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$ClosedException: null
at org.apache.flink.streaming.connectors.kafka.internals.Handover.close(Handover.java:177)
at org.apache.flink.streaming.connectors.kafka.internals.KafkaFetcher.cancel(KafkaFetcher.java:164)
at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.cancel(FlinkKafkaConsumerBase.java:945)
at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.lambda$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: null
at 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) disconnected
2023-10-07 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:
@Override
public 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:
@Override
public final void initializeState(FunctionInitializationContext context) throws Exception {
OperatorStateStore stateStore = context.getOperatorStateStore();
this.unionOffsetStates =
stateStore.getUnionListState(
new ListStateDescriptor<>(
OFFSETS_STATE_NAME,
createStateSerializer(getRuntimeContext().getExecutionConfig())));
...
}
@Override
public void open(Configuration configuration) throws Exception {
// determine the offset commit mode
this.offsetCommitMode =
OffsetCommitModes.fromConfiguration(
getIsAutoCommitEnabled(),
enableCommitOnCheckpoints,
((StreamingRuntimeContext) getRuntimeContext()).isCheckpointingEnabled());
// create the partition discoverer
this.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")));
}
@Override
public 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 method
this.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() {
@Override
public void onSuccess() {
successfulCommits.inc();
}
@Override
public 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();
}
}
@Override
public 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()));
}
...
}
}
@Override
public 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 //这里会随机选择配置的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 做的就是随机选择配置的broker节点，对每个节点进行请求，request.timeout.ms超时后，再随机选择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 exits partitionDiscoverer.wakeup(); joinDiscoveryLoopThread(); // rethrow any fetcher errors final 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