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Flink 中kafka broker缩容导致Task一直重启

news 2025/9/16 16:39:02

背景

Flink版本 1.12.2
Kafka 客户端 2.4.1
在公司的Flink平台运行了一个读Kafka计算DAU的流程序，由于公司Kafka的缩容，直接导致了该程序一直在重启，重启了一个小时都还没恢复（具体的所容操作是下掉了四台kafka broker，而当时flink配置了12台kafka broker），当时具体的现场如下：

JobManaer上的日志如下：
2023-10-07 10:02:52.975 INFO  org.apache.flink.runtime.executiongraph.ExecutionGraph - Source: TableSourceScan(table=[[default_catalog, default_database, ubt_start, watermark=[-(LOCALTIMESTAMP, 1000:INTERVAL SECOND)]]]) (34/64) (e33d9ad0196a71e8eb551c181eb779b5) switched from RUNNING to FAILED on container_e08_1690538387235_2599_01_000010 @ task-xxxx-shanghai.emr.aliyuncs.com (dataPort=xxxx).
org.apache.flink.streaming.connectors.kafka.internals.Handover$ClosedException: nullat org.apache.flink.streaming.connectors.kafka.internals.Handover.close(Handover.java:177)at org.apache.flink.streaming.connectors.kafka.internals.KafkaFetcher.cancel(KafkaFetcher.java:164)at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.cancel(FlinkKafkaConsumerBase.java:945)at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.lambda$createAndStartDiscoveryLoop$2(FlinkKafkaConsumerBase.java:913)at java.lang.Thread.run(Thread.java:750)对应的 TaskManager（task-xxxx-shanghai.emr.aliyuncs.com）上的日志如下：2023-10-07 10:02:24.604 WARN  org.apache.kafka.clients.NetworkClient - [Consumer clientId=xxxx] Connection to node 46129 (sh-bs-b1-303-i14-kafka-129-46.ximalaya.local/192.168.129.46:9092) could not be established. Broker may not be available.2023-10-07 10:02:52.939 WARN  org.apache.flink.runtime.taskmanager.Task - Source: TableSourceScan(t) (34/64)#0 (e33d9ad0196a71e8eb551c181eb779b5) switched from RUNNING to FAILED.
org.apache.flink.streaming.connectors.kafka.internals.Handover$ClosedException: nullat org.apache.flink.streaming.connectors.kafka.internals.Handover.close(Handover.java:177)at org.apache.flink.streaming.connectors.kafka.internals.KafkaFetcher.cancel(KafkaFetcher.java:164)at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.cancel(FlinkKafkaConsumerBase.java:945)at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.lambda$createAndStartDiscoveryLoop$2(FlinkKafkaConsumerBase.java:913)at java.lang.Thread.run(Thread.java:750)2023-10-07 10:04:58.205 WARN  org.apache.kafka.clients.NetworkClient - [Consumer clientId=xxx, groupId=xxxx] Connection to node -4 (xxxx:909) could not be established. Broker may not be available.
2023-10-07 10:04:58.205 WARN  org.apache.kafka.clients.NetworkClient - [Consumer clientId=xxx, groupId=xxxx] Bootstrap broker sxxxx:909 (id: -4 rack: null) disconnected
2023-10-07 10:04:58.206 WARN  org.apache.kafka.clients.NetworkClient - [Consumer clientId=xxx, groupId=xxxxu] Connection to node -5 (xxxx:9092) could not be established. Broker may not be available.
2023-10-07 10:04:58.206 WARN  org.apache.kafka.clients.NetworkClient - [Consumer clientId=xxx, groupId=xxxxu] Bootstrap broker xxxx:9092 (id: -5 rack: null) disconnected2023-10-07 10:08:15.541 WARN  org.apache.flink.runtime.taskmanager.Task - Source: TableSourceScan(xxx) switched from RUNNING to FAILED.
org.apache.kafka.common.errors.TimeoutException: Timeout expired while fetching topic metadata

当时Flink中kafka source的相关配置如下：

scan.topic-partition-discovery.interval  300000
restart-strategy.type fixed-delay
restart-strategy.fixed-delay.attempts 50000000
jobmanager.execution.failover-strategy region

