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<li class="md-nav__item">
<a href="#_2" class="md-nav__link">
为什么要使用消息队列？
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<a href="#kafka" class="md-nav__link">
kafka是什么？
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kafka如何做到高可用的？
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Kafka 为什么不支持读写分离?
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如何解决kafka消息重复消费问题？
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Kafka消息是采用Pull模式，还是Push模式？
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kafka中如何防止消息丢失？
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kafka中的 zookeeper 起到什么作用，可以不用zookeeper吗？
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kafka 为什么那么快？
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Kafka中是怎么体现消息顺序性的？
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kafka如何实现延迟队列？
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kafka中consumer group 是什么概念？
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Kafka 中位移(offset)的作用？
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阐述下Kafka 中的领导者副本(Leader Replica)和追随者副本 (Follower Replica)的区别？
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<h1 id="_1">消息队列<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h1>
<h2 id="_2">为什么要使用消息队列？<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<ol>
<li>业务解耦</li>
<li>异步处理</li>
<li>流量削峰</li>
</ol>
<h2 id="kafka">kafka是什么？<a class="headerlink" href="#kafka" title="Permanent link">&para;</a></h2>
<p>Kafka是高吞吐低延迟的高并发、高性能的消息中间件，配置良好的Kafka集群甚至可以做到每秒几十万、上百万的超高并发写入。Kafka是**一个分布式消息队列**。Kafka对消息保存时根据Topic进行归类，发送消息者称为Producer，消息接受者称为Consumer，此外kafka集群有多个kafka实例组成，每个实例(server)称为broker。无论是kafka集群，还是consumer都依赖于zookeeper集群保存一些meta信息，来保证系统可用性。</p>
<p><a class="glightbox" href="https://static.cyub.vip/images/202107/kafka_arch.png" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="" src="https://static.cyub.vip/images/202107/kafka_arch.png" /></a></p>
<h2 id="kafka_1">kafka如何做到高可用的？<a class="headerlink" href="#kafka_1" title="Permanent link">&para;</a></h2>
<p>从topic的Partition的副本来看：</p>
<p><a class="glightbox" href="https://static.cyub.vip/images/202107/kafka_ha.jpeg" data-type="image" data-width="auto" data-height="auto" data-desc-position="bottom"><img alt="" src="https://static.cyub.vip/images/202107/kafka_ha.jpeg" /></a></p>
<p>上图中只有一个Topic，它有3个Partition。</p>
<h2 id="kafka_2">Kafka 为什么不支持读写分离?<a class="headerlink" href="#kafka_2" title="Permanent link">&para;</a></h2>
<p>自 Kafka 2.4 之后，Kafka 提供了有限度的读写分离，也就是说，Follower 副本能够对外提供读服务。</p>
<ol>
<li>业务场景不适用。读写分离适用于那种读负载很大，而写操作相对不频繁的场景，可 Kafka 不属于这样的场景。</li>
