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href="/zh-CN/docs/1.5.0/contact">联系我们</a></li></ul></nav></div></div></aside><main class="docMainContainer_gTbr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="theme-doc-version-banner alert alert--warning margin-bottom--md" role="alert"><div>This is documentation for <!-- -->Apache InLong<!-- --> <b>1.5.0</b>, which is no longer actively maintained.</div><div class="margin-top--md">For up-to-date documentation, see the <b><a href="/zh-CN/docs/modules/tubemq/overview">latest version</a></b> (<!-- -->1.11.0<!-- -->).</div></div><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="页面路径"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="主页面" class="breadcrumbs__link" href="/zh-CN/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">组件介绍</span><meta itemprop="position" content="1"></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">TubeMQ</span><meta itemprop="position" content="2"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">总览</span><meta itemprop="position" content="3"></li></ul></nav><span class="theme-doc-version-badge badge badge--secondary">版本:1.5.0</span><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>总览</h1></header><h2 class="anchor anchorWithStickyNavbar_LWe7" id="1-apache-inlong-tubemq模块的架构">1 Apache InLong TubeMQ模块的架构<a href="#1-apache-inlong-tubemq模块的架构" class="hash-link" aria-label="Direct link to heading" title="Direct link to heading"></a></h2><p>经过多年演变,TubeMQ集群分为如下5个部分:
<img loading="lazy" src="/zh-CN/assets/images/sys_structure-8696e2c7dfebcbe30e47b2d99e185485.png" width="1031" height="507" class="img_ev3q"></p><ul><li><p><strong>Portal</strong>: 负责对外交互和运维操作的Portal部分,包括API和Web两块,API对接集群之外的管理系统,Web是在API基础上对日常运维功能做的页面封装;</p></li><li><p><strong>Master</strong>: 负责集群控制的Control部分,该部分由1个或多个Master节点组成,Master HA通过Master节点间心跳保活、实时热备切换完成(这是大家使用TubeMQ的Lib时需要填写对应集群所有Master节点地址的原因),主Master负责管理整个集群的状态、资源调度、权限检查、元数据查询等;</p></li><li><p><strong>Broker</strong>: 负责实际数据存储的Store部分,该部分由相互之间独立的Broker节点组成,每个Broker节点对本节点内的Topic集合进行管理,包括Topic的增、删、改、查,Topic内的消息存储、消费、老化、分区扩容、数据消费的offset记录等,集群对外能力,包括Topic数目、吞吐量、容量等,通过水平扩展Broker节点来完成;</p></li><li><p><strong>Client</strong>: 负责数据生产和消费的Client部分,该部分我们以Lib形式对外提供,大家用得最多的是消费端,相比之前,消费端现支持Push、Pull两种数据拉取模式,数据消费行为支持顺序和过滤消费两种。对于Pull消费模式,支持业务通过客户端重置精确offset以支持业务exactly-once消费,同时,消费端新推出跨集群切换免重启的Consumer客户端;</p></li><li><p><strong>ZooKeeper</strong>: 负责offset存储的ZooKeeper部分,该部分功能已弱化到仅做offset的持久化存储,考虑到接下来的多节点副本功能该模块暂时保留。</p></li></ul><h2 class="anchor anchorWithStickyNavbar_LWe7" id="2-apache-inlong-tubemq模块的系统特点">2 Apache InLong TubeMQ模块的系统特点<a href="#2-apache-inlong-tubemq模块的系统特点" class="hash-link" aria-label="Direct link to heading" title="Direct link to heading"></a></h2><ul><li><p><strong>纯Java实现语言</strong>
TubeMQ采用纯Java语言开发,便于开发人员快速熟悉项目及问题处理;</p></li><li><p><strong>引入协调节点</strong>
TubeMQ系统采用自管理的元数据仲裁机制方式进行,Master节点通过采用内嵌数据库BDB完成集群内元数据的存储、更新以及HA热切功能,负责TubeMQ集群的运行管控和配置管理操作,对外提供接口等;通过Master节点,TubeMQ集群里的Broker配置设置、变更及查询实现了完整的自动化闭环管理,减轻了系统维护的复杂度;</p></li><li><p><strong>服务器侧消费负载均衡</strong>
TubeMQ采用的是服务侧负载均衡的方案,而不是客户端侧操作,提升系统的管控能力同时简化客户端实现,更便于均衡算法升级;</p></li><li><p><strong>系统行级锁操作</strong>
对于Broker消息读写中存在中间状态的并发操作采用行级锁,避免重复问题;</p></li><li><p><strong>Offset管理调整</strong>
Offset由各个Broker独自管理,ZK只作数据持久化存储用(最初考虑完全去掉ZK依赖,考虑到后续的功能扩展就暂时保留);</p></li><li><p><strong>消息读取机制的改进</strong>
TubeMQ采用消息随机读取模式,同时为了降低消息时延又增加了内存缓存读写,使其满足业务快速生产消费的需求(后面章节详细介绍);</p></li><li><p><strong>消费者行为管控</strong>
支持通过策略实时动态地控制系统接入的消费者行为,包括系统负载高时对特定业务的限流、暂停消费,动态调整数据拉取的频率等;</p></li><li><p><strong>服务分级管控</strong>
