Flink Operator State 實例 實現CheckpointedFunction

public interface CheckpointedFunction {
  
  // Checkpoint觸發時會調用這個方法，我們要實現具體的snapshot邏輯，比如將哪些本地狀態持久化
  void snapshotState(FunctionSnapshotContext context) throws Exception;
  // 第一次初始化時會調用這個方法，或者從之前的檢查點恢復時也會調用這個方法
  void initializeState(FunctionInitializationContext context) throws Exception;
}

 可以自定義把哪些變量如何保存到檢查點，以及如何初始化、如何從檢查點恢復

  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