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Flink源码阅读之Checkpoint执行过程
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前言
对应Flink来说checkpoint的作用及重要性就不细说了，前面文章写过checkpoint的详细过程和checkpoint周期性触发过程。本篇我们在一起根据源码看下checkpoint的详细执行过程。
checkpoint过程
源头
我们都知道checkpoint的周期性触发是由jobmanager中的一个叫做CheckpointCoordinator角色发起的，具体执行在CheckpointCoordinator.triggerCheckpoint中，这个方法代码逻辑很长，概括一下主要包括：
预检查。包括：
是否需要强制进行 checkpoint
当前正在排队的并发 checkpoint 的数目是否超过阈值
距离上一次成功 checkpoint 的间隔时间是否过小
如果上述条件不满足则不会进行这次checkpoint。
检查需要触发的task是否都是running状态，否则放弃。之前踩过坑，请见记一次flink不做checkpoint的问题。
检查所有需要ack checkpoint完成的task是否都是running状态。否则放弃。上面的检查都通过之后就可以做checkpoint啦。
生成唯一自增的checkpointID。
初始化CheckpointStorageLocation，用于存储这次checkpoint快照的路径，不同的backend有区别。
生成 PendingCheckpoint，这表示一个处于中间状态的 checkpoint，并保存在 checkpointId -> PendingCheckpoint 这样的映射关系中。
注册一个调度任务，在 checkpoint 超时后取消此次 checkpoint，并重新触发一次新的 checkpoint
调用 Execution.triggerCheckpoint() 方法向所有需要 trigger 的 task 发起 checkpoint 请求
for (Execution execution: executions) {
if (props.isSynchronous()) {
execution.triggerSynchronousSavepoint(checkpointID, timestamp, checkpointOptions, advanceToEndOfTime);
} else {
execution.triggerCheckpoint(checkpointID, timestamp, checkpointOptions);
}
}
最终通过 RPC 调用 TaskExecutorGateway.triggerCheckpoint，即请求执行 TaskExecutor.triggerCheckpoin()。因为一个 TaskExecutor 中可能有多个 Task 正在运行，因而要根据触发 checkpoint 的 ExecutionAttemptID 找到对应的 Task，然后调用 Task.triggerCheckpointBarrier() 方法
private void triggerCheckpointHelper(long checkpointId, long timestamp, CheckpointOptions checkpointOptions, boolean advanceToEndOfEventTime) {
final CheckpointType checkpointType = checkpointOptions.getCheckpointType();
if (advanceToEndOfEventTime && !(checkpointType.isSynchronous() && checkpointType.isSavepoint())) {
throw new IllegalArgumentException("Only synchronous savepoints are allowed to advance the watermark to MAX.");
}
final LogicalSlot slot = assignedResource;
if (slot != null) {
final TaskManagerGateway taskManagerGateway = slot.getTaskManagerGateway();
taskManagerGateway.triggerCheckpoint(attemptId, getVertex().getJobId(), checkpointId, timestamp, checkpointOptions, advanceToEndOfEventTime);
} else {
LOG.debug("The execution has no slot assigned. This indicates that the execution is no longer running.");
}
}
@Override
public CompletableFuture<Acknowledge> triggerCheckpoint(
ExecutionAttemptID executionAttemptID,
long checkpointId,
long checkpointTimestamp,
CheckpointOptions checkpointOptions,
boolean advanceToEndOfEventTime) {
log.debug("Trigger checkpoint {}@{} for {}.", checkpointId, checkpointTimestamp, executionAttemptID);
final CheckpointType checkpointType = checkpointOptions.getCheckpointType();
if (advanceToEndOfEventTime && !(checkpointType.isSynchronous() && checkpointType.isSavepoint())) {
throw new IllegalArgumentException("Only synchronous savepoints are allowed to advance the watermark to MAX.");
}
final Task task = taskSlotTable.getTask(executionAttemptID);
if (task != null) {
task.triggerCheckpointBarrier(checkpointId, checkpointTimestamp, checkpointOptions, advanceToEndOfEventTime);
return CompletableFuture.completedFuture(Acknowledge.get());
} else {
final String message = "TaskManager received a checkpoint request for unknown task " + executionAttemptID + '.';
log.debug(message);
return FutureUtils.completedExceptionally(new CheckpointException(message, CheckpointFailureReason.TASK_CHECKPOINT_FAILURE));
}
}
Task 执行 checkpoint 的真正逻辑被封装在 AbstractInvokable.triggerCheckpointAsync(...) 中，
public void triggerCheckpointBarrier(
final long checkpointID,
final long checkpointTimestamp,
final CheckpointOptions checkpointOptions,
final boolean advanceToEndOfEventTime) {
final AbstractInvokable invokable = this.invokable;
final CheckpointMetaData checkpointMetaData = new CheckpointMetaData(checkpointID, checkpointTimestamp);
if (executionState == ExecutionState.RUNNING && invokable != null) {
try {
invokable.triggerCheckpointAsync(checkpointMetaData, checkpointOptions, advanceToEndOfEventTime);
}
catch (RejectedExecutionException ex) {
// This may happen if the mailbox is closed. It means that the task is shutting down, so we just ignore it.
