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各种 NoSQL 的比较 TODO: 中英文之间需要半角空格
即使关系型数据库依然是非常有用的工具，它们持续几十年的垄断地位就要走到头了。现在已经存在无数能撼动关系型数据库地位的 NoSQL，当然，这些 NoSQL 还无法完全取代它们。（也就是说，关系型数据库还是处理关系型事务的最佳方式。）
NoSQL 与 NoSQL 之间的区别，要远大于 SQL 与 SQL 之间的区别。所以软件架构师必须要在项目一开始就选好一款合适的 NoSQL。
考虑到这种情况，本文为大家介绍以下几种 NoSQL 之间的区别：Cassandra, Mongodb, CouchDB, Redis, Riak, Couchbase (ex-Membase), Hypertable, ElasticSearch, Accumulo, VoltDB, Kyoto Tycoon, Scalaris, Neo4j和HBase:
##最流行的 NoSQL
###MongoDB 2.2版
开发语言： C++
主要特性： 保留 SQL 中一些用户友好的特性（查询、索引等）。
许可证： AGPL (发起者: Apache)
数据传输、存储的格式： 自定义，二进制（ BSON 文档格式）
- 主/从备份（支持自动故障切换功能）
- 自带数据分片功能
- 通过 javascript 表达式提供数据查询
- 服务器端完全支持 javascript 脚本
- 比 CouchDB 更好的升级功能
- 数据存储使用内存映射文件技术
- 功能丰富，性能不俗
- 最好开启日志功能（使用 --journal 参数）
- 在 32 位系统中，内存限制在 2.5GB
- 空数据库占用 192MB 空间
- 使用 GridFS（不是真正的文件系统）来保存大数据和元数据
- 支持对数据建立索引
- 数据中心意识
**应用场景：**动态查询；需要定义索引而不是 map/reduce 功能；提高大数据库性能；想使用 CouchDB 但数据的 IO 吞吐量太大，CouchDB 无法满足要求。MongoDB 可以满足你的需求。
**使用案例：**想布署 MySQL 或 PostgreSQL，但它们存在的预定义处理语句和预定义变量让你望而却步。这个时候，MongoDB 是你可以考虑的选项。
###Riak 1.2版
开发语言： Erlang、C、以及一些 JavaScript
**主要特性：**容错机制（当一份数据失效，服务会自动切换到备份数据，保证服务一直在线 —— 译者注）
许可证： Apache
数据传输、存储的格式： HTTP/REST 架构，自定义二进制格式
- 可存储 BLOB（binary large object，二进制大对象，比如一张图片、一个声音文件 —— 译者注）。
- 可在分部式存储和备份存储之间作协调。
- 为了保证可验证性和安全性，Riak 在 JS 和 Erlaing 中提供提交前（pre-commit）和提交后（post-commit）钩子（hook）函数（你可以在提交数据前执行一个 hook，或者在提交数据后执行一个 hook —— 译者注）。
- JS 和 Erlang 提供映射和简化（map/reduce）编程模型。
- 使用 links 和 link walking 图形化数据库（link 用于描述对象之间的关系，link walking 是一个用于查询对象关系的进程 —— 译者注）。
- 次要标记（secondaty indeces，开发者在写数据时可用多个名称来标记一个对象 —— 译者注），一次只能用一个。
- 支持大数据对象（Luwak）（Luwak 是 Riak 中的一个服务层，为大数据量对象提供简单的、面向文档的抽象，弥补了 Riak 的 Key/Value 存储格式在处理大数据对象方面的不足 —— 译者注）。
- 提供“开源”和“企业”两个版本。
- 提供“全文搜索”（可能就是允许用户在不提供 table/volume 等信息，对一个表进行文本字段的搜索，瞎猜的，望指正 —— 译者注）。
- 正在将存储后端从“Bitcask”迁移到 Google 的“LevelDB”上。
- 企业版本提供多点备份（各点地位平等，非主从架构）和SNMP监控功能。
**应用场景：**假如你想要类似 Dynamo 的数据库，但不想要它的庞大和复杂；假如你需要良好的单点可扩展性、可用性和容错能力，但不想为多点备份买单。 Riak 能满足你的需求。
**使用案例：**销售点数据收集；工厂控制系统；必须实时在线的系统；需要易于升级的网站服务器。
###CouchDB 1.2版
开发语言： Erlang
**主要特性：**数据一致性；易于使用
许可证： Apache
数据传输格式： HTTP/REST
- 双向复制（一种同步技术，每个备份点都有一份它们自己的拷贝，允许用户在存储点断线的情况下修改数据，当存储节点重新上线时，CouchDB 会对所有节点同步这些修改 —— 译者注）。
- 支持持续同步或者点对点同步。
- 支持冲突检测。
