Flink之IntervalJoin介绍
InterValJoin算子
间隔流，一条流去join另一条流去过去一段时间内的数据，该算子将keyedStream与keyedStream转化为DataStream；再给定的时间边界内（默认包含边界），相当于一个窗口，按指定的key对俩个KeyedStream进行Join操作，把符合join条件的俩个event拉倒一起，然后咋么处理右用户来决定。
1、key1 == key2 && e1.timestamp +lowerBound <= e2.timestamp +upperBound
2、场景：把一定时间范围内相关的分组数据拉成一个宽表
语法规则：
leftKeyedStream
.intervalJoin(rightKeyedStream)
//时间间隔，设定下界和上界
.between(Time.minutes(-10),Time.seconds(0))
//不包含下界
.lowerBoundExclusive()
//不包含上界
.upperBoundExclusive()
//自定义ProcessJoinFunction 处理join到的元组
.process(ProcessJoinFunction)
该算子的注意事项：
1、俩条流都缓存在内部state中。leftElement到达，去获取State中rightElement响应时间范围内的数据，然后执行ProcessJoinFunciton进行Join操作；
2、时间间隔：leftElement默认和【leftElementEventTime + lowerBound，leftElementEventTime +upperBound】时间范围内的rightElement join；
3、举例：leftElementEventTime = 2019-11-16 17:30:00，lowerBound=-10minute,upperBound=0,则这条leftElement按Key和【2019-11-16 17:20:00,2019-11-16 17:30:00】时间范围内的rightElementJoin；
4、IntervalJoin目前只支持EventTime；
5、数据量比较大，可能使用RocksDBStateBackend
demo案列：
package Flink_API;
import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.TimeCharacteristic;
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.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.api.functions.co.ProcessJoinFunction;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
import org.apache.flink.streaming.util.serialization.KeyedDeserializationSchema;
import org.apache.flink.table.shaded.org.joda.time.DateTime;
import org.apache.flink.table.shaded.org.joda.time.format.DateTimeFormat;
import org.apache.flink.table.shaded.org.joda.time.format.DateTimeFormatter;
import org.apache.flink.util.Collector;
import java.io.Serializable;
import java.util.Properties;
public class TestInterViewJoin {
public static void main(String[] args) throws Exception {
//创建运行环境
StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
//Flink是以数据自带的时间戳字段为准
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
//设置并行度
env.setParallelism(1);
//1、获取第一个流，获取用户的浏览信息
DataStream browseStream = getUserBrowseDataStream(env);
//2、获取用户的点击信息
DataStream clickStream = getUserClickLogDataStream(env);
//打印结果
browseStream.print();
clickStream.print();
//核心：双流进行IntervalJoin操作：每个用户的点击信息Join这个用户最近10分钟内的浏览信息
//browseStream(左流)关联clickStream（右流）
KeyedStream userClickLogStringKeyedStream = clickStream.keyBy(new KeySelector(){
@Override
public String getKey(UserClickLog userClickLog) throws Exception {
return userClickLog.userID;
}
});
KeyedStream userBrowseLogStringKeyedStream1=browseStream.keyBy(new KeySelector(){
@Override
public String getKey(UserBrowseLog userBrowseLog) throws Exception {
return userBrowseLog.userID;
}
});
//每个用户的点击Join这个用户最近的10分钟内的浏览
DataStream processData = userClickLogStringKeyedStream.intervalJoin(userBrowseLogStringKeyedStream1)
.between(Time.minutes(-10),Time.seconds(0))//下界：10分钟，上界：当前EventTime时刻（左流去右流10分钟之前去找数据）
.process(new ProcessJoinFunction() {
//leftElement到达，去获取State中rightElement响应范围内的数据，然后执行ProcessJoinFunction进行Join操作：
@Override
public void processElement(UserClickLog left, UserBrowseLog right, Context context, Collector collector) throws Exception {
collector.collect(left+""+right);
}
});
processData.print();
//程序的入口类
env.execute("TestInterViewJoin");
}
private static DataStream getUserClickLogDataStream(StreamExecutionEnvironment env) {
Properties consumerProperties = new Properties();
consumerProperties.setProperty("bootstrap.severs","page01:9002");
consumerProperties.setProperty("grop.id","browsegroup");
DataStreamSource dataStreamSource=env.addSource(new FlinkKafkaConsumer010("browse_topic1", (KeyedDeserializationSchema) new SimpleStringSchema(),consumerProperties));
DataStream processData=dataStreamSource.process(new ProcessFunction() {
@Override
public void processElement(String s, Context context, Collector collector) throws Exception {
try{