LOG.debug(
"Triggering checkpoint {} for {} ({}) was rejected by the mailbox",
checkpointID, taskNameWithSubtask, executionId);
}
catch (Throwable t) {
if (getExecutionState() == ExecutionState.RUNNING) {
failExternally(new Exception(
"Error while triggering checkpoint " + checkpointID + " for " +
taskNameWithSubtask, t));
} else {
LOG.debug("Encountered error while triggering checkpoint {} for " +
"{} ({}) while being not in state running.", checkpointID,
taskNameWithSubtask, executionId, t);
}
}
}
else {
LOG.debug("Declining checkpoint request for non-running task {} ({}).", taskNameWithSubtask, executionId);
// send back a message that we did not do the checkpoint
checkpointResponder.declineCheckpoint(jobId, executionId, checkpointID,
new CheckpointException("Task name with subtask : " + taskNameWithSubtask, CheckpointFailureReason.CHECKPOINT_DECLINED_TASK_NOT_READY));
}
}
triggerCheckpointAsync方法分别被SourceStreamTask和普通StreamTask覆盖，主要逻辑还是在StreamTask中
private boolean performCheckpoint(
CheckpointMetaData checkpointMetaData,
CheckpointOptions checkpointOptions,
CheckpointMetrics checkpointMetrics,
boolean advanceToEndOfTime) throws Exception {
LOG.debug("Starting checkpoint ({}) {} on task {}",
checkpointMetaData.getCheckpointId(), checkpointOptions.getCheckpointType(), getName());
final long checkpointId = checkpointMetaData.getCheckpointId();
if (isRunning) {
actionExecutor.runThrowing(() -> {
if (checkpointOptions.getCheckpointType().isSynchronous()) {
setSynchronousSavepointId(checkpointId);
if (advanceToEndOfTime) {
advanceToEndOfEventTime();
}
}
// All of the following steps happen as an atomic step from the perspective of barriers and
// records/watermarks/timers/callbacks.
// We generally try to emit the checkpoint barrier as soon as possible to not affect downstream
// checkpoint alignments
// Step (1): Prepare the checkpoint, allow operators to do some pre-barrier work.
// The pre-barrier work should be nothing or minimal in the common case.
operatorChain.prepareSnapshotPreBarrier(checkpointId);
// Step (2): Send the checkpoint barrier downstream
operatorChain.broadcastCheckpointBarrier(
checkpointId,
checkpointMetaData.getTimestamp(),
checkpointOptions);
// Step (3): Take the state snapshot. This should be largely asynchronous, to not
// impact progress of the streaming topology
checkpointState(checkpointMetaData, checkpointOptions, checkpointMetrics);
});
return true;
} else {
actionExecutor.runThrowing(() -> {
// we cannot perform our checkpoint - let the downstream operators know that they
// should not wait for any input from this operator
// we cannot broadcast the cancellation markers on the 'operator chain', because it may not
// yet be created
final CancelCheckpointMarker message = new CancelCheckpointMarker(checkpointMetaData.getCheckpointId());
recordWriter.broadcastEvent(message);
});
return false;
}
}
主要做三件事：1）checkpoint的准备操作，这里通常不进行太多操作；2）发送 CheckpointBarrier；3）存储检查点快照。
广播Barrier
public void broadcastCheckpointBarrier(long id, long timestamp, CheckpointOptions checkpointOptions) throws IOException {
CheckpointBarrier barrier = new CheckpointBarrier(id, timestamp, checkpointOptions);
for (RecordWriterOutput<?> streamOutput : streamOutputs) {
streamOutput.broadcastEvent(barrier);
}
}
进行快照
private void checkpointState(
CheckpointMetaData checkpointMetaData,
CheckpointOptions checkpointOptions,
CheckpointMetrics checkpointMetrics) throws Exception {
//checkpoint的存储地址及元数据信息
CheckpointStreamFactory storage = checkpointStorage.resolveCheckpointStorageLocation(
checkpointMetaData.getCheckpointId(),
checkpointOptions.getTargetLocation());
//将checkpoint的过程封装为CheckpointingOperation对象
CheckpointingOperation checkpointingOperation = new CheckpointingOperation(
this,
checkpointMetaData,
checkpointOptions,
storage,
checkpointMetrics);
checkpointingOperation.executeCheckpointing();
