a state).
+Those features give graphs a great deal of flexibility for data modeling, as illustrated in [Figure 3-6](ch03.html#fig_datamodels_graph). The figure shows a few things that would be difficult to express in a traditional relational schema, such as different kinds of regional structures in different countries (France has *départements* and *régions*, whereas the US has *counties* and *states*), quirks of history such as a country within a country (ignoring for now the intricacies of sovereign states and nations), and varying granularity of data (Lucy’s current residence is specified as a city, whereas her place of birth is specified only at the level of a state).
 
 You could imagine extending the graph to also include many other facts about Lucy and Alain, or other people. For instance, you could use it to indicate any food allergies they have (by introducing a vertex for each allergen, and an edge between a person and an allergen to indicate an allergy), and link the allergens with a set of vertices that show which foods contain which substances. Then you could write a query to find out what is safe for each person to eat. Graphs are good for evolvability: as you add features to your application, a graph can easily be extended to accommodate changes in your application’s data structures.
 
@@ -500,11 +500,11 @@ You could imagine extending the graph to also include many other facts about Luc
 
 ### Cypher查询语言
 
-*Cypher* is a query language for property graphs, originally created for the Neo4j graph database, and later developed into an open standard as *openCypher* [[37](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Francis2018)]. Besides Neo4j, Cypher is supported by Memgraph, KùzuDB [[34](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Feng2023)], Amazon Neptune, Apache AGE (with storage in PostgreSQL), and others. It is named after a character in the movie *The Matrix* and is not related to ciphers in cryptography [[38](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#EifremTweet)].
+*Cypher* is a query language for property graphs, originally created for the Neo4j graph database, and later developed into an open standard as *openCypher* [[37](ch03.html#Francis2018)]. Besides Neo4j, Cypher is supported by Memgraph, KùzuDB [[34](ch03.html#Feng2023)], Amazon Neptune, Apache AGE (with storage in PostgreSQL), and others. It is named after a character in the movie *The Matrix* and is not related to ciphers in cryptography [[38](ch03.html#EifremTweet)].
 
-[Example 3-4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_cypher_create) shows the Cypher query to insert the lefthand portion of [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph) into a graph database. The rest of the graph can be added similarly. Each vertex is given a symbolic name like `usa` or `idaho`. That name is not stored in the database, but only used internally within the query to create edges between the vertices, using an arrow notation: `(idaho) -[:WITHIN]-> (usa)` creates an edge labeled `WITHIN`, with `idaho` as the tail node and `usa` as the head node.
+[Example 3-4](ch03.html#fig_cypher_create) shows the Cypher query to insert the lefthand portion of [Figure 3-6](ch03.html#fig_datamodels_graph) into a graph database. The rest of the graph can be added similarly. Each vertex is given a symbolic name like `usa` or `idaho`. That name is not stored in the database, but only used internally within the query to create edges between the vertices, using an arrow notation: `(idaho) -[:WITHIN]-> (usa)` creates an edge labeled `WITHIN`, with `idaho` as the tail node and `usa` as the head node.
 
-##### Example 3-4. A subset of the data in [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph), represented as a Cypher query
+##### Example 3-4. A subset of the data in [Figure 3-6](ch03.html#fig_datamodels_graph), represented as a Cypher query
 
 ```
 CREATE
@@ -516,9 +516,9 @@ CREATE
   (lucy)  -[:BORN_IN]-> (idaho)
 ```
 
-When all the vertices and edges of [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph) are added to the database, we can start asking interesting questions: for example, *find the names of all the people who emigrated from the United States to Europe*. That is, find all the vertices that have a `BORN_IN` edge to a location within the US, and also a `LIVING_IN` edge to a location within Europe, and return the `name` property of each of those vertices.
+When all the vertices and edges of [Figure 3-6](ch03.html#fig_datamodels_graph) are added to the database, we can start asking interesting questions: for example, *find the names of all the people who emigrated from the United States to Europe*. That is, find all the vertices that have a `BORN_IN` edge to a location within the US, and also a `LIVING_IN` edge to a location within Europe, and return the `name` property of each of those vertices.
 
-[Example 3-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_cypher_query) shows how to express that query in Cypher. The same arrow notation is used in a `MATCH` clause to find patterns in the graph: `(person) -[:BORN_IN]-> ()` matches any two vertices that are related by an edge labeled `BORN_IN`. The tail vertex of that edge is bound to the variable `person`, and the head vertex is left unnamed.
+[Example 3-5](ch03.html#fig_cypher_query) shows how to express that query in Cypher. The same arrow notation is used in a `MATCH` clause to find patterns in the graph: `(person) -[:BORN_IN]-> ()` matches any two vertices that are related by an edge labeled `BORN_IN`. The tail vertex of that edge is bound to the variable `person`, and the head vertex is left unnamed.
 
 ##### Example 3-5. Cypher query to find people who emigrated from the US to Europe
 
@@ -546,7 +546,7 @@ But equivalently, you could start with the two `Location` vertices and work back
 
 ### SQL中的图查询
 
-[Example 3-3](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_sql_schema) suggested that graph data can be represented in a relational database. But if we put graph data in a relational structure, can we also query it using SQL?
+[Example 3-3](ch03.html#fig_graph_sql_schema) suggested that graph data can be represented in a relational database. But if we put graph data in a relational structure, can we also query it using SQL?
 
 The answer is yes, but with some difficulty. Every edge that you traverse in a graph query is effectively a join with the `edges` table. In a relational database, you usually know in advance which joins you need in your query. On the other hand, in a graph query, you may need to traverse a variable number of edges before you find the vertex you’re looking for—that is, the number of joins is not fixed in advance.
 
@@ -554,9 +554,9 @@ In our example, that happens in the `() -[:WITHIN*0..]-> ()` pattern in the Cyph
 
 In Cypher, `:WITHIN*0..` expresses that fact very concisely: it means “follow a `WITHIN` edge, zero or more times.” It is like the `*` operator in a regular expression.
 
-Since SQL:1999, this idea of variable-length traversal paths in a query can be expressed using something called *recursive common table expressions* (the `WITH RECURSIVE` syntax). [Example 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_sql_query) shows the same query—finding the names of people who emigrated from the US to Europe—expressed in SQL using this technique. However, the syntax is very clumsy in comparison to Cypher.
+Since SQL:1999, this idea of variable-length traversal paths in a query can be expressed using something called *recursive common table expressions* (the `WITH RECURSIVE` syntax). [Example 3-6](ch03.html#fig_graph_sql_query) shows the same query—finding the names of people who emigrated from the US to Europe—expressed in SQL using this technique. However, the syntax is very clumsy in comparison to Cypher.
 
-> Example 3-6. The same query as [Example 3-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_cypher_query), written in SQL using recursive common table expressions
+> Example 3-6. The same query as [Example 3-5](ch03.html#fig_cypher_query), written in SQL using recursive common table expressions
 
 ```postgresql
 WITH RECURSIVE
@@ -602,33 +602,33 @@ JOIN born_in_usa     ON vertices.vertex_id = born_in_usa.vertex_id
 JOIN lives_in_europe ON vertices.vertex_id = lives_in_europe.vertex_id;
 ```
 
-- [![1](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/1.png)](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#co_data_models_and_query_languages_CO2-1)
+- [![1](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/1.png)](ch03.html#co_data_models_and_query_languages_CO2-1)
 
   First find the vertex whose `name` property has the value `"United States"`, and make it the first element of the set of vertices `in_usa`.
 