针对系统运维、业务特点、机器负载状态的不同需求,系统支持运维通过策略来动态控制不同消费者的消费行为,比如是否有权限消费、消费时延分级保证、消费限流控制,以及数据拉取频率控制等;</p></li><li><p><strong>系统安全管控</strong>
根据业务不同的数据服务需要,以及系统运维安全的考虑,TubeMQ系统增加了TLS传输层加密管道,生产和消费服务的认证、授权,以及针对分布式访问控制的访问令牌管理,满足业务和系统运维在系统安全方面的需求;</p></li><li><p><strong>资源利用率提升改进</strong>
TubeMQ采用连接复用模式,减少连接资源消耗;通过逻辑分区构造,减少系统对文件句柄数的占用,通过服务器端过滤模式,减少网络带宽资源使用率;通过剥离对Zookeeper的使用,减少Zookeeper的强依赖及瓶颈限制;</p></li><li><p><strong>客户端改进</strong>
基于业务使用上的便利性以,我们简化了客户端逻辑,使其做到最小的功能集合,我们采用基于响应消息的接收质量统计算法来自动剔出坏的Broker节点,基于首次使用时作连接尝试来避免大数据量发送时发送受阻(具体内容见后面章节介绍)。</p></li></ul><h2 class="anchor anchorWithStickyNavbar_LWe7" id="3-broker文件存储方案改进">3 Broker文件存储方案改进<a href="#3-broker文件存储方案改进" class="hash-link" aria-label="Direct link to heading" title="Direct link to heading"></a></h2><p>以磁盘为数据持久化媒介的系统都面临各种因磁盘问题导致的系统性能问题,TubeMQ系统也不例外,性能提升很大程度上是在解决消息数据如何读写及存储的问题。在这个方面TubeMQ进行了比较多的改进,我们采用存储实例来作为最小的Topic数据管理单元,每个存储实例包括一个文件存储块和一个内存缓存块,每个Topic可以分配多个存储实例:</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="31-文件存储块">3.1 文件存储块<a href="#31-文件存储块" class="hash-link" aria-label="Direct link to heading" title="Direct link to heading"></a></h3><p> TubeMQ的磁盘存储方案类似Kafka,但又不尽相同,如下图示,每个文件存储块由一个索引文件和一个数据文件组成,partiton为数据文件里的逻辑分区,每个Topic单独维护管理文件存储块的相关机制,包括老化周期,partition个数,是否可读可写等。
<img loading="lazy" src="/zh-CN/assets/images/store_file-afacb93118082ca4afc7f0ee3176af70.png" width="1211" height="700" class="img_ev3q"></p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="32-内存缓存块">3.2 内存缓存块<a href="#32-内存缓存块" class="hash-link" aria-label="Direct link to heading" title="Direct link to heading"></a></h3><p> 在文件存储块基础上,我们额外增加了一个单独的内存缓存块,即在原有写磁盘基础上增加一块内存,隔离硬盘的慢速影响,数据先刷到内存缓存块,然后由内存缓存块批量地将数据刷到磁盘文件。
<img loading="lazy" src="/zh-CN/assets/images/store_mem-05a41b24e2b5e68a769c9236d1e063d4.png" width="794" height="604" class="img_ev3q"></p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="4-apache-inlong-tubemq模块的客户端演进">4 Apache InLong TubeMQ模块的客户端演进:<a href="#4-apache-inlong-tubemq模块的客户端演进" class="hash-link" aria-label="Direct link to heading" title="Direct link to heading"></a></h2><p>业务与TubeMQ接触得最多的是消费侧,怎样更适应业务特点、更方便业务使用我们在这块做了比较多的改进:</p><ul><li><p><strong>数据拉取模式支持Push、Pull:</strong></p><ul><li><p><strong>Push客户端:</strong> TubeMQ最初消费端版本只提供Push模式的消费,这种模式能比较快速地消费数据,减轻服务端压力,但同时也带来一个问题,业务使用的时候因为无法控制拉取频率,从而容易形成数据积压数据处理不过来;</p></li><li><p><strong>带消费中止/继续的Push客户端:</strong> 在收到业务反馈能否控制Push拉取动作的需求后,我们增加了resumeConsume()/pauseConsume()函数对,让业务可以模拟水位线控制机制,状态比较繁忙时调用pauseConsume()函数来中止Lib后台的数据拉取,在状态恢复后,再调用resumeConsume()通知Lib后台继续拉取数据;</p></li><li><p><strong>Pull客户端:</strong> 我们后来版本里增加了Pull客户端,该客户端有别于Push客户端,是由业务而非Lib主动的拉取消息并对数据处理的结果进行成功与否的确认,将数据处理的主动权留给业务。这样处理后,虽然服务端压力有所提升,但业务消费时积压情况可大大缓解。</p></li></ul></li><li><p><strong>数据消费行为支持顺序和过滤消费:</strong> 在TubeMQ设计初我们考虑是不同业务使用不同的Topic,实际运营中我们发现不少业务实际上是通过代理模式上报的数据,数据通过Topic下的文件ID或者表ID属性来区分,业务为了消费自己的一份数据是需要全量消费该Topic下的所有数据。我们通过tid字段支持指定属性的过滤消费模式,将数据过滤放到服务端来做,减少出流量以及客户端的数据处理压力。</p></li><li><p><strong>支持业务exactly-once消费:</strong> 为了解决业务处理数据时需要精确回档的需求,在客户端版本里提供了通过客户端重置精确offset功能,业务重启系统时,只需通过客户端提供待回拨时间点的消费上下文,TubeMQ即可按照指定的精确位置接续消费。该特性目前已在Flink这类实时计算框架使用,依托Flink基于checkpoint机制进行exactly-once数据处理。</p></li></ul><hr><a href="#top">Back to top</a></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="theme-doc-footer-edit-meta-row row"><div class="col"><a href="https://github.com/apache/inlong-website/edit/master/versioned_docs/version-1.5.0/modules/tubemq/overview.md" target="_blank" rel="noreferrer noopener" class="theme-edit-this-page"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" 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