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Join算子:两个数据流通过内部相同的key分区,将窗口内两个数据流相同key数据元素计算后,合并输出(类似于mysql表的inner join操作)
示例环境
java.version: 1.8.x flink.version: 1.11.1
示例数据源 (项目码云下载)
Flink 系例 之 搭建开发环境与数据
Join.java
package com.flink.examples.functions; import com.flink.examples.DataSource; import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner; import org.apache.flink.api.common.eventtime.WatermarkStrategy; import org.apache.flink.api.common.functions.FlatJoinFunction; import org.apache.flink.api.java.functions.KeySelector; import org.apache.flink.api.java.tuple.Tuple3; import org.apache.flink.streaming.api.TimeCharacteristic; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows; import org.apache.flink.streaming.api.windowing.time.Time; import org.apache.flink.util.Collector; import java.time.Duration; import java.util.Arrays; import java.util.List; /** * @Description Join算子:两个数据流通过内部相同的key分区,将窗口内两个数据流相同key数据元素计算后,合并输出(类似于mysql表的inner join操作) */ public class Join { /** * Flink支持了两种Join:Window Join(窗口连接)和Interval Join(时间间隔连接),本示例演示的为Window Join * 官方文档:https://ci.apache.org/projects/flink/flink-docs-release-1.11/zh/dev/stream/operators/joining.html */ /** * 两个数据流集合,对相同key进行内联,分配到同一个窗口下,合并并打印 * @param args * @throws Exception */ public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setParallelism(4); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); // //watermark 自动添加水印调度时间 // env.getConfig().setAutoWatermarkInterval(200); List<Tuple3<String, String, Integer>> tuple3List1 = DataSource.getTuple3ToList(); List<Tuple3<String, String, Integer>> tuple3List2 = Arrays.asList( new Tuple3<>("伍七", "girl", 18), new Tuple3<>("吴八", "man", 30) ); //Datastream 1 DataStream<Tuple3<String, String, Integer>> dataStream1 = env.fromCollection(tuple3List1) //添加水印窗口,如果不添加,则时间窗口会一直等待水印事件时间,不会执行apply .assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, String, Integer>>forBoundedOutOfOrderness(Duration.ofSeconds(2)) .withTimestampAssigner((element, timestamp)->System.currentTimeMillis())); //Datastream 2 DataStream<Tuple3<String, String, Integer>> dataStream2 = env.fromCollection(tuple3List2) //添加水印窗口,如果不添加,则时间窗口会一直等待水印事件时间,不会执行apply .assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, String, Integer>>forBoundedOutOfOrderness(Duration.ofSeconds(2)) .withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, String, Integer>>() { @Override public long extractTimestamp(Tuple3<String, String, Integer> element, long timestamp) { return System.currentTimeMillis(); } })); //Datastream 3 DataStream<String> newDataStream = dataStream1.join(dataStream2) .where(new KeySelector<Tuple3<String, String, Integer>, String>() { @Override public String getKey(Tuple3<String, String, Integer> value) throws Exception { System.out.println("first name:" + value.f0 + ",sex:" + value.f1); return value.f1; } }) .equalTo(new KeySelector<Tuple3<String, String, Integer>, String>() { @Override public String getKey(Tuple3<String, String, Integer> value) throws Exception { System.out.println("second name:" + value.f0 + ",sex:" + value.f1); return value.f1; } }) .window(TumblingEventTimeWindows.of(Time.seconds(1)) .apply(new FlatJoinFunction<Tuple3<String, String, Integer>, Tuple3<String, String, Integer>, String>() { @Override public void join(Tuple3<String, String, Integer> first, Tuple3<String, String, Integer> second, Collector<String> out) throws Exception { out.collect(first.f0 + "|" + first.f1 + "|" + first.f2 + "|" + second.f0 + "|" + second.f1 + "|" + second.f2); } }) ; newDataStream.print(); env.execute("flink Join job"); } }
打印结果
4> 李四|girl|24|伍七|girl|18 4> 刘六|girl|32|伍七|girl|18 4> 伍七|girl|18|伍七|girl|18 2> 张三|man|20|吴八|man|30 2> 王五|man|29|吴八|man|30 2> 吴八|man|30|吴八|man|30
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