结论以及解决

目前在kafka 消费端有两个参数default.api.timeout.ms（默认60000），request.timeout.ms（默认30000），这两个参数来控制kakfa的客户端从服务端请求超时，也就是说每次请求的超时时间是30s，超时之后可以再重试，如果在60s内请求没有得到任何回应，则会报TimeOutException,具体的见如下分析，
我们在flink kafka connector中通过设置如下参数来解决：

`properties.default.api.timeout.ms` = '600000',
`properties.request.timeout.ms` = '5000',
// max.block.ms是设置kafka producer的超时
`properties.max.block.ms` = '600000',

分析

在Flink中对于Kafka的Connector的DynamicTableSourceFactory是KafkaDynamicTableFactory，这里我们只讨论kafka作为source的情况，
而该类的方法createDynamicTableSource最终会被调用，至于具体的调用链可以参考Apache Hudi初探(四)(与flink的结合)–Flink Sql中hudi的createDynamicTableSource/createDynamicTableSink/是怎么被调用–只不过把Sink改成Source就可以了，所以最终会到KafkaDynamicSource类:

@Overridepublic ScanRuntimeProvider getScanRuntimeProvider(ScanContext context) {final DeserializationSchema<RowData> keyDeserialization =createDeserialization(context, keyDecodingFormat, keyProjection, keyPrefix);final DeserializationSchema<RowData> valueDeserialization =createDeserialization(context, valueDecodingFormat, valueProjection, null);final TypeInformation<RowData> producedTypeInfo =context.createTypeInformation(producedDataType);final FlinkKafkaConsumer<RowData> kafkaConsumer =createKafkaConsumer(keyDeserialization, valueDeserialization, producedTypeInfo);return SourceFunctionProvider.of(kafkaConsumer, false);}

该类的getScanRuntimeProvider方法会被调用，所有kafka相关的操作都可以追溯到FlinkKafkaConsumer类（继承FlinkKafkaConsumerBase）中，对于该类重点的方法如下:

    @Overridepublic final void initializeState(FunctionInitializationContext context) throws Exception {OperatorStateStore stateStore = context.getOperatorStateStore();this.unionOffsetStates =stateStore.getUnionListState(new ListStateDescriptor<>(OFFSETS_STATE_NAME,createStateSerializer(getRuntimeContext().getExecutionConfig())));... }@Overridepublic void open(Configuration configuration) throws Exception {// determine the offset commit modethis.offsetCommitMode =OffsetCommitModes.fromConfiguration(getIsAutoCommitEnabled(),enableCommitOnCheckpoints,((StreamingRuntimeContext) getRuntimeContext()).isCheckpointingEnabled());// create the partition discovererthis.partitionDiscoverer =createPartitionDiscoverer(topicsDescriptor,getRuntimeContext().getIndexOfThisSubtask(),getRuntimeContext().getNumberOfParallelSubtasks());this.partitionDiscoverer.open();subscribedPartitionsToStartOffsets = new HashMap<>();final List<KafkaTopicPartition> allPartitions = partitionDiscoverer.discoverPartitions();if (restoredState != null) {...} else {// use the partition discoverer to fetch the initial seed partitions,// and set their initial offsets depending on the startup mode.// for SPECIFIC_OFFSETS and TIMESTAMP modes, we set the specific offsets now;// for other modes (EARLIEST, LATEST, and GROUP_OFFSETS), the offset is lazily// determined// when the partition is actually read.switch (startupMode) {。。。default:for (KafkaTopicPartition seedPartition : allPartitions) {subscribedPartitionsToStartOffsets.put(seedPartition, startupMode.getStateSentinel());}}if (!subscribedPartitionsToStartOffsets.isEmpty()) {switch (startupMode) {...case GROUP_OFFSETS:LOG.info("Consumer subtask {} will start reading the following {} partitions from the committed group offsets in Kafka: {}",getRuntimeContext().getIndexOfThisSubtask(),subscribedPartitionsToStartOffsets.size(),subscribedPartitionsToStartOffsets.keySet());}} else {LOG.info("Consumer subtask {} initially has no partitions to read from.",getRuntimeContext().getIndexOfThisSubtask());}}this.deserializer.open(RuntimeContextInitializationContextAdapters.deserializationAdapter(getRuntimeContext(), metricGroup -> metricGroup.addGroup("user")));}@Overridepublic void run(SourceContext<T> sourceContext) throws Exception {if (subscribedPartitionsToStartOffsets == null) {throw new Exception("The partitions were not set for the consumer");}// initialize commit metrics and default offset callback methodthis.successfulCommits =this.getRuntimeContext().getMetricGroup().counter(COMMITS_SUCCEEDED_METRICS_COUNTER);this.failedCommits =this.getRuntimeContext().getMetricGroup().counter(COMMITS_FAILED_METRICS_COUNTER);final int subtaskIndex = this.getRuntimeContext().getIndexOfThisSubtask();this.offsetCommitCallback =new KafkaCommitCallback() {@Overridepublic void onSuccess() {successfulCommits.inc();}@Overridepublic void onException(Throwable cause) {LOG.warn(String.format("Consumer subtask %d failed async Kafka commit.",subtaskIndex),cause);failedCommits.inc();}};// mark the subtask as temporarily idle if there are no initial seed partitions;// once this subtask discovers some partitions and starts collecting records, the subtask's// status will automatically be triggered back to be active.if (subscribedPartitionsToStartOffsets.isEmpty()) {sourceContext.markAsTemporarilyIdle();}LOG.info("Consumer subtask {} creating fetcher with offsets {}.",getRuntimeContext().getIndexOfThisSubtask(),subscribedPartitionsToStartOffsets);// from this point forward://   - 'snapshotState' will draw offsets from the fetcher,//     instead of being built from `subscribedPartitionsToStartOffsets`//   - 'notifyCheckpointComplete' will start to do work (i.e. commit offsets to//     Kafka through the fetcher, if configured to do so)this.kafkaFetcher =createFetcher(sourceContext,subscribedPartitionsToStartOffsets,watermarkStrategy,(StreamingRuntimeContext) getRuntimeContext(),offsetCommitMode,getRuntimeContext().getMetricGroup().addGroup(KAFKA_CONSUMER_METRICS_GROUP),useMetrics);if (!running) {return;}if (discoveryIntervalMillis == PARTITION_DISCOVERY_DISABLED) {kafkaFetcher.runFetchLoop();} else {runWithPartitionDiscovery();}}@Overridepublic final void snapshotState(FunctionSnapshotContext context) throws Exception {...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()) {unionOffsetStates.add(Tuple2.of(kafkaTopicPartitionLongEntry.getKey(),kafkaTopicPartitionLongEntry.getValue()));}... }}@Overridepublic final void notifyCheckpointComplete(long checkpointId) throws Exception {...fetcher.commitInternalOffsetsToKafka(offsets, offsetCommitCallback);...}