-- [![2](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/2.png)](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#co_data_models_and_query_languages_CO2-2)
+- [![2](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/2.png)](ch03.html#co_data_models_and_query_languages_CO2-2)
 
   Follow all incoming `within` edges from vertices in the set `in_usa`, and add them to the same set, until all incoming `within` edges have been visited.
 
-- [![3](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/3.png)](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#co_data_models_and_query_languages_CO2-3)
+- [![3](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/3.png)](ch03.html#co_data_models_and_query_languages_CO2-3)
 
   Do the same starting with the vertex whose `name` property has the value `"Europe"`, and build up the set of vertices `in_europe`.
 
-- [![4](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/4.png)](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#co_data_models_and_query_languages_CO2-4)
+- [![4](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/4.png)](ch03.html#co_data_models_and_query_languages_CO2-4)
 
   For each of the vertices in the set `in_usa`, follow incoming `born_in` edges to find people who were born in some place within the United States.
 
-- [![5](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/5.png)](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#co_data_models_and_query_languages_CO2-5)
+- [![5](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/5.png)](ch03.html#co_data_models_and_query_languages_CO2-5)
 
   Similarly, for each of the vertices in the set `in_europe`, follow incoming `lives_in` edges to find people who live in Europe.
 
-- [![6](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/6.png)](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#co_data_models_and_query_languages_CO2-6)
+- [![6](https://learning.oreilly.com/api/v2/epubs/urn:orm:book:9781098119058/files/assets/6.png)](ch03.html#co_data_models_and_query_languages_CO2-6)
 
   Finally, intersect the set of people born in the USA with the set of people living in Europe, by joining them.
 
-The fact that a 4-line Cypher query requires 31 lines in SQL shows how much of a difference the right choice of data model and query language can make. And this is just the beginning; there are more details to consider, e.g., around handling cycles, and choosing between breadth-first or depth-first traversal [[39](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Tisiot2021)]. Oracle has a different SQL extension for recursive queries, which it calls *hierarchical* [[40](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Goel2020)].
+The fact that a 4-line Cypher query requires 31 lines in SQL shows how much of a difference the right choice of data model and query language can make. And this is just the beginning; there are more details to consider, e.g., around handling cycles, and choosing between breadth-first or depth-first traversal [[39](ch03.html#Tisiot2021)]. Oracle has a different SQL extension for recursive queries, which it calls *hierarchical* [[40](ch03.html#Goel2020)].
 
-However, the situation may be improving: at the time of writing, there are plans to add a graph query language called GQL to the SQL standard [[41](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Deutsch2022), [42](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Green2019)], which will provide a syntax inspired by Cypher, GSQL [[43](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Deutsch2018)], and PGQL [[44](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#vanRest2016)].
+However, the situation may be improving: at the time of writing, there are plans to add a graph query language called GQL to the SQL standard [[41](ch03.html#Deutsch2022), [42](ch03.html#Green2019)], which will provide a syntax inspired by Cypher, GSQL [[43](ch03.html#Deutsch2018)], and PGQL [[44](ch03.html#vanRest2016)].
 
 
 --------
@@ -641,16 +641,16 @@ In a triple-store, all information is stored in the form of very simple three-pa
 
 The subject of a triple is equivalent to a vertex in a graph. The object is one of two things:
 
-1. A value of a primitive datatype, such as a string or a number. In that case, the predicate and object of the triple are equivalent to the key and value of a property on the subject vertex. Using the example from [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph), (*lucy*, *birthYear*, *1989*) is like a vertex `lucy` with properties `{"birthYear": 1989}`.
+1. A value of a primitive datatype, such as a string or a number. In that case, the predicate and object of the triple are equivalent to the key and value of a property on the subject vertex. Using the example from [Figure 3-6](ch03.html#fig_datamodels_graph), (*lucy*, *birthYear*, *1989*) is like a vertex `lucy` with properties `{"birthYear": 1989}`.
 2. Another vertex in the graph. In that case, the predicate is an edge in the graph, the subject is the tail vertex, and the object is the head vertex. For example, in (*lucy*, *marriedTo*, *alain*) the subject and object *lucy* and *alain* are both vertices, and the predicate *marriedTo* is the label of the edge that connects them.
 
 > **注意**
 >
-> To be precise, databases that offer a triple-like data model often need to store some additional metadata on each tuple. For example, AWS Neptune uses quads (4-tuples) by adding a graph ID to each triple [[45](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#NeptuneDataModel)]; Datomic uses 5-tuples, extending each triple with a transaction ID and a boolean to indicate deletion [[46](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#DatomicDataModel)]. Since these databases retain the basic *subject-predicate-object* structure explained above, this book nevertheless calls them triple-stores.
+> To be precise, databases that offer a triple-like data model often need to store some additional metadata on each tuple. For example, AWS Neptune uses quads (4-tuples) by adding a graph ID to each triple [[45](ch03.html#NeptuneDataModel)]; Datomic uses 5-tuples, extending each triple with a transaction ID and a boolean to indicate deletion [[46](ch03.html#DatomicDataModel)]. Since these databases retain the basic *subject-predicate-object* structure explained above, this book nevertheless calls them triple-stores.
 
-[Example 3-7](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_n3_triples) shows the same data as in [Example 3-4](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_cypher_create), written as triples in a format called *Turtle*, a subset of *Notation3* (*N3*) [[47](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Beckett2011)].
+[Example 3-7](ch03.html#fig_graph_n3_triples) shows the same data as in [Example 3-4](ch03.html#fig_cypher_create), written as triples in a format called *Turtle*, a subset of *Notation3* (*N3*) [[47](ch03.html#Beckett2011)].
 
-> Example 3-7. A subset of the data in [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph), represented as Turtle triples
+> Example 3-7. A subset of the data in [Figure 3-6](ch03.html#fig_datamodels_graph), represented as Turtle triples
 
 ```
 @prefix : <urn:example:>.
@@ -672,9 +672,9 @@ _:namerica :type   "continent".
 
 In this example, vertices of the graph are written as `_:*someName*`. The name doesn’t mean anything outside of this file; it exists only because we otherwise wouldn’t know which triples refer to the same vertex. When the predicate represents an edge, the object is a vertex, as in `_:idaho :within _:usa`. When the predicate is a property, the object is a string literal, as in `_:usa :name "United States"`.
 
-It’s quite repetitive to repeat the same subject over and over again, but fortunately you can use semicolons to say multiple things about the same subject. This makes the Turtle format quite readable: see [Example 3-8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_n3_shorthand).
+It’s quite repetitive to repeat the same subject over and over again, but fortunately you can use semicolons to say multiple things about the same subject. This makes the Turtle format quite readable: see [Example 3-8](ch03.html#fig_graph_n3_shorthand).
 
-> Example 3-8. A more concise way of writing the data in [Example 3-7](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_n3_triples)
+> Example 3-8. A more concise way of writing the data in [Example 3-7](ch03.html#fig_graph_n3_triples)
 
 ```
 @prefix : <urn:example:>.
@@ -686,15 +686,15 @@ _:namerica a :Location; :name "North America"; :type "continent".
 
