Flink学习连载文章11--双流Join
双流 Join 和两个流合并是不一样的
两个流合并:两个流变为 1 个流 union connect
双流 join: 两个流 join,其实这两个流还是原来的,只是满足条件的数据会变为一个新的流。
可以结合 sql 语句中的 union 和 join 的区别。
在离线 Hive 中,我们经常会使用 Join 进行多表关联。那么在实时中我们应该如何实现两条流的 Join 呢?Flink DataStream API 为我们提供了3个算子来实现双流 join,分别是:
- join -- 类似于我们以前学过的内连接 inner join
- coGroup -- 类似于我们以前学过的 内连接,左连接,右连接
- intervalJoin -- 一个流中的数据可以关联另一个流中一个时间段的所有数据
下面我们分别详细看一下这3个算子是如何实现双流 Join 的。
1. Join
Join 算子提供的语义为 “Window join”,即按照指定字段和(滚动/滑动/会话)窗口进行内连接(InnerJoin)。Join 将有相同 Key 并且位于同一窗口中的两条流的元素进行关联。
Join 可以支持处理时间(processing time)和事件时间(event time)两种时间特征。
Join 通用用法如下:
stream.join(otherStream)
.where(<KeySelector>)
.equalTo(<KeySelector>)
.window(<WindowAssigner>)
.apply(<JoinFunction>)
Join 语义类似与离线 Hive 的 InnnerJoin (内连接),这意味着如果一个流中的元素在另一个流中没有相对应的元素,则不会输出该元素。
下面我们看一下 Join 算子在不同类型窗口上的具体表现。
1.1 滚动窗口Join
当在滚动窗口上进行 Join 时,所有有相同 Key 并且位于同一滚动窗口中的两条流的元素两两组合进行关联,并最终传递到 JoinFunction 或 FlatJoinFunction 进行处理。
如上图所示,我们定义了一个大小为 2 秒的滚动窗口,最终产生 [0,1],[2,3],… 这种形式的数据。上图显示了每个窗口中橘色流和绿色流的所有元素成对组合。需要注意的是,在滚动窗口 [6,7] 中,由于绿色流中不存在要与橘色流中元素 6、7 相关联的元素,因此该窗口不会输出任何内容。
下面我们一起看一下如何实现上图所示的滚动窗口 Join:
可以通过两个socket流,将数据合并为一个三元组,key,value1,value2
package com.bigdata.day07;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple3;
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 java.text.ParseException;
import java.text.SimpleDateFormat;
import java.time.Duration;
import java.util.Arrays;
import java.util.Date;
/**
* @基本功能:
* @program:FlinkDemo
* @author: 闫哥
* @create:2023-11-27 09:31:57
**/
public class _ShuangLiuJoinDemo {
public static void main(String[] args) throws Exception {
//1. env-准备环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
// 并行度不为1 ,效果很难出来,因为本地的并行度是16,只有16个并行度都触发才能看到效果
env.setParallelism(1);
//2. source-加载数据 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> greenStream = env.socketTextStream("localhost", 8888)
.map(new MapFunction<String, Tuple3<String, Integer, String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("绿色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0], Integer.valueOf(arr[1]), arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("绿色的时间:"+timeStamp);
System.out.println(element.f0);
return timeStamp;
}
})
);
;
// 以后这个9999少用,因为kafka占用这个端口 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> orangeStream = env.socketTextStream("localhost", 7777)
.map(new MapFunction<String, Tuple3<String,Integer,String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("橘色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0],Integer.valueOf(arr[1]),arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("橘色的时间:"+timeStamp);
return timeStamp;
}
})
);
//3. transformation-数据处理转换
DataStream resultStream = greenStream.join(orangeStream)
.where(tup3 -> tup3.f0)
.equalTo(tup3 -> tup3.f0)
.window(TumblingEventTimeWindows.of(Time.seconds(5)))
.apply(new JoinFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, Tuple3<String, Integer, Integer>>() {
@Override