}
每一个算子的快照被抽象为 OperatorSnapshotFutures，包含了 operator state 和 keyed state 的快照结果：
public class OperatorSnapshotFutures {
@Nonnull
private RunnableFuture<SnapshotResult<KeyedStateHandle>> keyedStateManagedFuture;
@Nonnull
private RunnableFuture<SnapshotResult<KeyedStateHandle>> keyedStateRawFuture;
@Nonnull
private RunnableFuture<SnapshotResult<OperatorStateHandle>> operatorStateManagedFuture;
@Nonnull
private RunnableFuture<SnapshotResult<OperatorStateHandle>> operatorStateRawFuture;
}
由于每一个 StreamTask 可能包含多个算子，因而内部使用一个 Map 维护 OperatorID -> OperatorSnapshotFutures 的关系。
private final Map<OperatorID, OperatorSnapshotFutures> operatorSnapshotsInProgress;
快照的过程分同步和异步两个部分
public void executeCheckpointing() throws Exception {
startSyncPartNano = System.nanoTime();
try {
//同步
for (StreamOperator<?> op : allOperators) {
checkpointStreamOperator(op);
}
if (LOG.isDebugEnabled()) {
LOG.debug("Finished synchronous checkpoints for checkpoint {} on task {}",
checkpointMetaData.getCheckpointId(), owner.getName());
}
startAsyncPartNano = System.nanoTime();
checkpointMetrics.setSyncDurationMillis((startAsyncPartNano - startSyncPartNano) / 1_000_000);
// we are transferring ownership over snapshotInProgressList for cleanup to the thread, active on submit
//异步
// checkpoint 可以配置成同步执行，也可以配置成异步执行的
// 如果是同步执行的，在这里实际上所有的 runnable future 都是已经完成的状态
AsyncCheckpointRunnable asyncCheckpointRunnable = new AsyncCheckpointRunnable(
owner,
operatorSnapshotsInProgress,
checkpointMetaData,
checkpointMetrics,
startAsyncPartNano);
owner.cancelables.registerCloseable(asyncCheckpointRunnable);
owner.asyncOperationsThreadPool.execute(asyncCheckpointRunnable);
if (LOG.isDebugEnabled()) {
LOG.debug("{} - finished synchronous part of checkpoint {}. " +
"Alignment duration: {} ms, snapshot duration {} ms",
owner.getName(), checkpointMetaData.getCheckpointId(),
checkpointMetrics.getAlignmentDurationNanos() / 1_000_000,
checkpointMetrics.getSyncDurationMillis());
}
} catch (Exception ex) {
// Cleanup to release resources
for (OperatorSnapshotFutures operatorSnapshotResult : operatorSnapshotsInProgress.values()) {
if (null != operatorSnapshotResult) {
try {
operatorSnapshotResult.cancel();
} catch (Exception e) {
LOG.warn("Could not properly cancel an operator snapshot result.", e);
}
}
}
if (LOG.isDebugEnabled()) {
LOG.debug("{} - did NOT finish synchronous part of checkpoint {}. " +
"Alignment duration: {} ms, snapshot duration {} ms",
owner.getName(), checkpointMetaData.getCheckpointId(),
checkpointMetrics.getAlignmentDurationNanos() / 1_000_000,
checkpointMetrics.getSyncDurationMillis());
}
if (checkpointOptions.getCheckpointType().isSynchronous()) {
// in the case of a synchronous checkpoint, we always rethrow the exception,
// so that the task fails.
// this is because the intention is always to stop the job after this checkpointing
// operation, and without the failure, the task would go back to normal execution.
throw ex;
} else {
owner.getEnvironment().declineCheckpoint(checkpointMetaData.getCheckpointId(), ex);
}
}
}
在同步执行阶段，会依次调用每一个算子的 StreamOperator.snapshotState，返回结果是一个 runnable future。根据 checkpoint 配置成同步模式和异步模式的区别，这个 future 可能处于完成状态，也可能处于未完成状态：
private void checkpointStreamOperator(StreamOperator<?> op) throws Exception {
if (null != op) {
//同步过程调用算子的snapshotState方法，返回OperatorSnapshotFutures可能已完成或未完成
OperatorSnapshotFutures snapshotInProgress = op.snapshotState(
checkpointMetaData.getCheckpointId(),
checkpointMetaData.getTimestamp(),
checkpointOptions,
storageLocation);
operatorSnapshotsInProgress.put(op.getOperatorID(), snapshotInProgress);
}
}
详细过程在AbstractStreamOperator#snapshotState
public final OperatorSnapshotFutures snapshotState(long checkpointId, long timestamp, CheckpointOptions checkpointOptions,
CheckpointStreamFactory factory) throws Exception {
KeyGroupRange keyGroupRange = null != keyedStateBackend ?
