Flink旁路输出OutputTag

本文主要是介绍Flink旁路输出OutputTag，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

文章目录

前言
代码示例
- 1.流复制
- 2.条件分流
- 3.迟到数据分流

前言

除了由 DataStream 操作产生的主要流之外，还可以产生任意数量的旁路输出结果流。结果流中的数据类型不必与主要流中的数据类型相匹配，并且不同旁路输出的类型也可以不同。当你需要拆分数据流时，通常必须复制该数据流，然后从每个流中过滤掉不需要的数据。

使用旁路输出时，首先需要定义用于标识旁路输出流的 OutputTag：

//需要使用匿名内部类，其中T是泛型
OutputTag<T> outputTag = new OutputTag<T>("side-output") {};

可以通过以下方法将数据发送到旁路输出：

ProcessFunction
KeyedProcessFunction
CoProcessFunction
KeyedCoProcessFunction
ProcessWindowFunction
ProcessAllWindowFunction

代码示例

1.流复制

将流复制两份发到测输出流stream1 和stream2,代码如下（示例）：


import com.alibaba.fastjson.JSONObject;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;public class SideOutputTest {public static final String TYPE = "type";public static void main(String[] args) throws Exception {//获取执行环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();final ParameterTool params = ParameterTool.fromArgs(args);String hostName = params.get("hostname", "10.68.8.59");int port = params.getInt("port", 9999);// nc -l 9999DataStream<String> sourceStream = env.socketTextStream(hostName, port, "\n");SingleOutputStreamOperator<JSONObject> jsonObjectStream = sourceStream.map(s -> JSONObject.parseObject(s));//定义OutputTagOutputTag<JSONObject> outputTag1 = new OutputTag<JSONObject>("stream1") {};OutputTag<JSONObject> outputTag2 = new OutputTag<JSONObject>("stream2") {};//将流复制两份 发到测输出流stream1 和stream2SingleOutputStreamOperator<JSONObject> outputStream = jsonObjectStream.process(new ProcessFunction<JSONObject, JSONObject>() {@Overridepublic void processElement(JSONObject jsonObject, Context context, Collector<JSONObject> collector)throws Exception {context.output(outputTag1, jsonObject);context.output(outputTag2, jsonObject);}});DataStream<JSONObject> stream1 = outputStream.getSideOutput(outputTag1);DataStream<JSONObject> stream2 = outputStream.getSideOutput(outputTag2);//数据去向//stream1stream1.map(e -> {e.put("stream", "stream1");return e;}).print();//stream2stream2.map(e -> {e.put("stream", "stream2");return e;}).print();env.execute("SocketStreamTest");}
}

2.条件分流

可以根据自定义条件将数据分流。

public class SplitDemo {public static final OutputTag<Integer> evenTag = new OutputTag<Integer>("even"){};public static final OutputTag<Integer> oddTag = new OutputTag<Integer>("odd"){};public static void main(String[] args) throws Exception {StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<Integer> source = executionEnvironment.fromElements(1, 2, 3, 4, 5);SingleOutputStreamOperator<Integer> process = source.process(new ProcessFunction<Integer, Integer>() {@Overridepublic void processElement(Integer value, ProcessFunction<Integer, Integer>.Context ctx, Collector<Integer> out) throws Exception {if (value % 2 == 0) {// 这里不使用out.collect，而是使用ctx.output// 这个方法多了一个参数，可以指定output tag，从而实现数据分流ctx.output(evenTag, value);} else {ctx.output(oddTag, value);}}});// 依赖OutputTag获取对应的旁路输出DataStream<Integer> evenStream = process.getSideOutput(evenTag);DataStream<Integer> oddStream = process.getSideOutput(oddTag);// 分别打印两个旁路输出流中的数据evenStream.process(new ProcessFunction<Integer, String>() {@Overridepublic void processElement(Integer value, ProcessFunction<Integer, String>.Context ctx, Collector<String> out) throws Exception {out.collect("Even: " + value);}}).print();oddStream.process(new ProcessFunction<Integer, String>() {@Overridepublic void processElement(Integer value, ProcessFunction<Integer, String>.Context ctx, Collector<String> out) throws Exception {out.collect("Odd: " + value);}}).print();executionEnvironment.execute();}
}

3.迟到数据分流

public class OutOfOrderDemo {// 创建tagpublic static final OutputTag<Tuple2<String, Integer>> lateTag = new OutputTag<Tuple2<String, Integer>>("late"){};public static void main(String[] args) throws Exception {StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();// 示例数据，其中D乱序，I来迟（H到来的时候认为15000ms之前的数据已经到齐）SingleOutputStreamOperator<Tuple2<String, Integer>> source = executionEnvironment.fromElements(new Tuple2<>("A", 0),new Tuple2<>("B", 1000),new Tuple2<>("C", 2000),new Tuple2<>("D", 7000),new Tuple2<>("E", 3000),new Tuple2<>("F", 4000),new Tuple2<>("G", 5000),new Tuple2<>("H", 20000),new Tuple2<>("I", 8000)).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple2<String, Integer>>forGenerator(new WatermarkGeneratorSupplier<Tuple2<String, Integer>>() {// 这里自定义WatermarkGenerator的原因是Flink按照运行时间周期发送watermark，但我们的例子是单次执行的，可以认为数据是一瞬间到来// 因此我们改写为每到来一条数据发送一次watermark，watermark的时间戳为数据的事件事件减去5000毫秒，意思是最多容忍数据来迟5000毫秒@Overridepublic WatermarkGenerator<Tuple2<String, Integer>> createWatermarkGenerator(Context context) {return new WatermarkGenerator<Tuple2<String, Integer>>() {@Overridepublic void onEvent(Tuple2<String, Integer> event, long eventTimestamp, WatermarkOutput output) {long watermark = eventTimestamp - 5000L < 0 ? 0L : eventTimestamp - 5000L;output.emitWatermark(new Watermark(watermark));}@Overridepublic void onPeriodicEmit(WatermarkOutput output) {}};}// 取第二个字段为watermark}).withTimestampAssigner((element, timestamp) -> element.f1));// 窗口大小5秒，允许延迟5秒// watermark和allowedLateness的区别是，watermark决定了什么时候窗口数据触发计算，allowedLateness决定什么数据被认为是lateElement，从而发送到sideOutput// 设置side output tagsource.windowAll(TumblingEventTimeWindows.of(Time.seconds(5))).allowedLateness(Time.seconds(5)).sideOutputLateData(lateTag).process(new ProcessAllWindowFunction<Tuple2<String, Integer>, Object, TimeWindow>() {@Overridepublic void process(ProcessAllWindowFunction<Tuple2<String, Integer>, Object, TimeWindow>.Context context, Iterable<Tuple2<String, Integer>> elements, Collector<Object> out) throws Exception {Iterator<Tuple2<String, Integer>> iterator = elements.iterator();System.out.println("--------------------");while(iterator.hasNext()) {System.out.println(iterator.next());}}// 打印sideoutput流内容}).getSideOutput(lateTag).process(new ProcessFunction<Tuple2<String, Integer>, Object>() {@Overridepublic void processElement(Tuple2<String, Integer> value, ProcessFunction<Tuple2<String, Integer>, Object>.Context ctx, Collector<Object> out) throws Exception {System.out.println("Late element: " + value);}});executionEnvironment.execute();}
}