kafka消息可靠性保证(二)

kafka 消息保证是老生常谈的事情, 总结都做了n遍, 现在结合源码(kakfa版本v2.6.2)再来一遍,可靠性是由3个部分来进行保证的

消费者保证
生产者保证
broker保证

消费者保证

消费者对应的包为client项目下,其中重点包和生产者类似

**org.apache.kafka.clients.consumer 包 **

消费场景设置

autoCommit vs 手动commit

enable.auto.commit = true

org.apache.kafka.clients.consumer.ConsumerConfig#ENABLE_AUTO_COMMIT_DOC

  // 设置自动提交参数 的自定义, 如果没有设置就为false
  boolean maybeOverrideEnableAutoCommit() {
     		  // 获取group id 
          Optional<String> groupId = Optional.ofNullable(getString(CommonClientConfigs.GROUP_ID_CONFIG));
          // 获取系统中设置的自动提交参数
    			boolean enableAutoCommit = getBoolean(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG);
          
    			if (!groupId.isPresent()) { // overwrite in case of default group id where the config is not explicitly provided
            	// 默认false
              if (!originals().containsKey(ENABLE_AUTO_COMMIT_CONFIG)) {
                  enableAutoCommit = false;
              } else if (enableAutoCommit) {
                // 没有设置groupId 不能自动提交
                  throw new InvalidConfigurationException(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG + " cannot be set to true when default group id (null) is used.");
              }
          }
          return enableAutoCommit;
      }

auto.commit.interval.ms

org.apache.kafka.clients.consumer.ConsumerConfig#AUTO_COMMIT_INTERVAL_MS_DOC

    	//默认值为5000 
   .define(AUTO_COMMIT_INTERVAL_MS_CONFIG,
                                           Type.INT,
                                           5000,
                                           atLeast(0),
                                           Importance.LOW,
                                           AUTO_COMMIT_INTERVAL_MS_DOC)
     //

偏移量提交

org.apache.kafka.clients.consumer.KafkaConsumer#poll(org.apache.kafka.common.utils.Timer, boolean)

在consumerpoll 拉取数据的时候,会有coordinator.poll()

   private ConsumerRecords<K, V> poll(final Timer timer, final boolean includeMetadataInTimeout) {
           // 控制是当前线程消费的,非当前线程消费直接抛出异常
           acquireAndEnsureOpen();
           try {
               // 消费者记录下消息消费的开始时间
               this.kafkaConsumerMetrics.recordPollStart(timer.currentTimeMs());
   
               if (this.subscriptions.hasNoSubscriptionOrUserAssignment()) {
                   throw new IllegalStateException("Consumer is not subscribed to any topics or assigned any partitions");
               }
   
               do {
                   // 消费者触发唤醒，看有没有唤醒，没有直接抛出异常
                   client.maybeTriggerWakeup();
   
                   if (includeMetadataInTimeout) {
                       // try to update assignment metadata BUT do not need to block on the timer for join group
                       updateAssignmentMetadataIfNeeded(timer, false);
                   } else {
                       // 同步消费者元数据，这里要通过coordinator 去同步出去
                      // 要是没有就 rebalance 加到这个group里面去
                       while (!updateAssignmentMetadataIfNeeded(time.timer(Long.MAX_VALUE), true)) {
                           log.warn("Still waiting for metadata");
                       }
                   }
   
                   final Map<TopicPartition, List<ConsumerRecord<K, V>>> records = pollForFetches(timer);
                   if (!records.isEmpty()) {
                       // before returning the fetched records, we can send off the next round of fetches
                       // and avoid block waiting for their responses to enable pipelining while the user
                       // is handling the fetched records.
                       //
                       // NOTE: since the consumed position has already been updated, we must not allow
                       // wakeups or any other errors to be triggered prior to returning the fetched records.
                       if (fetcher.sendFetches() > 0 || client.hasPendingRequests()) {
                           client.transmitSends();
                       }
   
                       return this.interceptors.onConsume(new ConsumerRecords<>(records));
                   }
               } while (timer.notExpired());
   
               return ConsumerRecords.empty();
           } finally {
               release();
               this.kafkaConsumerMetrics.recordPollEnd(timer.currentTimeMs());
           }
       }
   
