flink学习（14）—— 双流join

概述

Join:内连接

CoGroup：内连接，左连接，右连接

Interval Join：点对面

Join

1、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。
2、Join 可以支持处理时间（processing time）和事件时间(event time)两种时间特征。
3、Join 通用用法如下：stream.join(otherStream).where(<KeySelector>).equalTo(<KeySelector>).window(<WindowAssigner>).apply(<JoinFunction>)

滚动窗口

package com.bigdata.day07;import org.apache.commons.lang3.time.DateUtils;
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.functions.KeySelector;
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.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;import java.text.ParseException;
import java.time.Duration;
import java.util.Date;/*** 内连接* 可以通过两个socket流，将数据合并为一个三元组，key,value1,value2*/
public class _01_双流join_join_内连接 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);// 绿色的流DataStreamSource<String> source = env.socketTextStream("localhost", 7777);SingleOutputStreamOperator<Tuple3<String, Integer, String>> greenSource = source.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}}));// 红色的流DataStreamSource<String> source2 = env.socketTextStream("localhost", 7778);SingleOutputStreamOperator<Tuple3<String, Integer, String>> redSource = source2.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}}));// 双流joinDataStream<Tuple3<String, Integer, Integer>> rsSource = greenSource.join(redSource).where(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}}).equalTo(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}// 滚动窗口}).window(TumblingEventTimeWindows.of(Time.seconds(5))).apply(new JoinFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, Tuple3<String, Integer, Integer>>() {@Overridepublic 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);}});redSource.print("红色的流：");greenSource.print("绿色的流：");rsSource.print("合并后的流：");env.execute();}
}

滑动窗口

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>>() {@Overridepublic 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>>() {@Overridepublic 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 12:09:00SimpleDateFormat 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>>() {@Overridepublic 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>>() {@Overridepublic 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 12:09:00SimpleDateFormat 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)// 滑动窗口.window(SlidingEventTimeWindows.of(Time.seconds(5),Time.seconds(1))).apply(new JoinFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, Tuple3<String, Integer, Integer>>() {@Overridepublic 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();}
}

CoGroup

1、优势：可以实现内连接，左连接，右连接
2、劣势：内存压力大
3、和上面的写法区别：将join换成coGroup，apply中实现的具体方法有区别 
4、流程
stream.coGroup(otherStream).where(<KeySelector>).equalTo(<KeySelector>).window(<WindowAssigner>).apply(<CoGroupFunction>);

内连接

package com.bigdata.day07;import org.apache.commons.lang3.time.DateUtils;
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.functions.KeySelector;
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.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;import java.text.ParseException;
import java.time.Duration;
import java.util.Date;/*** 内连接*/
public class _02_双流join_CoGroup_内连接 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);// 绿色的流DataStreamSource<String> source = env.socketTextStream("localhost", 7777);SingleOutputStreamOperator<Tuple3<String, Integer, String>> greenSource = source.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}}));// 红色的流DataStreamSource<String> source2 = env.socketTextStream("localhost", 7778);SingleOutputStreamOperator<Tuple3<String, Integer, String>> redSource = source2.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}}));// 连接DataStream<Tuple3<String, String, String>> rsSource = greenSource.coGroup(redSource).where(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}}).equalTo(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}}).window(TumblingEventTimeWindows.of(Time.seconds(5))).apply(new CoGroupFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, Tuple3<String, String, String>>() {@Overridepublic void coGroup(Iterable<Tuple3<String, Integer, String>> first, Iterable<Tuple3<String, Integer, String>> second, Collector<Tuple3<String, String, String>> out) throws Exception {for (Tuple3<String, Integer, String> firesTuple3 : first) {for (Tuple3<String, Integer, String> secondTuple3 : second) {out.collect(Tuple3.of(firesTuple3.f0,"green"+firesTuple3.f1,"red"+secondTuple3.f1));}}}});redSource.print("红色的流：");greenSource.print("绿色的流：");rsSource.print("合并后的流：");env.execute();}
}