- 支持主主互备（多个数据库时时同步数据，起到备份和分摊用户并行访问量的作用 —— 译者注）。
- 多版本并发控制（MVCC），写操作时不需要阻塞读操作（或者说不需要锁住数据库）。
- 向下兼容。
- 可靠的 crash-only 设计（所谓 crash-only，就是程序出错时，只需重启下程序，丢弃内存的所有数据，不需要执行复杂的数据恢复操作 —— 译者注）。
- 需要实时压缩数据。
- 视图（文档是 CouchDB 的核心概念，CouchDB 中的视图声明了如何从文档中提取数据，以及如何对提取出来的数据进行处理 —— 译者注）：内嵌映射和简化（map/reduce）编程模型。
- 格式化的views字段：lists（包含把视图运行结果转换成非 JSON 格式的方法）和 shows（包含把文档转换成非 JSON 格式的方法）（在 CouchDB 中，一个 Web 应用是与一个设计文档相对应的。在设计文档中可以包含一些特殊的字段，views 字段包含永久的视图定义 —— 译者注）。
- 可能会提供服务器端文档验证的功能。
- 可能提供身份认证功能。
- 通过 _changes 函数实时更新数据。
- 链接处理（attachment：couchDB 的每份文档都可以有一个 attachment，就像一份 email 有它的网址 —— 译者注）。
- 有个 CouchApps（第三方JS的应用）。
**应用场景：**用于随机数据量多、需要预定义查询的地方；用于版本控制比较重要的地方。
**使用案例：**可用于客户关系管理（CRM），内容管理系统（CMS）；可用于主主互备甚至多机互备。
###Redis 2.4版
开发语言： C/C++
**主要特性：**快到掉渣
许可证： BSD
数据传输方式： 类似 Telnet
- Redis 是一个内存数据库（in-memory database，简称 IMDB，将数据放在内存进行读写，这才是“快到掉渣”的真正原因 —— 译者注），磁盘只是提供数据持久化（即将内存的数据写到磁盘）的功能（这类数据库被称为“disk backed”数据库）。
- 当前不支持将磁盘作为 swap 分区，虚拟内存（VM）和 Diskstore 方式都没加到此版本（Redis 的数据持久化共有4种方式：定时快照、基于语句追加、虚拟内存、diskstore。其中 VM 方式由于性能不好以及不稳定的问题，已经被作者放弃，而 diskstore 方式还在实验阶段 —— 译者注）。
- 主从备份
- 存储结构为简单的 key/value 或 hash 表。
- 但是操作比较复杂，比如：ZREVRANGEBYSCORE。
- 支持 INCR（INCR key 就是将key中存储的数值加一 —— 译者注）命令（对限速和统计有帮助）。
- 支持sets数据类型（以及 union/diff/inter）。
- 支持 lists （以及 queue/blocking pop）。
- 支持 hash sets （多级对象）。
- 支持 sorted sets（高效率的表，在范围查找方面有优势）。
- 支持事务处理。
- 缓存中的数据可被标记为过期
- Pub/Sub 操作能让用户发送信息。
**应用场景：**适合布署快速多变的小规模数据（可以完全运行在存在中）。
**使用案例：**股价系统、分析系统、实时数据收集系统、实时通信系统、以及取代 memcached。
##Clones of Google's Bigtable
###HBase (V0.92.0)
Written in: Java
Main point: Billions of rows X millions of columns
License: Apache
Protocol: HTTP/REST (also Thrift)
- Modeled after Google's BigTable
- Uses Hadoop's HDFS as storage
- Map/reduce with Hadoop
- Query predicate push down via server side scan and get filters
- Optimizations for real time queries
- A high performance Thrift gateway
- HTTP supports XML, Protobuf, and binary
- Jruby-based (JIRB) shell
- Rolling restart for configuration changes and minor upgrades
- Random access performance is like MySQL
- A cluster consists of several different types of nodes
Best used: Hadoop is probably still the best way to run Map/Reduce jobs on huge datasets. Best if you use the Hadoop/HDFS stack already.