 #### The Semantic Web
 
-Some of the research and development effort on triple stores was motivated by the *Semantic Web*, an early-2000s effort to facilitate internet-wide data exchange by publishing data not only as human-readable web pages, but also in a standardized, machine-readable format. Although the Semantic Web as originally envisioned did not succeed [[48](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Target2018), [49](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#MendelGleason2022)], the legacy of the Semantic Web project lives on in a couple of specific technologies: *linked data* standards such as JSON-LD [[50](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Sporny2014)], *ontologies* used in biomedical science [[51](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#MichiganOntologies)], Facebook’s Open Graph protocol [[52](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#OpenGraph)] (which is used for link unfurling [[53](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Haughey2015)]), knowledge graphs such as Wikidata, and standardized vocabularies for structured data maintained by [`schema.org`](https://schema.org/).
+Some of the research and development effort on triple stores was motivated by the *Semantic Web*, an early-2000s effort to facilitate internet-wide data exchange by publishing data not only as human-readable web pages, but also in a standardized, machine-readable format. Although the Semantic Web as originally envisioned did not succeed [[48](ch03.html#Target2018), [49](ch03.html#MendelGleason2022)], the legacy of the Semantic Web project lives on in a couple of specific technologies: *linked data* standards such as JSON-LD [[50](ch03.html#Sporny2014)], *ontologies* used in biomedical science [[51](ch03.html#MichiganOntologies)], Facebook’s Open Graph protocol [[52](ch03.html#OpenGraph)] (which is used for link unfurling [[53](ch03.html#Haughey2015)]), knowledge graphs such as Wikidata, and standardized vocabularies for structured data maintained by [`schema.org`](https://schema.org/).
 
 Triple-stores are another Semantic Web technology that has found use outside of its original use case: even if you have no interest in the Semantic Web, triples can be a good internal data model for applications.
 
 #### The RDF data model
 
-The Turtle language we used in [Example 3-8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_n3_shorthand) is actually a way of encoding data in the *Resource Description Framework* (RDF) [[54](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#W3CRDF)], a data model that was designed for the Semantic Web. RDF data can also be encoded in other ways, for example (more verbosely) in XML, as shown in [Example 3-9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_rdf_xml). Tools like Apache Jena can automatically convert between different RDF encodings.
+The Turtle language we used in [Example 3-8](ch03.html#fig_graph_n3_shorthand) is actually a way of encoding data in the *Resource Description Framework* (RDF) [[54](ch03.html#W3CRDF)], a data model that was designed for the Semantic Web. RDF data can also be encoded in other ways, for example (more verbosely) in XML, as shown in [Example 3-9](ch03.html#fig_graph_rdf_xml). Tools like Apache Jena can automatically convert between different RDF encodings.
 
-> Example 3-9. The data of [Example 3-8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graph_n3_shorthand), expressed using RDF/XML syntax
+> Example 3-9. The data of [Example 3-8](ch03.html#fig_graph_n3_shorthand), expressed using RDF/XML syntax
 
 ```
 <rdf:RDF xmlns="urn:example:"
@@ -730,11 +730,11 @@ The URL `<http://my-company.com/namespace>` doesn’t necessarily need to resolv
 
 #### SPARQL查询语言
 
-*SPARQL* is a query language for triple-stores using the RDF data model [[55](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Harris2013)]. (It is an acronym for *SPARQL Protocol and RDF Query Language*, pronounced “sparkle.”) It predates Cypher, and since Cypher’s pattern matching is borrowed from SPARQL, they look quite similar.
+*SPARQL* is a query language for triple-stores using the RDF data model [[55](ch03.html#Harris2013)]. (It is an acronym for *SPARQL Protocol and RDF Query Language*, pronounced “sparkle.”) It predates Cypher, and since Cypher’s pattern matching is borrowed from SPARQL, they look quite similar.
 
-The same query as before—finding people who have moved from the US to Europe—is similarly concise in SPARQL as it is in Cypher (see [Example 3-10](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_sparql_query)).
+The same query as before—finding people who have moved from the US to Europe—is similarly concise in SPARQL as it is in Cypher (see [Example 3-10](ch03.html#fig_sparql_query)).
 
-> Example 3-10. The same query as [Example 3-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_cypher_query), expressed in SPARQL
+> Example 3-10. The same query as [Example 3-5](ch03.html#fig_cypher_query), expressed in SPARQL
 
 ```
 PREFIX : <urn:example:>
@@ -762,22 +762,22 @@ Because RDF doesn’t distinguish between properties and edges but just uses pre
 ?usa :name "United States".    # SPARQL
 ```
 
-SPARQL is supported by Amazon Neptune, AllegroGraph, Blazegraph, OpenLink Virtuoso, Apache Jena, and various other triple stores [[35](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Besta2019)].
+SPARQL is supported by Amazon Neptune, AllegroGraph, Blazegraph, OpenLink Virtuoso, Apache Jena, and various other triple stores [[35](ch03.html#Besta2019)].
 
 
 --------
 
 ### Datalog：递归关系查询
 
-Datalog is a much older language than SPARQL or Cypher: it arose from academic research in the 1980s [[56](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Green2013), [57](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Ceri1989), [58](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Abiteboul1995)]. It is less well known among software engineers and not widely supported in mainstream databases, but it ought to be better-known since it is a very expressive language that is particularly powerful for complex queries. Several niche databases, including Datomic, LogicBlox, CozoDB, and LinkedIn’s LIquid [[59](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Meyer2020)] use Datalog as their query language.
+Datalog is a much older language than SPARQL or Cypher: it arose from academic research in the 1980s [[56](ch03.html#Green2013), [57](ch03.html#Ceri1989), [58](ch03.html#Abiteboul1995)]. It is less well known among software engineers and not widely supported in mainstream databases, but it ought to be better-known since it is a very expressive language that is particularly powerful for complex queries. Several niche databases, including Datomic, LogicBlox, CozoDB, and LinkedIn’s LIquid [[59](ch03.html#Meyer2020)] use Datalog as their query language.
 
 Datalog is actually based on a relational data model, not a graph, but it appears in the graph databases section of this book because recursive queries on graphs are a particular strength of Datalog.
 
 The contents of a Datalog database consists of *facts*, and each fact corresponds to a row in a relational table. For example, say we have a table *location* containing locations, and it has three columns: *ID*, *name*, and *type*. The fact that the US is a country could then be written as `location(2, "United States", "country")`, where `2` is the ID of the US. In general, the statement `table(val1, val2, …)` means that `table` contains a row where the first column contains `val1`, the second column contains `val2`, and so on.
 
-[Example 3-11](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datalog_triples) shows how to write the data from the left-hand side of [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph) in Datalog. The edges of the graph (`within`, `born_in`, and `lives_in`) are represented as two-column join tables. For example, Lucy has the ID 100 and Idaho has the ID 3, so the relationship “Lucy was born in Idaho” is represented as `born_in(100, 3)`.
+[Example 3-11](ch03.html#fig_datalog_triples) shows how to write the data from the left-hand side of [Figure 3-6](ch03.html#fig_datamodels_graph) in Datalog. The edges of the graph (`within`, `born_in`, and `lives_in`) are represented as two-column join tables. For example, Lucy has the ID 100 and Idaho has the ID 3, so the relationship “Lucy was born in Idaho” is represented as `born_in(100, 3)`.
 
-> Example 3-11. A subset of the data in [Figure 3-6](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datamodels_graph), represented as Datalog facts
+> Example 3-11. A subset of the data in [Figure 3-6](ch03.html#fig_datamodels_graph), represented as Datalog facts
 