public Tuple3<String, Integer, Integer> join(Tuple3<String, Integer, String> t1, Tuple3<String, Integer, String> t2) throws Exception {
System.out.println(t1.f2);
System.out.println(t2.f2);
return Tuple3.of(t1.f0, t1.f1, t2.f1);
}
});
//4. sink-数据输出
resultStream.print();
//5. execute-执行
env.execute();
}
}
总结非常重要:
1) 要想测试这个效果,需要将并行度设置为1
2)窗口中数据的打印是需要触发的,没有触发的数据,窗口内是不会进行计算的,所以记得输入触发的数据。
假如使用了EventTime 作为时间语义,不管是窗口开始和结束时间还是触发的条件,都跟系统时间没有关系,而跟输入的数据有关系,举例:
假如你的第一条数据是:key,0,2021-03-26 12:09:01 窗口的大小是5s,水印是3秒 ,窗口的开始时间为:
2021-03-26 12:09:00 结束时间是 2021-03-26 12:09:05 ,触发时间是2021-03-26 12:09:08
为什么呢? 水印时间 >= 结束时间
水印时间是:2021-03-26 12:09:08 - 3 = 2021-03-26 12:09:05 >=2021-03-26 12:09:05
时间窗口如果是 00~05 ,05 这个时间点的数据是不包含在本窗口内的,而是归于下一个窗口,所谓的前包后不包。
如上代码所示为绿色流和橘色流指定 BoundedOutOfOrdernessWatermarks Watermark 策略,设置100毫秒的最大可容忍的延迟时间,同时也会为流分配事件时间戳。假设输入流为 格式,两条流输入元素如下所示:
绿色流:
key,0,2021-03-26 12:09:00
key,1,2021-03-26 12:09:01
key,2,2021-03-26 12:09:02
key,4,2021-03-26 12:09:04
key,5,2021-03-26 12:09:05
key,8,2021-03-26 12:09:08
key,9,2021-03-26 12:09:09
key,11,2021-03-26 12:09:11
橘色流:
key,0,2021-03-26 12:09:00
key,1,2021-03-26 12:09:01
key,2,2021-03-26 12:09:02
key,3,2021-03-26 12:09:03
key,4,2021-03-26 12:09:04
key,6,2021-03-26 12:09:06
key,7,2021-03-26 12:09:07
key,11,2021-03-26 12:09:11
1.2 滑动窗口Join [解释一下即可,不用深究 ]
当在滑动窗口上进行 Join 时,所有有相同 Key 并且位于同一滑动窗口中的两条流的元素两两组合进行关联,并最终传递到 JoinFunction 进行处理。
如上图所示,我们定义了一个窗口大小为 2 秒、滑动步长为 1 秒的滑动窗口。需要注意的是,一个元素可能会落在不同的窗口中,因此会在不同窗口中发生关联,例如,绿色流中的0元素。当滑动窗口中一个流的元素在另一个流中没有相对应的元素,则不会输出该元素。
下面我们一起看一下如何实现上图所示的滑动窗口 Join:
package com.bigdata.day07;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.time.Duration;
import java.util.Date;
/**
* @基本功能: 演示join的滑动窗口
* @program:FlinkDemo
* @author: 闫哥
* @create:2024-05-20 09:11:13
**/
public class Demo02Join {
public static void main(String[] args) throws Exception {
//1. env-准备环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
// 将并行度设置为1,否则很难看到现象
env.setParallelism(1);
// 创建一个绿色的流
DataStreamSource<String> greenSource = env.socketTextStream("localhost", 8899);
// key,0,2021-03-26 12:09:00 将它变为三元组
SingleOutputStreamOperator<Tuple3<String, Integer, String>> greenDataStream = greenSource.map(new MapFunction<String, Tuple3<String, Integer, String>>() {
@Override
public Tuple3<String, Integer, String> map(String value) throws Exception {
String[] arr = value.split(",");
return new Tuple3<>(arr[0], Integer.valueOf(arr[1]), arr[2]);
}
}).assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
// 为什么这个地方的代码比之前要长,原因是以前获取的数据都是long类型,并且都是毫秒值
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
// 指定你的数据中哪一个是时间戳,并且时间戳必须是long类型,必须是毫秒为单位的。
String time = element.f2; //2021-03-26 12:09:00
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
try {
Date date = sdf.parse(time);
return date.getTime();
} catch (ParseException e) {
throw new RuntimeException(e);
}
}
})
);
// 创建一个橘色的流
DataStreamSource<String> orangeSource = env.socketTextStream("localhost", 9988);
// key,0,2021-03-26 12:09:00 将它变为三元组
