背景
之前我们在一片文章里简单介绍过Flink的多流合并算子
java Flink（三十六）Flink多流合并算子UNION、CONNECT、CoGroup、Join
今天我们通过Flink 1.14的源码对Flink的Interval Join进行深入的理解。
Interval Join不是两个窗口做关联，更适用于处理乱序数据流之间的关联。它的作用更类似于从左流中a元素本身出发，对右流中一段时间内的数据进行关联（Inner Join：只关联相同Key的数据）。
如图所示：
下边这条流中的2关联到上范围内的0/1
源码解析
Flink版本1.14.4
按住Ctrl+鼠标左键，点击process进入源码
这里process方法是在KeydStream.java下IntervalJoined类下的方法
包装返回类型的TypeInfomation（TypeInfo的介绍可以看上一篇）
返回的outputType
SingleOutputStreamOperator使用给定的用户函数完成联接操作，该函数针对每个联接的元素对执行。这种方法允许传递输出类型的显式类型信息。
IntervalJoinOperator初始化
左边界<=右边界检查
获取左流还有右流数据对应的序列化（从TypeInfo获取的）
继续看IntervalJoinOperator中的其余关键实现
open方法用来注册定时器
初始化两个流的map状态
处理左侧流中的数据。每当数据到达左流时，它就会被添加到左缓冲区。将从右侧缓冲区中查找该元素可能的候选联接，如果该对位于用户定义的边界内，则将其传递给 ProcessJoinFunction
同理处理右流
进入数据处理函数
获取数据，取出事件时间
超过当前watermark的数据进行过滤
数据没问题的话，将数据添加到状态
遍历另一条流的状态，遍历其中的数据，把满足时间要求的数据进行collect
注册一个当前事件时间戳+右边界的定时器
定时器触发后，清空map状态中时间戳-左边界的那条数据
简单实例
pom
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.example</groupId>
<artifactId>FlinkCode</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
<jdk.version>1.8</jdk.version>
<jar.name>ubs-data-converter</jar.name>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<!--Flink 版本-->
<flink.version>1.14.4</flink.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka_2.11</artifactId>
<version>${flink.version}</version>
<exclusions>
<exclusion>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-runtime-web_2.11</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
<version>4.5.10</version>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.8</version>
</dependency>
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
<version>1.9.2</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpcore</artifactId>
<version>4.4.1</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.16</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.16</version>
<scope>compile</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.avro</groupId>
<artifactId>avro-maven-plugin</artifactId>
<version>1.9.2</version>
<executions>
<execution>
<phase>generate-sources</phase>
<goals>
<goal>schema</goal>
</goals>
<configuration>
<sourceDirectory>${project.basedir}/src/main/resources/</sourceDirectory>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>${jdk.version}</source>
<target>${jdk.version}</target>
<encoding>${project.build.sourceEncoding}</encoding>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.1.1</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<finalName>${jar.name}</finalName>
<artifactSet>
<excludes>
<exclude>com.google.code.findbugs:jsr305</exclude>
<exclude>org.slf4j:*</exclude>
<exclude>log4j:*</exclude>
<exclude>org.glassfish.jersey.core:jersey-common</exclude>
</excludes>
</artifactSet>
<relocations>
<relocation>
<pattern>com.google.common</pattern>
<shadedPattern>com.shade.google.common</shadedPattern>
</relocation>
<relocation>
<pattern>org.apache.kafka</pattern>
<shadedPattern>org.shade.apache.kafka</shadedPattern>
</relocation>
</relocations>
<filters>
<filter>
<artifact>*</artifact>
<includes>
<include>org/apache/htrace/**</include>
<include>org/apache/avro/**</include>
<include>org/apache/flink/streaming/**</include>
<include>org/apache/flink/connector/**</include>
<include>org/apache/kafka/**</include>
<include>org/apache/hive/**</include>
<include>org/apache/hadoop/hive/**</include>
<include>org/apache/curator/**</include>
<include>org/apache/zookeeper/**</include>
<include>org/apache/jute/**</include>
<include>org/apache/thrift/**</include>
