Flink DataStream API
Map
输入一个元素,输出一个元素
DataStream → DataStream
DataStream<Integer> dataStream = //...
dataStream.map(new MapFunction<Integer, Integer>() {
@Override
public Integer map(Integer value) throws Exception {
return 2 * value;
}
});
FlatMap
输入一个元素,输出零个或多个元素
DataStream → DataStream
dataStream.flatMap(new FlatMapFunction<String, String>() {
@Override
public void flatMap(String value, Collector<String> out)
throws Exception {
for(String word: value.split(" ")){
out.collect(word);
}
}
});
Filter
把流中的每一个元素用一个返回类型为boolean的方法计算,返回true的保留
DataStream → DataStream
dataStream.filter(new FilterFunction<Integer>() {
@Override
public boolean filter(Integer value) throws Exception {
return value != 0;
}
});
KeyBy
逻辑上把一个流分割成互斥的分区,每个分区都包含具有相同key的元素.内部由hash partitioning实现.下面的转换返回一个KeyedDataStream
DataStream → KeyedStream
dataStream.keyBy("someKey") // Key by field "someKey"
dataStream.keyBy(0) // Key by the first element of a Tuple
Reduce
在一个有键的数据流上滚动的reduce,在最近被reduce处理过的值上合并当前的元素并且提交新值
KeyedStream → DataStream
keyedStream.reduce(new ReduceFunction<Integer>() {
@Override
public Integer reduce(Integer value1, Integer value2)
throws Exception {
return value1 + value2;
}
});
Fold
在一个有键的数据流上以一个初始值滚动的fold,在最近被flod处理过的值上合并当前的元素并且提交新值
KeyedStream → DataStream
当输入为1,2,3,4,5的时候,输出"start-1", "start-1-2", "start-1-2-3", ...
DataStream<String> result =
keyedStream.fold("start", new FoldFunction<Integer, String>() {
@Override
public String fold(String current, Integer value) {
return current + "-" + value;
}
});
Aggregations
在一个有键的数据流上以一个初始值滚动的聚合,min和minBy的不同是:min返回最小值,minBy包含最小值的元素(max和maxBy同理)
KeyedStream → DataStream
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
窗口可以定义在已经分区的KeyedStream上.窗口根据每个特性对每个键中的数据进行分组(例如最近5秒内到达的数据)
KeyedStream → WindowedStream
dataStream.keyBy(0).window(TumblingEventTimeWindows.of(Time.seconds(5)));
// Last 5 seconds of data
WindowAll
一个可以定义在规则的DataStream上的窗口.窗口根据一些特征将所有流分组(例如最近5秒内到达的数据)
警告:在很多情况这是一个不能并发的transformation,所有的记录会被聚集在一个任务中进行WindowAll操作
DataStream → AllWindowedStream
dataStream.windowAll(TumblingEventTimeWindows.of(Time.seconds(5))); // Last 5 seconds of data
Window Apply
对一个窗口整体应用一个函数
WindowedStream → DataStream
AllWindowedStream → DataStream
注意:如果你使用WindowAll,你需要用AllWindowFunction
下面是一个手动计算一个窗口内所有元素的和的函数
windowedStream.apply (new WindowFunction<Tuple2<String,Integer>, Integer, Tuple, Window>() {
public void apply (Tuple tuple,
Window window,
Iterable<Tuple2<String, Integer>> values,
Collector<Integer> out) throws Exception {
int sum = 0;
for (value t: values) {
sum += t.f1;
}
out.collect (new Integer(sum));
}
});
// applying an AllWindowFunction on non-keyed window stream
allWindowedStream.apply (new AllWindowFunction<Tuple2<String,Integer>, Integer, Window>() {
public void apply (Window window,
Iterable<Tuple2<String, Integer>> values,
Collector<Integer> out) throws Exception {
int sum = 0;
for (value t: values) {
sum += t.f1;
}
out.collect (new Integer(sum));
}
});
Window Reduce
在一个窗口上应用一个reduce函数,返回一个reduce处理过的值
WindowedStream → DataStream
windowedStream.reduce (new ReduceFunction<Tuple2<String,Integer>>() {
public Tuple2<String, Integer> reduce(Tuple2<String, Integer> value1,
Tuple2<String, Integer> value2) throws Exception {
return new Tuple2<String,Integer>(value1.f0, value1.f1 + value2.f1);
}
});
Window Fold
在一个窗口上应用一个fold函数,返回一个fold处理过的值
WindowedStream → DataStream
下面的例子输入1,2,3,4,5,输出字符串在一个窗口上应用一个reduce函数,返回一个reduce处理过的值
windowedStream.fold("start", new FoldFunction<Integer, String>() {
public String fold(String current, Integer value) {