SingleOutputStreamOperator<Tuple3<String, Integer, String>> orangeDataStream = orangeSource.map(new MapFunction<String, Tuple3<String, Integer, String>>() {
@Override
public Tuple3<String, Integer, String> map(String value) throws Exception {
String[] arr = value.split(",");
return new Tuple3<>(arr[0], Integer.valueOf(arr[1]), arr[2]);
}
}).assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
// 为什么这个地方的代码比之前要长,原因是以前获取的数据都是long类型,并且都是毫秒值
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
// 指定你的数据中哪一个是时间戳,并且时间戳必须是long类型,必须是毫秒为单位的。
String time = element.f2; //2021-03-26 12:09:00
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
try {
Date date = sdf.parse(time);
return date.getTime();
} catch (ParseException e) {
throw new RuntimeException(e);
}
}
})
);
//2. source-加载数据
//3. transformation-数据处理转换
DataStream<Tuple3<String, Integer, Integer>> resultStream = greenDataStream.join(orangeDataStream)
.where(tuple3 -> tuple3.f0)
.equalTo(tuple3 -> tuple3.f0)
.window(SlidingEventTimeWindows.of(Time.seconds(5),Time.seconds(1)))
.apply(new JoinFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, Tuple3<String, Integer, Integer>>() {
@Override
public Tuple3<String, Integer, Integer> join(Tuple3<String, Integer, String> first, Tuple3<String, Integer, String> second) throws Exception {
return Tuple3.of(first.f0, first.f1, second.f1);
}
});
//4. sink-数据输出
greenDataStream.print("绿色的流:");
orangeDataStream.print("橘色的流:");
resultStream.print("最终的结果:");
//5. execute-执行
env.execute();
}
}
假设输入流为 格式,两条流输入元素如下所示:
绿色流:
key,0,2021-03-26 12:09:00
key,3,2021-03-26 12:09:03
key,4,2021-03-26 12:09:04
key,9,2021-03-26 12:09:09
橘色流:
key,0,2021-03-26 12:09:00
key,1,2021-03-26 12:09:01
key,2,2021-03-26 12:09:02
key,3,2021-03-26 12:09:03
key,4,2021-03-26 12:09:04
key,9,2021-03-26 12:09:09
2. CoGroup
CoGroup 算子是将两条数据流按照 Key 进行分组,然后将相同 Key 的数据进行处理。要实现 CoGroup 功能需要为两个输入流分别指定 KeySelector 和 WindowAssigner。它的调用方式类似于 Join 算子,但是 CoGroupFunction 比 JoinFunction 更加灵活,可以按照用户指定的逻辑匹配左流或者右流的数据,基于此我们可以实现内连接(InnerJoin)、左连接(LeftJoin)以及右连接(RightJoin)。
目前,这些分组中的数据是在内存中保存的,因此需要确保保存的数据量不能太大,否则,JVM 可能会崩溃。
CoGroup 通用用法如下:
stream.coGroup(otherStream)
.where(<KeySelector>)
.equalTo(<KeySelector>)
.window(<WindowAssigner>)
.apply(<CoGroupFunction>);
下面我们看一下如何使用 CoGroup 算子实现内连接(InnerJoin)、左连接(LeftJoin)以及右连接(RightJoin)。
最大的优势是可以实现内连接,左连接,右连接,但是缺点是内存压力大,而上面的join只能实现内连接。
CoGroup 从写法上,是coGroup 和 join的区别,而且apply 里面的函数也是不一样的,一定要注意观察。
2.1 InnerJoin
下面我们看一下如何使用 CoGroup 实现内连接:
如上图所示,我们定义了一个大小为 2 秒的滚动窗口。InnerJoin 只有在两个流对应窗口中都存在元素时,才会输出。
我们以滚动窗口为例来实现 InnerJoin
package com.bigdata.day07;
import org.apache.flink.api.common.RuntimeExecutionMode;
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.common.functions.JoinFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.CoGroupedStreams;
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.text.ParseException;
import java.text.SimpleDateFormat;
import java.time.Duration;
import java.util.Arrays;
import java.util.Date;
/**
* @基本功能:
* @program:FlinkDemo
