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<!DOCTYPE HTML>
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Kafka消费顺序-消费可靠性-不重复消费
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<h3 id="Kafka-如何保证消息的消费顺序？"><a href="#Kafka-如何保证消息的消费顺序？" class="headerlink" title="Kafka 如何保证消息的消费顺序？"></a>Kafka 如何保证消息的消费顺序？</h3><p>Kafka 中 Partition(分区)是真正保存消息的地方，我们发送的消息都被放在了这里。而 Partition(分区) 又存在于 Topic(主题) 这个概念中，并且可以给特定 Topic 指定多个 Partition。</p>
<p><img src="/kafka-reliability-sequential/image-20230723154008671.png" alt="image-20230723154008671"></p>
<p>每次添加消息到 Partition(分区) 的时候都会采用尾加法。 <strong>Kafka 只能保证 Partition(分区) 中的消息有序。</strong></p>
<p>消息在被追加到 Partition(分区)的时候都会分配一个特定的偏移量（offset）。Kafka 通过偏移量（offset）来保证消息在分区内的顺序性。</p>
<p>Kafka 中发送 1 条消息的时候，可以指定 topic, partition, key,data（数据） 4 个参数。如果发送消息的时候指定了 Partition 的话，所有消息都会被发送到指定的 Partition。并且，同一个 key 的消息可以保证只发送到同一个 partition，这个我们可以采用表/对象的 id 来作为 key 。</p>
<h3 id="Kafka-如何保证消息不丢失"><a href="#Kafka-如何保证消息不丢失" class="headerlink" title="Kafka 如何保证消息不丢失"></a>Kafka 如何保证消息不丢失</h3><h4 id="生产者丢失消息"><a href="#生产者丢失消息" class="headerlink" title="生产者丢失消息"></a>生产者丢失消息</h4><p>采用为其添加回调函数的形式</p>
<pre><code>@RestController
public class KafkaController {
// 注入KafkaTemplate
@Autowired
private KafkaTemplate<String, String> kafkaTemplate;
// 定义topic
private static final String topic = "second";
// 定义发送消息的方法
@GetMapping("/send")
public String send(String message) {
// 创建ProducerRecord对象，指定topic、分区、key和value
ProducerRecord<String, String> record = new ProducerRecord<String, String>(topic, 0, "congge", message);
// 调用send方法，传入回调函数
kafkaTemplate.send(record).addCallback(
success -> {
// 获取元数据信息
String topic = success.getRecordMetadata().topic();
int partition = success.getRecordMetadata().partition();
long offset = success.getRecordMetadata().offset();
System.out.println("topic:" + topic + " partition:" + partition + " offset:" + offset);
},
failure -> {
// 获取异常信息
String message1 = failure.getMessage();
System.out.println(message1);
}
);
return "success";
}
}
</code></pre><p>如果消息发送失败的话，我们检查失败的原因之后重新发送即可！</p>
<h4 id="消费者丢失消息的情况"><a href="#消费者丢失消息的情况" class="headerlink" title="消费者丢失消息的情况"></a>消费者丢失消息的情况</h4><p>息在被追加到 Partition(分区)的时候都会分配一个特定的偏移量（offset）。偏移量（offset)表示 Consumer 当前消费到的 Partition(分区)的所在的位置。Kafka 通过偏移量（offset）可以保证消息在分区内的顺序性。</p>
<p><img src="/kafka-reliability-sequential/image-20230723162202396.png" alt="image-20230723162202396"></p>