<li>同步机制。Kafka 采用 PULL 方式实现 Follower 的同步，因此Follower 与 Leader 存 在不一致性窗口。如果允许读 Follower 副本，就势必要处理消息滞后(Lagging)的问题。</li>
</ol>
<h2 id="kafka_3">如何解决kafka消息重复消费问题？<a class="headerlink" href="#kafka_3" title="Permanent link">&para;</a></h2>
<p>将消息的唯一标识保存到外部介质中，每次消费时判断是否处理过即可。这个解决办法适合其他消息系统。</p>
<h2 id="kafkapullpush">Kafka消息是采用Pull模式，还是Push模式？<a class="headerlink" href="#kafkapullpush" title="Permanent link">&para;</a></h2>
<p>kafka遵循了一种大部分消息系统共同的传统的设计：producer将消息推送(push)到broker，consumer从broker拉取(pull)消息。同redis的bpop命令类似，Kafka有个参数可以让consumer阻塞知道新消息到达，可以防止consumer不断在循环中轮询。</p>
<h2 id="kafka_4">kafka中如何防止消息丢失？<a class="headerlink" href="#kafka_4" title="Permanent link">&para;</a></h2>
<p>Kafka消息发送有两种方式：同步（sync）和异步（async），默认是同步方式，可通过producer.type属性进行配置。Kafka通过配置request.required.acks属性来确认消息的生产：</p>
<ul>
<li>
<p>0</p>
<p>表示producer不等待来自broker同步完成的确认继续发送下一条消息；</p>
</li>
<li>
<p>1</p>
<p>表示producer在leader已成功收到的数据并得到确认后发送下一条message，默认状态</p>
</li>
<li>
<p>-1</p>
<p>表示producer在header,follower副本确认接收到数据后才算一次发送完成；</p>
</li>
</ul>
<p>综上所述，有6种消息生产的情况，下面分情况来分析消息丢失的场景：</p>
<p>（1）acks=0，不和Kafka集群进行消息接收确认，则当网络异常、缓冲区满了等情况时，消息可能丢失；</p>
<p>（2）acks=1、同步模式下，只有Leader确认接收成功后但挂掉了，副本没有同步，数据可能丢失；</p>
<p>可见在同步模式下，ack=-1时候，可以防止消息丢失。但这牺牲了吞吐量。</p>
<h2 id="kafka-zookeeper-zookeeper">kafka中的 zookeeper 起到什么作用，可以不用zookeeper吗？<a class="headerlink" href="#kafka-zookeeper-zookeeper" title="Permanent link">&para;</a></h2>
<p>早期版本的kafka用zk做meta信息存储，consumer的消费状态，group的管理以及 offset的值。新的consumer使用了kafka内部的group coordination协议，也减少了对zookeeper的依赖，但是broker依然依赖于ZK，zookeeper 在kafka中还用来选举controller 和 检测broker是否存活等等。</p>
<h2 id="kafka_5">kafka 为什么那么快？<a class="headerlink" href="#kafka_5" title="Permanent link">&para;</a></h2>
<ol>
<li>
<p>Page cache技术</p>
<p>Kafka每次接收到数据都会往磁盘上去写。但并不是直接写入磁盘的，而是写入OS cache上面，然后在写到磁盘</p>
</li>
<li>
<p>顺序读写磁盘</p>
<p>磁盘读写时候，是顺序读写的。此时数据在磁盘上存取代价为O(1)。</p>
</li>
<li>
<p>零拷贝技术</p>
<p>Customer从broker读取数据，采用零拷贝技术。将磁盘文件读到OS内核缓冲区后，直接转到socket buffer进行网络发送。</p>
<p>传统的数据发送需要发送4次上下文切换，采用sendfile系统调用之后，数据直接在内核态交换，系统上下文切换减少为2次。</p>
</li>
</ol>
<h2 id="kafka_6">Kafka中是怎么体现消息顺序性的？<a class="headerlink" href="#kafka_6" title="Permanent link">&para;</a></h2>
<p>kafka每个partition中的消息在写入时都是有序的，消费时，每个partition只能被每一个group中的一个消费者消费，保证了消费时也是有序的。整个topic不保证有序。如果为了保证topic整个有序，那么将partition调整为1。</p>
<h2 id="kafka_7">kafka如何实现延迟队列？<a class="headerlink" href="#kafka_7" title="Permanent link">&para;</a></h2>
<p>kafka基于**时间轮**可以将插入和删除操作的时间复杂度都降为O(1)。</p>
<h2 id="kafkaconsumer-group">kafka中consumer group 是什么概念？<a class="headerlink" href="#kafkaconsumer-group" title="Permanent link">&para;</a></h2>
