【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（2）- interval join
Flink 系列文章
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-
1、Flink 部署系列
本部分介绍Flink的部署、配置相关基础内容。 -
2、Flink基础系列
本部分介绍Flink 的基础部分，比如术语、架构、编程模型、编程指南、基本的datastream api用法、四大基石等内容。 -
3、Flik Table API和SQL基础系列
本部分介绍Flink Table Api和SQL的基本用法，比如Table API和SQL创建库、表用法、查询、窗口函数、catalog等等内容。 -
4、Flik Table API和SQL提高与应用系列
本部分是table api 和sql的应用部分，和实际的生产应用联系更为密切，以及有一定开发难度的内容。 -
5、Flink 监控系列
本部分和实际的运维、监控工作相关。
二、Flink 示例专栏
Flink 示例专栏是 Flink 专栏的辅助说明，一般不会介绍知识点的信息，更多的是提供一个一个可以具体使用的示例。本专栏不再分目录，通过链接即可看出介绍的内容。
两专栏的所有文章入口点击：Flink 系列文章汇总索引
文章目录
- Flink 系列文章
- 三、interval join
-
- 1、示例
-
- 1）、数据结构及bean
- 2）、定义商品和订单数据源
- 3）、interval join 实现方式二
- 4）、interval join 实现方式二
- 5）、运行结果
本文主要介绍Flink 的常用的operator interval join 及详细示例。
如果需要了解更多内容，可以在本人Flink 专栏中了解更新系统的内容。
本文除了maven依赖外，没有其他依赖。本文maven依赖参考【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（1）- window join中的依赖。
本专题分为四篇文章介绍，即
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（1）- window join
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（2）- interval join
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（3）- 数据倾斜处理、分区示例
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例-完整版
三、interval join
Interval join 组合元素的条件为：两个流（我们暂时称为 A 和 B）中 key 相同且 B 中元素的 timestamp 处于 A 中元素 timestamp 的一定范围内。
这个条件可以更加正式地表示为 b.timestamp ∈ [a.timestamp + lowerBound; a.timestamp + upperBound] 或 a.timestamp + lowerBound <= b.timestamp <= a.timestamp + upperBound
这里的 a 和 b 为 A 和 B 中共享相同 key 的元素。上界和下界可正可负，只要下界永远小于等于上界即可。 Interval join 目前仅执行 inner join。
当一对元素被传递给 ProcessJoinFunction，他们的 timestamp 会从两个元素的 timestamp 中取最大值 （timestamp 可以通过 ProcessJoinFunction.Context 访问）。
Interval join 截至版本1.17 仅支持 event time。
上例中，我们 join 了橙色和绿色两个流，join 的条件是：以 -2 毫秒为下界、+1 毫秒为上界。 默认情况下，上下界也被包括在区间内，但 .lowerBoundExclusive() 和 .upperBoundExclusive() 可以将它们排除在外。
图中三角形所表示的条件也可以写成更加正式的表达式：
orangeElem.ts + lowerBound <= greenElem.ts <= orangeElem.ts + upperBound
- 示例代码
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.functions.co.ProcessJoinFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
...
DataStream<Integer> orangeStream = ...;
DataStream<Integer> greenStream = ...;
orangeStream
.keyBy(<KeySelector>)
.intervalJoin(greenStream.keyBy(<KeySelector>))
.between(Time.milliseconds(-2), Time.milliseconds(1))
.process (new ProcessJoinFunction<Integer, Integer, String>(){
@Override
public void processElement(Integer left, Integer right, Context ctx, Collector<String> out) {
out.collect(left + "," + right);
}
});
1、示例
本示例与上述的window joing构造数据一样，不同的就是数据聚合方式不同，也就是window join与interval join的功能不同，其他都一样。
本示例功能是通过系统模拟生成订单数据，然后通过订单关联商品信息，统计订单的金额。
本示例有2种实现方式，其区别就是WatermarkStrategy的实现方式不同，一个是匿名类，一个是实现接口。
1）、数据结构及bean
- 商品类
package org.datastreamapi.operator.window.bean;
import java.math.BigDecimal;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import com.alibaba.fastjson.JSON;
import lombok.Data;
/**
* @author alanchan
*
*/
// 商品类(商品id,商品名称,商品价格)
@Data
public class Goods {
private String goodsId;
private String goodsName;
private BigDecimal goodsPrice;
public static List<Goods> GOODSLIST;
public static Random r;
static {
r = new Random();
GOODSLIST = new ArrayList<>();
GOODSLIST.add(new Goods("1", "iphone11", new BigDecimal(6000)));
GOODSLIST.add(new Goods("2", "iphone12", new BigDecimal(7000)));
GOODSLIST.add(new Goods("3", "MacBookPro", new BigDecimal(15000)));
GOODSLIST.add(new Goods("4", "iphone13", new BigDecimal(8000)));
GOODSLIST.add(new Goods("5", "iphone14", new BigDecimal(9000)));
GOODSLIST.add(new Goods("6", "iphone15", new BigDecimal(10000)));
}
public static Goods randomGoods() {
int rIndex = r.nextInt(GOODSLIST.size());
return GOODSLIST.get(rIndex);
}
public Goods() {
}
public Goods(String goodsId, String goodsName, BigDecimal goodsPrice) {
this.goodsId = goodsId;
this.goodsName = goodsName;
this.goodsPrice = goodsPrice;
}
@Override
public String toString() {
return JSON.toJSONString(this);
