flink-connector-kafka consumer checkpoint源码分析

转发请注明原创地址：http://www.cnblogs.com/dongxiao-yang/p/7700600.html 

 

《flink-connector-kafka consumer的topic分区分配源码》一文提到了在flink-connector-kafka的consumer初始化的时候有三种offset提交模式：KAFKA_PERIODIC，DISABLED和ON_CHECKPOINTS。

其中ON_CHECKPOINTS表示在flink做完checkpoint后主动向kafka提交offset的方法，本文主要分析一下flink-connector-kafka在源码如何使用checkpoint机制实现offset的恢复和提交。

 flink conusmer的实现基类FlinkKafkaConsumerBase定义如下，这个类实现了了与checkpoin相关的三个接口CheckpointedFunction，CheckpointedRestoring<HashMap<KafkaTopicPartition, Long>>，CheckpointListener。根据官网文档，CheckpointedRestoring的restoreState()方法已经被CheckpointedFunction的initializeState取代，所以重点关注三个方法实现

1initializeState()  实例初始化或者recover的时候调用

2snapshotState() 每次创建checkpoint的时候调用

3 notifyCheckpointComplete() 每次checkpoint结束的时候调用

public abstract class FlinkKafkaConsumerBase<T> extends RichParallelSourceFunction<T> implements 
        CheckpointListener,
        ResultTypeQueryable<T>,
        CheckpointedFunction,
        CheckpointedRestoring<HashMap<KafkaTopicPartition, Long>> {

 

initializeState

    @Override
    public final void initializeState(FunctionInitializationContext context) throws Exception {

        // we might have been restored via restoreState() which restores from legacy operator state
        if (!restored) {
            restored = context.isRestored();
        }

        OperatorStateStore stateStore = context.getOperatorStateStore();
        offsetsStateForCheckpoint = stateStore.getSerializableListState(DefaultOperatorStateBackend.DEFAULT_OPERATOR_STATE_NAME);

        if (context.isRestored()) {
            if (restoredState == null) {
                restoredState = new HashMap<>();
                for (Tuple2<KafkaTopicPartition, Long> kafkaOffset : offsetsStateForCheckpoint.get()) {
                    restoredState.put(kafkaOffset.f0, kafkaOffset.f1);
                }

                LOG.info("Setting restore state in the FlinkKafkaConsumer.");
                if (LOG.isDebugEnabled()) {
                    LOG.debug("Using the following offsets: {}", restoredState);
                }
            }
        } else {
            LOG.info("No restore state for FlinkKafkaConsumer.");
        }
    }

这个方法的逻辑比较简单，在task恢复的时候从stateStore中序列化出来之前存储的ListState<Tuple2<KafkaTopicPartition, Long>> 状态数据，并放到restoredState这个变量，用于下面open方法直接恢复对应的分区和offset起始值。

 

snapshotState

    @Override
    public final void snapshotState(FunctionSnapshotContext context) throws Exception {
        if (!running) {
            LOG.debug("snapshotState() called on closed source");
        } else {

            offsetsStateForCheckpoint.clear();

            final AbstractFetcher<?, ?> fetcher = this.kafkaFetcher;
            if (fetcher == null) {
                // the fetcher has not yet been initialized, which means we need to return the
                // originally restored offsets or the assigned partitions
                for (Map.Entry<KafkaTopicPartition, Long> subscribedPartition : subscribedPartitionsToStartOffsets.entrySet()) {
                    offsetsStateForCheckpoint.add(Tuple2.of(subscribedPartition.getKey(), subscribedPartition.getValue()));
                }

                if (offsetCommitMode == OffsetCommitMode.ON_CHECKPOINTS) {
                    // the map cannot be asynchronously updated, because only one checkpoint call can happen
                    // on this function at a time: either snapshotState() or notifyCheckpointComplete()
                    pendingOffsetsToCommit.put(context.getCheckpointId(), restoredState);
                }
            } else {
                HashMap<KafkaTopicPartition, Long> currentOffsets = fetcher.snapshotCurrentState();

