From fe3df4c1356d7ebe2846c396e66815c6b1f1452f Mon Sep 17 00:00:00 2001 From: isno Date: Sun, 1 Dec 2024 18:57:21 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=85=B1=E8=AF=86=E7=AE=97?= =?UTF-8?q?=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Observability/logging.md | 32 +++---- Observability/tracing.md | 6 +- architecture/conclusion.md | 2 +- assets/paxos-p1.png | Bin 181671 -> 512833 bytes assets/paxos.svg | 2 +- consensus/Basic-Paxos.md | 118 ++++++++++---------------- consensus/Paxos-history.md | 2 +- consensus/Replicated-State-Machine.md | 16 ++-- consensus/consensus.md | 30 +++++-- consensus/raft-leader-election.md | 9 +- http/Edge-Acceleration.md | 6 +- http/quic.md | 4 +- network/conntrack.md | 8 +- 13 files changed, 109 insertions(+), 126 deletions(-) diff --git a/Observability/logging.md b/Observability/logging.md index 730d0bfd..0ee536e5 100644 --- a/Observability/logging.md +++ b/Observability/logging.md @@ -6,17 +6,17 @@ ## 1. 全文索引方案 Elastic Stack -在讨论如何实现一套完整的日志系统时,工程师们或多或少听说过以下几个名词:ELK、ELKB 或 Elastic Stack。 +讨论如何实现一套完整的日志系统时,工程师们或多或少都听说过这几个名词:ELK、ELKB 或 Elastic Stack。 -实际上,它们指的是同一套用于日志处理的开源组件。Elastic Stack(为明确统一,本文统称 Elastic Stack)是由 Elastic 公司开发的一组开源工具,专门用于数据海量的收集、搜索、分析和可视化处理。 +实际上,它们指向的是同一套用于日志处理的开源组件。Elastic Stack(为明确统一,本文统称 Elastic Stack)是由 Elastic 公司开发的一组开源工具,专门用于数据海量的收集、搜索、分析和可视化处理。 图 9-6 展示了一套基于 Elastic Stack 的日志处理方案: -- **数据收集**:Beats 组件部署在日志生成节点上,负责收集原始数据。 -- **数据缓冲**:使用消息队列(MQ)进行数据缓冲,以提高数据吞吐量。 -- **数据清洗**:数据发送到 Logstash 进行清洗和处理。 -- **数据存储**:处理后的数据存储在 Elasticsearch 集群中,并生成索引。 -- **数据可视化**:用户通过 Kibana 进行数据可视化、检索和分析。如果需要,还可以通过 Nginx 实现访问控制。 +- **数据收集**:Beats 组件部署在日志生成节点,负责收集原始数据。 +- **数据缓冲**:使用消息队列(RabbitMQ)缓冲数据,以提高数据吞吐量。 +- **数据清洗**:数据发送到 Logstash 清洗。 +- **数据存储**:清洗后的数据存储在 Elasticsearch 集群,并生成索引。 +- **数据可视化**:Kibana 负责数据检索、分析、可视化处理。如果需要,还可以再部署一套 Nginx 实现访问控制。 :::center ![](../assets/ELK.png)
@@ -29,7 +29,7 @@ Elastic 公司的发展始于创始人 Shay Banon 的个人兴趣,从开源、 Elastic Stack 套件中,最核心的组件是 Elasticsearch —— 一个基于 Apache Lucene 构建的开源的搜索与分析引擎。值得一提的是,Lucene 的作者就是大名鼎鼎的 Doug Cutting,如果你不知道他是谁是?那你一定听过他儿子玩具的名字 —— Hadoop。 -Elasticsearch 能够在海量数据中快速检索关键词,其关键技术之一是 Lucene 中的反向索引(Inverted Index)。与反向索引相对的是正向索引,两者的区别是: +Elasticsearch 能够在海量数据中快速检索关键词,其关键技术之一是 Lucene 中的“反向索引”(Inverted Index)。与反向索引相对的是正向索引,两者的区别是: - **正向索引(Forward Index)**:正向索引是一种传统的索引方法,它将文档集合中的每个单词作为键,将包含该单词的文档列表作为值。正向索引适用于快速检索特定标识符的文档,常用于数据库管理系统中的主键索引。 - **反向索引(Inverted Index)**:反向索引通常被译为“倒排索引”,但“倒排”容易让人误以为与排序有关,实际上它与排序无关。反向索引的工作原理是将文本分割成词条,并构建“<词条->文档编号>”的索引,以便快速定位某个词出现在哪些文档中。 @@ -48,16 +48,14 @@ Elasticsearch 能够在海量数据中快速检索关键词,其关键技术之 "it": {0, 1, 2} "what": {0, 1} ``` -进行检索时,条件“what”, “is” 和 “it” 将对应这个集合:$\{0, 1\}\cap\{0,1,2\}\cap\{0,1,2\} = \{0,1\}$。 +检索时,条件“what”, “is” 和 “it” 将对应这个集合:$\{0, 1\}\cap\{0,1,2\}\cap\{0,1,2\} = \{0,1\}$。可以看出,反向索引使得搜索操作能够快速定位包含特定关键词的文档,而无需逐一扫描所有文档。 -在 Elasticsearch 中,反向索引使得搜索操作能够快速定位包含特定关键词的文档,而无需逐一扫描所有文档。 - -Elasticsearch 另一项关键技术是分片机制(sharding)。Elasticsearch 中的每个分片相当于一个独立的 Lucene 实例,类似于一个完整的数据库。 +Elasticsearch 另一项关键技术是“分片”(sharding),每个分片相当于一个独立的 Lucene 实例,类似于一个完整的数据库。 - 文档写入时,Elasticsearch 通过哈希函数(通常基于文档 ID)计算该文档应存储的分片,从而将文档有序地分配到不同的分片中。 - 查询文档时,查询请求并行地在多个分片上执行计算,最终将结果聚合后返回给客户端,这显著提升了查询的吞吐量。 -为了追求极致的查询性能,Elasticsearch 在数据写入吞吐量和存储空间占用方面付出了代价: +追求极致查询性能的背后,Elasticsearch 也付出了相应的代价: - **写入吞吐量下降**:文档写入过程中需要进行分词和构建排序表等 CPU 和内存密集型操作,导致写入性能下降。 - **存储空间占用高**:Elasticsearch 存储原始数据和反向索引,为了加速分析,可能还需要额外存储一份列式数据。 @@ -151,17 +149,16 @@ CREATE TABLE example ( ORDER BY id; ``` -近几年来,经常能在国内各个技术公众号看到使用 ClickHouse 降低存储成本的实践分享。在 B 站的技术文章《B 站基于 Clickhouse 的下一代日志体系建设实践》中,我们看到相较于 Elasticsearch ,B 站使用 ClickHouse 后降低了 60%+ 的存储成本[^2]。 +近几年来,经常在国内各个技术公众号看到 ClickHouse 降低存储成本的实践分享。在 B 站的技术文章《B 站基于 Clickhouse 的下一代日志体系建设实践》中,我们看到相较于 Elasticsearch ,B 站使用 ClickHouse 后降低了 60%+ 的存储成本[^2]。 :::center ![](../assets/es-vs-clickhouse.png)