 ```
 location(1, "North America", "continent").
@@ -791,9 +791,9 @@ person(100, "Lucy").
 born_in(100, 3). /* Lucy was born in Idaho */
 ```
 
-Now that we have defined the data, we can write the same query as before, as shown in [Example 3-12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datalog_query). It looks a bit different from the equivalent in Cypher or SPARQL, but don’t let that put you off. Datalog is a subset of Prolog, a programming language that you might have seen before if you’ve studied computer science.
+Now that we have defined the data, we can write the same query as before, as shown in [Example 3-12](ch03.html#fig_datalog_query). It looks a bit different from the equivalent in Cypher or SPARQL, but don’t let that put you off. Datalog is a subset of Prolog, a programming language that you might have seen before if you’ve studied computer science.
 
-> Example 3-12. The same query as [Example 3-5](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_cypher_query), expressed in Datalog
+> Example 3-12. The same query as [Example 3-5](ch03.html#fig_cypher_query), expressed in Datalog
 
 ```
 within_recursive(LocID, PlaceName) :- location(LocID, PlaceName, _). /* Rule 1 */
@@ -813,11 +813,11 @@ us_to_europe(Person) :- migrated(Person, "United States", "Europe"). /* Rule 4 *
 
 Cypher and SPARQL jump in right away with `SELECT`, but Datalog takes a small step at a time. We define *rules* that derive new virtual tables from the underlying facts. These derived tables are like (virtual) SQL views: they are not stored in the database, but you can query them in the same way as a table containing stored facts.
 
-In [Example 3-12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datalog_query) we define three derived tables: `within_recursive`, `migrated`, and `us_to_europe`. The name and columns of the virtual tables are defined by what appears before the `:-` symbol of each rule. For example, `migrated(PName, BornIn, LivingIn)` is a virtual table with three columns: the name of a person, the name of the place where they were born, and the name of the place where they are living.
+In [Example 3-12](ch03.html#fig_datalog_query) we define three derived tables: `within_recursive`, `migrated`, and `us_to_europe`. The name and columns of the virtual tables are defined by what appears before the `:-` symbol of each rule. For example, `migrated(PName, BornIn, LivingIn)` is a virtual table with three columns: the name of a person, the name of the place where they were born, and the name of the place where they are living.
 
 The content of a virtual table is defined by the part of the rule after the `:-` symbol, where we try to find rows that match a certain pattern in the tables. For example, `person(PersonID, PName)` matches the row `person(100, "Lucy")`, with the variable `PersonID` bound to the value `100` and the variable `PName` bound to the value `"Lucy"`. A rule applies if the system can find a match for *all* patterns on the righthand side of the `:-` operator. When the rule applies, it’s as though the lefthand side of the `:-` was added to the database (with variables replaced by the values they matched).
 
-One possible way of applying the rules is thus (and as illustrated in [Figure 3-7](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datalog_naive)):
+One possible way of applying the rules is thus (and as illustrated in [Figure 3-7](ch03.html#fig_datalog_naive)):
 
 1. `location(1, "North America", "continent")` exists in the database, so rule 1 applies. It generates `within_recursive(1, "North America")`.
 2. `within(2, 1)` exists in the database and the previous step generated `within_recursive(1, "North America")`, so rule 2 applies. It generates `within_recursive(2, "North America")`.
@@ -827,11 +827,11 @@ By repeated application of rules 1 and 2, the `within_recursive` virtual table c
 
 ![ddia 0206](img/ddia_0206.png)
 
-> Figure 3-7. Determining that Idaho is in North America, using the Datalog rules from [Example 3-12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datalog_query).
+> Figure 3-7. Determining that Idaho is in North America, using the Datalog rules from [Example 3-12](ch03.html#fig_datalog_query).
 
 Now rule 3 can find people who were born in some location `BornIn` and live in some location `LivingIn`. Rule 4 invokes rule 3 with `BornIn = 'United States'` and `LivingIn = 'Europe'`, and returns only the names of the people who match the search. By querying the contents of the virtual `us_to_europe` table, the Datalog system finally gets the same answer as in the earlier Cypher and SPARQL queries.
 
-The Datalog approach requires a different kind of thinking compared to the other query languages discussed in this chapter. It allows complex queries to be built up rule by rule, with one rule referring to other rules, similarly to the way that you break down code into functions that call each other. Just like functions can be recursive, Datalog rules can also invoke themselves, like rule 2 in [Example 3-12](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_datalog_query), which enables graph traversals in Datalog queries.
+The Datalog approach requires a different kind of thinking compared to the other query languages discussed in this chapter. It allows complex queries to be built up rule by rule, with one rule referring to other rules, similarly to the way that you break down code into functions that call each other. Just like functions can be recursive, Datalog rules can also invoke themselves, like rule 2 in [Example 3-12](ch03.html#fig_datalog_query), which enables graph traversals in Datalog queries.
 
 --------
 
@@ -839,9 +839,9 @@ The Datalog approach requires a different kind of thinking compared to the other
 
 GraphQL is a query language that, by design, is much more restrictive than the other query languages we have seen in this chapter. The purpose of GraphQL is to allow client software running on a user’s device (such as a mobile app or a JavaScript web app frontend) to request a JSON document with a particular structure, containing the fields necessary for rendering its user interface. GraphQL interfaces allow developers to rapidly change queries in client code without changing server-side APIs.
 
-GraphQL’s flexibility comes at a cost. Organizations that adopt GraphQL often need tooling to convert GraphQL queries into requests to internal services, which often use REST or gRPC (see [Link to Come]). Authorization, rate limiting, and performance challenges are additional concerns [[60](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Bessey2024)]. GraphQL’s query language is also limited since GraphQL come from an untrusted source. The language does not allow anything that could be expensive to execute, since otherwise users could perform denial-of-service attacks on a server by running lots of expensive queries. In particular, GraphQL does not allow recursive queries (unlike Cypher, SPARQL, SQL, or Datalog), and it does not allow arbitrary search conditions such as “find people who were born in the US and are now living in Europe” (unless the service owners specifically choose to offer such search functionality).
+GraphQL’s flexibility comes at a cost. Organizations that adopt GraphQL often need tooling to convert GraphQL queries into requests to internal services, which often use REST or gRPC (see [Link to Come]). Authorization, rate limiting, and performance challenges are additional concerns [[60](ch03.html#Bessey2024)]. GraphQL’s query language is also limited since GraphQL come from an untrusted source. The language does not allow anything that could be expensive to execute, since otherwise users could perform denial-of-service attacks on a server by running lots of expensive queries. In particular, GraphQL does not allow recursive queries (unlike Cypher, SPARQL, SQL, or Datalog), and it does not allow arbitrary search conditions such as “find people who were born in the US and are now living in Europe” (unless the service owners specifically choose to offer such search functionality).
 