UserClickLog browseLog = com.alibaba.fastjson.JSON.parseObject(s, UserClickLog.class);
if(browseLog !=null){
collector.collect(browseLog);
}
}catch(Exception e){
System.out.print("解析Json——UserBrowseLog异常："+e.getMessage());
}
}
});
//设置watermark
return processData.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor(Time.seconds(0)){
@Override
public long extractTimestamp(UserClickLog userBrowseLog) {
DateTimeFormatter dateTimeFormatter= DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
DateTime dateTime=DateTime.parse(userBrowseLog.getEventTime(),dateTimeFormatter);
//用数字表示时间戳，单位是ms，13位
return dateTime.getMillis();
}
});
}
private static DataStream getUserBrowseDataStream(StreamExecutionEnvironment env) {
Properties consumerProperties = new Properties();
consumerProperties.setProperty("bootstrap.severs","page01:9001");
consumerProperties.setProperty("grop.id","browsegroup");
DataStreamSource dataStreamSource=env.addSource(new FlinkKafkaConsumer010("browse_topic", (KeyedDeserializationSchema) new SimpleStringSchema(),consumerProperties));
DataStream processData=dataStreamSource.process(new ProcessFunction() {
@Override
public void processElement(String s, Context context, Collector collector) throws Exception {
try{
UserBrowseLog browseLog = com.alibaba.fastjson.JSON.parseObject(s, UserBrowseLog.class);
if(browseLog !=null){
collector.collect(browseLog);
}
}catch(Exception e){
System.out.print("解析Json——UserBrowseLog异常："+e.getMessage());
}
}
});
//设置watermark
return processData.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor(Time.seconds(0)) {
@Override
public long extractTimestamp(UserBrowseLog userBrowseLog) {
DateTimeFormatter dateTimeFormatter= DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
DateTime dateTime=DateTime.parse(userBrowseLog.getEventTime(),dateTimeFormatter);
//用数字表示时间戳，单位是ms，13位
return dateTime.getMillis();
}
});
}
//浏览类
public static class UserBrowseLog implements Serializable {
private String userID;
private String eventTime;
private String eventType;
private String productID;
private Integer productPrice;
public String getUserID() {
return userID;
}
public void setUserID(String userID) {
this.userID = userID;
}
public String getEventTime() {
return eventTime;
}
public void setEventTime(String eventTime) {
this.eventTime = eventTime;
}
public String getEventType() {
return eventType;
}
public void setEventType(String eventType) {
this.eventType = eventType;
}
public String getProductID() {
return productID;
}
public void setProductID(String productID) {
this.productID = productID;
}
public Integer getProductPrice() {
return productPrice;
}
public void setProductPrice(Integer productPrice) {
this.productPrice = productPrice;
}
@Override
public String toString() {
return "UserBrowseLog{" +
"userID='" + userID + '\'' +
", eventTime='" + eventTime + '\'' +
", eventType='" + eventType + '\'' +
", productID='" + productID + '\'' +
", productPrice=" + productPrice +
'}';
}
}
//点击类
public static class UserClickLog implements Serializable{
private String userID;
private String eventTime;
private String eventType;
private String pageID;
public String getUserID() {
return userID;
}
public void setUserID(String userID) {
this.userID = userID;
}
public String getEventTime() {
return eventTime;
}
public void setEventTime(String eventTime) {
this.eventTime = eventTime;
}
public String getEventType() {
return eventType;
}
public void setEventType(String eventType) {
this.eventType = eventType;
}
public String getPageID() {
return pageID;
}
public void setPageID(String pageID) {
this.pageID = pageID;
}
@Override
public String toString() {
return "UserClickLog{" +
"userID='" + userID + '\'' +
", eventTime='" + eventTime + '\'' +
", eventType='" + eventType + '\'' +
", pageID='" + pageID + '\'' +
'}';
}
}
}