主要是initializeState，open,run,snapshotState,notifyCheckpointComplete这四个方法，下面带着问题逐一介绍一下：
注意:对于initializeState和open方法的先后顺序，可以参考StreamTask类,其中如下的调用链：

invoke()||\/
beforeInvoke()||\/
operatorChain.initializeStateAndOpenOperators||\/
FlinkKafkaConsumerBase.initializeState||\/
FlinkKafkaConsumerBase.open

就可以知道 initializeState方法的调用是在open之前的

initializeState方法

这里做的事情就是从持久化的State中恢复kafkaTopicOffset信息,我们这里假设是第一次启动

open方法

offsetCommitMode
offsetCommitMode = OffsetCommitModes.fromConfiguration 这里获取设置的kafka offset的提交模式,这里会综合enable.auto.commit的配置（默认是true）,enableCommitOnCheckpoints默认是true，checkpointing设置为true(默认是false)，综合以上得到的值为OffsetCommitMode.ON_CHECKPOINTS

partitionDiscoverer
这里主要是进行kafka的topic的分区发现,主要路程是 partitionDiscoverer.discoverPartitions,这里的涉及的流程如下：

AbstractPartitionDiscoverer.discoverPartitions||\/
AbstractPartitionDiscoverer.getAllPartitionsForTopics ||\/
KafkaPartitionDiscoverer.kafkaConsumer.partitionsFor||\/
KafkaConsumer.partitionsFor(topic, Duration.ofMillis(defaultApiTimeoutMs)) //这里的defaultApiTimeoutMs 来自于*default.api.timeout.ms*||\/
Fetcher.getTopicMetadata //这里面最后抛出 new TimeoutException("Timeout expired while fetching topic metadata");||\/
Fetcher.sendMetadataRequest => NetworkClient.leastLoadedNode //这里会根据某种策略选择配置的broker的节点||\/
client.poll(future, timer) => NetworkClient.poll => selector.poll(Utils.min(timeout, metadataTimeout, defaultRequestTimeoutMs)); // 这里的 *defaultRequestTimeoutMs* 来自配置*request.timeout.ms*