-Nevertheless, GraphQL is useful. [Example 3-13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graphql_query) shows how you might implement a group chat application such as Discord or Slack using GraphQL. The query requests all the channels that the user has access to, including the channel name and the 50 most recent messages in each channel. For each message it requests the timestamp, the message content, and the name and profile picture URL for the sender of the message. Moreover, if a message is a reply to another message, the query also requests the sender name and the content of the message it is replying to (which might be rendered in a smaller font above the reply, in order to provide some context).
+Nevertheless, GraphQL is useful. [Example 3-13](ch03.html#fig_graphql_query) shows how you might implement a group chat application such as Discord or Slack using GraphQL. The query requests all the channels that the user has access to, including the channel name and the 50 most recent messages in each channel. For each message it requests the timestamp, the message content, and the name and profile picture URL for the sender of the message. Moreover, if a message is a reply to another message, the query also requests the sender name and the content of the message it is replying to (which might be rendered in a smaller font above the reply, in order to provide some context).
 
 > Example 3-13. Example GraphQL query for a group chat application
 
@@ -867,9 +867,9 @@ query ChatApp {
 }
 ```
 
-[Example 3-14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graphql_response) shows what a response to the query in [Example 3-13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graphql_query) might look like. The response is a JSON document that mirrors the structure of the query: it contains exactly those attributes that were requested, no more and no less. This approach has the advantage that the server does not need to know which attributes the client requires in order to render the user interface; instead, the client can simply request what it needs. For example, this query does not request a profile picture URL for the sender of the `replyTo` message, but if the user interface were changed to add that profile picture, it would be easy for the client to add the required `imageUrl` attribute to the query without changing the server.
+[Example 3-14](ch03.html#fig_graphql_response) shows what a response to the query in [Example 3-13](ch03.html#fig_graphql_query) might look like. The response is a JSON document that mirrors the structure of the query: it contains exactly those attributes that were requested, no more and no less. This approach has the advantage that the server does not need to know which attributes the client requires in order to render the user interface; instead, the client can simply request what it needs. For example, this query does not request a profile picture URL for the sender of the `replyTo` message, but if the user interface were changed to add that profile picture, it would be easy for the client to add the required `imageUrl` attribute to the query without changing the server.
 
-> Example 3-14. A possible response to the query in [Example 3-13](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graphql_query)
+> Example 3-14. A possible response to the query in [Example 3-13](ch03.html#fig_graphql_query)
 