For example: Search engines. Analysing log data. Any place where scanning huge, two-dimensional join-less tables are a requirement.
###Cassandra (1.2)
Written in: Java
Main point: Best of BigTable and Dynamo
License: Apache
Protocol: Thrift & custom binary CQL3
- Tunable trade-offs for distribution and replication (N, R, W)
- Querying by column, range of keys (Requires indices on anything that you want to search on)
- BigTable-like features: columns, column families
- Can be used as a distributed hash-table, with an "SQL-like" language, CQL (but no JOIN!)
- Data can have expiration (set on INSERT)
- Writes can be much faster than reads (when reads are disk-bound)
- Map/reduce possible with Apache Hadoop
- All nodes are similar, as opposed to Hadoop/HBase
- Very good and reliable cross-datacenter replication
Best used: When you write more than you read (logging). If every component of the system must be in Java. ("No one gets fired for choosing Apache's stuff.")
For example: Banking, financial industry (though not necessarily for financial transactions, but these industries are much bigger than that.) Writes are faster than reads, so one natural niche is data analysis.
###Hypertable (0.9.6.5)
Written in: C++
Main point: A faster, smaller HBase
License: GPL 2.0
Protocol: Thrift, C++ library, or HQL shell
- Implements Google's BigTable design
- Run on Hadoop's HDFS
- Uses its own, "SQL-like" language, HQL
- Can search by key, by cell, or for values in column families.
- Search can be limited to key/column ranges.
- Sponsored by Baidu
- Retains the last N historical values
- Tables are in namespaces
- Map/reduce with Hadoop
Best used: If you need a better HBase.
For example: Same as HBase, since it's basically a replacement: Search engines. Analysing log data. Any place where scanning huge, two-dimensional join-less tables are a requirement.
###Accumulo (1.4)
Written in: Java and C++
Main point: A BigTable with Cell-level security
License: Apache
Protocol: Thrift
- Another BigTable clone, also runs of top of Hadoop
- Cell-level security
- Bigger rows than memory are allowed
- Keeps a memory map outside Java, in C++ STL
- Map/reduce using Hadoop's facitlities (ZooKeeper & co)
- Some server-side programming
Best used: If you need a different HBase.
For example: Same as HBase, since it's basically a replacement: Search engines. Analysing log data. Any place where scanning huge, two-dimensional join-less tables are a requirement.
##Special-purpose
###Neo4j (V1.5M02)
Written in: Java
Main point: Graph database - connected data
License: GPL, some features AGPL/commercial
Protocol: HTTP/REST (or embedding in Java)
- Standalone, or embeddable into Java applications
- Full ACID conformity (including durable data)
- Both nodes and relationships can have metadata
- Integrated pattern-matching-based query language ("Cypher")
- Also the "Gremlin" graph traversal language can be used
- Indexing of nodes and relationships
- Nice self-contained web admin
- Advanced path-finding with multiple algorithms
- Indexing of keys and relationships
- Optimized for reads
- Has transactions (in the Java API)
- Scriptable in Groovy
- Online backup, advanced monitoring and High Availability is AGPL/commercial licensed
Best used: For graph-style, rich or complex, interconnected data. Neo4j is quite different from the others in this sense.
For example: For searching routes in social relations, public transport links, road maps, or network topologies.