   
   // coordinator.poll调用
   public boolean poll(Timer timer, boolean waitForJoinGroup) {
          ...
            // 先不管上面的逻辑
            // 在这里就回自动提交当前的消费的offset 
            //。具体就是掉send
           maybeAutoCommitOffsetsAsync(timer.currentTimeMs());
           return true;
       }
   
   // 异步提交偏移量
   private void doAutoCommitOffsetsAsync() {
           // 偏移量提交
           Map<TopicPartition, OffsetAndMetadata> allConsumedOffsets = subscriptions.allConsumed();
           log.debug("Sending asynchronous auto-commit of offsets {}", allConsumedOffsets);
   
           commitOffsetsAsync(allConsumedOffsets, (offsets, exception) -> {
               if (exception != null) {
                   if (exception instanceof RetriableCommitFailedException) {
                       log.debug("Asynchronous auto-commit of offsets {} failed due to retriable error: {}", offsets,
                           exception);
                       nextAutoCommitTimer.updateAndReset(rebalanceConfig.retryBackoffMs);
                   } else {
                       log.warn("Asynchronous auto-commit of offsets {} failed: {}", offsets, exception.getMessage());
                   }
               } else {
                   log.debug("Completed asynchronous auto-commit of offsets {}", offsets);
               }
           });
       }

rebalance

Kafka提供了一个角色：coordinator来执行对于consumer group的管理。坦率说kafka对于coordinator的设计与修改是一个很长的故事。最新版本的coordinator也与最初的设计有了很大的不同。这里我只想提及两次比较大的改变。

首先是0.8版本的coordinator，那时候的coordinator是依赖zookeeper来实现对于consumer group的管理的。Coordinator监听zookeeper的/consumers/<group>/ids的子节点变化以及/brokers/topics/<topic>数据变化来判断是否需要进行rebalance。group下的每个consumer都自己决定要消费哪些分区，并把自己的决定抢先在zookeeper中的/consumers/<group>/offsets/<topic>/<partition>下注册。很明显，这种方案要依赖于zookeeper的帮助，而且每个consumer是单独做决定的，没有那种“大家属于一个组，要协商做事情”的精神。

// 老版本和测试代码可以看到对应的定义,可见是在zookeeper上注册offsets
def getConsumersOffsetsZkPath(consumerGroup: String, topic: String, partition: Int): String = {
      s"/consumers/$consumerGroup/offsets/$topic/$partition"
}

基于这些潜在的弊端，0.9版本的kafka改进了coordinator的设计，提出了group coordinator——每个consumer group都会被分配一个这样的coordinator用于组管理和位移管理。这个group coordinator比原来承担了更多的责任，比如组成员管理、位移提交保护机制等。当新版本consumer group的第一个consumer启动的时候，它会去和kafka server确定谁是它们组的coordinator。之后该group内的所有成员都会和该coordinator进行协调通信。显而易见，这种coordinator设计不再需要zookeeper了，性能上可以得到很大的提升。后面的所有部分我们都将讨论最新版本的coordinator设计。

初始化

new ConsumerCoordinator(groupRebalanceConfig,
                        logContext,
                        this.client,
                        assignors,
                        this.metadata,
                        this.subscriptions,
                        metrics,
                        metricGrpPrefix,
                        this.time,
                        enableAutoCommit,
                        config.getInt(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG),
                        this.interceptors,
                        config.getBoolean(ConsumerConfig.THROW_ON_FETCH_STABLE_OFFSET_UNSUPPORTED));


// 这里注重看下groupRebalanceConfig  这个属性
public GroupRebalanceConfig(AbstractConfig config, ProtocolType protocolType) {
  		//。
        this.sessionTimeoutMs = config.getInt(CommonClientConfigs.SESSION_TIMEOUT_MS_CONFIG);

        // Consumer and Connect use different config names for defining rebalance timeout
  // 消费者和连接的使用的不同的rebalance timeout
        if (protocolType == ProtocolType.CONSUMER) {
          // 使用的是max.poll.interval.ms 默认是5分钟
            this.rebalanceTimeoutMs = config.getInt(CommonClientConfigs.MAX_POLL_INTERVAL_MS_CONFIG);
        } else {
          //
            this.rebalanceTimeoutMs = config.getInt(CommonClientConfigs.REBALANCE_TIMEOUT_MS_CONFIG);
        }