 ```
 {
@@ -896,9 +896,9 @@ query ChatApp {
           ...
 ```
 
-In [Example 3-14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graphql_response) the name and image URL of a message sender is embedded directly in the message object. If the same user sends multiple messages, this information is repeated on each message. In principle, it would be possible to reduce this duplication, but GraphQL makes the design choice to accept a larger response size in order to make it simpler to render the user interface based on the data.
+In [Example 3-14](ch03.html#fig_graphql_response) the name and image URL of a message sender is embedded directly in the message object. If the same user sends multiple messages, this information is repeated on each message. In principle, it would be possible to reduce this duplication, but GraphQL makes the design choice to accept a larger response size in order to make it simpler to render the user interface based on the data.
 
-The `replyTo` field is similar: in [Example 3-14](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_graphql_response), the second message is a reply to the first, and the content (“Hey!…”) and sender Aaliyah are duplicated under `replyTo`. It would be possible to instead return the ID of the message being replied to, but then the client would have to make an additional request to the server if that ID is not among the 50 most recent messages returned. Duplicating the content makes it much simpler to work with the data.
+The `replyTo` field is similar: in [Example 3-14](ch03.html#fig_graphql_response), the second message is a reply to the first, and the content (“Hey!…”) and sender Aaliyah are duplicated under `replyTo`. It would be possible to instead return the ID of the message being replied to, but then the client would have to make an additional request to the server if that ID is not among the 50 most recent messages returned. Duplicating the content makes it much simpler to work with the data.
 
 The server’s database can store the data in a more normalized form, and perform the necessary joins to process a query. For example, the server might store a message along with the user ID of the sender and the ID of the message it is replying to; when it receives a query like the one above, the server would then resolve those IDs to find the records they refer to. However, the client can only ask the server to perform joins that are explicitly offered in the GraphQL schema.
 
@@ -919,25 +919,25 @@ Even though the response to a GraphQL query looks similar to a response from a d
 
 In all the data models we have discussed so far, the data is queried in the same form as it is written—be it JSON documents, rows in tables, or vertices and edges in a graph. However, in complex applications it can sometimes be difficult to find a single data representation that is able to satisfy all the different ways that the data needs to be queried and presented. In such situations, it can be beneficial to write data in one form, and then to derive from it several representations that are optimized for different types of reads.
 
-We previously saw this idea in [“Systems of Record and Derived Data”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_derived), and ETL (see [“Data Warehousing”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_dwh)) is one example of such a derivation process. Now we will take the idea further. If we are going to derive one data representation from another anyway, we can choose different representations that are optimized for writing and for reading, respectively. How would you model your data if you only wanted to optimize it for writing, and if efficient queries were of no concern?
+We previously saw this idea in [“Systems of Record and Derived Data”](ch01.html#sec_introduction_derived), and ETL (see [“Data Warehousing”](ch01.html#sec_introduction_dwh)) is one example of such a derivation process. Now we will take the idea further. If we are going to derive one data representation from another anyway, we can choose different representations that are optimized for writing and for reading, respectively. How would you model your data if you only wanted to optimize it for writing, and if efficient queries were of no concern?
 
 Perhaps the simplest, fastest, and most expressive way of writing data is an *event log*: every time you want to write some data, you encode it as a self-contained string (perhaps as JSON), including a timestamp, and then append it to a sequence of events. Events in this log are *immutable*: you never change or delete them, you only ever append more events to the log (which may supersede earlier events). An event can contain arbitrary properties.
 
-[Figure 3-8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_event_sourcing) shows an example that could be taken from a conference management system. A conference can be a complex business domain: not only can individual attendees register and pay by card, but companies can also order seats in bulk, pay by invoice, and then later assign the seats to individual people. Some number of seats may be reserved for speakers, sponsors, volunteer helpers, and so on. Reservations may also be cancelled, and meanwhile, the conference organizer might change the capacity of the event by moving it to a different room. With all of this going on, simply calculating the number of available seats becomes a challenging query.
+[Figure 3-8](ch03.html#fig_event_sourcing) shows an example that could be taken from a conference management system. A conference can be a complex business domain: not only can individual attendees register and pay by card, but companies can also order seats in bulk, pay by invoice, and then later assign the seats to individual people. Some number of seats may be reserved for speakers, sponsors, volunteer helpers, and so on. Reservations may also be cancelled, and meanwhile, the conference organizer might change the capacity of the event by moving it to a different room. With all of this going on, simply calculating the number of available seats becomes a challenging query.
 
 ![ddia 0208](img/ddia_0208.png)
 
 > Figure 3-8. Using a log of immutable events as source of truth, and deriving materialized views from it.
 
-In [Figure 3-8](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_event_sourcing), every change to the state of the conference (such as the organizer opening registrations, or attendees making and cancelling registrations) is first stored as an event. Whenever an event is appended to the log, several *materialized views* (also known as *projections* or *read models*) are also updated to reflect the effect of that event. In the conference example, there might be one materialized view that collects all information related to the status of each booking, another that computes charts for the conference organizer’s dashboard, and a third that generates files for the printer that produces the attendees’ badges.
+In [Figure 3-8](ch03.html#fig_event_sourcing), every change to the state of the conference (such as the organizer opening registrations, or attendees making and cancelling registrations) is first stored as an event. Whenever an event is appended to the log, several *materialized views* (also known as *projections* or *read models*) are also updated to reflect the effect of that event. In the conference example, there might be one materialized view that collects all information related to the status of each booking, another that computes charts for the conference organizer’s dashboard, and a third that generates files for the printer that produces the attendees’ badges.
 
-The idea of using events as the source of truth, and expressing every state change as an event, is known as *event sourcing* [[61](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Betts2012), [62](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Young2014)]. The principle of maintaining separate read-optimized representations and deriving them from the write-optimized representation is called *command query responsibility segregation (CQRS)* [[63](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Young2010)]. These terms originated in the domain-driven design (DDD) community, although similar ideas have been around for a long time, for example in *state machine replication* (see [Link to Come]).
+The idea of using events as the source of truth, and expressing every state change as an event, is known as *event sourcing* [[61](ch03.html#Betts2012), [62](ch03.html#Young2014)]. The principle of maintaining separate read-optimized representations and deriving them from the write-optimized representation is called *command query responsibility segregation (CQRS)* [[63](ch03.html#Young2010)]. These terms originated in the domain-driven design (DDD) community, although similar ideas have been around for a long time, for example in *state machine replication* (see [Link to Come]).
 
 When a request from a user comes in, it is called a *command*, and it first needs to be validated. Only once the command has been executed and it has been determined to be valid (e.g., there were enough available seats for a requested reservation), it becomes a fact, and the corresponding event is added to the log. Consequently, the event log should contain only valid events, and a consumer of the event log that builds a materialized view is not allowed to reject an event.
 
 When modelling your data in an event sourcing style, it is recommended that you name your events in the past tense (e.g., “the seats were booked”), because an event is a record of the fact that something has happened in the past. Even if the user later decides to change or cancel, the fact remains true that they formerly held a booking, and the change or cancellation is a separate event that is added later.
 