* @author: 闫哥
* @create:2023-11-27 09:31:57
**/
public class _ShuangLiuCoGroupDemo {
public static void main(String[] args) throws Exception {
//1. env-准备环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
//2. source-加载数据 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> greenStream = env.socketTextStream("localhost", 8888)
.map(new MapFunction<String, Tuple3<String, Integer, String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("绿色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0], Integer.valueOf(arr[1]), arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(1))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("绿色的时间:"+timeStamp);
System.out.println(element.f0);
return timeStamp;
}
})
);
;
// 以后这个9999少用,因为kafka占用这个端口 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> orangeStream = env.socketTextStream("localhost", 7777)
.map(new MapFunction<String, Tuple3<String,Integer,String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("橘色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0],Integer.valueOf(arr[1]),arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(1))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("橘色的时间:"+timeStamp);
return timeStamp;
}
})
);
//3. transformation-数据处理转换
CoGroupedStreams<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>> coGroup = greenStream.coGroup(orangeStream);
coGroup.where(tup3 -> tup3.f0)
.equalTo(tup3 -> tup3.f0)
.window(TumblingEventTimeWindows.of(Time.seconds(5))).apply(new CoGroupFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, String>() {
@Override
public void coGroup(Iterable<Tuple3<String, Integer, String>> i1, Iterable<Tuple3<String, Integer, String>> i2, Collector<String> collector) throws Exception {
// 凭借这两个迭代器实现内连接,左右连接
// 内连接 外面这个循环和里面的循环必须都有数据才会进行输出,典型的内连接
for (Tuple3<String, Integer, String> t1 : i1) {
for (Tuple3<String, Integer, String> t2 : i2) {
collector.collect("key="+t1.f0+",t1.value="+t1.f1+",t2.value="+t2.f1);
}
}
}
}).print();
//5. execute-执行
env.execute();
}
}
如上代码所示,我们实现了 CoGroupFunction 接口,重写 coGroup 方法。一个流中有相同 Key 并且位于同一窗口的元素都会保存在同一个迭代器(Iterable),本示例中绿色流为 greenIterable,橘色流为 orangeIterable,如果要实现 InnerJoin ,只需要两个迭代器中的元素两两组合即可。两条流输入元素如下所示:
绿色流:
key,0,2021-03-26 12:09:00
key,1,2021-03-26 12:09:01
key,2,2021-03-26 12:09:02
key,4,2021-03-26 12:09:04
key,5,2021-03-26 12:09:05
key,8,2021-03-26 12:09:08
key,9,2021-03-26 12:09:09
key,11,2021-03-26 12:09:11
橘色流:
key,0,2021-03-26 12:09:00
key,1,2021-03-26 12:09:01
key,2,2021-03-26 12:09:02
key,3,2021-03-26 12:09:03
key,4,2021-03-26 12:09:04
key,6,2021-03-26 12:09:06
key,7,2021-03-26 12:09:07
key,11,2021-03-26 12:09:11
2.2 LeftJoin
下面我们看一下如何使用 CoGroup 实现左连接:
如上图所示,我们定义了一个大小为 2 秒的滚动窗口。LeftJoin 只要绿色流窗口中有元素时,就会输出。即使在橘色流对应窗口中没有相对应的元素。
我们以滚动窗口为例来实现 LeftJoin
package com.bigdata.day07;
import org.apache.flink.api.common.RuntimeExecutionMode;
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.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.CoGroupedStreams;