图 9-8 同一份日志在 Elasticsearch、ClickHouse 和 ClickHouse(zstd) 中的容量对比(结果越低越好) ::: -ClickHouse 支持分片(Sharding),也就是支持分布式并行计算。通过增加更多的节点,Clickhouse 能实现处理数百亿到数万亿条记录,以及数 PB 级别的数据。 - -根据 Yandex 的内部跑分结果来看(图 9-9),在一亿条记录的规模上,ClickHouse 比 Vertia(一款商业的 OLAP 分析软件)快约 5 倍、比 Hive 快 279 倍、比 InifniDB 快 31 倍。ClickHouse 表现的惊人的查询性能,当之无愧阐述 ClickHouse 介绍中“实时”(real-time)二字含义。 +ClickHouse 支持“分片”(Sharding)技术,也就是支持分布式并行计算。节点规模的上限即是 Clickhouse 处理能力的上限,只要有足够多的硬件资源,Clickhouse 能实现处理数百亿到数万亿条记录、数 PB 级别的数据。 +根据 Yandex 的内部跑分结果来看(图 9-9),一亿条记录的规模上,ClickHouse 比 Vertia(一款商业的 OLAP 分析软件)快约 5 倍、比 Hive 快 279 倍、比 InifniDB 快 31 倍。ClickHouse 表现的惊人的查询性能,当之无愧阐述 ClickHouse 介绍中“实时”(real-time)二字含义。 :::center ![](../assets/ClickHouse-benchmark.jpeg)
@@ -170,6 +167,5 @@ ClickHouse 支持分片(Sharding),也就是支持分布式并行计算。 正如 ClickHouse 的宣传所言,其他的开源系统太慢,商用的又太贵。只有 ClickHouse 在存储成本与查询性能之间做到了良好平衡,不仅快且还开源。 - [^1]: 以运营俄罗斯最受欢迎的搜索引擎闻名,被称为俄罗斯的 Google [^2]: 参见 https://mp.weixin.qq.com/s/dUs7WUKUDOf9lLG6tzdk0g \ No newline at end of file diff --git a/Observability/tracing.md b/Observability/tracing.md index 881e4a14..26704989 100644 --- a/Observability/tracing.md +++ b/Observability/tracing.md @@ -34,10 +34,10 @@ Dapper 系统为每一个跨度记录了一个可读的 span name、span id、pa 图 9-12 CNCF 下分布式链路追踪产品生态 ::: -上述各个链路追踪系统笔者无法一一介绍,但从链路追踪系统的实现来看,就是在服务调用的阶段收集 trace、span 信息,然后汇总构成追踪树结构。数据收集听起来并不复杂,但考虑现在分布式架构都是异构的、规模庞大且复杂的,实现分布式链路追踪系统最大的挑战来源于以下目标: +上述各个链路追踪系统笔者无法一一介绍,但从链路追踪系统的实现来看,就是在服务调用的阶段收集 trace、span 信息,然后汇总构成追踪树结构。收集数据并不复杂,但再兼顾下面两个目标,就变得不容易了: -- 低消耗:追踪系统对在线服务的影响应该做到足够小。在一些高度优化过的服务,即使一点点损耗也会很容易察觉到,而且有可能迫使在线服务的部署团队不得不将追踪系统关停。 -- 对业务透明:对于业务工程师而言,是不需要知道有追踪系统这回事的。如果一个追踪系统想生效,就必须需要依赖应用的开发者主动配合,那这个追踪系统未免太脆弱了。 +- **低消耗**:追踪系统对业务的影响应该足够小。一些对延迟极其敏感的业务,即使一点点延迟损耗也会很容易察觉到,这可能迫使业务团队不得不将追踪系统关停; +- **对业务透明**:对于业务工程师而言,不需要知道有追踪系统这回事。如果一个追踪系统必须依赖业务工程师的主动配合,那这个追踪系统也未免太脆弱了。 接下来,笔者将从数据采集层面阐述当前主流链路系统实现的逻辑。 diff --git a/architecture/conclusion.md b/architecture/conclusion.md index 5dd45563..cc2893cb 100644 --- a/architecture/conclusion.md +++ b/architecture/conclusion.md @@ -4,7 +4,7 @@ 云原生不是简单使用云计算平台运行现有的应用程序,也不是某个开源项目或者某种技术,而是一套指导软件和基础设施架构设计的思想。基于这套思想构建出来的应用,能够天然地与“云”融合,充分发挥“云”的能力和价值。 -最后,笔者还要补充的是,并不是因为云计算/容器技术的发展才出现云原生的概念,而是软件规模变得越来越大、越来越复杂,对软件的可靠性、迭代效率、资源成本的要求越来越高,这才有了云原生技术出现的契机和发展。 +最后,笔者还要补充的是,并不是因为云计算/容器技术的发展才出现云原生的概念,而是软件规模变得越来越大、越来越复杂的同时,对软件的可靠性、迭代效率、资源成本的要求也越来越高,这才有了云原生技术出现的契机和发展。 从根本上讲,驱动技术发展的动力有很多种,但能让技术大行其道的动力,都是来源于业务发展的需要。 diff --git a/assets/paxos-p1.png b/assets/paxos-p1.png index 9acecb6df8b57e28c2688667ec3f0edbd6c08cfd..115b400c494bd1f7a075b006a1cc39cca27fe3cb 100644 GIT binary patch literal 512833 zcmeFZWmH_V65Zps>3n4&&hQ{69CAc> zq}My;3r*QDFsP;0;^Hc@;^OaBTpTT|?aX0dWTH~i-l!)pVh5eP6i0kX#oGU}_Ok)z z^Cv457QBqQ(suG#rLqAbcOdB7KM|};G zB&6k}^=3RPoBzU`!