-A similarity between event sourcing and a star schema fact table, as discussed in [“Stars and Snowflakes: Schemas for Analytics”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#sec_datamodels_analytics), is that both are collections of events that happened in the past. However, rows in a fact table all have the same set of columns, wheras in event sourcing there may be many different event types, each with different properties. Moreover, a fact table is an unordered collection, while in event sourcing the order of events is important: if a booking is first made and then cancelled, processing those events in the wrong order would not make sense.
+A similarity between event sourcing and a star schema fact table, as discussed in [“Stars and Snowflakes: Schemas for Analytics”](ch03.html#sec_datamodels_analytics), is that both are collections of events that happened in the past. However, rows in a fact table all have the same set of columns, wheras in event sourcing there may be many different event types, each with different properties. Moreover, a fact table is an unordered collection, while in event sourcing the order of events is important: if a booking is first made and then cancelled, processing those events in the wrong order would not make sense.
 
 Event sourcing and CQRS have several advantages:
 
@@ -945,7 +945,7 @@ Event sourcing and CQRS have several advantages:
 - A key principle of event sourcing is that the materialized views are derived from the event log in a reproducible way: you should always be able to delete the materialized views and recompute them by processing the same events in the same order, using the same code. If there was a bug in the view maintenance code, you can just delete the view and recompute it with the new code. It’s also easier to find the bug because you can re-run the view maintenance code as often as you like and inspect its behavior.
 - You can have multiple materialized views that are optimized for the particular queries that your application requires. They can be stored either in the same database as the events or a different one, depending on your needs. They can use any data model, and they can be denormalized for fast reads. You can even keep a view only in memory and avoid persisting it, as long as it’s okay to recompute the view from the event log whenever the service restarts.
 - If you decide you want to present the existing information in a new way, it is easy to build a new materialized view from the existing event log. You can also evolve the system to support new features by adding new types of events, or new properties to existing event types (any older events remain unmodified). You can also chain new behaviors off existing events (for example, when a conference attendee cancels, their seat could be offered to the next person on the waiting list).
-- If an event was written in error you can delete it again, and then you can rebuild the views without the deleted event. On the other hand, in a database where you update and delete data directly, a committed transaction is often difficult to reverse. Event sourcing can therefore reduce the number of irreversible actions in the system, making it easier to change (see [“Evolvability: Making Change Easy”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch02.html#sec_introduction_evolvability)).
+- If an event was written in error you can delete it again, and then you can rebuild the views without the deleted event. On the other hand, in a database where you update and delete data directly, a committed transaction is often difficult to reverse. Event sourcing can therefore reduce the number of irreversible actions in the system, making it easier to change (see [“Evolvability: Making Change Easy”](ch02.html#sec_introduction_evolvability)).
 - The event log can also serve as an audit log of everything that happened in the system, which is valuable in regulated industries that require such auditability.
 
 However, event sourcing and CQRS also have downsides:
@@ -965,7 +965,7 @@ The only important requirement is that the event storage system must guarantee t
 
 ## 数据框、矩阵和数组
 
-本章迄今为止我们看到的数据模型通常用于事务处理和分析目的（见[“事务处理与分析对比”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_analytics)）。还有一些数据模型，你可能在分析或科学上下文中遇到，但它们很少出现在OLTP系统中：数据框和数字的多维数组，如矩阵。
+本章迄今为止我们看到的数据模型通常用于事务处理和分析目的（见[“事务处理与分析对比”](ch01.html#sec_introduction_analytics)）。还有一些数据模型，你可能在分析或科学上下文中遇到，但它们很少出现在OLTP系统中：数据框和数字的多维数组，如矩阵。
 
 数据框是R语言、Python的Pandas库、Apache Spark、ArcticDB、Dask等系统支持的数据模型。它们是数据科学家准备训练机器学习模型的数据时常用的工具，但也广泛用于数据探索、统计数据分析、数据可视化及类似目的。
 
@@ -973,11 +973,11 @@ The only important requirement is that the event storage system must guarantee t
 
 数据框通常不是通过像SQL这样的声明性查询操作，而是通过一系列修改其结构和内容的命令进行操纵。这符合数据科学家的典型工作流程，他们逐步“整理”数据，使其能够找到他们正在询问的问题的答案。这些操作通常发生在数据科学家的私有数据集副本上，通常在他们的本地机器上，尽管最终结果可能与其他用户共享。
 
-数据框API还提供了远超关系数据库所提供的各种操作，而且数据模型的使用方式通常与典型的关系数据建模非常不同 [[64](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Petersohn2020)]。例如，数据框的一个常见用途是将数据从类似关系的表示转换为矩阵或多维数组表示，这是许多机器学习算法所期望的输入形式。
+数据框API还提供了远超关系数据库所提供的各种操作，而且数据模型的使用方式通常与典型的关系数据建模非常不同 [[64](ch03.html#Petersohn2020)]。例如，数据框的一个常见用途是将数据从类似关系的表示转换为矩阵或多维数组表示，这是许多机器学习算法所期望的输入形式。
 
-一个这样的转换的简单示例显示在[图3-9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dataframe_to_matrix)中。左边是一个关系表，显示不同用户对各种电影的评分（在1到5的范围内），右边的数据被转换成一个矩阵，每一列是一部电影，每一行是一个用户（类似于电子表格中的*数据透视表*）。该矩阵是*稀疏的*，这意味着许多用户-电影组合没有数据，但这是可以的。这个矩阵可能有成千上万的列，因此不适合在关系数据库中存储，但数据框和提供稀疏数组的库（如Python的NumPy）可以轻松处理这种数据
+一个这样的转换的简单示例显示在[图3-9](ch03.html#fig_dataframe_to_matrix)中。左边是一个关系表，显示不同用户对各种电影的评分（在1到5的范围内），右边的数据被转换成一个矩阵，每一列是一部电影，每一行是一个用户（类似于电子表格中的*数据透视表*）。该矩阵是*稀疏的*，这意味着许多用户-电影组合没有数据，但这是可以的。这个矩阵可能有成千上万的列，因此不适合在关系数据库中存储，但数据框和提供稀疏数组的库（如Python的NumPy）可以轻松处理这种数据
 
-The data models we have seen so far in this chapter are generally used for both transaction processing and analytics purposes (see [“Transaction Processing versus Analytics”](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch01.html#sec_introduction_analytics)). There are also some data models that you are likely to encounter in an analytical or scientific context, but that rarely feature in OLTP systems: dataframes and multidimensional arrays of numbers such as matrices.
+The data models we have seen so far in this chapter are generally used for both transaction processing and analytics purposes (see [“Transaction Processing versus Analytics”](ch01.html#sec_introduction_analytics)). There are also some data models that you are likely to encounter in an analytical or scientific context, but that rarely feature in OLTP systems: dataframes and multidimensional arrays of numbers such as matrices.
 
 Dataframes are a data model supported by the R language, the Pandas library for Python, Apache Spark, ArcticDB, Dask, and other systems. They are a popular tool for data scientists preparing data for training machine learning models, but they are also widely used for data exploration, statistical data analysis, data visualization, and similar purposes.
 
@@ -985,9 +985,9 @@ At first glance, a dataframe is similar to a table in a relational database or a
 
 Instead of a declarative query such as SQL, a dataframe is typically manipulated through a series of commands that modify its structure and content. This matches the typical workflow of data scientists, who incrementally “wrangle” the data into a form that allows them to find answers to the questions they are asking. These manipulations usually take place on the data scientist’s private copy of the dataset, often on their local machine, although the end result may be shared with other users.
 
-Dataframe APIs also offer a wide variety of operations that go far beyond what relational databases offer, and the data model is often used in ways that are very different from typical relational data modelling [[64](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Petersohn2020)]. For example, a common use of dataframes is to transform data from a relational-like representation into a matrix or multidimensional array representation, which is the form that many machine learning algorithms expect of their input.
+Dataframe APIs also offer a wide variety of operations that go far beyond what relational databases offer, and the data model is often used in ways that are very different from typical relational data modelling [[64](ch03.html#Petersohn2020)]. For example, a common use of dataframes is to transform data from a relational-like representation into a matrix or multidimensional array representation, which is the form that many machine learning algorithms expect of their input.
 
-A simple example of such a transformation is shown in [Figure 3-9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dataframe_to_matrix). On the left we have a relational table of how different users have rated various movies (on a scale of 1 to 5), and on the right the data has been transformed into a matrix where each column is a movie and each row is a user (similarly to a *pivot table* in a spreadsheet). The matrix is *sparse*, which means there is no data for many user-movie combinations, but this is fine. This matrix may have many thousands of columns and would therefore not fit well in a relational database, but dataframes and libraries that offer sparse arrays (such as NumPy for Python) can handle such data easily.