<p>consumer group是Kafka实现单播和广播两种消息模型的手段。同一个topic的数据，会广播给不同的group；同一个group中的worker，只有一个worker能拿到这个数据。换句话说，对于同一个topic，每个group都可以拿到同样的所有数据，但是数据进入group后只能被其中的一个worker消费。group内的worker可以使用多线程或多进程来实现，也可以将进程分散在多台机器上，worker的数量通常不超过partition的数量，且二者最好保持整数倍关系，因为Kafka在设计时假定了一个partition只能被一个worker消费（同一group内）。</p>
<h2 id="kafka-offset">Kafka 中位移(offset)的作用？<a class="headerlink" href="#kafka-offset" title="Permanent link">&para;</a></h2>
<p>在 Kafka 中，每个 主题分区下的每条消息都被赋予了一个唯一的 ID 数值，用于标识它在分区中的位置。这个 ID 数值，就被称为位移，或者叫偏移量。一旦消息被写入到分区日志，它的位移值将不能 被修改。</p>
<h2 id="kafka-leader-replica-follower-replica">阐述下Kafka 中的领导者副本(Leader Replica)和追随者副本 (Follower Replica)的区别？<a class="headerlink" href="#kafka-leader-replica-follower-replica" title="Permanent link">&para;</a></h2>
<p>Kafka 副本当前分为领导者副本和追随者副本。只有 Leader 副本才能 对外提供读写服务，响应 Clients 端的请求。Follower 副本只是采用拉(PULL)的方 式，被动地同步 Leader 副本中的数据，并且在 Leader 副本所在的 Broker 宕机后，随时准备应聘 Leader 副本。</p>
<p>自 Kafka 2.4 版本开始，社区通过引入新的 Broker 端参数，允许 Follower 副本有限度地提供读服务。</p>
<h2 id="_3">消息传递语义是什么概念？<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h2>
<p>message delivery semantic 也就是消息传递语义。通用的概念，也就是消息传递过程中消息传递的保证性。分为三种：</p>
<ul>
<li>
<p><strong>最多一次（at most once）</strong></p>
<p>消息可能丢失也可能被处理，但最多只会被处理一次。可能丢失，不会重复。</p>
<p><strong>只管发送，不管对方收没收到。</strong></p>
</li>
<li>
<p><strong>至少一次（at least once）</strong></p>
<p>消息不会丢失，但可能被处理多次。可能重复 不会丢失。</p>
<p><strong>发送之后，会等待对方确认之后才会停止发送。</strong></p>
</li>
<li>
<p><strong>精确传递一次（exactly once）</strong></p>
<p>消息被处理且只会被处理一次。不丢失，不重复，就一次。</p>
</li>
</ul>
<h2 id="beanstalk">介绍一下beanstalk?<a class="headerlink" href="#beanstalk" title="Permanent link">&para;</a></h2>
<p>beanstalk是轻量级的，易使用的，C语言实现的消息队列中间件。支持特性有：</p>
<ul>
<li>
<p>延迟（delay）</p>
<p>延迟意味着可以定义任务什么时间才开始被消费</p>
</li>
<li>
<p>优先级（priority）</p>
<p>优先级就意味 支持任务插队（数字越小，优先级越高，0的优先级最高）</p>
</li>
<li>
<p>持久化（persistent data）</p>
<p>Beanstalkd 支持定时将文件刷到日志文件里，即使beanstalkd宕机，重启之后仍然可以找回文件</p>
</li>
<li>
<p>任务超时重发（time-to-run）</p>
<p>消费者必须在指定的时间内处理完这个任务，否则就认为消费者处理失败，任务会被重新放到队列，等待消费</p>
</li>
</ul>
<p>Beanstalk由四部分构成：</p>
<ul>
<li>
<p>管道（tube）</p>
<p>相当于kafka的Topic概念，是消息的归类。</p>
</li>
<li>
<p>任务（job）</p>
<p>相当于kafka中的消息</p>
</li>
<li>
<p>producer</p>
<p>job的生产者，通过put命令来将一个job放到一个tube中</p>
</li>
<li>
<p>consumer</p>
<p>job的消费者，通过reserve、release、bury、delete命令来获取job或改变job的状态</p>
</li>
</ul>
<p>任务从进入管道到离开管道一共有5个状态（ready，delayed，reserved，buried，delete）：</p>
<ol>
<li>
<p>生产者将任务放到管道中，任务的状态可以是ready（表示任务已经准备好，随时可以被消费者读取），也可以是delayed（任务在被生产者放入管道时，设置了延迟，比如设置了5s延迟，意味着5s之后，这个任务才会变成ready状态，才可以被消费者读取）</p>
</li>
<li>
<p>消费者消费任务（消费者将处于ready状态的任务读出来后，被读取处理的任务状态变为reserved），可以设置reserved的时间，若在这段时间没有处理完成，那么任务会重新放回消息队列中，再次被别人消费</p>
</li>
<li>
<p>消费者处理完任务后，任务的状态可能是delete（删除，处理成功），可能是buried（预留，意味着先把任务放一边，等待条件成熟还要用），可能是ready，也可能是delayed，需要根据具体业务场景自己进行判断定义</p>
</li>
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