keyedStateBackend.getKeyGroupRange() : KeyGroupRange.EMPTY_KEY_GROUP_RANGE;
OperatorSnapshotFutures snapshotInProgress = new OperatorSnapshotFutures();
StateSnapshotContextSynchronousImpl snapshotContext = new StateSnapshotContextSynchronousImpl(
checkpointId,
timestamp,
factory,
keyGroupRange,
getContainingTask().getCancelables());
try {
//对状态进行快照，包括KeyedState和OperatorState
snapshotState(snapshotContext);
snapshotInProgress.setKeyedStateRawFuture(snapshotContext.getKeyedStateStreamFuture());
snapshotInProgress.setOperatorStateRawFuture(snapshotContext.getOperatorStateStreamFuture());
//写入operatorState快照
if (null != operatorStateBackend) {
snapshotInProgress.setOperatorStateManagedFuture(
operatorStateBackend.snapshot(checkpointId, timestamp, factory, checkpointOptions));
}
//写入keyedState快照
if (null != keyedStateBackend) {
snapshotInProgress.setKeyedStateManagedFuture(
keyedStateBackend.snapshot(checkpointId, timestamp, factory, checkpointOptions));
}
} catch (Exception snapshotException) {
try {
snapshotInProgress.cancel();
} catch (Exception e) {
snapshotException.addSuppressed(e);
}
String snapshotFailMessage = "Could not complete snapshot " + checkpointId + " for operator " +
getOperatorName() + ".";
if (!getContainingTask().isCanceled()) {
LOG.info(snapshotFailMessage, snapshotException);
}
try {
snapshotContext.closeExceptionally();
} catch (IOException e) {
snapshotException.addSuppressed(e);
}
throw new CheckpointException(snapshotFailMessage, CheckpointFailureReason.CHECKPOINT_DECLINED, snapshotException);
}
return snapshotInProgress;
}
我们知道state还分为raw state（原生state）和managed state（flink管理的state），timer定时器属于raw state，也需要写到snapshot中。
/**
* Stream operators with state, which want to participate in a snapshot need to override this hook method.
*
* @param context context that provides information and means required for taking a snapshot
*/
public void snapshotState(StateSnapshotContext context) throws Exception {
final KeyedStateBackend<?> keyedStateBackend = getKeyedStateBackend();
//TODO all of this can be removed once heap-based timers are integrated with RocksDB incremental snapshots
// 所有的 timer 都作为 raw keyed state 写入
if (keyedStateBackend instanceof AbstractKeyedStateBackend &&
((AbstractKeyedStateBackend<?>) keyedStateBackend).requiresLegacySynchronousTimerSnapshots()) {
KeyedStateCheckpointOutputStream out;
try {
out = context.getRawKeyedOperatorStateOutput();
} catch (Exception exception) {
throw new Exception("Could not open raw keyed operator state stream for " +
getOperatorName() + '.', exception);
}
try {
KeyGroupsList allKeyGroups = out.getKeyGroupList();
for (int keyGroupIdx : allKeyGroups) {
out.startNewKeyGroup(keyGroupIdx);
timeServiceManager.snapshotStateForKeyGroup(
new DataOutputViewStreamWrapper(out), keyGroupIdx);
}
} catch (Exception exception) {
throw new Exception("Could not write timer service of " + getOperatorName() +
" to checkpoint state stream.", exception);
} finally {
try {
out.close();
} catch (Exception closeException) {
LOG.warn("Could not close raw keyed operator state stream for {}. This " +
"might have prevented deleting some state data.", getOperatorName(), closeException);
}
}
}
}
上面是AbstractStreamOperator中的snapshotState做的操作，还有个子类AbstractUdfStreamOperator
public void snapshotState(StateSnapshotContext context) throws Exception {
//先调用父类方法，写入timer
super.snapshotState(context);
StreamingFunctionUtils.snapshotFunctionState(context, getOperatorStateBackend(), userFunction);
}
public static void snapshotFunctionState(
StateSnapshotContext context,
OperatorStateBackend backend,
Function userFunction) throws Exception {
Preconditions.checkNotNull(context);
Preconditions.checkNotNull(backend);
while (true) {
if (trySnapshotFunctionState(context, backend, userFunction)) {
break;
}
// inspect if the user function is wrapped, then unwrap and try again if we can snapshot the inner function
if (userFunction instanceof WrappingFunction) {
userFunction = ((WrappingFunction<?>) userFunction).getWrappedFunction();
} else {
break;
}
}
}
private static boolean trySnapshotFunctionState(
StateSnapshotContext context,
OperatorStateBackend backend,
Function userFunction) throws Exception {
//如果用户函数实现了CheckpointedFunction接口，则调用udf中的snapshotState方法进行快照
if (userFunction instanceof CheckpointedFunction) {
((CheckpointedFunction) userFunction).snapshotState(context);
return true;
}
// 如果用户函数实现了 ListCheckpointed
if (userFunction instanceof ListCheckpointed) {
//先调用 snapshotState 方法获取当前状态
@SuppressWarnings("unchecked")
List<Serializable> partitionableState = ((ListCheckpointed<Serializable>) userFunction).
snapshotState(context.getCheckpointId(), context.getCheckpointTimestamp());
//获取状态后端存储引用
ListState<Serializable> listState = backend.
