简介
Join和coGroup都是flinkSQL中用于连接多个流的算子，但是有一定的区别，推荐能使用coGroup不要使用Join，因为coGroup更强大。下面让我们简单看一下两个算子的用法
Window Join
DataStream，DataStream→DataStream
在给定密钥和公共窗口上连接两个数据流。
dataStream.join(otherStream)
.where(<key selector>).equalTo(<key selector>)
.window(TumblingEventTimeWindows.of(Time.seconds(3)))
.apply { ... }
Window CoGroup
DataStream，DataStream→DataStream在给定密钥和公共窗口上对两个数据流进行Cogroup。
dataStream.coGroup(otherStream)
.where(0).equalTo(1)
.window(TumblingEventTimeWindows.of(Time.seconds(3)))
.apply {}
CoGrouped源码
joinedStream源码 对比结论
结论一：.使用时，where里面要传入数据流T1要与数据流T2匹配的key，equalTo中传入T2要和T1匹配的key。
结论二：join和coGroup的最大区别就是apply方法提供的参数类型不一样，
join的apply
coGroup的apply参数
两种算子apply方法中的参数类型不一样，join中提供的apply方法，参数是T1与T2泛型类型。而coGroup中提供的apply方法，参数是Iterator[T1]与Iterator[2]迭代器，基于这2种方式，如果我们的逻辑不仅仅是对一条record做处理，而是要与上一record或更复杂的判断与比较，甚至是对结果排序，那么join中的apply方法显得比较困难，所以推荐使用coGroup。
结论三：apply方法的好处
我们想要在Flink中实现实时的流计算，就可以通过joinedStream或coGroupedStream来实现。但是在join之后实施更复杂的运算，例如判断、迭代等，仅仅通过SQL实现，恐怕会很麻烦，只能通过PL/SQL块来实现，而Flink提供了apply方法，用户可以自己编写复杂的函数来实现。
案例 双流jion
scala版
package flinkSQL
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.StreamExecutionEnvironment
import org.apache.flink.util.Collector
import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.table.api.scala._
/**
* Created by ${WangZhiHua} on 2018/11/13
*/
object JoinDemo {
def main(args: Array[String]): Unit = {
val env = StreamExecutionEnvironment.getExecutionEnvironment
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
//获取接口传送的数据
val dataStream1 = env.readTextFile("C:/flink_data/scoket1.txt")
val dataStream2 = env.readTextFile("C:/flink_data/scoket2.txt")
val format = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS")
//使用样例类StockTransaction封装获取的数据
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)
//使用样例类StockSnapshot封装获取的数据
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)
/**
* 进行双流join
* 限定范围是：3秒钟的Event time时间窗口
*/
val joinStream = dataStreamMap1.coGroup(dataStreamMap2)
.where(_.tx_code)
.equalTo(_.md_code)
.window(TumblingEventTimeWindows.of(Time.seconds(3)))
val innerJoinStream = joinStream.apply(new InnerJoinFunction)
innerJoinStream.name("innerJoin").print()
print("===================== end =========================")
env.execute("join demo")
}
}
//定义样例类封装接收的数据
case class StockTransaction(tx_time:String, tx_code:String,tx_value:Double)
case class StockSnapshot(md_time:String, md_code:String,md_value:Double)
//定义一个内连接函数，继承CoCroup
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")
}
}
}
}
java版
public class DoubleJoin {
public static void main(String[] args) {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStreamSource<String> source1 = env.readTextFile("/Users/apple/Downloads/1.txt");
DataStreamSource<String> source2 = env.readTextFile("/Users/apple/Downloads/2.txt");
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
/**
* 数据流1
*/
SingleOutputStreamOperator<Row> stream1 = source1.map(new MapFunction<String, Row>() {
@Override
public Row map(String value) throws Exception {
String[] split = value.split(",");
String timeStamp = split[0];
String name = split[1];
String city = split[2];
Row row = new Row(3);
row.setField(0,timeStamp);
row.setField(1,name);
row.setField(2,city);
return row;
}
}).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<Row>() {
long currentMaxTimestamp = 0L;
long maxOutOfOrderness = 10000L;
Watermark watermark=null;
//最大允许的乱序时间是10s
@Nullable
@Override
public Watermark getCurrentWatermark() {
watermark = new Watermark(currentMaxTimestamp - maxOutOfOrderness);
return watermark;
}
@Override
public long extractTimestamp(Row element, long previousElementTimestamp) {
long timeStamp = 0;
try {
timeStamp = simpleDateFormat.parse(element.getField(0).toString()).getDate();
} catch (ParseException e) {
e.printStackTrace();
}
currentMaxTimestamp = Math.max(timeStamp, currentMaxTimestamp);
return timeStamp ;
}
}
);
stream1.print();
/**
* 数据流2
*/
SingleOutputStreamOperator<Row> stream2 = source2.map(new MapFunction<String, Row>() {
@Override
public Row map(String value) throws Exception {
String[] split = value.split(",");
String timeStamp = split[0];
String name = split[1];
String age = split[2];
String school= split[3];
Row row = new Row(4);
row.setField(0,timeStamp);
row.setField(1,name);
row.setField(2,age);
row.setField(3,school);
return row;
}
}).assignTimestampsAndWatermarks(new AssignerWithPeriodicWatermarks<Row>() {
long currentMaxTimestamp = 0L;
long maxOutOfOrderness = 10000L;
Watermark watermark=null;
//最大允许的乱序时间是10s
@Nullable
@Override
public Watermark getCurrentWatermark() {
watermark = new Watermark(currentMaxTimestamp - maxOutOfOrderness);
return watermark;
}
@Override
public long extractTimestamp(Row element, long previousElementTimestamp) {
long timeStamp = 0;
try {
timeStamp = simpleDateFormat.parse(element.getField(0).toString()).getDate();
} catch (ParseException e) {
e.printStackTrace();
}
currentMaxTimestamp = Math.max(timeStamp, currentMaxTimestamp);
return timeStamp ;
}
});
stream2.print();
/**
* 双流join
*/
stream1.coGroup(stream2)
.where(new KeySelector<Row, String>() {
@Override
public String getKey(Row value) throws Exception {
System.out.println("stream1"+value.toString());
return value.getField(1).toString();
}
})
.equalTo(new KeySelector<Row, String>() {
@Override
public String getKey(Row value) throws Exception {
System.out.println("stream2"+value.toString());
return value.getField(1).toString();
}
}).window(TumblingEventTimeWindows.of(Time.seconds(5)))
.apply(new CoGroupFunction<Row, Row, Row>() {
@Override
public void coGroup(Iterable<Row> first, Iterable<Row> second, Collector<Row> out) throws Exception {
first.forEach(t ->
second.forEach(x ->
{
//双流join 选取需要的字段
Row row = new Row(3);
Object field1 = t.getField(0);
Object field2 = x.getField(1);
Object field3 = x.getField(2);
//使用row封装数据
row.setField(0, field1);
row.setField(1, field2);
row.setField(2, field3);
out.collect(row);
}
));
System.out.println("join"+first.toString());
}
}).printToErr();
try {
env.execute("ddcddd");
} catch (Exception e) {
e.printStackTrace();
}
}
}
注意：我们在测试的时候尽量使用CountTrigge去触发窗口执行，如果使用默认的EventTimeTrigger，我们还需要设置具体的时间戳，不然可能测试时出现获取不到数据的假象，其实是窗口一直没触发。
使用案例：十条数据就触发一次窗口的执行
.window(TumblingEventTimeWindows.of(Time.seconds(5)))
.trigger(CountTrigger.of(10))本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
