From d9b2b6eaba2c73498d40cb39b8512eeda24ff213 Mon Sep 17 00:00:00 2001 From: Chowss Date: Tue, 24 Apr 2018 18:17:05 +0800 Subject: [PATCH 1/3] =?UTF-8?q?=E8=AF=A6=E7=BB=86=E4=BF=AE=E6=94=B9?= =?UTF-8?q?=E4=BA=86=E5=90=8E=E8=AE=B0=E4=B8=AD=E5=92=8C=E5=8D=B0=E5=BA=A6?= =?UTF-8?q?=E9=87=8E=E7=8C=AA=E7=9B=B8=E5=85=B3=E7=9A=84=E6=8F=8F=E8=BF=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- colophon.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/colophon.md b/colophon.md index 0c963e4..f0149bb 100644 --- a/colophon.md +++ b/colophon.md @@ -2,7 +2,7 @@ ## 关于作者 -Martin Kleppmann是英国康桥大学分布式系统的研究员。此前他曾在互联网公司担任过软件工程师和企业家,其中包括LinkedIn和Rapportive,负责大规模数据基础架构。在这个过程中,他以艰难的方式学习了一些东西,他希望这本书能够让你避免重蹈覆辙。 +**Martin Kleppmann**是英国剑桥大学分布式系统的研究员。此前他曾在互联网公司担任过软件工程师和企业家,其中包括LinkedIn和Rapportive,负责大规模数据基础架构。在这个过程中,他以艰难的方式学习了一些东西,他希望这本书能够让你避免重蹈覆辙。 Martin是一位常规会议演讲者,博主和开源贡献者。他认为,每个人都应该有深刻的技术理念,深层次的理解能帮助我们开发出更好的软件。 @@ -22,12 +22,14 @@ Alibaba+-Finplus 架构师/全栈工程师 (2015 ~ 2017) ## 后记 -《设计数据密集型应用》封面上的动物是**印度野猪(Sus scrofa cristatus)**,它是在印度,缅甸,尼泊尔,斯里兰卡和泰国发现的一种野猪的亚种。它们与欧洲公猪不同,它们具有较高的背部刷毛,没有羊毛底毛,以及更大,更直的头骨。 +《设计数据密集型应用》封面上的动物是**印度野猪(Sus scrofa cristatus)**,它是在印度、缅甸、尼泊尔、斯里兰卡和泰国发现的一种野猪的亚种。与欧洲野猪不同,它们有更高的背部鬃毛,没有体表绒毛,以及更大更直的头骨。 -印度的野猪有一头灰色或黑色的头发,脊椎上有僵硬的硬毛。雄性有突出的犬齿(称为T),用来与对手战斗或抵御掠食者。雄性比雌性大,但这些物种平均肩高33-35英寸,体重200-300磅。他们的天敌包括熊,老虎和各种大型猫科动物。 +印度的野猪有一头灰色或黑色的头发,脊背上有短而硬的毛。雄性有突出的犬齿(称为T),用来与对手战斗或抵御掠食者。雄性比雌性大,这些物种平均肩高33-35英寸,体重200-300磅。他们的天敌包括熊、老虎和各种大型猫科动物。 -这些动物夜行且杂食 —— 它们吃各种各样的东西,包括根,昆虫,腐肉,坚果,浆果和小动物。野猪也被称为通过垃圾和作物田地,造成大量的破坏,并赢得农民的仇恨。他们需要每天吃4,000 ~ 4,500卡路里。公猪有着发达的嗅觉,这有助于它们寻找地下的植物材料和挖掘动物。但是它们的视力很差。 +这些动物夜行且杂食——它们吃各种各样的东西,包括根、昆虫、腐肉、坚果、浆果和小动物。野猪经常因为破坏农作物的根被人们所熟知,他们造成大量的破坏,并被农民所敌视。他们每天需要摄入4,000 ~ 4,500卡路里的能量。野猪有发达的嗅觉,这有助于寻找地下植物和挖掘动物。然而,它们的视力很差。 -野猪在人类文化中一直具有重要意义。在印度教传说中,野猪是毗湿奴神的化身。在古希腊的丧葬纪念碑中,它是一个勇敢失败者的象征(与胜利的狮子相反)。由于它的侵略,它被描绘在斯堪的纳维亚,日耳曼和盎格鲁 ~ 撒克逊战士的盔甲和武器上。在中国十二生肖中,它象征着决心和急躁。 +野猪在人类文化中一直具有重要意义。在印度教传说中,野猪是毗湿奴神的化身。在古希腊的丧葬纪念碑中,它是一个勇敢失败者的象征(与胜利的狮子相反)。