_(qe_+>1E9Hv@Gnp!Sw8&(F#B3j%}Xis&D-ED;}C=v7R9y}=q zuXry20|O>}$%MdO{8;Q*RVZ;$CE4Zl9vWdE(03 z`gi9DpT0;Uw-gl?USNqtRldazy_HeBBRpH*mVMM6_p(;Ppi>>@^v%F)Eq*WlZCLv% zkZj;3wN`z|4jo=XR@P)4tCxY&(nFMH)o{4`w)49--f0oEm|u32SY%`c032ny;LCmf zT~`teTa*3%)I$Ekth&b{BX0v&U}WLjI$A; zA|&fO+GU9JcT7M?nh~J&4GtE<#(NHlVDi!!Hk6`nqDol1S8-_nU`BMwLnOTT zv(b0Emm%1Va3UDQ7j;&%cBcLu)N}v&3 zD#@L{q}rhB|4Vg-jtfumk*gZ zNIT=9PJFo8AI1^!zd#`z&fbk}jnKxJMzWRn$5aML;IM{%U#RKnCqImKf;_49lmcluY3-=wz z-G{pZCdoOyROXRneO6ssUDjo)2f6t7#jFG0bQ#i^HJLnhj#F2D34L4>%cYq}GNd!4 z4ODv0eQbo2?2@C9p-?m~>t+u-MD2;@lZ2OKmCQA%*fj1NiH3joPBN|ghtr2FdTDx1 z`cQSI*~H-@S~av^biZF$c2n-H=rdvC6y*!;i*tUg7W(HFvnTK; zh&qn6E_n-Ih|rgPXkolaxkx$u=9?l~HmT97{#o6ll>&JZ8ie#B&59Je0&ugv`99 zY|W;z+!h4I1Sh=59Kniz6uv49D>!6y7hM;@O+OW_Ua((~?xO8#j&@i|ahPVBXUwKw zWlfL5kGhT$j~Z(4Y4>XVskLgjwXn9#u<`CaPfGcblBW@`kvvOmi(^~io^eG=Tpiai z=949rHD6y;@3#zJe{)3dfp)~RT({(3=UIurJinB{x5wYh|Hrj);5$bMp)TP(341(Q z{2^%;sVM)Vpt(Sf%l*jWD1YBw`o$?+58@-DG#;RFuhB^lqv^8gw?0%4v-!Hoy2W{g zVI{Zeu=!#op&64sE@NmEI;u&QN@kK!Lq@Kp1gZn!DSsLZb(|)r+WXIByDZH}_X_y0#@_&e zcMk;L2X}XdzsKsuSi-%eUSRX`J_W~A#)ROfF#C|1;8xTA6#j_rR7=d1Rc> z9oJ2>QU2EDZPnY^x8#(7DsQ!)aK=-)T@8+Dns^iuQ>01?b0ef;ZK#CNbY6$b|Gt)e%8kE3D z3CuPqsr=knZJIJv%3Ky+-BuH55N9x^zuGM2JJ!$rWSe1&Sspsq2e#D*w}^N?L6Gv$ zHHf2#)p(v$O*7QeFC5giii-5&R?wT^MQ6UePd= zU>#=lY4BY`{qb%$G$ix}^Nyqx%=U|3Bvv@v{q`;?lIlp5GoV7G)Tb7*H!o*wZm1!np<5^J ze#L%ex@-#}k{ug7TY&1*2`O{UWzLe77R=&iBhb;%89rj)jI63X7waf>Y72JlN%>lmg<_O(5{dj%FRkUW-%f>6c<|FTi1Jdi9thV5`RnMN2wGMn=@QKLD^J(bJ z8!)+raJEjVf(-pN)IKMj4WxCg>~8J$ ziR*~Z_RRH+QLRzwv*L9S_(4pEP*|*Z88Dxkm zk=-aLFs&sBK_uufg=iV%coU=3({N+0@CrD$%0is3$}z$)b&(lFFY}qTaL9N{M;FTve?EaPApW+) zeqxv?GGcJpi9ra|?_vMj!|RkFd&4-X(4>ED+JC&F6I-QI8SUf0{7&jhOvHDm%Mr;x zC}jV2iQk=06a9;S@-J!sh{Ga2UT^%glKEFv^g0{yu@>oH(dz$v_CZAqw!@$A1JT!i zX-)pW*|4JJ|NUkBFX1{x-l=ptk}E}rQ~&F;UzgMNUtf@aFXw;9+y1?re}^#t_RfEf zO8@rG|3bO{n<@PpZ2x(X`FHI6JE;D5?EG&K$A43yzvH|A&zY_0PqC&iE=@t7N@7j! 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    Prepare(n)
    Prepare(n)
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    操作请求
    操作请求
    Actor
    Act...
    Prepare 阶段
    Prepare 阶段
    Accept(n,val)
    Accept(n,val)
    Accepted(n)
    Accepted(n)
    Accept(n,val)
    Accept(n,val)
    Accepted(n)
    Accepted(n)
    Accept 阶段
    Accept 阶段
    Learner
    Learner
    形成决议,同步至 Learner
    形成决议,同步至 Learner
    构造提案 n
    构造提案 n
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    Proposer
    Proposer
    Acceptor
    Acceptor
    Acceptor
    Acceptor
    Prepare(n)
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    Promise(n,val)
    Promise(n,val)
    Prepare(n)
    Prepare(n)
    Promise(n,val)
    Promise(n,val)
    操作请求
    操作请求
    Actor
    Act...
    Prepare 阶段
    Prepare 阶段
    Accepted(n)
    Accepted(n)
    Accepted(n)
    Accepted(n)
    Accept 阶段
    Accept 阶段
    Learner
    Learner
    形成决议,同步至 Learner
    形成决议,同步至 Learner
    构造提案 n
    构造提案 n
    判断之前是否“批准”过 ≥n 的提案:
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    判断之前是否“批准”过 ≥n 的提案:...
    Prepare Quorum 达成
    Prepare Quorum 达成
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    Accept Quorum 达成
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    Accept(n,val)
    Accept(n,val)
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    \ No newline at end of file diff --git a/consensus/Basic-Paxos.md b/consensus/Basic-Paxos.md index 570814c4..f9b26bcd 100644 --- a/consensus/Basic-Paxos.md +++ b/consensus/Basic-Paxos.md @@ -1,135 +1,105 @@ -# 6.3.2 Basic Paxos +# 6.3.2 Paxos 算法 -希望你没有对前篇 Paxos 的“复杂”做的铺垫所吓倒,共识问题已经算是一个古老的领域,30 余年间已经有无数简洁直白的视频、论文等资料进行过解读。譬如在网络中流传甚广的 raft 和 paxos 视频讲解[^1],即使没有多少技术背景,也能通俗地理解 Paxos。 +希望你没有对前篇 Paxos 的“复杂”做的铺垫所吓倒,共识问题已经算是一个古老的领域,30 余年间已经有无数简洁直白的视频、论文等资料进行过解读。网络中流传甚广的 raft 和 paxos 视频讲解[^1],即使没有多少技术背景,也能通俗地理解 Paxos。 -实现多个节点 x 值一致的复杂度主要来源于以下两个因素的共同影响: +## 1. Paxos 算法背景 -- 分布式系统是不可靠的:节点可能宕机,节点间通信可能延迟、丢失、乱序; -- 操作是并发的:如果是串行操作,单纯使用少数服从多数原则,就足以保证正确读写数据。但并发操作就变成了“**分布式环境下多个节点并发操作共享数据**”的问题。 +在 Paxos 算法中,节点分为三种角色。 -第一问题相对好解决。一个节点不可靠无所谓,只要一群节点中多个节点可靠就行。按照少数服从多数原则,也就是 Quorum 机制。 +- **提议者(Proposer)**:提议者是启动共识过程的节点,它提出一个值,并请求其他节点对这个值进行投票,提出值的行为被称为“提案"(Proposal)。注意的是,Paxos 算法是一个典型的为“操作转移”模型设计的算法,为简化表述,本文把提案类比成“变量赋值”操作,但你应该理解它是“操作日志”相似的概念,后面介绍的 Raft 算法中,直接就把“提案”称做“日志”了。 +- **决策者(Acceptor)**:接受或拒绝 Proposer 的提案,如果一个提案被超过半数的 Acceptor 接受,那提案意味着被“批准”(accepted)。提案一旦被批准,意味着在所有节点中达到共识,便不可改变、永久生效。 +- **记录者(Learner)**:记录者发不起提案,也不参与决策提案,但它们需要学习并知道哪些提案被接受作为最终决策。 -:::tip Quorum +在 Paxos 算法中,节点都是平等的,它们都可以承担一种或者多种角色。例如,一个节点即可发起提案,也可以就一个提案进行表决,但为了达成 Quorum,表决提案的角色要保证有奇数个。 -Quorum(法定人数)指的是在一个分布式系统中,能够进行合法决策所需的最小节点数,也就是集群节点总数的一半加一(即 ⌈N/2⌉ + 1,其中 N 是集群节点总数)。Quorum 的大小决定了系统的容错能力,一个集群最多能容忍 ⌊N/2⌋ 个节点失效。例如: -- 3 节点集群:Quorum 为 2,允许 1 个节点失效; -- 5 节点集群:Quorum 为 3,允许 2 个节点失效。 - -如果失效节点超过 ⌊N/2⌋,系统无法形成 Quorum,集群就会停止提供服务以避免一致性问题。 -::: - - -但是问题还是存在:**每个决策节点该接受几个提案呢**? - -先看第一种情况:**按时序决策节点只接受它收到的第一个提案**。但考虑多个提议节点同时对一个提案进行提议,最后可能没有一个提议能够取得多数的投票,出现了平票问题(Split Votes)。如图 6-5 所示,red 和 blue 各有 2 票,没办法确定谁被选择?这也就意味着我们无法保证在一轮投票中达成共识,这违背活性(Liveness)要求。 +Paxos 是基于 Quorum 的算法,仅依靠“数服从多数原则”,一定会遇到提案冲突的问题。如图所示:。S~1~ 看到其他服务器没有接受某个值,之后提出 red 值。S~5~ 也没有看到其他服务器接受某个值,于是提出 blue 值。它们的提案 Quorum 都达成了,也就是说一个提案有两个值选中,这显然破坏了一致性原则。 :::center - ![](../assets/paxos_split_votes.png)
    - 图 6-5 多个决策节点会遇到平票问题 -::: - -再看第二种情况:**决策节点就需要允许接受多个不同的提案,用多数派的机制解决平票问题问题**。但新的问题是有了少数服从多数原则,就会碰到冲突的问题。如图 6-6 所示,不同提案节点提议不同的值,可能都会被选择(S~3~ 收到了 blue 和 red,S~3~ 该确认选择哪个值? ),这就破坏了每个提案只有一个值的原则,这违背了安全性(Safety)要求。 - -:::center - ![](../assets/paxos_conflict_choices.png)
    - 图 6-6 接受多个不同的提案会遇到冲突问题 -::: - -:::tip 注意,Paxos 强调 -Once a value has been chosen, future proposals must propose the same value. + ![](../assets/paxos_2pc_choice.png)
    + 图 6-7 网络延迟导致冲突 ::: -也就是说,我们讨论的 Basic-Paxos 只会 Chosen 一个值。基于此,就需要一个两阶段(2-phase)协议,对于已经 Chosen 的值,后面的提案要放弃自己的提议,提出已经被选中的值。例如,S~5~ 发起提案之前,先广播给 S~3~、S~4~、S~5~ 这 3 个节点,询问是否已经有接受的提案,如果已有,则撤销自己的提案,S~5~ 的题案由 blue 改为 red,这一阶段在 Basic Paxos 称为准备(Prepare)阶段。 +注意,你会发现矛盾点在于 S~3~,S~3~ 是两个多数派中的交集点,它的时间线上有两个不同的值被选中,这实际上是一个“并发”操作问题。 -:::tip 第一阶段实际是分布式抢占锁的过程 +我们知道,程序设计中的一个基本常识是:并发操作一个共享变量,一定要加上互斥锁,不然会出现各种意外情况。所以,S~3~ 的问题本质是:“在分布式环境下并发操作共享变量”。 -如果并发操作一个变量不使用锁,会出现各种意外情况,假设有一个变量 x 在当前系统存储的值是 2,同时有外部请求 A、B 分别对系统发送操作指令,把 x 的值加 1 和 把 x 值乘 3,如果不加任何控制,将可能得到 `(2+1)*3=9` 或者`(2*3+1)=7`。 +分布式环境下可能随时会出现通信故障,如果一个节点获得锁之后,释放锁之前故障了,那么整个系统都会被无限期等待阻塞。因此,我们不能简单“套用”进程加锁的方式,必须提供一种可供其他节点抢占锁的机制,避免因通信故障出现的死锁问题。分布式抢占锁的机制和 5.4.2 节提到的“乐观锁”有“异曲同工之妙”。回顾乐观锁的 SQL 示例,WHERE 条件的作用是判断在它操作之前,数据是否被修改。如果修改过,则请求最新的数据,更新版本号,然后用重试的方式再次修改。 -到了分布式环境下,由于要考虑到分布式系统内可能任何时刻出现的通信故障,如果一个节点取得锁之后、释放锁之前发生故障,这将导致整个系统被无限期的等待所阻塞,因此分布式环境中的加锁就不能完全等同于单机系统并发控制中以互斥量实现的加锁,还要提供一个其他节点能抢占锁的机制,以避免因通信问题出现死锁。 +```SQL +UPDATE accounts +SET balance = balance + ?, + version = version + 1 +WHERE id = ? AND version = ?; +``` -::: - -仅单纯使用二阶段协议仍然无法解决这个问题,分布式系统中的网络延迟无法忽视。如图 6-7 所示,S~1~ 和 S~5~ 在第一个阶段都发现没有其他的值被选中,因此提出自己的提案,但在这个时序下会有两个不同的值被选中。 - -:::center - ![](../assets/paxos_2pc_choice.png)