return current + "-" + value;
}
});
Aggregations on windows
聚合一个窗口的内容,min和minBy的不同是:min返回最小值,minBy包含最小值的元素(max和maxBy同理)
WindowedStream → DataStream
windowedStream.sum(0);
windowedStream.sum("key");
windowedStream.min(0);
windowedStream.min("key");
windowedStream.max(0);
windowedStream.max("key");
windowedStream.minBy(0);
windowedStream.minBy("key");
windowedStream.maxBy(0);
windowedStream.maxBy("key");
Union
联合两个或以上数据流,产生一个包含所有流中元素的数据流
注意:如果你联合同一个流你会得到一个包含两个相同元素的流
DataStream* → DataStream
dataStream.union(otherStream1, otherStream2, ...);
Window Join
在给定的key和相同的窗口内Join两个流
DataStream,DataStream → DataStream
dataStream.join(otherStream)
.where(<key selector>).equalTo(<key selector>)
.window(TumblingEventTimeWindows.of(Time.seconds(3)))
.apply (new JoinFunction () {...});
Window CoGroup
在给定的key和相同的窗口内Cogroup两个流
DataStream,DataStream → DataStream
dataStream.coGroup(otherStream)
.where(0).equalTo(1)
.window(TumblingEventTimeWindows.of(Time.seconds(3)))
.apply (new CoGroupFunction () {...});
Connect
Connect两个数据流,保留他们的类型,Connect允许两个流共享state
DataStream,DataStream → ConnectedStreams
DataStream<Integer> someStream = //...
DataStream<String> otherStream = //...
ConnectedStreams<Integer, String> connectedStreams = someStream.connect(otherStream);
CoMap, CoFlatMap
Similar to map and flatMap on a connected data stream
ConnectedStreams → DataStream
connectedStreams.map(new CoMapFunction<Integer, String, Boolean>() {
@Override
public Boolean map1(Integer value) {
return true;
}
@Override
public Boolean map2(String value) {
return false;
}
});
connectedStreams.flatMap(new CoFlatMapFunction<Integer, String, String>() {
@Override
public void flatMap1(Integer value, Collector<String> out) {
out.collect(value.toString());
}
@Override
public void flatMap2(String value, Collector<String> out) {
for (String word: value.split(" ")) {
out.collect(word);
}
}
});
Split
根据规则把一个流拆分成两个或更多的流
DataStream → SplitStream
SplitStream<Integer> split = someDataStream.split(new OutputSelector<Integer>() {
@Override
public Iterable<String> select(Integer value) {
List<String> output = new ArrayList<String>();
if (value % 2 == 0) {
output.add("even");
}
else {
output.add("odd");
}
return output;
}
});
Select
从SplitStream中选择一个或多个流
SplitStream → DataStream
SplitStream<Integer> split;
DataStream<Integer> even = split.select("even");
DataStream<Integer> odd = split.select("odd");
DataStream<Integer> all = split.select("even","odd");
Iterate
Creates a "feedback" loop in the flow, by redirecting the output of one operator to some previous operator. This is especially useful for defining algorithms that continuously update a model. The following code starts with a stream and applies the iteration body continuously. Elements that are greater than 0 are sent back to the feedback channel, and the rest of the elements are forwarded downstream.
DataStream → IterativeStream → DataStream
IterativeStream<Long> iteration = initialStream.iterate();
DataStream<Long> iterationBody = iteration.map (/*do something*/);
DataStream<Long> feedback = iterationBody.filter(new FilterFunction<Long>(){
@Override
public boolean filter(Integer value) throws Exception {
return value > 0;
}
});
iteration.closeWith(feedback);
DataStream<Long> output = iterationBody.filter(new FilterFunction<Long>(){
@Override
public boolean filter(Integer value) throws Exception {
return value <= 0;
}
});
Extract Timestamps
为了使用窗口从记录中提取时间戳
DataStream → DataStream
stream.assignTimestamps (new TimeStampExtractor() {...});