getSerializableListState(DefaultOperatorStateBackend.DEFAULT_OPERATOR_STATE_NAME);
//清空
listState.clear();
//当前状态写入状态后端存储
if (null != partitionableState) {
try {
for (Serializable statePartition : partitionableState) {
listState.add(statePartition);
}
} catch (Exception e) {
listState.clear();
throw new Exception("Could not write partitionable state to operator " +
"state backend.", e);
}
}
return true;
}
return false;
}
到这里我们知道了checkpoint过程中如何调用到我们自己实现的快照方法。再看下flink管理的状态是如何写入快照的。
if (null != operatorStateBackend) {
snapshotInProgress.setOperatorStateManagedFuture(
operatorStateBackend.snapshot(checkpointId, timestamp, factory, checkpointOptions));
}
if (null != keyedStateBackend) {
snapshotInProgress.setKeyedStateManagedFuture(
keyedStateBackend.snapshot(checkpointId, timestamp, factory, checkpointOptions));
}
首先来看看 operator state。DefaultOperatorStateBackend 将实际的工作交给 DefaultOperatorStateBackendSnapshotStrategy 完成。首先，会为对当前注册的所有 operator state（包含 list state 和 broadcast state）做深度拷贝，然后将实际的写入操作封装在一个异步的 FutureTask 中，这个 FutureTask 的主要任务包括：1）打开输出流 2）写入状态元数据信息 3）写入状态 4）关闭输出流，获得状态句柄。如果不启用异步checkpoint模式，那么这个 FutureTask 在同步阶段就会立刻执行。
public RunnableFuture<SnapshotResult<OperatorStateHandle>> snapshot(
final long checkpointId,
final long timestamp,
@Nonnull final CheckpointStreamFactory streamFactory,
@Nonnull final CheckpointOptions checkpointOptions) throws IOException {
if (registeredOperatorStates.isEmpty() && registeredBroadcastStates.isEmpty()) {
return DoneFuture.of(SnapshotResult.empty());
}
final Map<String, PartitionableListState<?>> registeredOperatorStatesDeepCopies =
new HashMap<>(registeredOperatorStates.size());
final Map<String, BackendWritableBroadcastState<?, ?>> registeredBroadcastStatesDeepCopies =
new HashMap<>(registeredBroadcastStates.size());
ClassLoader snapshotClassLoader = Thread.currentThread().getContextClassLoader();
Thread.currentThread().setContextClassLoader(userClassLoader);
try {
// eagerly create deep copies of the list and the broadcast states (if any)
// in the synchronous phase, so that we can use them in the async writing.
//获得已注册的所有 list state 和 broadcast state 的深拷贝
if (!registeredOperatorStates.isEmpty()) {
for (Map.Entry<String, PartitionableListState<?>> entry : registeredOperatorStates.entrySet()) {
PartitionableListState<?> listState = entry.getValue();
if (null != listState) {
listState = listState.deepCopy();
}
registeredOperatorStatesDeepCopies.put(entry.getKey(), listState);
}
}
if (!registeredBroadcastStates.isEmpty()) {
for (Map.Entry<String, BackendWritableBroadcastState<?, ?>> entry : registeredBroadcastStates.entrySet()) {
BackendWritableBroadcastState<?, ?> broadcastState = entry.getValue();
if (null != broadcastState) {
broadcastState = broadcastState.deepCopy();
}
registeredBroadcastStatesDeepCopies.put(entry.getKey(), broadcastState);
}
}
} finally {
Thread.currentThread().setContextClassLoader(snapshotClassLoader);
}
//将主要写入操作封装为一个异步的FutureTask
AsyncSnapshotCallable<SnapshotResult<OperatorStateHandle>> snapshotCallable =
new AsyncSnapshotCallable<SnapshotResult<OperatorStateHandle>>() {
@Override
protected SnapshotResult<OperatorStateHandle> callInternal() throws Exception {
// 创建状态输出流
CheckpointStreamFactory.CheckpointStateOutputStream localOut =
streamFactory.createCheckpointStateOutputStream(CheckpointedStateScope.EXCLUSIVE);
snapshotCloseableRegistry.registerCloseable(localOut);
// 收集元数据
// get the registered operator state infos ...
List<StateMetaInfoSnapshot> operatorMetaInfoSnapshots =
new ArrayList<>(registeredOperatorStatesDeepCopies.size());
for (Map.Entry<String, PartitionableListState<?>> entry :
registeredOperatorStatesDeepCopies.entrySet()) {
operatorMetaInfoSnapshots.add(entry.getValue().getStateMetaInfo().snapshot());
}
// 写入元数据
// ... get the registered broadcast operator state infos ...
List<StateMetaInfoSnapshot> broadcastMetaInfoSnapshots =
new ArrayList<>(registeredBroadcastStatesDeepCopies.size());
for (Map.Entry<String, BackendWritableBroadcastState<?, ?>> entry :
registeredBroadcastStatesDeepCopies.entrySet()) {
broadcastMetaInfoSnapshots.add(entry.getValue().getStateMetaInfo().snapshot());
}
// ... write them all in the checkpoint stream ...