外连接

package com.bigdata.day07;import org.apache.commons.lang3.time.DateUtils;
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.functions.KeySelector;
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.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;import java.text.ParseException;
import java.time.Duration;
import java.util.Date;/*** 外连接*/
public class _03_双流join_CoGroup_外连接 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);// 绿色的流DataStreamSource<String> source = env.socketTextStream("localhost", 7777);SingleOutputStreamOperator<Tuple3<String, Integer, String>> greenSource = source.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}}));// 红色的流DataStreamSource<String> source2 = env.socketTextStream("localhost", 7778);SingleOutputStreamOperator<Tuple3<String, Integer, String>> redSource = source2.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}}));DataStream<Tuple3<String, String, String>> rsSource = greenSource.coGroup(redSource).where(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}}).equalTo(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}}).window(TumblingEventTimeWindows.of(Time.seconds(5))).apply(new CoGroupFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, Tuple3<String, String, String>>() {@Overridepublic void coGroup(Iterable<Tuple3<String, Integer, String>> first, Iterable<Tuple3<String, Integer, String>> second, Collector<Tuple3<String, String, String>> out) throws Exception {// 内连接，左连接，右连接的区别只在这里面存在，两层循环for (Tuple3<String, Integer, String> firesTuple3 : first) {boolean isExist = false;for (Tuple3<String, Integer, String> secondTuple3 : second) {isExist = true;out.collect(Tuple3.of(firesTuple3.f0,"green"+firesTuple3.f1,"red"+secondTuple3.f1));}if (!isExist){out.collect(Tuple3.of(firesTuple3.f0,"green"+firesTuple3.f1,"red null"));}}}});redSource.print("红色的流：");greenSource.print("绿色的流：");rsSource.print("合并后的流：");env.execute();}
}

Interval Join

1、Join以及CoGroup 原因是 Join和CoGroup是窗口Join，必须给定窗口
2、Interval Join不需要给窗口。Interval Join 必须先分组才能使用。 
3、先对数据源进行keyBy
4、 外流.intervalJoin（内流）.between(-2,2).processbetween 左不包，右包
内部的流为下面的流（取单个值）

 代码实现

package com.bigdata.day07;import org.apache.commons.lang3.time.DateUtils;
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.functions.KeySelector;
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.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
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.time.Duration;
import java.util.Date;public class _04_双流join_Interval_Join {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);//绿色的流DataStreamSource<String> source = env.socketTextStream("localhost", 7777);KeyedStream<Tuple3<String, Integer, String>, String> greenSource = source.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}// 水印}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}// keyBy})).keyBy(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}});// 红色的流DataStreamSource<String> source2 = env.socketTextStream("localhost", 7778);KeyedStream<Tuple3<String, Integer, String>, String> redSource = source2.map(new MapFunction<String, Tuple3<String, Integer, String>>() {@Overridepublic Tuple3<String, Integer, String> map(String line) throws Exception {String[] split = line.split(",");return Tuple3.of(split[0], Integer.valueOf(split[1]), split[2]);}// 水印}).assignTimestampsAndWatermarks(WatermarkStrategy.<Tuple3<String, Integer, String>>forBoundedOutOfOrderness(Duration.ofSeconds(3)).withTimestampAssigner(new SerializableTimestampAssigner<Tuple3<String, Integer, String>>() {@Overridepublic long extractTimestamp(Tuple3<String, Integer, String> tuple3, long recordTimestamp) {String timeStr = tuple3.f2;try {Date date = DateUtils.parseDate(timeStr, "yyyy-MM-dd hh-mm-ss");return date.getTime();} catch (ParseException e) {throw new RuntimeException(e);}}// 分组})).keyBy(new KeySelector<Tuple3<String, Integer, String>, String>() {@Overridepublic String getKey(Tuple3<String, Integer, String> tuple3) throws Exception {return tuple3.f0;}});// 实现SingleOutputStreamOperator<String> rsSource = greenSource.intervalJoin(redSource).between(Time.seconds(-2), Time.seconds(2)).process(new ProcessJoinFunction<Tuple3<String, Integer, String>, Tuple3<String, Integer, String>, String>() {@Overridepublic 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);}});redSource.print("红色的流：");greenSource.print("绿色的流：");rsSource.print("合并后的流：");env.execute();
/*** 红色的为下面的流* 范围：* 假如现在是10* 9 10 11 12*/}
}

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