综上所述，discoverPartitions做的就是根据某种策略选择配置的broker节点，对每个节点进行请求，request.timeout.ms超时后，再根据策略选择broker，直至总的时间达到了配置的default.api.timeout.ms,这里默认default.api.timeout.ms 为60秒，request.timeout.ms为30秒

subscribedPartitionsToStartOffsets
根据startupMode模式，默认是StartupMode.GROUP_OFFSETS(默认从上次消费的offset开始消费),设置开启的kafka offset,这在kafkaFetcher中会用到

run方法

设置一些指标successfulCommits/failedCommits

KafkaFetcher
这里主要是从kafka获取数据以及如果有分区发现则循环进kafka的topic分区发现，这里会根据配置scan.topic-partition-discovery.interval默认配置为0,实际中设置的为300000，即5分钟。该主要的流程为在方法runWithPartitionDiscovery:

  private void runWithPartitionDiscovery() throws Exception {final AtomicReference<Exception> discoveryLoopErrorRef = new AtomicReference<>();createAndStartDiscoveryLoop(discoveryLoopErrorRef);kafkaFetcher.runFetchLoop();// make sure that the partition discoverer is waked up so that// the discoveryLoopThread exitspartitionDiscoverer.wakeup();joinDiscoveryLoopThread();// rethrow any fetcher errorsfinal Exception discoveryLoopError = discoveryLoopErrorRef.get();if (discoveryLoopError != null) {throw new RuntimeException(discoveryLoopError);}}

createAndStartDiscoveryLoop 这个会启动单个线程以while sleep方式实现以scan.topic-partition-discovery.interval为间隔来轮询进行Kafka的分区发现，注意这里会吞没Execption,并不会抛出异常

 private void createAndStartDiscoveryLoop(AtomicReference<Exception> discoveryLoopErrorRef) {discoveryLoopThread =new Thread(...while (running) {...try {discoveredPartitions =partitionDiscoverer.discoverPartitions();} catch (AbstractPartitionDiscoverer.WakeupException| AbstractPartitionDiscoverer.ClosedException e) {break;}if (running && !discoveredPartitions.isEmpty()) {kafkaFetcher.addDiscoveredPartitions(discoveredPartitions);}if (running && discoveryIntervalMillis != 0) {try {Thread.sleep(discoveryIntervalMillis);} catch (InterruptedException iex) {break;}}}} catch (Exception e) {discoveryLoopErrorRef.set(e);} finally {// calling cancel will also let the fetcher loop escape// (if not running, cancel() was already called)if (running) {cancel();}}},"Kafka Partition Discovery for "+ getRuntimeContext().getTaskNameWithSubtasks());discoveryLoopThread.start();
}

这里的kafkaFetcher.addDiscoveredPartitions(discoveredPartitions);中subscribedPartitionStates变量会把发现分区信息保存起来，这在kafkaFetcher.runFetchLoop中会设置已经提交的offset信息,并且会在snapshotState会用到

kafkaFetcher.runFetchLoop 这里会从kafka拉取数据，并设置kafka的offset,具体的流程如下：

 runFetchLoop ||\/subscribedPartitionStates 这里会获取*subscribedPartitionStates*变量||\/partitionConsumerRecordsHandler||\/emitRecordsWithTimestamps||\/emitRecordsWithTimestamps||\/partitionState.setOffset(offset);

这里的offset就是从消费的kafka记录中获取的

snapshotState方法

这里会对subscribedPartitionStates中的信息进行处理，主要是加到pendingOffsetsToCommit变量中

offsetCommitMode
这里上面说到是OffsetCommitMode.ON_CHECKPOINTS,如果是ON_CHECKPOINTS,则会从fetcher.snapshotCurrentState获取subscribedPartitionStates
并加到pendingOffsetsToCommit，并持久化到unionOffsetStates中，这实际的kafka offset commit操作在notifyCheckpointComplete中，

notifyCheckpointComplete方法

获取到要提交的kafka offset信息，并持久化保存kafka中

参考

open 和 initailizeState的初始化顺序
A single failing Kafka broker may cause jobs to fail indefinitely with TimeoutException: Timeout expired while fetching topic metadata

背景