    - 图 6-7 网络延迟导致冲突 -::: - -所以你会发现,矛盾的点其实就是这个 S~3~,也就是少数服从多数原则,能保证任意的大多数都是有交集的。交集中的点会发现矛盾(和之前接受的值有矛盾理应选择拒绝)。 +对于 图 6-7 所示的因延迟导致的冲突,根本的解决方式是“抢占”。S~1~ 发起提案时,S~3~ 收到 S~1~ 提案时,应该意识到 blue 的 Quorum 达成,S~1~ 的提案 red 太老。为了就提案达成一致,S~1~ 应该更新提案,把 red 修改为 blue(相当于乐观锁中的操作失败,请求最新数据),然后再次重试。 :::tip 思考:3 个节点的容忍度是 1,那么 4 节点的容忍度是多少? 答案也是 1,因为要形成发现矛盾的交集对于 4 来说,要达到 3/4,才能构成大多数,这就是为什么集群选单数的原因,因为双数从算法的角度来说没什么帮助。 ::: -如图 S~3~ 应该拒绝 S~1~ 的提案,这样就可以保证 S~5~ 的提案成功,S~1~的提案因为冲突而失败。这种方式我们需要对提案进行排序,有了排序,决策节点就可以拒绝老的提议。如果你熟悉分布式系统,应该能想到《Time, Clocks and the Ordering of Events in a Distributed System》[^2] 这篇论文,我们不能简单用时间来判断提案的先后顺序。 - - -## 1. Basic Paxos 算法描述 +## 1. Paxos 算法描述 -“准备阶段”(Prepare)相当于抢占锁的过程。某个提案节点准备发起提案,必须先向所有的决策节点广播一个许可申请(称为 Prepare 请求)。Prepare 请求附带一个全局唯一且单调递增的数字 n 作为提案 ID。决策节点收到请求后,将给予提案节点两个承诺与一个应答: -- [承诺1]:不再接受提案 ID <= n 的 Prepare 请求; -- [承诺2]:不再接受提案 ID < n 的 Accept 请求; -- [应答]:不违背历史承诺的前提下,回复形成决议最大的那个提案所设定的值和提案 ID: - - 如果这个值没有被任何提案设定过,则返回空值 - - 如果违反历史承诺,即收到的提案 ID 并不是决策节点收到的最大的 ID,对 Prepare 请求不予理会。 +相信通过以上铺垫,你已经具备了足够的前置知识来理解 Paxos 算法。简而言之,Paxos 算法本质上是一个支持多次提案的二阶段提交协议。 -提案节点收到多数决策节点的应答(称为 Promise 应答)后,就可以开始“批准”(Accept)阶段了。 +Paxos 算法的第一个阶段称“准备阶段”(Prepare)。该阶段相当于提案的抢占,Proposer 选择一个提案编号 $\mathit{n}$(通常是单调递增的数字,相当于乐观锁中的 version,更高的编号意味着更高的优先级),向所有的 Acceptor 广播一个许可申请(称为 $\mathit{Prepare(n)}$ 请求),如果 Acceptor : +- 尚未承诺 $\mathit{≥n}$ 编号的提案:则“承诺”(promise)不再接受任何编号小于 𝑛 的提案,返回一个响应,其中包含承诺的提案编号以及对应的提案值(如果有); +- 已承诺 $\mathit{≥n}$ 编号的提案:拒绝当前的 Prepare 请求,不返回任何响应。 -提议者向所有接受者广播 accept(n, v) 请求,请求接受提案编号为 n 和提案值为 v。 -- 如果提案编号 n >= 该接受者之前所承诺的编号,它将接受该提案,并返回 accepted(n, v) 消息。 -- 如果提案编号 n < 该接受者所承诺的编号,它将拒绝该提案。 +Proposer 从多数(Quorum)Acceptor 获得了“承诺”(Promise),则“准备阶段”达成。接着,Proposer 选择提案值:如果 Acceptor 的响应中返回了提案值,从中选择编号最高的提案值;如果没有返回的提案值,则使用 Proposer 自己设定的初始提案值。 -如果提案节点收到了大多数决策节点的应答(accepted),协商结束,共识协议形成,然后将形成的决议发送给所有记录节点进行学习。 +完成以上操作后,Proposer 进入下一个阶段。 +Paxos 算法的第二个阶段称“批准阶段”(Accept)。在该阶段,Proposer 向所有 Acceptor 广播一个批准申请(称为 $\mathit{accept(n, v)}$ 请求),请求批准:“提案编号 $\mathit{n}$ 提案值 $\mathit{v}$”。如果 Acceptor 发现提案编号 不小于它已承诺的最大编号,则“批准”(accepted)该提案;否则,Acceptor 拒绝该提案。当多数(Quorum)的 Acceptor 批准提案时,Proposer 认为本轮提案成功。一旦提案成功,Proposer 会将最终的决议广播给所有 Learner 节点,供它们学习并记录最终结果。 :::center ![](../assets/paxos.svg)
    图 6-8 Basic Paxos 流程 ::: -## 2. Basic Paxos 验证 +## 2. Paxos 算法验证 -那这样的二阶段提交算法是否可以解决前面提到的问题?我们通过具体的例子来分析。 +Paxos 难以理解是指其推导过程复杂,证明 Paxos 的正确性比重新实现 Paxos 算法还难。我们没必须推导 Paxos 的正确性,通过以下几个例子来验证 Paxos 算法的运行机制。 -假设共有五个节点 S~1~、S~2~、S~3~、S~4~、S~5~,这 5 个节点同时扮演着提案节点和决策节点的角色。此时,有两个并发请求希望将同一个值分别设定为 X(由 S~3~ 作为提案节点提出)和 Y(由 S~5~ 作为提案节点提出),图中的 P 代表准备阶段,A 代表批准阶段,这时会发生以下几种情况。 +假设有五个节点 S~1~、S~2~、S~3~、S~4~、S~5~,其中 S~1~、S~5~ 扮演 Proposer,它们同是又都是 Acceptor 角色。我们根据上述 Paxos 的二阶段算法,分析它们就一个“提案选取值 X 还是 Y ”进行决议,会出现什么情况。 -**情况一:提案已 Chosen** 譬如,S~1~ 选定的提案 ID 是 3.1(全局唯一 ID 加上节点编号),先取得了多数派决策节点的 Promise 和 Accepted 应答,此时 S~5~ 选定提案 ID 是 4.5,发起 Prepare 请求,收到的多数派应答中至少会包含 1 个此前应答过 S~1~ 的决策节点,假设是 S~3~,那么 S~3~ 提供的 Promise 中必将包含 S~1~ 已设定好的值 X,S~5~ 就必须无条件地用 X 代替 Y 作为自己提案的值,由此整个系统对“取值为 X”这个事实达成一致。整个流程如下图所示。 +**情况一:提案已达成**,假设 S~1~ 选定提案编号 3.1(节点编号加上自增 ID),优先取得了多数 Acceptor 的 Promise 和 Accepted 应答。另外,S~5~ 选定提案编号 4.5,广播 Prepare 消息,收到的应答中至少会包含 1 个此前应答过 S~1~ 的 Acceptor,假设是 S~3~,那么 S~3~ 的响应 Promise 中必将包含 S~1~ 已设定好的值 X。那么,S~5~ 就必须无条件地用 X 代替 Y 作为自己提案的值,由此整个系统对“取值为 X”这个事实达成一致。整个流程如下图所示。 :::center ![](../assets/paxos-p1.png)
    - 图 6-9 提案已 Chosen + 图 6-9 提案已 Chosen,P 代表 “Prepare 阶段”,A 代表“Accept 阶段” ::: -**情况二:提案未 Chosen,Proposer 可见** 事实上,对于情况一,X 被选定为最终值是必然结果,但从上图中可以看出,X 被选定为最终值并不是必定需要多数派的共同批准,只取决于 S~5~ 提案时 Promise 应答中是否已包含了批准过 X 的决策节点,譬如图 6-3 所示,S~5~ 发起提案的 Prepare 请求时,X 并未获得多数派批准,但由于 S~3~ 已经批准的关系,最终共识的结果仍然是 X。 +**情况二:事实上,对于情况一,也就是“取值为 X”并不是一定需要多数派批准,S~5~ 提案时 Promise 应答中是否包含了批准过 X 的 Acceptor 也影响决策**。例如。图 6-3 所示,S~5~ 广播 Prepare 请求,X 并未获得多数派批准,但由于 S~3~ 已经批准的关系,最终共识的结果仍然是 X。 :::center - ![](../assets/paxos-conflict-2.png)
    + ![](../assets/paxos-p2.png)