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.text.ParseException;
import java.text.SimpleDateFormat;
import java.time.Duration;
import java.util.Arrays;
import java.util.Date;
/**
* @基本功能:
* @program:FlinkDemo
* @author: 闫哥
* @create:2023-11-27 09:31:57
**/
public class _ShuangLiuCoGroupLeftDemo {
public static void main(String[] args) throws Exception {
//1. env-准备环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
env.setParallelism(1);
//2. source-加载数据 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> greenStream = env.socketTextStream("localhost", 8888)
.map(new MapFunction<String, Tuple3<String, Integer, String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("绿色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0], Integer.valueOf(arr[1]), arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("绿色的时间:"+timeStamp);
System.out.println(element.f0);
return timeStamp;
}
})
);
;
// 以后这个9999少用,因为kafka占用这个端口 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> orangeStream = env.socketTextStream("localhost", 7777)
.map(new MapFunction<String, Tuple3<String,Integer,String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("橘色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0],Integer.valueOf(arr[1]),arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("橘色的时间:"+timeStamp);
return timeStamp;
}
})
);
//3. transformation-数据处理转换
CoGroupedStreams<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>> coGroup = greenStream.coGroup(orangeStream);
coGroup.where(tup3 -> tup3.f0)
.equalTo(tup3 -> tup3.f0)
.window(TumblingEventTimeWindows.of(Time.seconds(5))).apply(new CoGroupFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, String>() {
@Override
public void coGroup(Iterable<Tuple3<String, Integer, String>> i1, Iterable<Tuple3<String, Integer, String>> i2, Collector<String> collector) throws Exception {
// 凭借这两个迭代器实现内连接,左右连接
// 内连接
for (Tuple3<String, Integer, String> t1 : i1) {
boolean noEelement = true;
for (Tuple3<String, Integer, String> t2 : i2) {
noEelement = false;
collector.collect("key="+t1.f0+",t1.value="+t1.f1+",t2.value="+t2.f1);
}
if(noEelement){
collector.collect("key="+t1.f0+",t1.value="+t1.f1+",t2.value="+null);
}
}
}
}).print();
//5. execute-执行
env.execute();
}
}
如上代码所示,我们实现了 CoGroupFunction 接口,重写 coGroup 方法。一个流中有相同 Key 并且位于同一窗口的元素都会保存在同一个迭代器(Iterable),本示例中绿色流为 green Iterable,橘色流为 orange Iterable,如果要实现 LeftJoin ,需要保证 orange Iterable 中没有元素,green Iterable 中的元素也能输出。因此我们定义了一个 noElements 变量来判断 orange Iterable 是否有元素,如果 orange Iterable 中没有元素,单独输出 greenIterable 中的元素即可。Join 效果如下所示:
2.3 RightJoin
下面我们看一下如何使用 CoGroup 实现右连接:
如上图所示,我们定义了一个大小为 2 秒的滚动窗口。LeftJoin 只要橘色流窗口中有元素时,就会输出。即使在绿色流对应窗口中没有相对应的元素。
我们以滚动窗口为例来实现 RightJoin
// Join流
CoGroupedStreams coGroupStream = greenStream.coGroup(orangeStream);
DataStream<String> result = coGroupStream
// 绿色流
.where(new KeySelector<Tuple3<String, String, String>, String>() {
@Override
public String getKey(Tuple3<String, String, String> tuple3) throws Exception {
