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<a href="/2024/02/java-synchronization-implementation/" class="post-title-link" itemprop="url">Java 线程同步实现原理深度解析</a>
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<p>本文内容主要是，以官方文档资料和 Java 8 的 JDK 和 HotSpot 源码为依据，对 Java 的基于 <code>synchronized</code> 和 <code>ReentrantLock</code> 线程同步的底层实现原理进行总结梳理和深度解析。同时，也在广度上对线程同步相关的基础概念做总结概括。</p>
<h1 id="基础概念">基础概念</h1>
<p>在并发编程模型中，需要处理的两个最关键的问题就是<strong>通信</strong>（communication）和<strong>同步</strong>（synchronization）<sup class="footnote-ref"><a href="#fn1" id="fnref1">[1]</a></sup>。通信指线程可用于获得其他线程产生的信息的各种机制。通信机制通常都基于<strong>共享内存</strong>（<a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Shared_memory">shared memory</a>）或<strong>消息传递</strong>（<a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Message_passing">message passing</a>）。在共享内存的编程模型中，某些或全部程序变量可以由多个线程访问。如果一对线程之间需要通信，只要一个线程将值写入某个变量，另一线程来读它即可。在消息传递编程模型中，不同线程没有公共的状态。当一对线程之间需要通信时，其中的一个必须执行一次明确的 send 操作，将数据传送给另一个线程。</p>
<p><strong>同步</strong><sup class="footnote-ref"><a href="#fn1" id="fnref1:1">[1:1]</a></sup>是控制不同线程之间操作发生的相对顺序的各种机制，用以排除导致不正确结果的交错。消息传递模型中的同步通常是隐式的，消息的发送必须在接收之前。如果某个线程企图接收一个尚未发送的消息，那么它就必须等到发送方赶上来。在共享内存编程模型中，同步通常不是隐式的，除非我们做了某些特殊的事情，否则“接收方”就可能在某个变量被“发送方”修改之前读到其中的“老”值。</p>
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<p>为了提升系统的<strong>可扩展性</strong>（scalability），分布式数据库或分布式存储系统通常支持数据<strong>分区</strong>（partitioning）或<strong>分片</strong>（sharding），即将完整的数据拆分存放在多个服务器节点上，拆分后的部分数据称为“partition”或“<a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Shard_%28database_architecture%29">shard</a>”。数据被拆分后多个服务器节点能分摊负载压力，从而提升系统性能。“分区”和分片”，这两个术语，在很多情况下不区分，可以混用。如果严格区分的话，<strong>分片</strong>拆分的数据分布在多个服务器节点上，而<strong>分区</strong>拆分的数据在单个服务器节点。另外，<strong>复制</strong>（<a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Replication_%28computing%29">replication</a>）也典型的分布式技术，多个数据副本能实现读请求的负载均衡，提升系统性能。同时复制也提供了冗余容错的能力，提升系统的<strong>可用性</strong>（availability）。本文关注消息中间件的消息存储系统，解析并对比 RocketMQ 和 Kafka 的消息数据的分片和复制的具体实现策略。</p>
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<p>为了应对负载的增长，提升系统性能，目前大型网站普遍都是分布式架构，采用微服务架构风格。分布式系统的最重要的架构特性是<strong>伸缩性</strong>（scalability），伸缩性的系统具备应对增长的工作负载的能力。关于性能和伸缩性，可以参阅笔者的文章《<a href="https://nullwy.me/2023/12/website-performance-scalability/">大型网站的性能和可伸缩性</a>》。相对于采用单体架构的系统，分布式系统中有大量的服务器及设备，各服务之间存在错综复杂的依赖关系，存在更多的不确定性。整个系统的故障率会随服务节点的增加而呈指数级增加，单一节点问题可能会被无限放大，日常运行过程中一定会伴随故障发生。所以构建分布式系统需要关注的另外一个重要架构特性是<strong>稳定性</strong>（stability）。有关减少系统故障以及快速从故障中恢复的工程实践，国内通常称为“稳定性建设”，而国外类似的工程实践更多称为“站点可靠性工程”（SRE, <a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Site_reliability_engineering">Site reliability engineering</a>）。稳定性（stability）、可靠性（reliability）、韧性（resilience）、可用性（availability）等架构特性，相似并且相关，虽然严格区分的话，含义并不相同，但是很多时候在探讨这些架构特性时往往涵盖的是类似的内容。本文的内容主要是总结稳定性、可靠性、韧性这些架构特性的内涵，以及如何建设分布式系统的这些特性。</p>