}
}
- 订单类
package org.datastreamapi.operator.window.bean;
import com.alibaba.fastjson.JSON;
import lombok.Data;
/**
* @author alanchan
*
*/
// 订单明细类(订单id,商品id,商品数量)
@Data
public class Order {
private String itemId;
private String goodsId;
private Integer count;
@Override
public String toString() {
return JSON.toJSONString(this);
}
}
- 商品和订单关联类
package org.datastreamapi.operator.window.bean;
import java.math.BigDecimal;
import com.alibaba.fastjson.JSON;
import lombok.Data;
/**
* @author alanchan
*
*/
// 商品类(商品id,商品名称,商品价格)
// 订单明细类(订单id,商品id,商品数量)
// 关联结果(商品id,商品名称,商品数量,商品价格*商品数量)
@Data
public class OrderItem {
private String goodsId;
private String goodsName;
private BigDecimal count;
private BigDecimal total;
@Override
public String toString() {
return JSON.toJSONString(this);
}
}
2）、定义商品和订单数据源
- 商品数据源
package org.datastreamapi.operator.window.source;
import java.util.concurrent.TimeUnit;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.datastreamapi.operator.window.bean.Goods;
/**
* @author alanchan
*
*/
public class GoodsSource extends RichSourceFunction<Goods> {
private Boolean isCancel;
@Override
public void open(Configuration parameters) throws Exception {
isCancel = false;
}
@Override
public void run(SourceContext sourceContext) throws Exception {
while (!isCancel) {
Goods.GOODSLIST.stream().forEach(goods -> sourceContext.collect(goods));
TimeUnit.SECONDS.sleep(1);
}
}
@Override
public void cancel() {
isCancel = true;
}
}
- 订单数据源
package org.datastreamapi.operator.window.source;
import java.util.Random;
import java.util.UUID;
import java.util.concurrent.TimeUnit;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.datastreamapi.operator.window.bean.Goods;
import org.datastreamapi.operator.window.bean.Order;
/**
* @author alanchan
*
*/
public class OrderSource extends RichSourceFunction<Order>{
private Boolean isCancel;
private Random r;
@Override
public void open(Configuration parameters) throws Exception {
isCancel = false;
r = new Random();
}
@Override
public void run(SourceContext sourceContext) throws Exception {
while (!isCancel) {
Goods goods = Goods.randomGoods();
Order order = new Order();
order.setGoodsId(goods.getGoodsId());
order.setCount(r.nextInt(10) + 1);
order.setItemId(UUID.randomUUID().toString());
sourceContext.collect(order);
// 模拟一个订单中有多个商品
order.setGoodsId("10");
sourceContext.collect(order);
TimeUnit.SECONDS.sleep(1);
}
}
@Override
public void cancel() {
isCancel = true;
}
}
3）、interval join 实现方式二
/**
* @author alanchan
*/
package org.datastreamapi.operator.window;
import java.math.BigDecimal;
import java.time.Duration;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
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.functions.co.ProcessJoinFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;
import org.datastreamapi.operator.window.bean.Goods;
import org.datastreamapi.operator.window.bean.Order;
import org.datastreamapi.operator.window.bean.OrderItem;
import org.datastreamapi.operator.window.source.GoodsSource;
import org.datastreamapi.operator.window.source.OrderSource;
/**
* @author alanchan
*
*/
public class TestIntervalJoinDemo {
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
// 1.source
// 商品数据流
DataStreamSource<Goods> goodsDS = env.addSource(new GoodsSource());
// 订单数据流
DataStreamSource<Order> orderDS = env.addSource(new OrderSource());
// 给数据添加水印(直接使用系统时间作为事件时间)
// 方式一
SingleOutputStreamOperator<Order> orderDSWithWatermark = orderDS.assignTimestampsAndWatermarks(WatermarkStrategy.<Order>forBoundedOutOfOrderness(Duration.ofSeconds(3))// 指定maxOutOfOrderness最大无序度/最大允许的延迟时间/乱序时间
.withTimestampAssigner((element, timestamp) -> System.currentTimeMillis()));
SingleOutputStreamOperator<Goods> goodsDSWithWatermark = goodsDS.assignTimestampsAndWatermarks(WatermarkStrategy.<Goods>forBoundedOutOfOrderness(Duration.ofSeconds(3))// 指定maxOutOfOrderness最大无序度/最大允许的延迟时间/乱序时间