    图 6-10 提案未 Chosen,Proposer 可见 ::: -**情况三:提案未提交,Proposer 不可见** 当然,另外一种可能的结果是 S~5~ 提案时 Promise 应答中并未包含批准过 X 的决策节点,譬如应答 S~5~ 提案时,节点 S~1~ 已经批准了 X,节点 S~2~ 、S~3~ 未批准但返回了 Promise 应答,此时 S~5~ 以更大的提案 ID 获得了 S~3~、S~4~、S~5~ 的 Promise,这 3 个节点均未批准过任何值,那么 S~3~ 将不会再接收来自 S~1~ 的 Accept 请求,因为它的提案 ID 已经不是最大的了,这 3 个节点将批准 Y 的取值,整个系统最终会对“取值为 Y”达成一致,如图下图所示。 +**情况三:另外一种可能得情况是 S~5~ 提案时 Promise 应答中并未包含批准过 X 的决策节点**。例如 S~5~ 提案期间,节点 S~2~ 、S~3~ 返回 Promise 应答,节点 S~1~ 批准了 X。此时,S~5~ 以更大的提案 ID 获得了 S~3~、S~4~、S~5~ 的 Promise,这 3 个节点均未批准过任何值。那么,因为 S~1~ 提案编号已经不是最大的了,S~3~ 将拒绝 S~1~ 的 Accept 请求,这 3 个节点将批准 Y 的取值,整个系统最终对“取值为 Y”达成一致,如下图所示。 :::center - ![](../assets/paxos-conflict3.png)
    + ![](../assets/paxos-p3.png)
    图 6-11 提案未提交,Proposer 不可见 ::: -**情况四:出现活锁** - -从情况三可以推导出另一种极端的情况,如果 2 个提案节点交替使用更大的提案 ID 使得准备阶段成功,但是批准阶段失败的话,这个过程理论上可以无限持续下去,形成活锁(Live Lock)。例如 S~3~、S~4~、S~5~ 拿着更高的提交号导致 S~1~、S~2~、S~3~ 的 accept 被拒绝重新进行提交,又把 S~3~、S~4~、S~5~ 给拒绝了,提议者没有看到先前提议的情况下,当 S~1~ 发现自己的提议没有通过,就会发起新一轮 Prepare RPC,然后就有可能又封锁了 S~5~ 的提议,S~5~ 又会回到 Prepare 阶段,有概率双方都轮流封锁对方的协议,导致无法达成共识。 +**情况四:从情况三可以推导出另一种极端的情况,如果 2 个提案节点交替使用更大的提案编号使得“准备阶段成功,但是批准阶段失败”的话,这个过程理论上可以无限持续下去,形成活锁**。例如,S~3~、S~4~、S~5~ 拿着更高的提案编号导致 S~1~、S~2~、S~3~ 的 accept 阶段失败重新发起的 Prepare 阶段,又把 S~3~、S~4~、S~5~ 给拒绝了,Proposer 没有看到先前提议的情况下,当 S~1~ 发现自己的提议没有通过,就会发起新一轮 Prepare RPC,然后就有可能又封锁了 S~5~ 的提议,S~5~ 又会回到 Prepare 阶段,有概率双方都轮流封锁对方的协议,导致无法达成共识。 :::center - ![](../assets/paxos-liveness.png)
    + ![](../assets/paxos-p4.png)
    图 6-12 出现活锁问题 ::: -解决这个问题的办法就是把重试时间进行一些随机化,减少这种巧合发生,或者把重试的时间指数增长等等。 +在计算机工程领域解决这个问题并复杂,随机化重试时间减少这种巧合发生,或者把重试的时间指数增长等等。 总结 Paxos 中保证一致性的最核心的两个原则其实就是**少数服从多数**和**后者认同前者**。Paxos Basic 只能对一个值形成决议,而且决议形成至少需要两次网络来回,高并发情况还有可能形成活锁,因此 Basic Paxos 几乎只是用来做理论研究,并不直接应用在实际工程中。 +2014 年,斯坦福的学者 Diego Ongaro 和 John Ousterhout 发表了论文《In Search of an Understandable Consensus Algorithm》,提出了 Raft 算法。Raft 算法明确了节点的角色,以日志复制为模型在工程领域解决了共识问题。该论文斩获 USENIX ATC 2014 大会 Best Paper 荣誉,Raft 算法更是成为 etcd、Consul 等分布式系统的实现基础。 + + [^1]: 讲解作者是斯坦福教授 John Ousterhunt,他还指导了 Diego Ongaro 写出了 Raft 的论文。本章配图也多来源于 John Ousterhunt 所发表的内容。 [^2]: 参见 https://lamport.azurewebsites.net/pubs/time-clocks.pdf diff --git a/consensus/Paxos-history.md b/consensus/Paxos-history.md index b8efd605..3a2a727d 100644 --- a/consensus/Paxos-history.md +++ b/consensus/Paxos-history.md @@ -32,7 +32,7 @@ Paxos 最初的论文名称为《The Part-Time Parliament》,翻译成中文 :::center ![](../assets/paxos.png)
    - 图 5-4 Paxos 论文摘要 + 图 6-4 Paxos 论文摘要 ::: 然而,这篇论文还是非常难以理解,引用斯坦福学者 Diego Ongaro 和 John Ousterhout 设计 Raft 时发表的论文[^4]中对 Paxos 的描述: diff --git a/consensus/Replicated-State-Machine.md b/consensus/Replicated-State-Machine.md index 827ff2ed..92e614f1 100644 --- a/consensus/Replicated-State-Machine.md +++ b/consensus/Replicated-State-Machine.md @@ -4,25 +4,25 @@ 这里的“日志“,并不是工程师熟悉的,通过 log4j 或者 syslog 输出的描述发生事情的文本。而是 MySQL 中的 binlog(Binary Log)、MongoDB 中的 Oplog(Operations Log)、Redis 中的 AOF(Append Only File)、PostgreSQL 中的 WAL(Write-Ahead Log)...。这些日志名字不同,但它们的全都是**只能追加、完全有序的记录序列**。 -图 5-1 展示了日志结构,是有序的、持久化的记录序列,以追加的方式在末尾添加记录,读取时则从左到右顺序扫描。 +图 6-1 展示了日志结构,日志是有序的、持久化的记录序列,日志以追加的方式在末尾添加记录,读取时则从左到右顺序扫描。 :::center ![](../assets/log.png)