由于它的侵略,它被描绘在斯堪的纳维亚、日耳曼和盎格鲁撒克逊战士的盔甲和武器上。在中国十二生肖中,它象征着决心和急躁。 -O'Reilly封面上的许多动物都受到威胁;所有这些对世界都很重要。要了解有关如何提供帮助的更多信息,请访问animals.oreilly.com。封面图片来自Shaw's Zoology。封面字体是URW Typewriter和Guardian Sans。文字字体是Adobe Minion Pro;图中的字体是Adobe Myriad Pro;标题字体是Adobe Myriad Condensed;代码字体是Dalton Maag的Ubuntu Mono。 \ No newline at end of file +O'Reilly封面上的许多动物都受到威胁,这些动物对世界都很重要。要了解有关如何提供帮助的更多信息,请访问animals.oreilly.com。 + +封面图片来自Shaw's Zoology。封面字体是URW Typewriter和Guardian Sans。文字字体是Adobe Minion Pro;图中的字体是Adobe Myriad Pro;标题字体是Adobe Myriad Condensed;代码字体是Dalton Maag的Ubuntu Mono。 From a238f2851ea9b8440ffa52c8f1a17cd21c85ecd2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=91=A8=E6=98=A5=E5=85=89?= Date: Thu, 26 Apr 2018 09:01:11 +0800 Subject: [PATCH 2/3] Translate glossary --- glossary.md | 189 +++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 164 insertions(+), 25 deletions(-) diff --git a/glossary.md b/glossary.md index 6da36e5..304b9b6 100644 --- a/glossary.md +++ b/glossary.md @@ -10,7 +10,7 @@ ### 异步(asynchronous) -不等待某些事情完成(例如,通过网络将数据发送到另一个节点),并且不会假设要花多长时间。请参阅第153页上的“同步与异步复制”,第284页上的“同步与异步网络”,以及第306页上的“系统模型与现实”。 +不等待某些事情完成(例如,将数据发送到网络中的另一个节点),并且不会假设要花多长时间。请参阅第153页上的“同步与异步复制”,第284页上的“同步与异步网络”,以及第306页上的“系统模型与现实”。 @@ -18,14 +18,13 @@ 1.在并发操作的上下文中:描述一个在单个时间点看起来生效的操作,所以另一个并发进程永远不会遇到处于“半完成”状态的操作。另见隔离。 -2.在事务的上下文中:即使出现故障,将一组必须全部提交或全部回滚的写入组合在一起。参见第223页的“原子性”和第354页的“原子提交和两阶段提交(2PC)”。 +2.在事务的上下文中:将一些写入操作分为一组,这组写入要么全部提交成功,要么遇到错误时全部回滚。参见第223页的“原子性”和第354页的“原子提交和两阶段提交(2PC)”。 ### 背压(backpressure) -强制一些数据的发送者减慢,因为收件人不能保留 -与它一起。也称为流量控制。请参阅第441页上的“消息系统”。 +接收方接收数据速度较慢时,强制降低发送方的数据发送速度。也称为流量控制。请参阅第441页上的“消息系统”。 @@ -37,19 +36,19 @@ ### 边界(bounded) -有一些已知的上限或大小。例如,在网络延迟的情况下(请参阅“超时和未定义的延迟”在本页281)和数据集(请参阅第11章的介绍)。 +有一些已知的上限或大小。例如,网络延迟情况(请参阅“超时和未定义的延迟”在本页281)和数据集(请参阅第11章的介绍)。 ### 拜占庭故障(Byzantine fault) -以任意方式表现不正确的节点,例如通过向其他节点发送矛盾或恶意消息。请参阅第304页上的“拜占庭故障”。 +表现异常的节点,这种异常可能以任意方式出现,例如向其他节点发送矛盾或恶意消息。请参阅第304页上的“拜占庭故障”。 ### 缓存(cache) -最近记住使用数据的组件,以加快未来对相同数据的读取速度。