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這篇文章主要介紹“Flink的CoGroup如何使用”,在日常操作中,相信很多人在Flink的CoGroup如何使用問(wèn)題上存在疑惑,小編查閱了各式資料,整理出簡(jiǎn)單好用的操作方法,希望對(duì)大家解答”Flink的CoGroup如何使用”的疑惑有所幫助!接下來(lái),請(qǐng)跟著小編一起來(lái)學(xué)習(xí)吧!
CoGroup算子:將兩個(gè)數(shù)據(jù)流按照key進(jìn)行g(shù)roup分組,并將數(shù)據(jù)流按key進(jìn)行分區(qū)的處理,最終合成一個(gè)數(shù)據(jù)流(與join有區(qū)別,不管key有沒(méi)有關(guān)聯(lián)上,最終都會(huì)合并成一個(gè)數(shù)據(jù)流)
示例環(huán)境
java.version: 1.8.x flink.version: 1.11.1
示例數(shù)據(jù)源 (項(xiàng)目碼云下載)
Flink 系例 之 搭建開(kāi)發(fā)環(huán)境與數(shù)據(jù)
CoGroup.java
package com.flink.examples.functions; import com.flink.examples.DataSource; import com.google.gson.Gson; import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner; import org.apache.flink.api.common.eventtime.WatermarkStrategy; import org.apache.flink.api.common.functions.CoGroupFunction; 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 CoGroup算子:將兩個(gè)數(shù)據(jù)流按照key進(jìn)行g(shù)roup分組,并將數(shù)據(jù)流按key進(jìn)行分區(qū)的處理,最終合成一個(gè)數(shù)據(jù)流(與join有區(qū)別,不管key有沒(méi)有關(guān)聯(lián)上,最終都會(huì)合并成一個(gè)數(shù)據(jù)流) */ public class CoGroup { /** * 兩個(gè)數(shù)據(jù)流集合,對(duì)相同key進(jìn)行內(nèi)聯(lián),分配到同一個(gè)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(1); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); //watermark 自動(dòng)添加水印調(diào)度時(shí)間 //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<>("吳八", "man", 30) ); //Datastream 1 DataStream<Tuple3<String, String, Integer>> dataStream1 = env.fromCollection(tuple3List1) //添加水印窗口,如果不添加,則時(shí)間窗口會(huì)一直等待水印事件時(shí)間,不會(huì)執(zhí)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,則時(shí)間窗口會(huì)一直等待水印事件時(shí)間,不會(huì)執(zhí)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(); } }) ); //對(duì)dataStream1和dataStream2兩個(gè)數(shù)據(jù)流進(jìn)行關(guān)聯(lián),沒(méi)有關(guān)聯(lián)也保留 //Datastream 3 DataStream<String> newDataStream = dataStream1.coGroup(dataStream2) .where(new KeySelector<Tuple3<String, String, Integer>, String>() { @Override public String getKey(Tuple3<String, String, Integer> value) throws Exception { return value.f1; } }) .equalTo(t3->t3.f1) .window(TumblingEventTimeWindows.of(Time.seconds(1))) .apply(new CoGroupFunction<Tuple3<String, String, Integer>, Tuple3<String, String, Integer>, String>() { @Override public void coGroup(Iterable<Tuple3<String, String, Integer>> first, Iterable<Tuple3<String, String, Integer>> second, Collector<String> out) throws Exception { StringBuilder sb = new StringBuilder(); Gson gson = new Gson(); //datastream1的數(shù)據(jù)流集合 for (Tuple3<String, String, Integer> tuple3 : first) { sb.append(gson.toJson(tuple3)).append("\n"); } //datastream2的數(shù)據(jù)流集合 for (Tuple3<String, String, Integer> tuple3 : second) { sb.append(gson.toJson(tuple3)).append("\n"); } out.collect(sb.toString()); } }); newDataStream.print(); env.execute("flink CoGroup job"); } }
打印結(jié)果
{"f0":"張三","f1":"man","f2":20} {"f0":"王五","f1":"man","f2":29} {"f0":"吳八","f1":"man","f2":30} {"f0":"吳八","f1":"man","f2":30} {"f0":"李四","f1":"girl","f2":24} {"f0":"劉六","f1":"girl","f2":32} {"f0":"伍七","f1":"girl","f2":18} {"f0":"伍七","f1":"girl","f2":18}
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