DataOutputView dov = new DataOutputViewStreamWrapper(localOut);
OperatorBackendSerializationProxy backendSerializationProxy =
new OperatorBackendSerializationProxy(operatorMetaInfoSnapshots, broadcastMetaInfoSnapshots);
backendSerializationProxy.write(dov);
// ... and then go for the states ...
// 写入状态
// we put BOTH normal and broadcast state metadata here
int initialMapCapacity =
registeredOperatorStatesDeepCopies.size() + registeredBroadcastStatesDeepCopies.size();
final Map<String, OperatorStateHandle.StateMetaInfo> writtenStatesMetaData =
new HashMap<>(initialMapCapacity);
for (Map.Entry<String, PartitionableListState<?>> entry :
registeredOperatorStatesDeepCopies.entrySet()) {
PartitionableListState<?> value = entry.getValue();
long[] partitionOffsets = value.write(localOut);
OperatorStateHandle.Mode mode = value.getStateMetaInfo().getAssignmentMode();
writtenStatesMetaData.put(
entry.getKey(),
new OperatorStateHandle.StateMetaInfo(partitionOffsets, mode));
}
// ... and the broadcast states themselves ...
for (Map.Entry<String, BackendWritableBroadcastState<?, ?>> entry :
registeredBroadcastStatesDeepCopies.entrySet()) {
BackendWritableBroadcastState<?, ?> value = entry.getValue();
long[] partitionOffsets = {value.write(localOut)};
OperatorStateHandle.Mode mode = value.getStateMetaInfo().getAssignmentMode();
writtenStatesMetaData.put(
entry.getKey(),
new OperatorStateHandle.StateMetaInfo(partitionOffsets, mode));
}
// ... and, finally, create the state handle.
OperatorStateHandle retValue = null;
if (snapshotCloseableRegistry.unregisterCloseable(localOut)) {
//关闭输出流，获得状态句柄，后面可以用这个句柄读取状态
StreamStateHandle stateHandle = localOut.closeAndGetHandle();
if (stateHandle != null) {
retValue = new OperatorStreamStateHandle(writtenStatesMetaData, stateHandle);
}
return SnapshotResult.of(retValue);
} else {
throw new IOException("Stream was already unregistered.");
}
}
@Override
protected void cleanupProvidedResources() {
// nothing to do
}
@Override
protected void logAsyncSnapshotComplete(long startTime) {
if (asynchronousSnapshots) {
logAsyncCompleted(streamFactory, startTime);
}
}
};
final FutureTask<SnapshotResult<OperatorStateHandle>> task =
snapshotCallable.toAsyncSnapshotFutureTask(closeStreamOnCancelRegistry);
//如果不是异步 checkpoint 那么在这里直接运行 FutureTask，即在同步阶段就完成了状态的写入
if (!asynchronousSnapshots) {
task.run();
}
return task;
}
keyed state 写入的基本流程与此相似，但由于 keyed state 在存储时有多种实现，包括基于堆内存和 RocksDB 的不同实现，此外基于 RocksDB 的实现还包括支持增量 checkpoint，因而相比于 operator state 要更复杂一些。
至此，我们介绍了快照操作的第一个阶段，即同步执行的阶段。异步执行阶段被封装为 AsyncCheckpointRunnable，主要的操作包括 1）执行同步阶段创建的 FutureTask 2）完成后向 CheckpointCoordinator 发送 Ack 响应。
protected static final class AsyncCheckpointRunnable implements Runnable, Closeable {
@Override
public void run() {
FileSystemSafetyNet.initializeSafetyNetForThread();
try {
TaskStateSnapshot jobManagerTaskOperatorSubtaskStates =
new TaskStateSnapshot(operatorSnapshotsInProgress.size());
TaskStateSnapshot localTaskOperatorSubtaskStates =
new TaskStateSnapshot(operatorSnapshotsInProgress.size());
// 完成每一个 operator 的状态写入
// 如果是同步 checkpoint，那么在此之前状态已经写入完成
// 如果是异步 checkpoint，那么在这里才会写入状态
for (Map.Entry<OperatorID, OperatorSnapshotFutures> entry : operatorSnapshotsInProgress.entrySet()) {
OperatorID operatorID = entry.getKey();
OperatorSnapshotFutures snapshotInProgress = entry.getValue();
// finalize the async part of all by executing all snapshot runnables
OperatorSnapshotFinalizer finalizedSnapshots =
new OperatorSnapshotFinalizer(snapshotInProgress);
jobManagerTaskOperatorSubtaskStates.putSubtaskStateByOperatorID(
operatorID,
finalizedSnapshots.getJobManagerOwnedState());
localTaskOperatorSubtaskStates.putSubtaskStateByOperatorID(
operatorID,
finalizedSnapshots.getTaskLocalState());
}
final long asyncEndNanos = System.nanoTime();
final long asyncDurationMillis = (asyncEndNanos - asyncStartNanos) / 1_000_000L;
checkpointMetrics.setAsyncDurationMillis(asyncDurationMillis);
if (asyncCheckpointState.compareAndSet(CheckpointingOperation.AsyncCheckpointState.RUNNING,
CheckpointingOperation.AsyncCheckpointState.COMPLETED)) {
//报告 snapshot 完成
reportCompletedSnapshotStates(
jobManagerTaskOperatorSubtaskStates,
localTaskOperatorSubtaskStates,
asyncDurationMillis);
} else {
LOG.debug("{} - asynchronous part of checkpoint {} could not be completed because it was closed before.",
owner.getName(),
checkpointMetaData.getCheckpointId());
}
} catch (Exception e) {
handleExecutionException(e);
} finally {
owner.cancelables.unregisterCloseable(this);
FileSystemSafetyNet.closeSafetyNetAndGuardedResourcesForThread();
}
}
}
private void reportCompletedSnapshotStates(
TaskStateSnapshot acknowledgedTaskStateSnapshot,
TaskStateSnapshot localTaskStateSnapshot,
long asyncDurationMillis) {
TaskStateManager taskStateManager = owner.getEnvironment().getTaskStateManager();
boolean hasAckState = acknowledgedTaskStateSnapshot.hasState();
boolean hasLocalState = localTaskStateSnapshot.hasState();
// we signal stateless tasks by reporting null, so that there are no attempts to assign empty state
// to stateless tasks on restore. This enables simple job modifications that only concern
// stateless without the need to assign them uids to match their (always empty) states.