    - 图 5-1 日志是有序的、持久化的记录序列 [图片来源](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying) + 图 6-1 日志是有序的、持久化的记录序列 [图片来源](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying) ::: -有序的日志记录了什么时候发生了什么,这句话的含义通过两种分布式系统数据复制模型来理解: +有序的日志记录了什么时候发生了什么,这句话的含义通过两种数据复制模型来理解: -- **主备模型(Primary-backup)**:主节点(Master)负责执行如“+1”、“-2”的操作,然后将这些操作的结果(如“1”、“3”、“6”)记录到日志中。备节点(Slave)根据日志直接同步结果; -- **复制状态机模型(State-Machine Replication)**:日志记录的不是最终结果,而是具体的操作指令,如“+1”、“-2”。这些指令按照日志中的顺序被依次复制到各个节点(Peer)。如果每个节点按顺序执行这些操作,将最终达到一致的状态。 +- **主备模型(Primary-backup)**:该模型也称“状态转移”模型,主节点(Master)负责执行如“+1”、“-2”的操作,然后将这些操作的结果(如“1”、“3”、“6”)记录到日志中。备节点(Slave)根据日志直接同步结果; +- **复制状态机模型(State-Machine Replication)**:该模型也称“操作转移”模型,日志记录的不是最终结果,而是具体的操作指令,如“+1”、“-2”。这些指令按照日志中的顺序被依次复制到各个节点(Peer)。如果每个节点按顺序执行这些操作,将最终达到一致的状态。 :::center ![](../assets/active_and_passive_arch.png)
    - 图 5-2 分布式系统数据复制模型 + 图 6-2 分布式系统数据复制模型 ::: 无论哪一种模型,它们都揭示了:“**顺序是节点之间保持一致性的关键因素**”。如果打乱了这些操作的顺序,就会得到不同的运算结果。 -再进一步解释以“复制状态机”(State Machine Replication)构建的分布式系统,其基本原理如图 5-3 所示。 +再进一步解释以“复制状态机”(State Machine Replication)构建的分布式系统,其基本原理如图 6-3 所示。 :::tip 复制状态机的基本原理 两个“相同的” (identical)、“确定的” (deterministic) 进程: @@ -44,7 +44,7 @@ :::center ![](../assets/Replicated-state-machine.webp)
    - 图 5-3 复制状态机(State Machine Replication)工作模型 [图片来源](https://raft.github.io/raft.pdf) + 图 6-3 复制状态机(State Machine Replication)工作模型 [图片来源](https://raft.github.io/raft.pdf) ::: diff --git a/consensus/consensus.md b/consensus/consensus.md index fd3c0dd4..be4687fe 100644 --- a/consensus/consensus.md +++ b/consensus/consensus.md @@ -1,19 +1,35 @@ # 6.1 什么是共识 -受翻译影响,网上讨论 Paxos 或 Raft 的内容多使用“分布式一致性协议”或者“分布式一致性算法”这样的描述。如 Google Chubby 系统的作者 Mike Burrows,他对 Paxos 的评价原话是:“There is only one consensus protocol...”,很多文章翻译成“世界上只有一种一致性算法...”。 +在讨论 Paxos 或 Raft 时,很多文献使用“分布式一致性协议”或“分布式一致性算法”来描述这些技术。例如,Google Chubby 系统的作者 Mike Burrows 曾评价 Paxos:“There is only one consensus protocol...”,这一句话常被翻译为“世界上只有一种一致性算法”。 -虽然“共识”和“一致”在汉语中含义相近,但在计算机领域它们有明显的差异: +尽管“共识”和“一致”在汉语中含义相近,但在计算机领域,这两个术语具有截然不同的含义: -- 共识(consensus):所有的节点就某一项提议(如选举、原子事务提交、日志复制、分布式锁等)达成一致的**过程及其算法**; -- 一致(consistency):描述多个节点中存储的数据之间不自相矛盾,侧重于数据最终达成稳定状态的**结果**。 +- 共识(Consensus):指所有节点就某项操作(如选主、原子事务提交、日志复制、分布式锁管理等)达成一致的过程及其算法。 +- 一致性(Consistency):描述多个节点的数据是否保持一致,关注数据最终达到稳定状态的结果。 Paxos、Raft、ZAB 等等属于 consensus 算法,明显使用“共识”描述更准确,而 CAP 定理中的 C 和数据库 ACID 的 C 才是真正的“一致性” —— consistency 问题。 -在充满不确定性的环境中(如节点故障、网络不可靠、消息延迟或丢失、顺序错乱...)可靠地达成共识是一件非常了不起的事情。一旦解决了共识问题,就能为分布式应用提供一套屏蔽内部复杂问题的抽象机制,满足应用层的很多目标需求。例如: +因此,Paxos、Raft 和 ZAB 等算法应归类为“共识”算法,而 CAP 定理中的 C 和数据库 ACID 模型中的 C 则描述的是“一致性”问题。将 Paxos 等算法称为“共识算法”更为准确。 + +在分布式系统中,节点故障是不可避免的。为提高系统可靠性,可以通过增加节点数量,依据“少数服从多数”的原则确保系统在多数节点(n/2+1)正常工作的情况下仍能达成决策。这种通过多数节点(Quorum)保证系统容错性的机制被称为 Quorum 机制。 + +:::tip Quorum 机制 + +Quorum 机制下,一个 N/2+1 节点个数的集群最多容忍 ⌊N/2⌋ 个节点失效。例如: +- 3 节点集群:Quorum 为 2,允许 1 个节点失效; +- 5 节点集群:Quorum 为 3,允许 2 个节点失效。 + +相应的,5 个节点的集群,即至少 3 个节点参与决策,才能达成 Quorum。 + +::: + +根据上述的讨论,以 Quorum 为基础,在充满不确定的环境下(如节点故障、网络不可靠、消息延迟或丢失),仍然能够协商出一致的决策,从而对外呈现出一致的运行结果,这个过程称为节点间”协商共识“。 + +一旦解决了共识问题,就能为分布式应用提供一套屏蔽内部复杂问题的抽象机制,为应用层提供一致性保证,满足多种目标需求,例如: - **主节点选举**:对于一个主从复制的数据库,所有的节点需要就“谁来当主节点”达成一致。如果由于网络问题出现节点间无法通信,很容易出现争议(也就是没有形成共识)。不解决争议问题,就会出现多个节点同时认为自己是主节点,也就是出现分布式系统中最头疼的情况 —— “脑裂”。 - **原子事务提交**:对于支持跨节点或者跨分区事务的数据库,可能会出现这样的情况:某些节点事务执行成功,某些节点事务执行失败。为了维护事务的原子性(即 ACID 特性),所有节点必须就事务的结果达成一致。 -- **分布式锁管理**:当多个请求同时尝试访问共享资源时,分布式锁需要就“谁成功加锁”达成一致。跟“主节点选举”的情况一样,即使网络问题或者节点故障,分布式锁也能正常工作,不然就会出现加锁争议,引发并发冲突、死锁或数据不一致等问题。 -- **日志复制**:日志复制是指将主节点的“操作日志条目”复制到从节点。日志条目的顺序必须在所有节点上形成一致,即日志条目需要按相同的顺序被写入(“顺序”相当重要,笔者将在下一节介绍)。 +- **分布式锁管理**:当多个请求尝试访问共享资源时,共识机制确保所有节点一致认定“谁成功获取锁”。即使出现网络故障或节点异常,也能避免加锁争议,防止并发冲突或数据不一致。 +- **日志复制**:日志复制是指将主节点的“操作日志”复制到从节点。日志条目的顺序必须在所有节点上形成一致,即日志条目需要按相同的顺序被写入(“顺序”相当重要,笔者将在下一节介绍)。 [^2]: Lamport 在分布式系统理论方面有非常多的成就,比如 Lamport 时钟、拜占庭将军问题、Paxos 算法等等。