Flink流计算编程：双流中实现Inner Join、Left Join与Right Join
一：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。
二、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:\nSocketTextStreamJoinType " )
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")
}
}
}
}三、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:\nSocketTextStreamJoinType " )
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
*/四、测试
首先，开启2个socket接口，分别使用9998和9999端口：
root@master:~# nc -lk 9998root@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 13:00:20.000,000001,10.2root@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)
五、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。
参考
https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/index.html
https://github.com/apache/flink/blob/master/flink-streaming-scala/src/main/scala/org/apache/flink/streaming/api/scala/CoGroupedStreams.scala