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<p>互联网网站在初期用户量和访问量一般都很小，往往只需要采用最简单的技术架构就能对外提供稳定服务。最简单的架构通常采用的是单机应用服务器、单机数据库服务器这样的单体架构。成功的互联网网站，比如电商平台，流量、用户量、交易量等核心指标是呈指数增长的，所以就需要提升网站系统的性能，来应对更大的负载。通过向系统中增加资源来提升系统性能的能力，被称为可伸缩性（<a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Scalability">scalability</a>）。为了提升系统的可伸缩性，典型的大型网站，比如 eBay、Amazon 和淘宝等，几乎都经历过从单体架构向分布式架构演进的过程。本文主要关注大型网站或 Web 服务这类系统，解释系统的性能和可伸缩性相关的核心概念，并介绍系统的性能指标、系统的扩展策略和分布式架构风格，同时也总结分析典型大型网站的可扩展性架构演进案例，案例包括 eBay、Amazon、淘宝等。</p>
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<p>本文整理总结主要的流行互联网网站技术栈，以及这些网站的技术栈和架构的历史演进过程。涉及的网站大部分都是当前或曾经访问量或月活用户量 Top 的网站（参见 Similarweb 网站的统计<sup class="footnote-ref"><a href="#fn1" id="fnref1">[1]</a></sup>，或访问量 Top 10 网站的历史演变<sup class="footnote-ref"><a href="#fn2" id="fnref2">[2]</a></sup>，或 wiki 整理的至少 1 亿月活用户量的社交平台<sup class="footnote-ref"><a href="#fn3" id="fnref3">[3]</a></sup>）。国内网站或 APP 涵盖了主流<sup class="footnote-ref"><a href="#fn4" id="fnref4">[4]</a></sup>的阿里、腾讯、百度、美团、字节、京东等大厂的互联网产品。整理的技术栈主要是流行网站的服务端业务系统的技术栈，包括编程语言、数据库、RPC 框架等，同时也简单整理了大数据技术栈，前端和客户端技术栈等不涉及。除了对大部分流行网站的技术栈做系统性梳理外，本文还挑选部分有代表性的网站，对这些网站的技术栈和架构的历史演进做详细解析。</p>
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<h1 id="SOA-与微服务">SOA 与微服务</h1>
<p>Amazon，1994 年创立，早期网站是单服务、单数据库的单体架构的系统<sup class="footnote-ref"><a href="#fn1" id="fnref1">[1]</a></sup><sup class="footnote-ref"><a href="#fn2" id="fnref2">[2]</a></sup><sup class="footnote-ref"><a href="#fn3" id="fnref3">[3]</a></sup>，全部代码由 C++ 编写，编译成单个二进制文件，整个代码仓库被命名为 <a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Obidos_(software)">Obidos</a>。Obidos 是底层是一个 Web 页面渲染引擎，是一个框架，业务逻辑基于这个框架开发，Obidos 渲染引擎和业务逻辑共同组成整个代码仓库。随着时间的推移，Obidos 变得越来越复杂，编译 Obidos 整个代码库耗时 12 小时，开发调试效率低下<sup class="footnote-ref"><a href="#fn2" id="fnref2:1">[2:1]</a></sup>。另外，全部业务逻辑在单个二进制文件中，导致紧耦合，新功能特性无法快速发布上线。1995 年，Amazon 网站的技术架构，如下图所示<sup class="footnote-ref"><a href="#fn3" id="fnref3:1">[3:1]</a></sup>：</p>
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<p>为了应对负载的增长，目前大型网站普遍都采用分布式架构。相对于采用单体架构的系统，分布式系统中有大量的服务器及设备，各模块之间存在错综复杂的依赖关系，存在更多的不确定性。整个系统的故障率会随设备的增加而呈指数级增加，单一节点问题可能会被无限放大，日常运行过程中一定会伴随故障发生。所以，可靠性开始成为大型网站关注的最重要的质量属性之一，并因此发展出了站点可靠性工程（<a target="_blank" rel="noopener" href="https://en.wikipedia.org/wiki/Site_reliability_engineering">Site reliability engineering</a>，SRE）。站点可靠性工程，是从可靠性工程发展而来的，从可靠性工程中借鉴了概念和成果。本文溯本求源，内容主要是总结概括，可靠性工程的历史演进和核心概念，软件可靠性工程的核心概念，以及可靠性设计的方法。</p>
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<a href="/2023/07/io-multiplexing-network-server/" class="post-title-link" itemprop="url">I/O 多路复用与网络服务器并发策略</a>
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<p>目前主流的网络服务器，网络 I/O 相关的底层最核心的技术都是 I/O 多路复用（I/O Multiplexing），比如 Apache HTTP Server、Nginx、Redis 等。本文尝试解释各种 I/O 模型，包括解释什么是 I/O 多路复用，同时也总结 I/O 多路复用底层的系统调用 select、poll、kqueue 和 epoll 的演进和区别，并编写了使用这些函数的示例代码。另外，本文还总结了各种基于 I/O 多路复用实现的网络服务器的并发策略的三种模式，包括对 Apache HTTP Server、Nginx 和 Redis 等网络服务器的并发策略的具体案例的解析。</p>
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