<p><strong>解决办法也比较粗暴，我们手动关闭自动提交 offset，每次在真正消费完消息之后再自己手动提交 offset 。</strong></p>
<p>这样会带来消息被重新消费的问题。比如你刚刚消费完消息之后，还没提交 offset，结果自己挂掉了，那么这个消息理论上就会被消费两次。</p>
<h4 id="Kafka-弄丢了消息"><a href="#Kafka-弄丢了消息" class="headerlink" title="Kafka 弄丢了消息"></a>Kafka 弄丢了消息</h4><p>Kafka 为分区（Partition）引入了多副本（Replica）机制。分区（Partition）中的多个副本之间会有一个叫做 leader 的家伙，其他副本称为 follower。我们发送的消息会被发送到 leader 副本，然后 follower 副本才能从 leader 副本中拉取消息进行同步。生产者和消费者只与 leader 副本交互。你可以理解为其他副本只是 leader 副本的拷贝，它们的存在只是为了保证消息存储的安全性。</p>
<p>假如 leader 副本所在的 broker 突然挂掉，那么就要从 follower 副本重新选出一个 leader ，但是 leader 的数据还有一些没有被 follower 副本的同步的话，就会造成消息丢失。</p>
<p><strong>设置 acks = all</strong></p>
<p>解决办法就是我们设置 <strong>acks = all</strong>。acks 是 Kafka 生产者(Producer) 很重要的一个参数。</p>
<p>acks 的默认值即为 1，代表我们的消息被 leader 副本接收之后就算被成功发送。当我们配置 <strong>acks = all</strong> 表示只有所有 ISR 列表的副本全部收到消息时，生产者才会接收到来自服务器的响应. 这种模式是最高级别的，也是最安全的，可以确保不止一个 Broker 接收到了消息. 该模式的延迟会很高.</p>
<p><strong>设置 replication.factor >= 3</strong></p>
<p>为了保证 leader 副本能有 follower 副本能同步消息，我们一般会为 topic 设置 <strong>replication.factor >= 3</strong>。这样就可以保证每个 分区(partition) 至少有 3 个副本。虽然造成了数据冗余，但是带来了数据的安全性。</p>
<p><strong>设置 min.insync.replicas > 1</strong></p>
<p>一般情况下我们还需要设置 <strong>min.insync.replicas> 1</strong> ，这样配置代表消息至少要被写入到 2 个副本才算是被成功发送。<strong>min.insync.replicas</strong> 的默认值为 1 ，在实际生产中应尽量避免默认值 1。</p>
<p>为了保证整个 Kafka 服务的高可用性，你需要确保 <strong>replication.factor > min.insync.replicas</strong> 。为什么呢？设想一下假如两者相等的话，只要是有一个副本挂掉，整个分区就无法正常工作了。这明显违反高可用性！一般推荐设置成 <strong>replication.factor = min.insync.replicas + 1</strong>。</p>
<p><strong>设置 unclean.leader.election.enable = false</strong></p>
<p>多个 follower 副本之间的消息同步情况不一样，当我们配置了 <strong>unclean.leader.election.enable = false</strong> 的话，当 leader 副本发生故障时就不会从 follower 副本中和 leader 同步程度达不到要求的副本中选择出 leader ，这样降低了消息丢失的可能性。</p>
<h3 id="Kafka-如何保证消息不重复消费"><a href="#Kafka-如何保证消息不重复消费" class="headerlink" title="Kafka 如何保证消息不重复消费"></a>Kafka 如何保证消息不重复消费</h3><p><strong>kafka 出现消息重复消费的原因：</strong></p>
<ul>
<li>服务端侧已经消费的数据没有成功提交 offset（根本原因）。</li>
<li>Kafka 侧 由于服务端处理业务时间长或者网络链接等等原因让 Kafka 认为服务假死，触发了分区 rebalance。</li>
</ul>
<p>消费消息服务做幂等校验，比如 Redis 的 set、MySQL 的主键等天然的幂等功能。这种方法最有效。</p>
<p>将 <strong><code>enable.auto.commit</code></strong> 参数设置为 false，关闭自动提交，开发者在代码中手动提交 offset。那么这里会有个问题：<strong>什么时候提交 offset 合适？</strong></p>
<ul>
<li>处理完消息再提交：依旧有消息重复消费的风险，和自动提交一样</li>
<li>拉取到消息即提交：会有消息丢失的风险。允许消息延时的场景，一般会采用这种方式。然后，通过定时任务在业务不繁忙（比如凌晨）的时候做数据兜底。</li>
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