.withTimestampAssigner((element, timestamp) -> System.currentTimeMillis()));
// 2.transformation
// 商品类(商品id,商品名称,商品价格)
// 订单明细类(订单id,商品id,商品数量)
// 关联结果(商品id,商品名称,商品数量,商品价格*商品数量)
// 代码示例
// orangeStream
// .keyBy()
// .intervalJoin(greenStream.keyBy())
// .between(Time.milliseconds(-2), Time.milliseconds(1))
// .process (new ProcessJoinFunction
//
// @Override
// public void processElement(Integer left, Integer right, Context ctx, Collector out) {
// out.collect(first + "," + second);
// }
// });
DataStream<OrderItem> resultDS = goodsDSWithWatermark.keyBy(goods -> goods.getGoodsId())
// join的条件:
// 条件1.id要相等
// 条件2. Order的时间戳 - 2 <=Goods的时间戳 <= Order的时间戳 + 1
.intervalJoin(orderDSWithWatermark.keyBy(orderItem -> orderItem.getGoodsId())).between(Time.seconds(-2), Time.seconds(1))
.process(new ProcessJoinFunction<Goods, Order, OrderItem>() {
@Override
public void processElement(Goods first, Order second, Context ctx, Collector<OrderItem> out) throws Exception {
OrderItem orderItem = new OrderItem();
orderItem.setGoodsId(first.getGoodsId());
orderItem.setGoodsName(first.getGoodsName());
orderItem.setCount(new BigDecimal(second.getCount()));
orderItem.setTotal(new BigDecimal(second.getCount()).multiply(first.getGoodsPrice()));
out.collect(orderItem);
}
});
// 3.sink
resultDS.print();
// 4.execute
env.execute();
}
}
4）、interval join 实现方式二
- GoodsWatermark
package org.datastreamapi.operator.window.watermark;
import org.apache.flink.api.common.eventtime.TimestampAssigner;
import org.apache.flink.api.common.eventtime.TimestampAssignerSupplier;
import org.apache.flink.api.common.eventtime.Watermark;
import org.apache.flink.api.common.eventtime.WatermarkGenerator;
import org.apache.flink.api.common.eventtime.WatermarkGeneratorSupplier;
import org.apache.flink.api.common.eventtime.WatermarkOutput;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.datastreamapi.operator.window.bean.Goods;
/**
* @author alanchan
* 使用系统时间构建水印分配器
*/
public class GoodsWatermark implements WatermarkStrategy<Goods> {
@Override
public TimestampAssigner<Goods> createTimestampAssigner(TimestampAssignerSupplier.Context context) {
return (element, recordTimestamp) -> System.currentTimeMillis();
}
@Override
public WatermarkGenerator<Goods> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
return new WatermarkGenerator<Goods>() {
@Override
public void onEvent(Goods event, long eventTimestamp, WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
@Override
public void onPeriodicEmit(WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
};
}
}
- OrderWatermark
package org.datastreamapi.operator.window.watermark;
import org.apache.flink.api.common.eventtime.TimestampAssigner;
import org.apache.flink.api.common.eventtime.TimestampAssignerSupplier;
import org.apache.flink.api.common.eventtime.Watermark;
import org.apache.flink.api.common.eventtime.WatermarkGenerator;
import org.apache.flink.api.common.eventtime.WatermarkGeneratorSupplier;
import org.apache.flink.api.common.eventtime.WatermarkOutput;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.datastreamapi.operator.window.bean.Order;
/**
* @author alanchan
* 使用系统时间构建水印分配器
*/
public class OrderWatermark implements WatermarkStrategy<Order> {
@Override
public TimestampAssigner<Order> createTimestampAssigner(TimestampAssignerSupplier.Context context) {
return (element, recordTimestamp) -> System.currentTimeMillis();
}
@Override
public WatermarkGenerator<Order> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
return new WatermarkGenerator<Order>() {
@Override
public void onEvent(Order event, long eventTimestamp, WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
@Override
public void onPeriodicEmit(WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
};
}
}
- interval Join实现
package org.datastreamapi.operator.window;
import java.math.BigDecimal;
import org.apache.flink.api.common.RuntimeExecutionMode;