taskStateManager.reportTaskStateSnapshots(
checkpointMetaData,
checkpointMetrics,
hasAckState ? acknowledgedTaskStateSnapshot : null,
hasLocalState ? localTaskStateSnapshot : null);
}
}
public class TaskStateManagerImpl implements TaskStateManager {
@Override
public void reportTaskStateSnapshots(
@Nonnull CheckpointMetaData checkpointMetaData,
@Nonnull CheckpointMetrics checkpointMetrics,
@Nullable TaskStateSnapshot acknowledgedState,
@Nullable TaskStateSnapshot localState) {
long checkpointId = checkpointMetaData.getCheckpointId();
localStateStore.storeLocalState(checkpointId, localState);
//发送 ACK 响应给 CheckpointCoordinator
checkpointResponder.acknowledgeCheckpoint(
jobId,
executionAttemptID,
checkpointId,
checkpointMetrics,
acknowledgedState);
}
}
Checkpoint 的确认
Task 对 checkpoint 的响应是通过 CheckpointResponder 接口完成的：
public interface CheckpointResponder {
/**
* Acknowledges the given checkpoint.
*/
void acknowledgeCheckpoint(
JobID jobID,
ExecutionAttemptID executionAttemptID,
long checkpointId,
CheckpointMetrics checkpointMetrics,
TaskStateSnapshot subtaskState);
/**
* Declines the given checkpoint.
*/
void declineCheckpoint(
JobID jobID,
ExecutionAttemptID executionAttemptID,
long checkpointId,
Throwable cause);
}
RpcCheckpointResponder 作为 CheckpointResponder 的具体实现，主要是通过 RPC 调用通知 CheckpointCoordinatorGateway，即通知给 JobMaster, JobMaster 调用 CheckpointCoordinator.receiveAcknowledgeMessage() 和 CheckpointCoordinator.receiveDeclineMessage() 进行处理。
确认完成
在一个 Task 完成 checkpoint 操作后，CheckpointCoordinator 接收到 Ack 响应，对 Ack 响应的处理流程主要如下：
根据 Ack 的 checkpointID 从 Map
pendingCheckpoints 中查找对应的 PendingCheckpoint ,>若存在对应的 PendingCheckpoint
这个 PendingCheckpoint 没有被丢弃，调用 PendingCheckpoint.acknowledgeTask 方法处理 Ack，根据处理结果的不同：
SUCCESS：判断是否已经接受了所有需要响应的 Ack，如果是，则调用 completePendingCheckpoint 完成此次 checkpoint
DUPLICATE：Ack 消息重复接收，直接忽略
UNKNOWN：未知的 Ack 消息，清理上报的 Ack 中携带的状态句柄
DISCARD：Checkpoint 已经被 discard，清理上报的 Ack 中携带的状态句柄
这个 PendingCheckpoint 已经被丢弃，抛出异常
若不存在对应的 PendingCheckpoint，则清理上报的 Ack 中携带的状态句柄相应代码：
class CheckpointCoordinator {
public boolean receiveAcknowledgeMessage(AcknowledgeCheckpoint message) throws CheckpointException {
if (shutdown || message == null) {
return false;
}
if (!job.equals(message.getJob())) {
LOG.error("Received wrong AcknowledgeCheckpoint message for job {}: {}", job, message);
return false;
}
final long checkpointId = message.getCheckpointId();
synchronized (lock) {
// we need to check inside the lock for being shutdown as well, otherwise we
// get races and invalid error log messages
if (shutdown) {
return false;
}
final PendingCheckpoint checkpoint = pendingCheckpoints.get(checkpointId);
if (checkpoint != null && !checkpoint.isDiscarded()) {
switch (checkpoint.acknowledgeTask(message.getTaskExecutionId(), message.getSubtaskState(), message.getCheckpointMetrics())) {
case SUCCESS:
LOG.debug("Received acknowledge message for checkpoint {} from task {} of job {}.",
checkpointId, message.getTaskExecutionId(), message.getJob());
if (checkpoint.isFullyAcknowledged()) {
completePendingCheckpoint(checkpoint);
}
break;
case DUPLICATE:
LOG.debug("Received a duplicate acknowledge message for checkpoint {}, task {}, job {}.",
message.getCheckpointId(), message.getTaskExecutionId(), message.getJob());