它通常是不完整的:因此,如果缓存中缺少某些数据,则必须从某些底层较慢的数据存储系统具有完整的数据副本。 +一种组件,通过存储最近使用过的数据,加快未来对相同数据的读取速度。缓存中通常存放部分数据:因此,如果缓存中缺少某些数据,则必须从某些底层较慢的数据存储系统中,获取完整的数据副本。 @@ -61,7 +60,7 @@ ### 因果关系(causality) -事件之间的依赖关系,当一件事发生在另一件事情之前。例如,后面的事件是对早期事件的回应,或者建立在更早的事件上,或者应该根据先前的事件来理解。请参阅第186页上的“发生之前的关系和并发性”和第339页上的“排序和因果关系”。 +事件之间的依赖关系,当一件事发生在另一件事情之前。例如,后面的事件是对早期事件的回应,或者依赖于更早的事件,或者应该根据先前的事件来理解。请参阅第186页上的“发生之前的关系和并发性”和第339页上的“排序和因果关系”。 @@ -79,37 +78,37 @@ ### 声明式(declarative) -描述某些东西应该具有的属性,但不知道如何实现它的确切步骤。在查询的上下文中,查询优化器采用声明性查询并决定如何最好地执行它。请参阅第42页上的“数据的查询语言”。 +描述某些东西应有的属性,但不知道如何实现它的确切步骤。在查询的上下文中,查询优化器采用声明性查询并决定如何最好地执行它。请参阅第42页上的“数据的查询语言”。 ### 非规范化(denormalize) -为了加速读取,在标准数据集中引入一些冗余或重复数据,通常采用缓存或索引的形式。非规范化的值是一种预先计算的查询结果,视图。请参见“单对象和多对象操作”(第228页)和“从同一事件日志中派生多个视图”(第461页)。 +为了加速读取,在标准数据集中引入一些冗余或重复数据,通常采用缓存或索引的形式。非规范化的值是一种预先计算的查询结果,像物化视图。请参见“单对象和多对象操作”(第228页)和“从同一事件日志中派生多个视图”(第461页)。 ### 派生数据(derived data) -通过可重复的流程从其他数据创建的数据集,如有必要,您可以再次运行该数据集。通常,需要派生数据来加速对数据的特定读访问。索引,缓存和物化视图是派生数据的示例。参见第三部分的介绍。 +一种数据集,根据其他数据通过可重复运行的流程创建。必要时,你可以运行该流程再次创建派生数据。派生数据通常用于提高特定数据的读取速度。常见的派生数据有索引、缓存和物化视图。参见第三部分的介绍。 ### 确定性(deterministic) -描述一个函数,如果给它相同的输入,则总是产生相同的输出。这意味着它不能依赖于随机数字,时间,网络通信或其他不可预测的事情。 +描述一个函数,如果给它相同的输入,则总是产生相同的输出。这意味着它不能依赖于随机数字、时间、网络通信或其他不可预测的事情。 ### 分布式(distributed) -在由网络连接的多个节点上运行。以部分故障为特征:系统的某些部分可能被破坏,而其他部分仍在工作,软件通常不可能知道究竟是什么被破坏。请参阅第274页上的“故障和部分故障”。 +在由网络连接的多个节点上运行。对于部分节点故障,具有容错性:系统的一部分发生故障时,其他部分仍可以正常工作,通常情况下,软件无需了解故障相关的确切情况。请参阅第274页上的“故障和部分故障”。 ### 持久(durable) -以某种方式存储数据,即使发生各种故障,也不会丢失数据。请参阅第226页上的“耐用性”。 +以某种方式存储数据,即使发生各种故障,也不会丢失数据。请参阅第226页上的“持久性”。 @@ -121,7 +120,7 @@ ### 故障转移(failover) -在具有单一领导者的系统中,故障转移是将领导角色从一个节点转移到另一个节点的过程。请参阅第156页的“处理节点中断”。 +在具有单一领导者的系统中,故障转移是将领导角色从一个节点转移到另一个节点的过程。请参阅第156页的“处理节点故障”。 @@ -139,100 +138,240 @@ ### 追随者(follower) -不直接接受来自客户端的任何写入的副本,但仅处理从领导者收到的数据更改。也称为辅助,从,只读副本或热备份。请参阅第152页上的“领导和追随者”。 +一种数据副本,仅处理领导者发出的数据变更,不直接接受来自客户端的任何写入。也称为辅助、仆从、只读副本或热备份。请参阅第152页上的“领导和追随者”。 ### 全文检索(full-text search) -通过任意关键字来搜索文本,通常具有附加特征,例如匹配类似的拼写词或同义词。全文索引是一种支持这种查询的次级索引。