Flink Operators

https://ci.apache.org/projects/flink/flink-docs-release-1.7/dev/stream/operators/

Operator	作用	流的转换
map	将一个元素转换成另外一个元素	DataStream → DataStream本
flapmap	将几个的一个元素转换为零个，一个或者多个	DataStream → DataStream
filter	保留集合中返回true的元素	DataStream → DataStream
keyBy	对数据流进行逻辑分区，相同的key在同一分区	DataStream → KeyedStream
reduce	遍历集合，依次合并元素最终生产一个元素	KeyedStream → DataStream
fold	遍历结合从第一个元素到最后一个元素依次连接起来	KeyedStream → DataStream
Aggregations	emmmm	KeyedStream → DataStream
Window	基于已经分区的stream，将元素划分窗口	KeyedStream → WindowedStream
WindowAll	基于未分区的stream，将所有元素集中到一个task	DataStream → AllWindowedStream
Apply(Window)	自定义函数处理窗口内所有的元素	WindowedStream → DataStream AllWindowedStream → DataStream
Window Reduce	窗口内所有元素reduce到一个结果	WindowedStream → DataStream
Window Fold	同stream的fold	WindowedStream → DataStream
Aggregations on windows	同stream的Aggregations	WindowedStream → DataStream
Union	将两个流合并	DataStream* → DataStream
Window Join	两个流join成一个流，指定分区key，在指定window，窗口是必须的	DataStream,DataStream → DataStream
Interval Join	流2 join 流1中一段时间的元素	KeyedStream,KeyedStream → DataStream
Window CoGroup	双流join，指定窗口	DataStream,DataStream → DataStream
Connect	联合两个流，保留各种state	DataStream,DataStream → ConnectedStreams
CoMap, CoFlatMap	同map, CoFlatMap	ConnectedStreams → DataStream
Split	流拆分	DataStream → SplitStream
Select	从SplitStream分离出DataStream	SplitStream → DataStream
Iterate	-	DataStream → IterativeStream → DataStream
-	-	-
Extract Timestamps	设置event time	DataStream → DataStream

• map 将每个元素乘以2

DataStream dataStream = //...
dataStream.map(new MapFunction() {
    @Override
    public Integer map(Integer value) throws Exception {
        return 2 * value;
    }
});

• flatMap 单词分隔

dataStream.flatMap(new FlatMapFunction() {
    @Override
    public void flatMap(String value, Collector out)
        throws Exception {
        for(String word: value.split(" ")){
            out.collect(word);
        }
    }
});

• filter 保留value=0的元素

dataStream.filter(new FilterFunction() {
    @Override
    public boolean filter(Integer value) throws Exception {
        return value != 0;
    }
});

• keyby

dataStream.keyBy("someKey") // Key by field "someKey"
dataStream.keyBy(0) // Key by the first element of a Tuple

• reduce 求和

keyedStream.reduce(new ReduceFunction() {
    @Override
    public Integer reduce(Integer value1, Integer value2)
    throws Exception {
        return value1 + value2;
    }
});

• fold
  A fold function that, when applied on the sequence (1,2,3,4,5), emits the sequence "start-1", "start-1-2", "start-1-2-3", ..

DataStream result =
  keyedStream.fold("start", new FoldFunction() {
    @Override
    public String fold(String current, Integer value) {
        return current + "-" + value;
    }
  });

• Aggregations

keyedStream.sum(0);
keyedStream.sum("key");
keyedStream.min(0);
keyedStream.min("key");
keyedStream.max(0);
keyedStream.max("key");
keyedStream.minBy(0);
keyedStream.minBy("key");
keyedStream.maxBy(0);
keyedStream.maxBy("key");

• Window Join

dataStream.join(otherStream)
    .where().equalTo()
    .window(TumblingEventTimeWindows.of(Time.seconds(3)))
    .apply (new JoinFunction () {...});

• Interval Join

// this will join the two streams so that
// key1 == key2 && leftTs - 2 < rightTs < leftTs + 2
keyedStream.intervalJoin(otherKeyedStream)
    .between(Time.milliseconds(-2), Time.milliseconds(2)) // lower and upper bound
    .upperBoundExclusive(true) // optional
    .lowerBoundExclusive(true) // optional
    .process(new IntervalJoinFunction() {...});

• Split

SplitStream split = someDataStream.split(new OutputSelector() {
    @Override
    public Iterable select(Integer value) {
        List output = new ArrayList();
        if (value % 2 == 0) {
            output.add("even");
        }
        else {
            output.add("odd");
        }
        return output;
    }
});