本文整理了Java中org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator.timeWindowAll()方法的一些代码示例，展示了SingleOutputStreamOperator.timeWindowAll()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。SingleOutputStreamOperator.timeWindowAll()方法的具体详情如下：
包路径：org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator
类名称：SingleOutputStreamOperator
方法名：timeWindowAll

SingleOutputStreamOperator.timeWindowAll介绍

暂无

代码示例

代码示例来源：origin: apache/flink

public static void main(String[] args) throws Exception {
  // Checking input parameters
  final ParameterTool params = ParameterTool.fromArgs(args);
  StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
  env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
  DataStream<Integer> trainingData = env.addSource(new FiniteTrainingDataSource());
  DataStream<Integer> newData = env.addSource(new FiniteNewDataSource());
  // build new model on every second of new data
  DataStream<Double[]> model = trainingData
      .assignTimestampsAndWatermarks(new LinearTimestamp())
      .timeWindowAll(Time.of(5000, TimeUnit.MILLISECONDS))
      .apply(new PartialModelBuilder());
  // use partial model for newData
  DataStream<Integer> prediction = newData.connect(model).map(new Predictor());
  // emit result
  if (params.has("output")) {
    prediction.writeAsText(params.get("output"));
  } else {
    System.out.println("Printing result to stdout. Use --output to specify output path.");
    prediction.print();
  }
  // execute program
  env.execute("Streaming Incremental Learning");
}

代码示例来源：origin: apache/flink

.timeWindowAll(Time.milliseconds(1), Time.milliseconds(1))
.process(new ProcessAllWindowFunction<Integer, Integer, TimeWindow>() {
  private static final long serialVersionUID = 1L;

代码示例来源：origin: apache/flink

.timeWindowAll(Time.milliseconds(1), Time.milliseconds(1))
.sideOutputLateData(lateDataTag)
.apply(new AllWindowFunction<Integer, Integer, TimeWindow>() {

代码示例来源：origin: com.alibaba.blink/flink-examples-streaming

public static void main(String[] args) throws Exception {
  // Checking input parameters
  final ParameterTool params = ParameterTool.fromArgs(args);
  StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
  env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
  DataStream<Integer> trainingData = env.addSource(new FiniteTrainingDataSource());
  DataStream<Integer> newData = env.addSource(new FiniteNewDataSource());
  // build new model on every second of new data
  DataStream<Double[]> model = trainingData
      .assignTimestampsAndWatermarks(new LinearTimestamp())
      .timeWindowAll(Time.of(5000, TimeUnit.MILLISECONDS))
      .apply(new PartialModelBuilder());
  // use partial model for newData
  DataStream<Integer> prediction = newData.connect(model).map(new Predictor());
  // emit result
  if (params.has("output")) {
    prediction.writeAsText(params.get("output"));
  } else {
    System.out.println("Printing result to stdout. Use --output to specify output path.");
    prediction.print();
  }
  // execute program
  env.execute("Streaming Incremental Learning");
}

代码示例来源：origin: vasia/gelly-streaming

public static void main(String[] args) throws Exception {
  if(!parseParameters(args)) {
    return;
  }
  StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
  SimpleEdgeStream<Long, NullValue> edges = getGraphStream(env);
  DataStream<Tuple2<Integer, Long>> triangleCount = 
    edges.slice(windowTime, EdgeDirection.ALL)
    .applyOnNeighbors(new GenerateCandidateEdges())
    .keyBy(0, 1).timeWindow(windowTime)
    .apply(new CountTriangles())
    .timeWindowAll(windowTime).sum(0);
  if (fileOutput) {
    triangleCount.writeAsText(outputPath);
  }
  else {
    triangleCount.print();
  }
  env.execute("Naive window triangle count");
}

代码示例来源：origin: vasia/gelly-streaming

@SuppressWarnings("unchecked")
@Override
public DataStream<T> run(final DataStream<Edge<K, EV>> edgeStream) {
  //For parallel window support we key the edge stream by partition and apply a parallel fold per partition.
  //Finally, we merge all locally combined results into our final graph aggregation property.
  TupleTypeInfo edgeTypeInfo = (TupleTypeInfo) edgeStream.getType();
  TypeInformation<S> returnType = TypeExtractor.createTypeInfo(EdgesFold.class, getUpdateFun().getClass(), 2, edgeTypeInfo.getTypeAt(0), edgeTypeInfo.getTypeAt(2));
  TypeInformation<Tuple2<Integer, Edge<K, EV>>> typeInfo = new TupleTypeInfo<>(BasicTypeInfo.INT_TYPE_INFO, edgeStream.getType());
  DataStream<S> partialAgg = edgeStream
      .map(new PartitionMapper<>()).returns(typeInfo)
      .keyBy(0)
      .timeWindow(Time.of(timeMillis, TimeUnit.MILLISECONDS))
      .fold(getInitialValue(), new PartialAgg<>(getUpdateFun(),returnType))
      .timeWindowAll(Time.of(timeMillis, TimeUnit.MILLISECONDS))
      .reduce(getCombineFun())
      .flatMap(getAggregator(edgeStream)).setParallelism(1);
  if (getTransform() != null) {
    return partialAgg.map(getTransform());
  }
  return (DataStream<T>) partialAgg;
}

代码示例来源：origin: vasia/gelly-streaming

@SuppressWarnings("unchecked")
@Override
public DataStream<T> run(final DataStream<Edge<K, EV>> edgeStream) {
  TypeInformation<Tuple2<Integer, Edge<K, EV>>> basicTypeInfo = new TupleTypeInfo<>(BasicTypeInfo.INT_TYPE_INFO, edgeStream.getType());
  TupleTypeInfo edgeTypeInfo = (TupleTypeInfo) edgeStream.getType();
  TypeInformation<S> partialAggType = TypeExtractor.createTypeInfo(EdgesFold.class, getUpdateFun().getClass(), 2, edgeTypeInfo.getTypeAt(0), edgeTypeInfo.getTypeAt(2));
  TypeInformation<Tuple2<Integer, S>> partialTypeInfo = new TupleTypeInfo<>(BasicTypeInfo.INT_TYPE_INFO, partialAggType);
  degree = (degree == -1) ? edgeStream.getParallelism() : degree;
  
  DataStream<S> partialAgg = edgeStream
      .map(new PartitionMapper<>()).returns(basicTypeInfo)
      .setParallelism(degree)
      .keyBy(0)
      .timeWindow(Time.of(timeMillis, TimeUnit.MILLISECONDS))
      .fold(getInitialValue(), new PartialAgg<>(getUpdateFun(), partialAggType)).setParallelism(degree);
  //split here
  DataStream<Tuple2<Integer, S>> treeAgg = enhance(partialAgg.map(new PartitionMapper<>()).setParallelism(degree).returns(partialTypeInfo), partialTypeInfo);
  DataStream<S> resultStream = treeAgg.map(new PartitionStripper<>()).setParallelism(treeAgg.getParallelism())
      .timeWindowAll(Time.of(timeMillis, TimeUnit.MILLISECONDS))
      .reduce(getCombineFun())
      .flatMap(getAggregator(edgeStream)).setParallelism(1);
  return (getTransform() != null) ? resultStream.map(getTransform()) : (DataStream<T>) resultStream;
}