break;
case UNKNOWN:
LOG.warn("Could not acknowledge the checkpoint {} for task {} of job {}, " +
"because the task's execution attempt id was unknown. Discarding " +
"the state handle to avoid lingering state.", message.getCheckpointId(),
message.getTaskExecutionId(), message.getJob());
discardSubtaskState(message.getJob(), message.getTaskExecutionId(), message.getCheckpointId(), message.getSubtaskState());
break;
case DISCARDED:
LOG.warn("Could not acknowledge the checkpoint {} for task {} of job {}, " +
"because the pending checkpoint had been discarded. Discarding the " +
"state handle tp avoid lingering state.",
message.getCheckpointId(), message.getTaskExecutionId(), message.getJob());
discardSubtaskState(message.getJob(), message.getTaskExecutionId(), message.getCheckpointId(), message.getSubtaskState());
}
return true;
}
else if (checkpoint != null) {
// this should not happen
throw new IllegalStateException(
"Received message for discarded but non-removed checkpoint " + checkpointId);
}
else {
boolean wasPendingCheckpoint;
// message is for an unknown checkpoint, or comes too late (checkpoint disposed)
if (recentPendingCheckpoints.contains(checkpointId)) {
wasPendingCheckpoint = true;
LOG.warn("Received late message for now expired checkpoint attempt {} from " +
"{} of job {}.", checkpointId, message.getTaskExecutionId(), message.getJob());
}
else {
LOG.debug("Received message for an unknown checkpoint {} from {} of job {}.",
checkpointId, message.getTaskExecutionId(), message.getJob());
wasPendingCheckpoint = false;
}
// try to discard the state so that we don't have lingering state lying around
discardSubtaskState(message.getJob(), message.getTaskExecutionId(), message.getCheckpointId(), message.getSubtaskState());
return wasPendingCheckpoint;
}
}
}
}
对于一个已经触发但还没有完成的 checkpoint，即 PendingCheckpoint，它是如何处理 Ack 消息的呢？在 PendingCheckpoint 内部维护了两个 Map，分别是：
Map
operatorStates; : 已经接收到 Ack 的算子的状态句柄 ,>Map
notYetAcknowledgedTasks;: 需要 Ack 但还没有接收到的 Task ,>
每当接收到一个 Ack 消息时，PendingCheckpoint 就从 notYetAcknowledgedTasks 中移除对应的 Task，并保存 Ack 携带的状态句柄保存。当 notYetAcknowledgedTasks 为空时，表明所有的 Ack 消息都接收到了。
一旦 PendingCheckpoint 确认所有 Ack 消息都已经接收，那么就可以完成此次 checkpoint 了，具体包括：
调用 PendingCheckpoint.finalizeCheckpoint() 将 PendingCheckpoint 转化为 CompletedCheckpoint
获取 CheckpointMetadataOutputStream，将所有的状态句柄信息通过 CheckpointMetadataOutputStream 写入到存储系统中
创建一个 CompletedCheckpoint 对象
将 CompletedCheckpoint 保存到 CompletedCheckpointStore 中
CompletedCheckpointStore 有两种实现，分别为 StandaloneCompletedCheckpointStore 和 ZooKeeperCompletedCheckpointStore StandaloneCompletedCheckpointStore 简单地将 CompletedCheckpointStore 存放在一个数组中 ZooKeeperCompletedCheckpointStore 提供高可用实现：先将 CompletedCheckpointStore 写入到 RetrievableStateStorageHelper 中（通常是文件系统），然后将文件句柄存在 ZK 中 保存的 CompletedCheckpointStore 数量是有限的，会删除旧的快照
移除被越过的 PendingCheckpoint，因为 CheckpointID 是递增的，那么所有比当前完成的 CheckpointID 小的 PendingCheckpoint 都可以被丢弃了
依次调用 Execution.notifyCheckpointComplete() 通知所有的 Task 当前 Checkpoint 已经完成
通过 RPC 调用 TaskExecutor.confirmCheckpoint() 告知对应的 Task
Task收到notifyCheckpointComplete确认后进行后续处理，比如kafkaproduce的两段式提交过程。
总结
本文分析了checkpoint进行snapshot的过程，包括广播barrier、进行snapshot以及checkpoint完成后的ACK过程。
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