        ...
    }

rebalance 触发条件一共有3种

组成员发生变更(新consumer加入组、已有consumer主动离开组或已有consumer崩溃了)
订阅主题数发生变更——这当然是可能的，如果你使用了正则表达式的方式进行订阅，那么新建匹配正则表达式的topic就会触发rebalance
订阅主题的分区数发生变更

下面以加入组为例子看下rebalance的触发

 @Override
    protected void onJoinPrepare(int generation, String memberId) {
        log.debug("Executing onJoinPrepare with generation {} and memberId {}", generation, memberId);
        // commit offsets prior to rebalance if auto-commit enabled
      // 该处的timeout 就是在上面设置好的 max.poll.interval.ms 
      // 如果还是在这个时间有效期内, 就一步自动提交一次offset
      // 校验 超时时间
        maybeAutoCommitOffsetsSync(time.timer(rebalanceConfig.rebalanceTimeoutMs));

        //出现错误或心跳超时时；在这种情况下，无论以前是什么
        //拥有的分区将丢失，我们应该触发回调并清理分配；
        //否则我们可以正常进行并根据协议撤销分区，
        //在这种情况下，我们应该仅在触发撤销回调后更改分配
        //因此用户仍然可以访问先前拥有的分区来提交偏移等。

        // the generation / member-id can possibly be reset by the heartbeat thread
        // upon getting errors or heartbeat timeouts; in this case whatever is previously
        // owned partitions would be lost, we should trigger the callback and cleanup the assignment;
        // otherwise we can proceed normally and revoke the partitions depending on the protocol,
        // and in that case we should only change the assignment AFTER the revoke callback is triggered
        // so that users can still access the previously owned partitions to commit offsets etc.
        Exception exception = null;
        final Set<TopicPartition> revokedPartitions;
        if (generation == Generation.NO_GENERATION.generationId &&
            memberId.equals(Generation.NO_GENERATION.memberId)) {
            revokedPartitions = new HashSet<>(subscriptions.assignedPartitions());

            if (!revokedPartitions.isEmpty()) {
                log.info("Giving away all assigned partitions as lost since generation has been reset," +
                    "indicating that consumer is no longer part of the group");
                exception = invokePartitionsLost(revokedPartitions);

                subscriptions.assignFromSubscribed(Collections.emptySet());
            }
        } else {
          //在eager 的情况下直接重分配所有的分区
            switch (protocol) {
                case EAGER:
                    // revoke all partitions
                    revokedPartitions = new HashSet<>(subscriptions.assignedPartitions());
                    exception = invokePartitionsRevoked(revokedPartitions);

                    subscriptions.assignFromSubscribed(Collections.emptySet());

                    break;

                case COOPERATIVE:
                //在这种情况下,只处理不再订阅的分区
                    // only revoke those partitions that are not in the subscription any more.
                    Set<TopicPartition> ownedPartitions = new HashSet<>(subscriptions.assignedPartitions());
                    revokedPartitions = ownedPartitions.stream()
                        .filter(tp -> !subscriptions.subscription().contains(tp.topic()))
                        .collect(Collectors.toSet());

                    if (!revokedPartitions.isEmpty()) {
                        exception = invokePartitionsRevoked(revokedPartitions);

                        ownedPartitions.removeAll(revokedPartitions);
                        subscriptions.assignFromSubscribed(ownedPartitions);
                    }

                    break;
            }
        }

        isLeader = false;
        subscriptions.resetGroupSubscription();

        if (exception != null) {
            throw new KafkaException("User rebalance callback throws an error", exception);
        }
    }

@Override
protected void onJoinComplete(int generation,
                                  String memberId,
                                  String assignmentStrategy,
                                  ByteBuffer assignmentBuffer) {
        log.debug("Executing onJoinComplete with generation {} and memberId {}", generation, memberId);

        // Only the leader is responsible for monitoring for metadata changes (i.e. partition changes)
        // 只有leader 才可以修改元数据
        if (!isLeader)
            assignmentSnapshot = null;
        // 查询Coordinator给消费者的分配策略 就是几个分区怎么分给几个消费者的
        //  如果没有，则提示策略异常
        ConsumerPartitionAssignor assignor = lookupAssignor(assignmentStrategy);
        if (assignor == null)
            throw new IllegalStateException("Coordinator selected invalid assignment protocol: " + assignmentStrategy);

