Flink Operator State 实例 实现CheckpointedFunction

public interface CheckpointedFunction {
  
  // Checkpoint触发时会调用这个方法，我们要实现具体的snapshot逻辑，比如将哪些本地状态持久化
  void snapshotState(FunctionSnapshotContext context) throws Exception;
  // 第一次初始化时会调用这个方法，或者从之前的检查点恢复时也会调用这个方法
  void initializeState(FunctionInitializationContext context) throws Exception;
}

 可以自定义把哪些变量如何保存到检查点，以及如何初始化、如何从检查点恢复

  public class MyFunction<T> implements MapFunction<T, T>, CheckpointedFunction {
 
      private ReducingState<Long> countPerKey;
      private ListState<Long> countPerPartition;
 
      private long localCount;
 
      public void initializeState(FunctionInitializationContext context) throws Exception {
          // get the state data structure for the per-key state
          countPerKey = context.getKeyedStateStore().getReducingState(
                  new ReducingStateDescriptor<>("perKeyCount", new AddFunction<>(), Long.class));
 
          // get the state data structure for the per-partition state
          countPerPartition = context.getOperatorStateStore().getOperatorState(
                  new ListStateDescriptor<>("perPartitionCount", Long.class));
 
          // initialize the "local count variable" based on the operator state
          for (Long l : countPerPartition.get()) {
              localCount += l;
          }
      }
 
      public void snapshotState(FunctionSnapshotContext context) throws Exception {
          // the keyed state is always up to date anyways
          // just bring the per-partition state in shape
          countPerPartition.clear();
          countPerPartition.add(localCount);
      }
 
      public T map(T value) throws Exception {
          // update the states
          countPerKey.add(1L);
          localCount++;
 
          return value;
      }
   }
  

flink 1.9.2, java1.8