return tuple3.f0;
}
})
// 橘色流
.equalTo(new KeySelector<Tuple3<String, String, String>, String>() {
@Override
public String getKey(Tuple3<String, String, String> tuple3) throws Exception {
return tuple3.f0;
}
})
// 滚动窗口
.window(TumblingEventTimeWindows.of(Time.seconds(2)))
.apply(new RightJoinFunction());
// 右连接
private static class RightJoinFunction implements CoGroupFunction<Tuple3<String, String, String>, Tuple3<String, String, String>, String> {
@Override
public void coGroup(Iterable<Tuple3<String, String, String>> greenIterable, Iterable<Tuple3<String, String, String>> orangeIterable, Collector<String> collector) throws Exception {
for (Tuple3<String, String, String> orangeTuple : orangeIterable) {
boolean noElements = true;
for (Tuple3<String, String, String> greenTuple : greenIterable) {
noElements = false;
LOG.info("[Join流] Key : {}, Value: {}, EventTime: {}",
greenTuple.f0, greenTuple.f1 + ", " + orangeTuple.f1, greenTuple.f2 + ", " + orangeTuple.f2
);
collector.collect(greenTuple.f1 + ", " + orangeTuple.f1);
}
if (noElements) {
LOG.info("[Join流] Key : {}, Value: {}, EventTime: {}",
orangeTuple.f0, "null, " + orangeTuple.f1, "null, " + orangeTuple.f2
);
collector.collect("null, " + orangeTuple.f2);
}
}
}
}
如上代码所示,我们实现了 CoGroupFunction 接口,重写 coGroup 方法。一个流中有相同 Key 并且位于同一窗口的元素都会保存在同一个迭代器(Iterable),本示例中绿色流为 greenIterable,橘色流为 orangeIterable,如果要实现 RightJoin,实现原理跟 LeftJoin 一样,需要保证 greenIterable 中没有元素,orangeIterable 中的元素也能输出。因此我们定义了一个 noElements 变量来判断 greenIterable 是否有元素,如果 greenIterable 中没有元素,单独输出 orangeIterable 中的元素即可。
3. Interval Join
Interval Join 不同于 Join以及CoGroup 原因是 Join和CoGroup 他们是窗口Join ,必须给定窗口的 ,Interval Join不需要给窗口。Interval Join 必须先分组才能使用。
Flink 中基于 DataStream 的 Join,只能实现在同一个窗口的两个数据流进行 Join,但是在实际中常常会存在数据乱序或者延时的情况,导致两个流的数据进度不一致,就会出现数据跨窗口的情况,那么数据就无法在同一个窗口内 Join。Flink 基于 KeyedStream 提供的 Interval Join 机制可以对两个keyedStream 进行 Join, 按照相同的 key 在一个相对数据时间的时间段内进行 Join。按照指定字段以及右流相对左流偏移的时间区间进行关联:
b.timestamp ∈ [a.timestamp + lowerBound, a.timestamp + upperBound]
或者
a.timestamp + lowerBound <= b.timestamp <= a.timestamp + upperBound
其中a和b分别是上图中绿色流和橘色流中的元素,并且有相同的 key。只需要保证 lowerBound 永远小于等于 upperBound 即可,均可以为正数或者负数。
从上面可以看出绿色流可以晚到 lowerBound(lowerBound为负的话)时间,也可以早到 upperBound(upperBound为正的话)时间。也可以理解为橘色流中的每个元素可以和绿色流中指定区间的元素进行 Join。需要注意的是 Interval Join 当前仅支持事件时间(EventTime):
public IntervalJoined<T1, T2, KEY> between(Time lowerBound, Time upperBound) {
if (timeBehaviour != TimeBehaviour.EventTime) {
throw new UnsupportedTimeCharacteristicException("Time-bounded stream joins are only supported in event time");
}
}
下面我们具体看看如何实现一个 Interval Join:
package com.bigdata.day07;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.ProcessJoinFunction;
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.text.ParseException;
import java.text.SimpleDateFormat;
import java.time.Duration;
import java.util.Arrays;
import java.util.Date;
/**
* @基本功能:
* @program:FlinkDemo
* @author: 闫哥
* @create:2023-11-27 09:31:57