        // Give the assignor a chance to update internal state based on the received assignment
        // 获取消费者组内最新的元数据（包含几个消费者 offset到哪里了）
        groupMetadata = new ConsumerGroupMetadata(rebalanceConfig.groupId, generation, memberId, rebalanceConfig.groupInstanceId);
        // 这个消费者订阅的分区set
        Set<TopicPartition> ownedPartitions = new HashSet<>(subscriptions.assignedPartitions());

        // should at least encode the short version
        // 内容长度一看就不对
        if (assignmentBuffer.remaining() < 2)
            throw new IllegalStateException("There are insufficient bytes available to read assignment from the sync-group response (" +
                "actual byte size " + assignmentBuffer.remaining() + ") , this is not expected; " +
                "it is possible that the leader's assign function is buggy and did not return any assignment for this member, " +
                "or because static member is configured and the protocol is buggy hence did not get the assignment for this member");
        // 内容buffer转成对应实体
        Assignment assignment = ConsumerProtocol.deserializeAssignment(assignmentBuffer);
        //coordinator传过来的 订阅的分区set
        Set<TopicPartition> assignedPartitions = new HashSet<>(assignment.partitions());
        // 对比下 发现现在的订阅的分区 和 传过来的对不上， 就重新加入消费者组
        if (!subscriptions.checkAssignmentMatchedSubscription(assignedPartitions)) {
            log.warn("We received an assignment {} that doesn't match our current subscription {}; it is likely " +
                "that the subscription has changed since we joined the group. Will try re-join the group with current subscription",
                assignment.partitions(), subscriptions.prettyString());
            // 重新加入- 这里只是改值，后续有个
            requestRejoin();
            // 终止
            return;
        }


        final AtomicReference<Exception> firstException = new AtomicReference<>(null);
        Set<TopicPartition> addedPartitions = new HashSet<>(assignedPartitions);
        addedPartitions.removeAll(ownedPartitions);

        // rebalance 协议
        //  EAGER  完全重新分配分区
        //  COOPERATIVE 消费者在下次rebalance之前保留其当前拥有的分区
        if (protocol == RebalanceProtocol.COOPERATIVE) {
            Set<TopicPartition> revokedPartitions = new HashSet<>(ownedPartitions);
            revokedPartitions.removeAll(assignedPartitions);

            log.info("Updating assignment with\n" +
                    "\tAssigned partitions:                       {}\n" +
                    "\tCurrent owned partitions:                  {}\n" +
                    "\tAdded partitions (assigned - owned):       {}\n" +
                    "\tRevoked partitions (owned - assigned):     {}\n",
                assignedPartitions,
                ownedPartitions,
                addedPartitions,
                revokedPartitions
            );

            if (!revokedPartitions.isEmpty()) {
                // Revoke partitions that were previously owned but no longer assigned;
                // note that we should only change the assignment (or update the assignor's state)
                // AFTER we've triggered  the revoke callback
                firstException.compareAndSet(null, invokePartitionsRevoked(revokedPartitions));

                // If revoked any partitions, need to re-join the group afterwards
                log.debug("Need to revoke partitions {} and re-join the group", revokedPartitions);
                // 重新加入消费组
                requestRejoin();
            }
        }

        // The leader may have assigned partitions which match our subscription pattern, but which
        // were not explicitly requested, so we update the joined subscription here.
        //在这边又同步下本地存的订阅信息
        maybeUpdateJoinedSubscription(assignedPartitions);

        // Catch any exception here to make sure we could complete the user callback.
        // 更新指定的信息
        firstException.compareAndSet(null, invokeOnAssignment(assignor, assignment));

        // Reschedule the auto commit starting from now
        // 自动提交 下次的自动提交时间
        if (autoCommitEnabled)
            this.nextAutoCommitTimer.updateAndReset(autoCommitIntervalMs);

        subscriptions.assignFromSubscribed(assignedPartitions);

        // Add partitions that were not previously owned but are now assigned
        firstException.compareAndSet(null, invokePartitionsAssigned(addedPartitions));

        // 期间有报错没
        if (firstException.get() != null) {
            if (firstException.get() instanceof KafkaException) {
                throw (KafkaException) firstException.get();
            } else {
                throw new KafkaException("User rebalance callback throws an error", firstException.get());
            }
        }
    }

posted @ 2021-11-28 20:24 _我在清水河边阅读(248) 评论(0) 编辑收藏举报

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kafka消息可靠性保证(二)