###ElasticSearch (0.20.1)
Written in: Java
Main point: Advanced Search
License: Apache
Protocol: JSON over HTTP (Plugins: Thrift, memcached)
- Stores JSON documents
- Has versioning
- Parent and children documents
- Documents can time out
- Very versatile and sophisticated querying, scriptable
- Write consistency: one, quorum or all
- Sorting by score (!)
- Geo distance sorting
- Fuzzy searches (approximate date, etc) (!)
- Asynchronous replication
- Atomic, scripted updates (good for counters, etc)
- Can maintain automatic "stats groups" (good for debugging)
- Still depends very much on only one developer (kimchy).
Best used: When you have objects with (flexible) fields, and you need "advanced search" functionality.
For example: A dating service that handles age difference, geographic location, tastes and dislikes, etc. Or a leaderboard system that depends on many variables.
##The "long tail"
(Not widely known, but definitely worthy ones)
###Couchbase (ex-Membase) (2.0)
Written in: Erlang & C
Main point: Memcache compatible, but with persistence and clustering
License: Apache
Protocol: memcached + extensions
- Very fast (200k+/sec) access of data by key
- Persistence to disk
- All nodes are identical (master-master replication)
- Provides memcached-style in-memory caching buckets, too
- Write de-duplication to reduce IO
- Friendly cluster-management web GUI
- Connection proxy for connection pooling and multiplexing (Moxi)
- Incremental map/reduce
- Cross-datacenter replication
Best used: Any application where low-latency data access, high concurrency support and high availability is a requirement.
For example: Low-latency use-cases like ad targeting or highly-concurrent web apps like online gaming (e.g. Zynga).
###VoltDB (2.8.4.1)
Written in: Java
Main point: Fast transactions and rapidly changing data
License: GPL 3
Protocol: Proprietary
- In-memory relational database.
- Can export data into Hadoop
- Supports ANSI SQL
- Stored procedures in Java
- Cross-datacenter replication
Best used: Where you need to act fast on massive amounts of incoming data.
For example: Point-of-sales data analysis. Factory control systems.
###Scalaris (0.5)
Written in: Erlang
Main point: Distributed P2P key-value store
License: Apache
Protocol: Proprietary & JSON-RPC
- In-memory (disk when using Tokyo Cabinet as a backend)
- Uses YAWS as a web server
- Has transactions (an adapted Paxos commit)
- Consistent, distributed write operations
- From CAP, values Consistency over Availability (in case of network partitioning, only the bigger partition - works)
Best used: If you like Erlang and wanted to use Mnesia or DETS or ETS, but you need something that is accessible from more languages (and scales much better than ETS or DETS).
For example: In an Erlang-based system when you want to give access to the DB to Python, Ruby or Java programmers.
###Kyoto Tycoon (0.9.56)
Written in: C++
Main point: A lightweight network DBM
License: GPL
Protocol: HTTP (TSV-RPC or REST)
- Based on Kyoto Cabinet, Tokyo Cabinet's successor
- Multitudes of storage backends: Hash, Tree, Dir, etc (everything from Kyoto Cabinet)
- Kyoto Cabinet can do 1M+ insert/select operations per sec (but Tycoon does less because of overhead)
- Lua on the server side
- Language bindings for C, Java, Python, Ruby, Perl, Lua, etc
- Uses the "visitor" pattern
- Hot backup, asynchronous replication
- background snapshot of in-memory databases
- Auto expiration (can be used as a cache server)
Best used: When you want to choose the backend storage algorithm engine very precisely. When speed is of the essence.
For example: Caching server. Stock prices. Analytics. Real-time data collection. Real-time communication. And wherever you used memcached before.
Of course, all these systems have much more features than what's listed here. I only wanted to list the key points that I base my decisions on. Also, development of all are very fast, so things are bound to change.
P.s.: And no, there's no date on this review. There are version numbers, since I update the databases one by one, not at the same time. And believe me, the basic properties of databases don't change that much.
via: http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis
译者：[译者ID][] 校对：校对者ID