<include>org/apache/http/**</include>
<include>org/I0Itec/**</include>
<include>jline/**</include>
<include>com/yammer/**</include>
<include>kafka/**</include>
<include>org/apache/hadoop/hbase/**</include>
<include>com/alibaba/fastjson/**</include>
<include>org/elasticsearch/action/**</include>
<include>io/confluent/**</include>
<include>com/fasterxml/**</include>
<include>org/elasticsearch/**</include>
<include>hbase-default.xml</include>
<include>hbase-site.xml</include>
</includes>
</filter>
<filter>
<artifact>org.apache.hadoop.hive.*:*</artifact>
<excludes>
<exclude></exclude>
<exclude></exclude>
<exclude></exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>user bean
package ubs.app.intervaljoin.bean;
import lombok.*;
@Data
@AllArgsConstructor
@Setter
@Getter
@NoArgsConstructor
public class User{
Integer id;
Long t;
}
order bean
package ubs.app.intervaljoin.bean;
import lombok.*;
@Data
@AllArgsConstructor
@Setter
@Getter
@NoArgsConstructor
public class Order {
Integer id;
Long price;
Long time;
}main
package ubs.app.intervaljoin;
import org.apache.flink.api.common.eventtime.*;
import org.apache.flink.streaming.api.datastream.DataStream;
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.time.Time;
import org.apache.flink.util.Collector;
import ubs.app.intervaljoin.bean.Order;
import ubs.app.intervaljoin.bean.User;
import ubs.app.intervaljoin.source.OrderSource;
import ubs.app.intervaljoin.source.UserSource;
import java.time.Duration;
public class IntervalJoinApp {
public static void main(String[] args) throws Exception{
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//设置watermark
WatermarkStrategy<User> userWatermarkStrategy = WatermarkStrategy.<User>forBoundedOutOfOrderness(Duration.ofSeconds(1))
.withTimestampAssigner(new SerializableTimestampAssigner<User>() {
@Override
public long extractTimestamp(User element, long recordTimestamp) {
return element.getT();
}
});
DataStream<User> userDataStreamSource = env.addSource(new UserSource()).assignTimestampsAndWatermarks(userWatermarkStrategy);
//设置watermark
WatermarkStrategy<Order> orderWatermarkStrategy = WatermarkStrategy.<Order>forBoundedOutOfOrderness(Duration.ofSeconds(1))
.withTimestampAssigner(new SerializableTimestampAssigner<Order>() {
@Override
public long extractTimestamp(Order element, long recordTimestamp) {
return element.getTime();
}
});
DataStream<Order> orderDataStreamSource = env.addSource(new OrderSource()).assignTimestampsAndWatermarks(orderWatermarkStrategy);
env.setParallelism(1);
SingleOutputStreamOperator<String> process = userDataStreamSource.keyBy(o -> o.getId())
.intervalJoin(orderDataStreamSource.keyBy(o -> o.getId()))
.between(Time.seconds(-5), Time.seconds(0))
.process(
new ProcessJoinFunction<User, Order, String>() {
@Override
public void processElement(User left, Order right, ProcessJoinFunction<User, Order, String>.Context ctx, Collector<String> out) throws Exception {
Integer lid = left.getId();
Long lt = left.getT();
Integer rid = right.getId();
long rt = right.getTime();
out.collect(String.format("左%s 左时间%s 右%s 右时间%s 关联到了 %s", lid, lt/1000, rid, rt/1000, rt/1000-lt/1000));
}
}
);
process.print();
env.execute();
}
}
![【PyTorch][chapter 22][李宏毅深度学习][ WGAN]【实战三】](https://img-blog.csdnimg.cn/direct/54522e6650df424e97d377af0e02ee69.png)
![使用ChatGPT高效完成简历制作[中篇3]-有爱AI实战教程(十）](https://img-blog.csdnimg.cn/direct/a82dd1b63b2448149423e8b2ef4c12e1.png)
![[AIGC] 在Spring Boot中指定请求体格式](https://img-blog.csdnimg.cn/direct/cd575d2000a34da69f729f18eddfa1ff.png)