                if (offsetCommitMode == OffsetCommitMode.ON_CHECKPOINTS) {
                    // the map cannot be asynchronously updated, because only one checkpoint call can happen
                    // on this function at a time: either snapshotState() or notifyCheckpointComplete()
                    pendingOffsetsToCommit.put(context.getCheckpointId(), currentOffsets);
                }

                for (Map.Entry<KafkaTopicPartition, Long> kafkaTopicPartitionLongEntry : currentOffsets.entrySet()) {
                    offsetsStateForCheckpoint.add(
                            Tuple2.of(kafkaTopicPartitionLongEntry.getKey(), kafkaTopicPartitionLongEntry.getValue()));
                }
            }

            if (offsetCommitMode == OffsetCommitMode.ON_CHECKPOINTS) {
                // truncate the map of pending offsets to commit, to prevent infinite growth
                while (pendingOffsetsToCommit.size() > MAX_NUM_PENDING_CHECKPOINTS) {
                    pendingOffsetsToCommit.remove(0);
                }
            }
        }
    }

snapshot方法创建checkpoint的做法是把当前的KafkaTopicPartition和目前消费到的offset值不断存放到offsetsStateForCheckpoint这个state对象里，然后把当前的checkpointid和对应的offset存到pendingOffsetsToCommit这个linkmap。当前offset的获取分两个情况，初始化的时候（if (fetcher == null) {...}）和fetcher已经初始化成功，初始化的时候从restoredState获取，正常运行中获取fetcher.snapshotCurrentState()。

 

notifyCheckpointComplete

public final void notifyCheckpointComplete(long checkpointId) throws Exception {
        if (!running) {
            LOG.debug("notifyCheckpointComplete() called on closed source");
            return;
        }

        final AbstractFetcher<?, ?> fetcher = this.kafkaFetcher;
        if (fetcher == null) {
            LOG.debug("notifyCheckpointComplete() called on uninitialized source");
            return;
        }

        if (offsetCommitMode == OffsetCommitMode.ON_CHECKPOINTS) {
            // only one commit operation must be in progress
            if (LOG.isDebugEnabled()) {
                LOG.debug("Committing offsets to Kafka/ZooKeeper for checkpoint " + checkpointId);
            }

            try {
                final int posInMap = pendingOffsetsToCommit.indexOf(checkpointId);
                if (posInMap == -1) {
                    LOG.warn("Received confirmation for unknown checkpoint id {}", checkpointId);
                    return;
                }

                @SuppressWarnings("unchecked")
                HashMap<KafkaTopicPartition, Long> offsets =
                    (HashMap<KafkaTopicPartition, Long>) pendingOffsetsToCommit.remove(posInMap);

                // remove older checkpoints in map
                for (int i = 0; i < posInMap; i++) {
                    pendingOffsetsToCommit.remove(0);
                }

                if (offsets == null || offsets.size() == 0) {
                    LOG.debug("Checkpoint state was empty.");
                    return;
                }

                fetcher.commitInternalOffsetsToKafka(offsets, offsetCommitCallback);
            } catch (Exception e) {
                if (running) {
                    throw e;
                }
                // else ignore exception if we are no longer running
            }
        }
    }

notifyCheckpointComplete主要是在checkpoint结束后在ON_CHECKPOINTS的情况下向kafka集群commit offset，方法调用时会拿到已经完成的checkpointid，从前文的pendingOffsetsToCommit列表里找到对应的offset。如果判断索引不存在，则直接退出。否则，移除该索引对应的快照信息，然后将小于当前索引（较旧的）的快照信息也一并移除（这一点我之前解释过，因为所有的检查点都是按时间递增有序的）。最后将当前完成的检查点对应的消息的偏移量进行commit，也即commitOffsets。只不过这里该方法被定义为抽象方法，因为Kafka不同版本的API差别的原因，由适配不同版本的consumer各自实现，目前kafka09和010实现都是在Kafka09Fetcher内实现的commitInternalOffsetsToKafka方法。

 

参考文档：

http://blog.csdn.net/yanghua_kobe/article/details/51503885