来源：https://mp.weixin.qq.com/s/mO2h_HNzx2rwFnOXlRDAJQ
简介
之前文章中提到JoinedStream与CoGroupedStream，例如下列代码：
dataStream.join(otherStream) .where(0).equalTo(1) .window(TumblingEventTimeWindows.of(Time.seconds(3))) .apply { ... }
由于joinedStream与coGroupedStream来自于一个特定的window，且在一个关联上的key中实现，因此，Flink中的双流join一定是在一个基于Inner Join的key的前提下的操作。
双流Join中的Inner、Left、Right Join操作，实际上是指在特定的window范围内的join。即Join的主体是window范围，如果窗口内都没有数据，则不发生join。
具体实现
这里我通过2个Socket接收数据，模拟双流，共3个参数，代码如下：
if (args.length != 3) { System.err.println("USAGE: SocketTextStreamJoinType <hostname> <port1> <port2>") return } val hostName = args(0) val port1 = args(1).toInt val port2 = args(2).toInt
接下来，我们创建了2个case class，来模拟2个socket的输入流数据，代码如下：
case class StockTransaction(tx_time:String, tx_code:String,tx_value:Double) case class StockSnapshot(md_time:String, md_code:String,md_value:Double)
最后要注意的地方就是如何实现Inner Join、Left Join与Right Join了。这里采用coGroup方式，通过对coGroupFunction中的2个Iterable集合判断是否为空来实现，例如：
if(scalaT1.nonEmpty && scalaT2.nonEmpty){ for(transaction <- scalaT1){ for(snapshot <- scalaT2){ out.collect(transaction.tx_code,transaction.tx_time, snapshot.md_time,transaction.tx_value,snapshot.md_value,"Inner Join Test") } } } }
完整的代码示例
package wikiedits import java.text.SimpleDateFormat import org.apache.flink.api.common.functions.CoGroupFunction import org.apache.flink.streaming.api.TimeCharacteristic import org.apache.flink.streaming.api.scala._ import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows import org.apache.flink.streaming.api.windowing.time.Time import org.apache.flink.util.Collector object InnerLeftRightJoinTest { // ************************************************************************* // PROGRAM // ************************************************************************* def main(args : Array[String]) : Unit ={ if (args.length != 3) { System.err.println("USAGE: SocketTextStreamJoinType <hostname> <port1> <port2>") return } val hostName = args(0) val port1 = args(1).toInt val port2 = args(2).toInt /** * 获取执行环境以及TimeCharacteristic */ val env = StreamExecutionEnvironment.getExecutionEnvironment env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) val dataStream1 = env.socketTextStream(hostName, port1) val dataStream2 = env.socketTextStream(hostName, port2) val format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS") /** * operator操作 * 数据格式如下： * TX：2016-07-28 13:00:01.000,000002,10.2 * MD: 2016-07-28 13:00:00.000,000002,10.1 * 这里由于是测试，固水位线采用升序（即数据的Event Time本身是升序输入的） */ val dataStreamMap1 = dataStream1.map(f => { val tokens1 = f.split(",") StockTransaction(tokens1(0), tokens1(1), tokens1(2).toDouble) }) .assignAscendingTimestamps(f => format.parse(f.tx_time).getTime) val dataStreamMap2 = dataStream2.map(f => { val tokens2 = f.split(",") StockSnapshot(tokens2(0), tokens2(1), tokens2(2).toDouble) }) .assignAscendingTimestamps(f => format.parse(f.md_time).getTime) /** * Join操作 * 限定范围是3秒钟的Event Time窗口 */ val joinedStream = dataStreamMap1 .coGroup(dataStreamMap2) .where(_.tx_code) .equalTo(_.md_code) .window(TumblingEventTimeWindows.of(Time.seconds(3))) val innerJoinedStream = joinedStream.apply(new InnerJoinFunction) val leftJoinedStream = joinedStream.apply(new LeftJoinFunction) val rightJoinedStream = joinedStream.apply(new RightJoinFunction) innerJoinedStream.name("InnerJoinedStream").print() leftJoinedStream.name("LeftJoinedStream").print() rightJoinedStream.name("RightJoinedStream").print() env.execute("3 Type of Double Stream Join") } // ************************************************************************* // USER FUNCTIONS // ************************************************************************* case class StockTransaction(tx_time:String, tx_code:String,tx_value:Double) case class StockSnapshot(md_time:String, md_code:String,md_value:Double) class InnerJoinFunction extends CoGroupFunction[StockTransaction,StockSnapshot,(String,String,String,Double,Double,String)]{ override def