请参见第88页上的“全文搜索和模糊索引”。 +通过任意关键字来搜索文本,通常具有附加特征,例如匹配类似的拼写词或同义词。全文索引是一种支持这种查询的次级索引。请参阅第88页上的“全文搜索和模糊索引”。 ### 图(graph) -由顶点组成的数据结构(可以引用的东西,也称为节点或实体)和边(从一个顶点到另一个顶点的连接,也称为关系或弧)。请参阅第49页上的“类似图形的数据模型”。 +一种数据结构,由顶点(可以指向的东西,也称为节点或实体)和边(从一个顶点到另一个顶点的连接,也称为关系或弧)组成。请参阅第49页上的“和图相似的数据模型”。 ### 散列(hash) -汇集有共同点的记录。在一个记录与另一个记录有关(外键,文档参考,图中的边)的情况下最常用,查询需要获 - -取参考所指向的记录。请参阅第33页上的“多对一和多对多关系”和第393页上的“减少端连接和分组”。 +将输入转换为看起来像随机数值的函数。相同的输入会转换为相同的数值,不同的输入一般会转换为不同的数值,也可能转换为相同数值(也被称为冲突)。请参阅第203页的“根据键的散列值分隔”。 ### 幂等(idempotent) +用于描述一种操作可以安全地重试执行,即执行多次的效果和执行一次的效果相同。请参阅第478页的“幂等”。 + + + ### 索引(index) -### 隔离(isolation) +一种数据结构。通过索引,你可以根据特定字段的值,在所有数据记录中进行高效检索。请参阅第70页的“让数据库更强大的数据结构”。 + + + +### 隔离性(isolation) + +在事务上下文中,用于描述并发执行事务的互相干扰程度。串行运行具有最强的隔离性,不过其它程度的隔离也通常被使用。请参阅第225页的“隔离”。 + + ### 连接(join) +汇集有共同点的记录。在一个记录与另一个记录有关(外键,文档参考,图中的边)的情况下最常用,查询需要获取参考所指向的记录。请参阅第33页上的“多对一和多对多关系”和第393页上的“减少端连接和分组”。 + + + ### 领导者(leader) -当数据或服务被复制到多个节点时,领导是被允许进行更改的指定副本。领导者可以通过某些协议选举产生,也可以由管理者手动选择。也被称为主或主。见“领导和F +当数据或服务被复制到多个节点时,由领导者分发已授权变更的数据副本。领导者可以通过某些协议选举产生,也可以由管理者手动选择。也被称为主人。请参阅第152页的“领导者和追随者”。 + + ### 线性化(linearizable) +表现为系统中只有一份通过原子操作更新的数据副本。请参阅第324页的“线性化”。 + + + ### 局部性(locality) +一种性能优化方式,如果经常在相同的时间请求一些离散数据,把这些数据放到一个位置。请参阅第41页的“请求数据的局部性”。 + + + ### 锁(lock) +一种保证只有一个线程、节点或事务可以访问的机制,如果其它线程、节点或事务想访问相同元素,则必须等待锁被释放。请参阅第257页的“两段锁(2PL)”和301页的“领导者和锁”。 + + + ### 日志(log) +日志是一个只能以追加方式写入的文件,用于存放数据。预写式日志用于在存储引擎崩溃时恢复数据(请参阅第82页的“使二叉树更稳定”);结构化日志存储引擎使用日志作为它的主要存储格式(请参阅第76页的“有序字符串表和日志结构的合并树”);复制型日志用于把写入从领导者复制到追随者(请参阅第152页的“领导者和追随者”);事件性日志可以表现为数据流(请参阅第446页的“分段日志”)。 + + + ### 物化(materialize) +急切地计算并写出结果,而不是在请求时计算。请参阅第101页的“聚合:数据立方和物化视图”和419页的“中间状态的物化”。 + + + ### 节点(node) +计算机上运行的一些软件的实例,通过网络与其他节点通信以完成某项任务。 + + + ### 规范化(normalized) +以没有冗余或重复的方式进行结构化。 在规范化数据库中,当某些数据发生变化时,您只需要在一个地方进行更改,而不是在许多不同的地方复制很多次。 请参阅第33页上的“多对一和多对多关系”。 + + + ### OLAP(Online Analytic Processing) +在线分析处理。 通过对大量记录进行聚合(例如,计数,总和,平均)来表征的访问模式。 请参阅第90页上的“交易处理或分析?”。 + + + ### OLTP(Online Transaction Processing) +在线事务处理。 