+A simple example of such a transformation is shown in [Figure 3-9](ch03.html#fig_dataframe_to_matrix). On the left we have a relational table of how different users have rated various movies (on a scale of 1 to 5), and on the right the data has been transformed into a matrix where each column is a movie and each row is a user (similarly to a *pivot table* in a spreadsheet). The matrix is *sparse*, which means there is no data for many user-movie combinations, but this is fine. This matrix may have many thousands of columns and would therefore not fit well in a relational database, but dataframes and libraries that offer sparse arrays (such as NumPy for Python) can handle such data easily.
 
 ![ddia 0207](img/ddia_0207.png)
 
@@ -996,21 +996,21 @@ A simple example of such a transformation is shown in [Figure 3-9](https://learn
 
 矩阵只能包含数字，各种技术被用来将非数字数据转换为矩阵中的数字。例如：
 
-- 日期（在[图3-9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dataframe_to_matrix)中的示例矩阵中被省略）可以缩放为某个适当范围内的浮点数。
+- 日期（在[图3-9](ch03.html#fig_dataframe_to_matrix)中的示例矩阵中被省略）可以缩放为某个适当范围内的浮点数。
 - 对于只能取固定小范围值的列（例如，电影数据库中电影的类型），通常使用*独热编码*：我们为每个可能的值创建一列（一列是“喜剧”，一列是“戏剧”，一列是“恐怖”等），并在代表电影的每一行中，在与该电影类型对应的列中放置1，在所有其他列中放置0。这种表示也很容易泛化到适用于多种类型的电影。
 
-一旦数据以数字矩阵的形式存在，就可以进行线性代数操作，这是许多机器学习算法的基础。例如，[图3-9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dataframe_to_matrix)中的数据可以是一个推荐系统的一部分，该系统可能会推荐用户可能喜欢的电影。数据框足够灵活，可以让数据从关系形式逐渐演变为矩阵表示，同时让数据科学家控制最适合实现数据分析或模型训练过程目标的表示。
+一旦数据以数字矩阵的形式存在，就可以进行线性代数操作，这是许多机器学习算法的基础。例如，[图3-9](ch03.html#fig_dataframe_to_matrix)中的数据可以是一个推荐系统的一部分，该系统可能会推荐用户可能喜欢的电影。数据框足够灵活，可以让数据从关系形式逐渐演变为矩阵表示，同时让数据科学家控制最适合实现数据分析或模型训练过程目标的表示。
 
-还有一些数据库，如TileDB [[65](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Papadopoulos2016)]，专门用于存储大量的多维数字数组；它们被称为*数组数据库*，最常用于存储科学数据集，如地理空间测量（在规则间隔的网格上的栅格数据）、医学成像或天文望远镜的观测 [[66](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Rusu2022)]。数据框也在金融行业中用于表示*时间序列数据*，如资产价格和随时间的交易 [[67](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Targett2023)]。
+还有一些数据库，如TileDB [[65](ch03.html#Papadopoulos2016)]，专门用于存储大量的多维数字数组；它们被称为*数组数据库*，最常用于存储科学数据集，如地理空间测量（在规则间隔的网格上的栅格数据）、医学成像或天文望远镜的观测 [[66](ch03.html#Rusu2022)]。数据框也在金融行业中用于表示*时间序列数据*，如资产价格和随时间的交易 [[67](ch03.html#Targett2023)]。
 
 A matrix can only contain numbers, and various techniques are used to transform non-numerical data into numbers in the matrix. For example:
 
-- Dates (which are omitted from the example matrix in [Figure 3-9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dataframe_to_matrix)) could be scaled to be floating-point numbers within some suitable range.
+- Dates (which are omitted from the example matrix in [Figure 3-9](ch03.html#fig_dataframe_to_matrix)) could be scaled to be floating-point numbers within some suitable range.
 - For columns that can only take one of a small, fixed set of values (for example, the genre of a movie in a database of movies), a *one-hot encoding* is often used: we create a column for each possible value (one for “comedy”, one for “drama”, one for “horror”, etc.), and for each row representing a movie, we put a 1 in the column corresponding to the genre of that movie, and a 0 in all the other columns. This representation also easily generalizes to movies that fit within several genres.
 
-Once the data is in the form of a matrix of numbers, it is amenable to linear algebra operations, which form the basis of many machine learning algorithms. For example, the data in [Figure 3-9](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#fig_dataframe_to_matrix) could be a part of a system for recommending movies that the user may like. Dataframes are flexible enough to allow data to be gradually evolved from a relational form into a matrix representation, while giving the data scientist control over the representation that is most suitable for achieving the goals of the data analysis or model training process.
+Once the data is in the form of a matrix of numbers, it is amenable to linear algebra operations, which form the basis of many machine learning algorithms. For example, the data in [Figure 3-9](ch03.html#fig_dataframe_to_matrix) could be a part of a system for recommending movies that the user may like. Dataframes are flexible enough to allow data to be gradually evolved from a relational form into a matrix representation, while giving the data scientist control over the representation that is most suitable for achieving the goals of the data analysis or model training process.
 
-There are also databases such as TileDB [[65](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Papadopoulos2016)] that specialize in storing large multidimensional arrays of numbers; they are called *array databases* and are most commonly used for scientific datasets such as geospatial measurements (raster data on a regularly spaced grid), medical imaging, or observations from astronomical telescopes [[66](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Rusu2022)]. Dataframes are also used in the financial industry for representing *time series data*, such as the prices of assets and trades over time [[67](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Targett2023)].
+There are also databases such as TileDB [[65](ch03.html#Papadopoulos2016)] that specialize in storing large multidimensional arrays of numbers; they are called *array databases* and are most commonly used for scientific datasets such as geospatial measurements (raster data on a regularly spaced grid), medical imaging, or observations from astronomical telescopes [[66](ch03.html#Rusu2022)]. Dataframes are also used in the financial industry for representing *time series data*, such as the prices of assets and trades over time [[67](ch03.html#Targett2023)].
 
 
 
@@ -1036,7 +1036,7 @@ One thing that non-relational data models have in common is that they typically
 
 Although we have covered a lot of ground, there are still data models left unmentioned. To give just a few brief examples:
 
-- Researchers working with genome data often need to perform *sequence-similarity searches*, which means taking one very long string (representing a DNA molecule) and matching it against a large database of strings that are similar, but not identical. None of the databases described here can handle this kind of usage, which is why researchers have written specialized genome database software like GenBank [[68](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Benson2007)].
+- Researchers working with genome data often need to perform *sequence-similarity searches*, which means taking one very long string (representing a DNA molecule) and matching it against a large database of strings that are similar, but not identical. None of the databases described here can handle this kind of usage, which is why researchers have written specialized genome database software like GenBank [[68](ch03.html#Benson2007)].
 - Many financial systems use *ledgers* with double-entry accounting as their data model. This type of data can be represented in relational databases, but there are also databases such as TigerBeetle that specialize in this data model. Cryptocurrencies and blockchains are typically based on distributed ledgers, which also have value transfer built into their data model.
 - *Full-text search* is arguably a kind of data model that is frequently used alongside databases. Information retrieval is a large specialist subject that we won’t cover in great detail in this book, but we’ll touch on search indexes and vector search in [Link to Come].
 
@@ -1047,141 +1047,141 @@ We have to leave it there for now. In the next chapter we will discuss some of t
 
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-[[41](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Deutsch2022-marker)] Alin Deutsch, Nadime Francis, Alastair Green, Keith Hare, Bei Li, Leonid Libkin, Tobias Lindaaker, Victor Marsault, Wim Martens, Jan Michels, Filip Murlak, Stefan Plantikow, Petra Selmer, Oskar van Rest, Hannes Voigt, Domagoj Vrgoč, Mingxi Wu, and Fred Zemke. [Graph Pattern Matching in GQL and SQL/PGQ](https://arxiv.org/abs/2112.06217). At *International Conference on Management of Data* (SIGMOD), pages 2246–2258, June 2022. [doi:10.1145/3514221.3526057](https://doi.org/10.1145/3514221.3526057)
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-[[61](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Betts2012-marker)] Dominic Betts, Julián Domínguez, Grigori Melnik, Fernando Simonazzi, and Mani Subramanian. [*Exploring CQRS and Event Sourcing*](https://learn.microsoft.com/en-us/previous-versions/msp-n-p/jj554200(v=pandp.10)). Microsoft Patterns & Practices, July 2012. ISBN: 1621140164, archived at [perma.cc/7A39-3NM8](https://perma.cc/7A39-3NM8)
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-[[62](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Young2014-marker)] Greg Young. [CQRS and Event Sourcing](https://www.youtube.com/watch?v=JHGkaShoyNs). At *Code on the Beach*, August 2014.
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-[[65](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Papadopoulos2016-marker)] Stavros Papadopoulos, Kushal Datta, Samuel Madden, and Timothy Mattson. [The TileDB Array Data Storage Manager](https://www.vldb.org/pvldb/vol10/p349-papadopoulos.pdf). *Proceedings of the VLDB Endowment*, volume 10, issue 4, pages 349–360, November 2016. [doi:10.14778/3025111.3025117](https://doi.org/10.14778/3025111.3025117)
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-[[68](https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/ch03.html#Benson2007-marker)] Dennis A. Benson, Ilene Karsch-Mizrachi, David J. Lipman, James Ostell, and David L. Wheeler. [GenBank](https://academic.oup.com/nar/article/36/suppl_1/D25/2507746). *Nucleic Acids Research*, volume 36, database issue, pages D25–D30, December 2007. [doi:10.1093/nar/gkm929](https://doi.org/10.1093/nar/gkm929)
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