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在Flink中，默认的StateBackend实现为MemoryStateBackend，本文以MemoryStateBackend为例说明StateBackend的设计与实现。

 
本文介绍MemoryStateBackend中如下三个主要组件的创建过程：

• HeapKeyedStateBackend
• OperatorStateBackend
• MemoryBackendCheckpointStorage

FsStateBackend和RocksDBStateBackend这两种状态后端存储的实现，功能和MemoryStateBackend类似，区别在于内部创建的KeyedStateBackend和CheckpointStorage。

 

1.基于MemoryStateBackend创建KeyedStateBackend

1.1. 状态初始化

AbstractStreamOperator.keyedStatedBackend()方法定义了创建和初始化KeyedStatedBackend的逻辑，具体如下。

protected <K> AbstractKeyedStateBackend<K> keyedStateBackend(TypeSerializer<K> keySerializer,String operatorIdentifierText,PrioritizedOperatorSubtaskState prioritizedOperatorSubtaskStates,CloseableRegistry backendCloseableRegistry,MetricGroup metricGroup) throws Exception {if (keySerializer == null) {return null;}String logDescription = "keyed state backend for " + operatorIdentifierText;//1. TaskInfo taskInfo = environment.getTaskInfo();final KeyGroupRange keyGroupRange = KeyGroupRangeAssignment.computeKeyGroupRangeForOperatorIndex(taskInfo.getMaxNumberOfParallelSubtasks(),taskInfo.getNumberOfParallelSubtasks(),taskInfo.getIndexOfThisSubtask());// 确保恢复状态过程中构建的数据流被关闭CloseableRegistry cancelStreamRegistryForRestore = new CloseableRegistry();backendCloseableRegistry.registerCloseable(cancelStreamRegistryForRestore);// 创建BackendRestorerProcedureBackendRestorerProcedure<AbstractKeyedStateBackend<K>, KeyedStateHandle> backendRestorer =new BackendRestorerProcedure<>((stateHandles) -> stateBackend.createKeyedStateBackend(environment,environment.getJobID(),operatorIdentifierText,keySerializer,taskInfo.getMaxNumberOfParallelSubtasks(),keyGroupRange,environment.getTaskKvStateRegistry(),TtlTimeProvider.DEFAULT,metricGroup,stateHandles,cancelStreamRegistryForRestore),backendCloseableRegistry,logDescription);try {return backendRestorer.createAndRestore(prioritizedOperatorSubtaskStates.getPrioritizedManagedKeyedState());} finally {if (backendCloseableRegistry.unregisterCloseable(cancelStreamRegistryForRestore)) {IOUtils.closeQuietly(cancelStreamRegistryForRestore);}}
}

1. 获取当前Task的TaskInfo，并基于TaskInfo的参数创建KeyGroupRange，表示当前Task实例中存储的Key分组区间。
2. 创建CloseableRegistry并注册到backendCloseableRegistry中，用于确保在任务取消的情况下关闭在恢复状态过程中构造的数据流。
3. 创建BackendRestorerProcedure，提供了stateBackend.createKeyedStateBackend()方法，也包含恢复历史状态数据的方法。
4. 创建KeyedStateBackend，同时对状态数据进行恢复。prioritizedOperatorSubtaskStates是从TaskStateManager中根据OperatorID获取的算子历史状态，通过prioritizedOperatorSubtaskStates获取当前算子的PrioritizedManagedKeyedState，并基于这些状态数据恢复算子的状态。

 

1.2. 创建状态

接下来我们看MemoryStateBackend.createKeyedStateBackend()方法的具体实现。

public <K> AbstractKeyedStateBackend<K> createKeyedStateBackend(Environment env,JobID jobID,String operatorIdentifier,TypeSerializer<K> keySerializer,int numberOfKeyGroups,KeyGroupRange keyGroupRange,TaskKvStateRegistry kvStateRegistry,TtlTimeProvider ttlTimeProvider,MetricGroup metricGroup,@Nonnull Collection<KeyedStateHandle> stateHandles,CloseableRegistry cancelStreamRegistry) throws BackendBuildingException {// 获取TaskStateManager实例TaskStateManager taskStateManager = env.getTaskStateManager();// 创建HeapPriorityQueueSetFactory实例HeapPriorityQueueSetFactory priorityQueueSetFactory =new HeapPriorityQueueSetFactory(keyGroupRange, numberOfKeyGroups, 128);// 创建HeapKeyedStateBackendBuilder实例HeapKeyedStateBackendreturn new HeapKeyedStateBackendBuilder<>(kvStateRegistry,keySerializer,env.getUserClassLoader(),numberOfKeyGroups,keyGroupRange,env.getExecutionConfig(),ttlTimeProvider,stateHandles,AbstractStateBackend.getCompressionDecorator(env.getExecutionConfig()),taskStateManager.createLocalRecoveryConfig(),priorityQueueSetFactory,isUsingAsynchronousSnapshots(),cancelStreamRegistry).build();
}

1. 从environment参数中获取TaskStateManager实例
2. 创建HeapPriorityQueueSetFactory实例，用于生成HeapPriorityQueueSet优先级队列，存储TimerHeapInternalTimer等数据。
3. 调用HeapKeyedStateBackendBuilder.build()方法创建HeapKeyedStateBackend。

 

2. 基于MemoryStateBackend创建OperatorStateBackend

和创建KeyedStateBackend的过程相似，AbstractStreamOperator.operatorStateBackend()方法实现了创建OperatorStateBackend的方法。

protected OperatorStateBackend operatorStateBackend(String operatorIdentifierText,PrioritizedOperatorSubtaskState prioritizedOperatorSubtaskStates,CloseableRegistry backendCloseableRegistry) throws Exception {String logDescription = "operator state backend for " + operatorIdentifierText;CloseableRegistry cancelStreamRegistryForRestore = new CloseableRegistry();backendCloseableRegistry.registerCloseable(cancelStreamRegistryForRestore);BackendRestorerProcedure<OperatorStateBackend, OperatorStateHandle> backendRestorer =new BackendRestorerProcedure<>((stateHandles) -> stateBackend.createOperatorStateBackend(environment,operatorIdentifierText,stateHandles,cancelStreamRegistryForRestore),backendCloseableRegistry,logDescription);try {return backendRestorer.createAndRestore(prioritizedOperatorSubtaskStates.getPrioritizedManagedOperatorState());} finally {if (backendCloseableRegistry.unregisterCloseable(cancelStreamRegistryForRestore)) {IOUtils.closeQuietly(cancelStreamRegistryForRestore);}}
}

1. 创建CloseableRegisty，确保在任务取消的情况下能够关闭在恢复状态时构造的数据流。
2. 创建BackendRestorerProcedure，封装了stateBackend.createOperatorStateBackend()方法，并包含恢复历史状态数据的操作。
3. 创建OperatorStateBackend，并恢复状态数据。

其中prioritizedOperatorSubtaskStates是从TaskStateManager中根据OperatorID获取的算子专有历史状态，可以通过prioritizedOperatorSubtaskStates获取当前算子中的PrioritizedManagedOperatorState，并基于这些状态数据恢复OperatorStateBackend中算子的状态。

 

3.基于MemoryStateBackend创建CheckpointStorage

在createCheckpointStorage()方法中，直接创建MemoryBackendCheckpointStorage实例并返回，没有涉及太多的流程

public CheckpointStorage createCheckpointStorage(JobID jobId) throws IOException {return new MemoryBackendCheckpointStorage(jobId, getCheckpointPath(), getSavepointPath(), maxStateSize);
}

 

参考：《Flink设计与实现：核心原理与源码解析》–张利兵