访问模式的特点是快速查询,读取或写入少量记录,这些记录通常通过键索引。 请参阅第90页上的“交易处理或分析?”。 + + + ### 分区(partitioning) +将单机上的大型数据集或计算结果拆分为较小部分,并将其分布到多台机器上。 也称为分片。 见第6章。 + + + ### 百分位点(percentile) +通过计算有多少值高于或低于某个阈值来衡量值分布的方法。 例如,某个时间段的第95个百分位响应时间是时间t,则该时间段中,95%的请求完成时间小于t,5%的请求完成时间要比t长。 请参阅第13页上的“描述性能”。 + + + ### 主键(primary key) +唯一标识记录的值(通常是数字或字符串)。 在许多应用程序中,主键由系统在创建记录时生成(例如,按顺序或随机); 它们通常不由用户设置。 另请参阅二级索引。 + + + ### 法定人数(quorum) +在操作完成之前,需要对操作进行投票的最少节点数量。 请参阅第179页上的“读和写的法定人数”。 + + + ### 再平衡(rebalance) +将数据或服务从一个节点移动到另一个节点以实现负载均衡。 请参阅第209页上的“再平衡分区”。 + + + ### 复制(replication) +在几个节点(副本)上保留相同数据的副本,以便在某些节点无法访问时,数据仍可访问。请参阅第5章。 + + + ### 模式(schema) +一些数据结构的描述,包括其字段和数据类型。 可以在数据生命周期的不同点检查某些数据是否符合模式(请参阅第39页上的“文档模型中的模式灵活性”),模式可以随时间变化(请参阅第4章)。 + + + ### 次级索引(secondary index) +与主要数据存储器一起维护的附加数据结构,使您可以高效地搜索与某种条件相匹配的记录。 请参阅第85页上的“其他索引结构”和第206页上的“分区和二级索引”。 + + + ### 可序列化(serializable) +保证多个并发事务同时执行时,它们的行为与按顺序逐个执行事务相同。 请参阅第251页上的“可序列化”。 + + + ### 无共享(shared-nothing) -### 偏差(skew) +与共享内存或共享磁盘架构相比,独立节点(每个节点都有自己的CPU,内存和磁盘)通过传统网络连接。 见第二部分的介绍。 + + + +### 偏斜(skew) + +1.各分区负载不平衡,例如某些分区有大量请求或数据,而其他分区则少得多。也被称为热点。请参阅第205页上的“工作负载偏斜和减轻热点”和第407页上的“处理偏斜”。 + +2.时间线异常导致事件以不期望的顺序出现。 请参阅第237页上的“快照隔离和可重复读取”中的关于读取偏斜的讨论,第246页上的“写入偏斜和模糊”中的写入偏斜以及第291页上的“订购事件的时间戳”中的时钟偏斜。 + + ### 脑裂(split brain) +两个节点同时认为自己是领导者的情况,这种情况可能违反系统担保。 请参阅第156页的“处理节点中断”和第300页的“真相由多数定义”。 + + + ### 存储过程(stored procdure) +一种对事务逻辑进行编码的方式,它可以完全在数据库服务器上执行,事务执行期间无需与客户端通信。 请参阅第252页的“实际串行执行”。 + + + ### 流处理(stream process) +持续运行的计算。可以持续接收事件流作为输入,并得出一些输出。 见第11章。 + + + ### 同步(synchronous) +异步的反义词。 + + + ### 记录系统(system of record) +一个保存主要权威版本数据的系统,也被称为真相的来源。首先在这里写入数据变更,其他数据集可以从记录系统派生。 参见第三部分的介绍。 + + + ### 超时(timeout) +检测故障的最简单方法之一,即在一段时间内观察是否缺乏响应。 但是,不可能知道超时是由于远程节点的问题还是网络中的问题造成的。 请参阅第281页上的“超时和无限延迟”。 + + + ### 全序(total order) +一种比较事物的方法(例如时间戳),可以让您总是说出两件事中哪一件更大,哪件更小。 总的来说,有些东西是无法比拟的(不能说哪个更大或更小)的顺序称为偏序。 请参见第341页的“因果顺序不是全序”。 + + + ### 事务(transaction) +为了简化错误处理和并发问题,将几个读写操作分组到一个逻辑单元中。 见第7章。 + + + ### 两阶段提交(2PC, two-phase commit) +一种确保多个数据库节点全部提交或全部中止事务的算法。 