**/
public class _ShuangLiuIntervalJoinDemo {
public static void main(String[] args) throws Exception {
//1. env-准备环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
// 并行度不为1 ,效果很难出来,因为本地的并行度是16,只有16个并行度都触发才能看到效果
env.setParallelism(1);
//2. source-加载数据 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> greenStream = env.socketTextStream("localhost", 8888)
.map(new MapFunction<String, Tuple3<String, Integer, String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("绿色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0], Integer.valueOf(arr[1]), arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("绿色的时间:"+timeStamp);
System.out.println(element.f0);
return timeStamp;
}
})
);
;
// 以后这个9999少用,因为kafka占用这个端口 key,0,2021-03-26 12:09:00
DataStream<Tuple3<String, Integer, String>> orangeStream = env.socketTextStream("localhost", 7777)
.map(new MapFunction<String, Tuple3<String,Integer,String>>() {
@Override
public Tuple3<String, Integer, String> map(String line) throws Exception {
String[] arr = line.split(",");
System.out.println("橘色:"+ Arrays.toString(arr));
return Tuple3.of(arr[0],Integer.valueOf(arr[1]),arr[2]);
}
})
// 因为用到了EventTime 所以势必用到水印,否则报错
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {
@Override
public long extractTimestamp(Tuple3<String, Integer, String> element, long recordTimestamp) {
Long timeStamp = 0L;
SimpleDateFormat simpleDateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
Date date = null;
try {
date = simpleDateFormat.parse(element.f2);
} catch (ParseException e) {
throw new RuntimeException(e);
}
timeStamp = date.getTime();
System.out.println("橘色的时间:"+timeStamp);
return timeStamp;
}
})
);
//3. transformation-数据处理转换
DataStream resultStream = greenStream.keyBy(tup -> tup.f0).intervalJoin(orangeStream.keyBy(tup -> tup.f0))
.between(Time.seconds(-2),Time.seconds(1))
.process(new ProcessJoinFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, String>() {
@Override
public void processElement(Tuple3<String, Integer, String> left, Tuple3<String, Integer, String> right, ProcessJoinFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, String>.Context ctx, Collector<String> out) throws Exception {
out.collect("left中的key:"+left.f0+",value="+left.f1+",time="+left.f2+",right中的key:"+right.f0+",value="+right.f1+",time="+right.f2);
}
});
//4. sink-数据输出
resultStream.print();
//5. execute-执行
env.execute();
}
}
需要注意的是 Interval Join 当前仅支持事件时间(EventTime),所以需要为流指定事件时间戳(毫秒值)。
两条流输入元素如下所示:
绿色流:
c,0,2021-03-23 12:09:00
c,1,2021-03-23 12:09:01
c,6,2021-03-23 12:09:06
c,7,2021-03-23 12:09:07
橘色流:
c,0,2021-03-23 12:09:00
c,2,2021-03-23 12:09:02
c,3,2021-03-23 12:09:03
c,4,2021-03-23 12:09:04
c,5,2021-03-23 12:09:05
c,7,2021-03-23 12:09:07
总结:
join、coGroup 都是基于窗口的join, join算子本身只支持内连接
coGroup 只可以实现内连接,左右连接
intervalJoin 是一个范围join, 一个数据流上的某一时刻数据可以join 另外一个数据流上的某一范围的数据。跟窗口无关。
原文地址:https://blog.csdn.net/wozhendeyumenle/article/details/144296710
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