除了计算机领域之外,其他领域的无数科研工作者也要成天和 Lamport 开发的一套软件打交道,目前科研行业应用最广泛的论文排版系统 —— LaTeX (名字中的 La 就是指 Lamport) \ No newline at end of file diff --git a/consensus/raft-leader-election.md b/consensus/raft-leader-election.md index 84918e81..8fa03212 100644 --- a/consensus/raft-leader-election.md +++ b/consensus/raft-leader-election.md @@ -1,8 +1,13 @@ # 6.4.1 领导者选举 -Raft 是一个强领导者算法。强领导意思是:接收客户端的所有请求,将客户端的请求操作复制给其他节点(Follower,跟随者)、不断发送心跳通知其他节点”我是 Leader,我还活者,你们现在不要发起新的选举“。 +在 Raft 算法中,节点间并不平等。不同角色的节点承担不用的职责。 + +- **Leader(领导者)**: +- **Follower(跟随者)**: +- **Candidate()**:过渡角色 Candidate,Leader 丧失时,Follower 会转为 Candidate 参与选举; + +Raft 是一个强领导者算法。强领导意思是:整个集群,只有一个节点接收客户端的所有请求,该节点将客户端的请求操作复制给其他节点(Follower,跟随者),其他节点无条件服从 Leader 的操作。 -Follower 的职责相对较为简单:即无条件从 Leader 同步日志。此外,还有一个过渡角色 Candidate,Leader 丧失时,Follower 会转为 Candidate 参与选举; 联想到现实世界中的领导人都有一段不等的任期。自然,Raft 中的 Leader 也对应的概念 —— term。 diff --git a/http/Edge-Acceleration.md b/http/Edge-Acceleration.md index db093d07..748893be 100644 --- a/http/Edge-Acceleration.md +++ b/http/Edge-Acceleration.md @@ -1,10 +1,8 @@ # 2.7 对请求进行“动态加速” -区别于静态文件缓存技术 CDN,“动态加速”并非依赖缓存数据,而是利用“网络边缘服务器”优化 IP 路由和传输层实现网络加速。 +区别于静态文件缓存技术 CDN,“动态加速”并非依赖“边缘节点”中的缓存数据,而是利用“边缘节点”优化 IP 路由和传输层实现网络加速。 -目前,主流的技术服务商,如 Akamai、Fastly、Amazon CloudFront 和 Microsoft Azure 等在全球多个地区部署了数量庞大的边缘服务器,构建了一个庞大的全球性加速网络。 - -使用上述服务商提供的“动态加速”操作简单,一般将域名的解析记录 CNAME 到服务商提供的域名后,整个加速过程就能自动实现。操作流程大致如下: +目前,主流的技术服务商,如 Akamai、Fastly、Amazon CloudFront 和 Microsoft Azure 等在全球多个地区部署了数量庞大的边缘服务器,构建了一个庞大的全球性加速网络。使用上述服务商提供的“动态加速”操作简单,一般将域名的解析记录 CNAME 到服务商提供的域名后,整个加速过程就能自动实现。操作流程大致如下: 1. 源站(Origin)将域名 CNAME 到 CDN 服务商提供的域名,例如将 www.thebyte.com.cn CNAME 到 thebyte.akamai.com。 2. 源站提供一个 20KB 左右的用于探测网络质量的文件资源。 diff --git a/http/quic.md b/http/quic.md index 868d72f3..1c73a076 100644 --- a/http/quic.md +++ b/http/quic.md @@ -96,7 +96,7 @@ QUIC 内部集成了 TLS 安全协议,无需像 TCP 先经过三次握手, 图 2-31 不同网络质量下,各协议失败率表现 ::: -综上所述,无论是在服务端还是客户端,集成 QUIC 协议并非一件易事: +综上所述,无论是服务端还是客户端,集成 QUIC 协议并非一件易事: - 服务端层面:**不仅需要适配 QUIC 协议,还要确保与 TCP 协议兼容**。此外,TCP 经过多年的深度优化,引发了一个问题:“QUIC 在实际应用中的效能表现是否能够与 TCP 相媲美?”。 -- 客户端层面:面临适配与收益之间的成本权衡。采用 QUIC 协议的客户端需要**具备降级容错能力**,并做好准备在**较长时间内同时维护新旧两种网络库**。 +- 客户端层面:面临适配与收益之间的成本权衡。采用 QUIC 协议的客户端需要**具备降级容错能力**,并做好**长时间同时维护新旧两种网络库的准备**。 diff --git a/network/conntrack.md b/network/conntrack.md index b212c9da..11196036 100644 --- a/network/conntrack.md +++ b/network/conntrack.md @@ -22,12 +22,10 @@ conntrack 连接记录是 iptables 连接状态匹配的基础,也是实现 SN 举一个具体的例子,客户端向 my-service(IP 10.0.0.10 )发送 HTTP 请求,端口 80。 -- 节点中的 kube-proxy 收到请求后,执行 DNAT 操作,将目标地址 10.0.0.10:80 转换为某个 Pod 的 IP 和端口,例如 192.168.1.2:8080。 -- Pod 处理请求,并返回响应,kube-proxy 执行 SNAT 操作,将响应的源地址 192.168.1.2:8080 转换为 Service IP 10.0.0.10:80。 +- 节点中的 kube-proxy 收到请求后,执行 DNAT 操作,将请求包的目标地址 10.0.0.10:80 转换为某个 Pod 的 IP 和端口 192.168.1.2:8080。 +- Pod 处理请求,并返回响应,kube-proxy 执行 SNAT 操作,将响应包的源地址 192.168.1.2:8080 转换为 Service IP 10.0.0.10:80。 -conntrack 模块维护的连接记录包含了从客户端到 Pod 的 DNAT 映射(10.0.0.10:80 到 192.168.1.2:8080)以及从 Pod 到客户端的 SNAT 映射(192.168.1.2:8080 到 10.0.0.10:80)。这样有来有回,是一条完整的 NAT 映射关系。 - -但如果发起请求的客户端和处理请求的 Pod 在同一个主机内(图 3-5),问题来了: +conntrack 模块维护的连接记录包含了从客户端到 Pod 的 DNAT 映射(10.0.0.10:80 到 192.168.1.2:8080)以及从 Pod 到客户端的 SNAT 映射(192.168.1.2:8080 到 10.0.0.10:80)。这样有来有回,是一条完整的 NAT 映射关系。但如果发起请求的客户端和处理请求的 Pod 在同一个主机内(图 3-5),问题来了: - 发起请求时,数据包经过网络层时,内核中的 conntrack 模块根据 iptables 规则,判断是否需要进行 DNAT; - 返回响应时,如果 Linux 网桥检测到目的 IP 位于同一网桥上,则直接通过链路层转发,并没有触发网络层的 conntrack 模块,也就是不进行 SNAT。
  2. 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