请参阅第354页上的“原子提交和两阶段提交(2PC)”。 + + + ### 两阶段锁定(2PL, two-phase locking) -### 无限制(unbounded) +一种用于实现可序列化隔离的算法,该算法通过事务获取对其读取或写入的所有数据的锁,直到事务结束。 请参阅第257页上的“两阶段锁定(2PL)”。 + + + +### 无边界(unbounded) + +没有任何已知的上限或大小。 反义词是边界(bounded)。 \ No newline at end of file From 270494983d98a35b3676562e889afeb95e799123 Mon Sep 17 00:00:00 2001 From: Vonng Date: Thu, 26 Apr 2018 11:57:16 +0800 Subject: [PATCH 3/3] ch8 fix --- ch8.md | 8 ++++++++ img/ddia-wexin.JPG | Bin 108440 -> 108623 bytes 2 files changed, 8 insertions(+) diff --git a/ch8.md b/ch8.md index 4e78fc6..b5511a2 100644 --- a/ch8.md +++ b/ch8.md @@ -278,6 +278,14 @@ 单调钟适用于测量持续时间(时间间隔),例如超时或服务的响应时间:Linux上的`clock_gettime(CLOCK_MONOTONIC)`,和Java中的`System.nanoTime()`都是单调时钟。这个名字来源于他们保证总是前进的事实(而时钟可以及时跳回)。 +你可以在某个时间点检查单调钟的值,做一些事情,且稍后再次检查它。这两个值之间的差异告诉你两次检查之间经过了多长时间。但单调钟的绝对值是毫无意义的:它可能是计算机启动以来的纳秒数,或类似的任意值。特别是比较来自两台不同计算机的单调钟的值是没有意义的,因为它们并不是一回事。 + +在具有多个CPU插槽的服务器上,每个CPU可能有一个单独的计时器,但不一定与其他CPU同步。操作系统会补偿所有的差异,并尝试向应用线程表现出单调钟的样子,即使这些线程被调度到不同的CPU上。当然,明智的做法是不要太把这种单调性保证当回事【40】。 + +如果NTP协议检测到计算机的本地石英钟比NTP服务器要更快或更慢,则可以调整单调钟向前走的频率(这称为**偏移(skewing)**时钟)。默认情况下,NTP允许时钟速率增加或减慢最高至0.05%,但NTP不能使单调时钟向前或向后跳转。单调时钟的分辨率通常相当好:在大多数系统中,它们能在几微秒或更短的时间内测量时间间隔。 + +在分布式系统中,使用单调钟测量**经过时间(elapsed time)**(比如超时)通常很好,因为它不假定不同节点的时钟之间存在任何同步,并且对测量的轻微不准确性不敏感。 + ### 时钟同步与准确性 单调钟不需要同步,但是时钟需要根据NTP服务器或其他外部时间源来设置才能有用。不幸的是,我们获取时钟的方法并不像你所希望的那样可靠或准确——硬件时钟和NTP可能会变幻莫测。举几个例子: diff --git a/img/ddia-wexin.JPG b/img/ddia-wexin.JPG index ade3f3d39c3d56a9b5501aaabaede814146d3a4e..6e8251c5cacab902d974acd63922bbb6b2e08100 100644 GIT binary patch delta 74698 zcmcG$c|4Tw+c!Q^+0xi|rtA{3r9~#mmPD&9rc&9$rx0q)B|?ZPQxs(srLs)+vW;CO z*+SMCqsW$-Xk3|@%kS)SfA8mBKKJW+e$Vgq`u$`WdiOl{Fq zyWVtQB0to{IefkPbP_i+7}lxcO2@LJ5(#q1C>hP=%fZ{hdDv2dn7gk_xNPRtmy`qH zr<)5-i)6@D&ZZqa<@iBJuS*F9IevCo3S2{NIIxC_iH90Jsb?VS5Pu`JLN?w)8RkDu zbYlLh>uT8*6s)hoktw-szLTM>zpGgMhu%uZO6k|l_6@}b6IBatYbXIzyjAbAf%Fhw 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