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.functions.co.ProcessJoinFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;
import org.datastreamapi.operator.window.bean.Goods;
import org.datastreamapi.operator.window.bean.Order;
import org.datastreamapi.operator.window.bean.OrderItem;
import org.datastreamapi.operator.window.source.GoodsSource;
import org.datastreamapi.operator.window.source.OrderSource;
import org.datastreamapi.operator.window.watermark.GoodsWatermark;
import org.datastreamapi.operator.window.watermark.OrderWatermark;
/**
* @author alanchan
*
*/
public class TestIntervalJoinDemo {
/**
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
// 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
// 1.source
// 商品数据流
DataStreamSource<Goods> goodsDS = env.addSource(new GoodsSource());
// 订单数据流
DataStreamSource<Order> orderDS = env.addSource(new OrderSource());
// 给数据添加水印(直接使用系统时间作为事件时间)
// 方式二
SingleOutputStreamOperator<Goods> goodsDSWithWatermark = goodsDS.assignTimestampsAndWatermarks(new GoodsWatermark());
SingleOutputStreamOperator<Order> orderDSWithWatermark = orderDS.assignTimestampsAndWatermarks(new OrderWatermark());
// 2.transformation
// 商品类(商品id,商品名称,商品价格)
// 订单明细类(订单id,商品id,商品数量)
// 关联结果(商品id,商品名称,商品数量,商品价格*商品数量)
// 代码示例
// orangeStream
// .keyBy()
// .intervalJoin(greenStream.keyBy())
// .between(Time.milliseconds(-2), Time.milliseconds(1))
// .process (new ProcessJoinFunction
//
// @Override
// public void processElement(Integer left, Integer right, Context ctx, Collector out) {
// out.collect(first + "," + second);
// }
// });
DataStream<OrderItem> resultDS = goodsDSWithWatermark.keyBy(goods -> goods.getGoodsId())
// join的条件:
// 条件1.id要相等
// 条件2. Order的时间戳 - 2 <=Goods的时间戳 <= Order的时间戳 + 1
.intervalJoin(orderDSWithWatermark.keyBy(orderItem -> orderItem.getGoodsId())).between(Time.seconds(-2), Time.seconds(1))
.process(new ProcessJoinFunction<Goods, Order, OrderItem>() {
@Override
public void processElement(Goods first, Order second, Context ctx, Collector<OrderItem> out) throws Exception {
OrderItem orderItem = new OrderItem();
orderItem.setGoodsId(first.getGoodsId());
orderItem.setGoodsName(first.getGoodsName());
orderItem.setCount(new BigDecimal(second.getCount()));
orderItem.setTotal(new BigDecimal(second.getCount()).multiply(first.getGoodsPrice()));
out.collect(orderItem);
}
});
// 3.sink
resultDS.print();
// 4.execute
env.execute();
}
}
5）、运行结果
Interval Join实现方式有2种，但运行结果类似，因为数据都是随机产生的，下述结果供参考。
5> {"count":3,"goodsId":"3","goodsName":"MacBookPro","total":45000}
1> {"count":6,"goodsId":"4","goodsName":"iphone13","total":48000}
5> {"count":3,"goodsId":"3","goodsName":"MacBookPro","total":45000}
1> {"count":6,"goodsId":"4","goodsName":"iphone13","total":48000}
7> {"count":6,"goodsId":"1","goodsName":"iphone11","total":36000}
7> {"count":6,"goodsId":"1","goodsName":"iphone11","total":36000}
5> {"count":3,"goodsId":"3","goodsName":"MacBookPro","total":45000}
1> {"count":3,"goodsId":"4","goodsName":"iphone13","total":24000}
5> {"count":3,"goodsId":"3","goodsName":"MacBookPro","total":45000}
7> {"count":2,"goodsId":"1","goodsName":"iphone11","total":12000}
1> {"count":3,"goodsId":"4","goodsName":"iphone13","total":24000}
7> {"count":2,"goodsId":"1","goodsName":"iphone11","total":12000}
7> {"count":2,"goodsId":"1","goodsName":"iphone11","total":12000}
7> {"count":2,"goodsId":"1","goodsName":"iphone11","total":12000}
7> {"count":8,"goodsId":"1","goodsName":"iphone11","total":48000}
4> {"count":10,"goodsId":"2","goodsName":"iphone12","total":70000}
7> {"count":8,"goodsId":"1","goodsName":"iphone11","total":48000}
以上，本文主要介绍Flink 的常用的operator interval join 及详细示例。
如果需要了解更多内容，可以在本人Flink 专栏中了解更新系统的内容。
本专题分为四篇文章介绍，即
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（1）- window join
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（2）- interval join
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例（3）- 数据倾斜处理、分区示例
【flink番外篇】2、flink的23种算子window join 和interval join 数据倾斜、分区介绍及详细示例-完整版