coGroup(T1: java.lang.Iterable[StockTransaction], T2: java.lang.Iterable[StockSnapshot], out: Collector[(String, String, String, Double, Double,String)]): Unit = { /** * 将Java中的Iterable对象转换为Scala的Iterable * scala的集合操作效率高，简洁 */ import scala.collection.JavaConverters._ val scalaT1 = T1.asScala.toList val scalaT2 = T2.asScala.toList /** * Inner Join要比较的是同一个key下，同一个时间窗口内的数据 */ if(scalaT1.nonEmpty && scalaT2.nonEmpty){ for(transaction <- scalaT1){ for(snapshot <- scalaT2){ out.collect(transaction.tx_code,transaction.tx_time, snapshot.md_time,transaction.tx_value,snapshot.md_value,"Inner Join Test") } } } } } class LeftJoinFunction extends CoGroupFunction[StockTransaction,StockSnapshot,(String,String,String,Double,Double,String)] { override def coGroup(T1: java.lang.Iterable[StockTransaction], T2: java.lang.Iterable[StockSnapshot], out: Collector[(String, String, String, Double,Double,String)]): Unit = { /** * 将Java中的Iterable对象转换为Scala的Iterable * scala的集合操作效率高，简洁 */ import scala.collection.JavaConverters._ val scalaT1 = T1.asScala.toList val scalaT2 = T2.asScala.toList /** * Left Join要比较的是同一个key下，同一个时间窗口内的数据 */ if(scalaT1.nonEmpty && scalaT2.isEmpty){ for(transaction <- scalaT1){ out.collect(transaction.tx_code,transaction.tx_time, "",transaction.tx_value,0,"Left Join Test") } } } } class RightJoinFunction extends CoGroupFunction[StockTransaction,StockSnapshot,(String,String,String,Double,Double,String)] { override def coGroup(T1: java.lang.Iterable[StockTransaction], T2: java.lang.Iterable[StockSnapshot], out: Collector[(String, String, String, Double,Double,String)]): Unit = { /** * 将Java中的Iterable对象转换为Scala的Iterable * scala的集合操作效率高，简洁 */ import scala.collection.JavaConverters._ val scalaT1 = T1.asScala.toList val scalaT2 = T2.asScala.toList /** * Right Join要比较的是同一个key下，同一个时间窗口内的数据 */ if(scalaT1.isEmpty && scalaT2.nonEmpty){ for(snapshot <- scalaT2){ out.collect(snapshot.md_code, "",snapshot.md_time,0,snapshot.md_value,"Right Join Test") } } } } } /** * 用于测试的数据 */ /** * Transaction: * 2016-07-28 13:00:01.820,000001,10.2 * 2016-07-28 13:00:01.260,000001,10.2 * 2016-07-28 13:00:02.980,000001,10.1 * 2016-07-28 13:00:03.120,000001,10.1 * 2016-07-28 13:00:04.330,000001,10.0 * 2016-07-28 13:00:05.570,000001,10.0 * 2016-07-28 13:00:05.990,000001,10.0 * 2016-07-28 13:00:14.000,000001,10.1 * 2016-07-28 13:00:20.000,000001,10.2 */ /** * Snapshot: * 2016-07-28 13:00:01.000,000001,10.2 * 2016-07-28 13:00:04.000,000001,10.1 * 2016-07-28 13:00:07.000,000001,10.0 * 2016-07-28 13:00:16.000,000001,10.1 */
测试
首先，开启2个socket接口，分别使用9998和9999端口：
root@master:~# nc -lk 9998
r> oot@master:~# nc -lk 9999
其次，打包程序，发布到集群：
mvn clean package
之后，在socket中模拟输入数据：
root@master:~# nc -lk 9998 2016-07-28 13:00:01.820,000001,10.2 2016-07-28 13:00:01.260,000001,10.2 2016-07-28 13:00:02.980,000001,10.1 2016-07-28 13:00:04.330,000001,10.0 2016-07-28 13:00:05.570,000001,10.0 2016-07-28 13:00:05.990,000001,10.0 2016-07-28 13:00:14.000,000001,10.1 2016-07-28 13:00:20.000,000001,10.2
root@master:~# nc -lk 9999 2016-07-28 13:00:01.000,000001,10.2 2016-07-28 13:00:04.000,000001,10.1 2016-07-28 13:00:07.000,000001,10.0 2016-07-28 13:00:16.000,000001,10.1
最后，看一下输出：
(000001,2016-07-28 13:00:01.820,2016-07-28 13:00:01.000,10.2,10.2,Inner Join Test) (000001,2016-07-28 13:00:01.260,2016-07-28 13:00:01.000,10.2,10.2,Inner Join Test) (000001,2016-07-28 13:00:02.980,2016-07-28 13:00:01.000,10.1,10.2,Inner Join Test) (000001,2016-07-28 13:00:04.330,2016-07-28 13:00:04.000,10.0,10.1,Inner Join Test) (000001,2016-07-28 13:00:05.570,2016-07-28 13:00:04.000,10.0,10.1,Inner Join Test) (000001,2016-07-28 13:00:05.990,2016-07-28 13:00:04.000,10.0,10.1,Inner Join Test) (000001,2016-07-28 13:00:14.000,,10.1,0.0,Left Join Test) (000001,,2016-07-28 13:00:07.000,0.0,10.0,Right Join Test)
总结
在实际的工作中，coGroupStream的逻辑往往更复杂，例如需要引入state（key/value类型或者List类型），因此要继承RichCoGroupFunction；而且需要考虑延迟的问题（即两个流根据Event Time，接收的数据不同步），导致窗口内经常缺失数据等。这些问题需要更加复杂的管理。