目录

0. 相关文章链接

1. 双流Join介绍

2. Window Join

2.1. Tumbling Window Join

2.2. Sliding Window Join

2.3. Session Window Join

3. Interval Join

4. 案例一

5. 案例二

---

0. 相关文章链接

Flink文章汇总

1. 双流Join介绍

介绍文章网址：

Apache Flink 1.12 Documentation: Joining

面试实时开发必问——Flink中如何做流Join呢？ - 知乎

十 Flink实现双流join_andy的博客-CSDN博客_flink双流join

具体介绍：

Join大体分类只有两种：Window Join和Interval Join。

• Window Join又可以根据Window的类型细分出3种：Tumbling Window Join、Sliding Window Join、Session Widnow Join。Windows类型的join都是利用window的机制，先将数据缓存在Window State中，当窗口触发计算时，执行join操作；
• interval join也是利用state存储数据再处理，区别在于state中的数据有失效机制，依靠数据触发数据清理；

目前Stream join的结果是数据的笛卡尔积

2. Window Join

2.1. Tumbling Window Join

        执行翻滚窗口联接时，具有公共键和公共翻滚窗口的所有元素将作为成对组合联接，并传递给JoinFunction或FlatJoinFunction。因为它的行为类似于内部连接，所以一个流中的元素在其滚动窗口中没有来自另一个流的元素，因此不会被发射！

        如图所示，我们定义了一个大小为2毫秒的翻滚窗口，结果窗口的形式为[0,1]、[2,3]、。。。。该图显示了每个窗口中所有元素的成对组合，这些元素将传递给JoinFunction。注意，在翻滚窗口[6,7]中没有发射任何东西，因为绿色流中不存在与橙色元素⑥和⑦结合的元素。

import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;...
DataStream<Integer> orangeStream = ...DataStream<Integer> greenStream = ...
orangeStream.join(greenStream).where(<KeySelector>).equalTo(<KeySelector>).window(TumblingEventTimeWindows.of(Time.milliseconds(2))).apply (new JoinFunction<Integer, Integer, String> (){@Overridepublic String join(Integer first, Integer second) {return first + "," + second;}});

2.2. Sliding Window Join

        在执行滑动窗口联接时，具有公共键和公共滑动窗口的所有元素将作为成对组合联接，并传递给JoinFunction或FlatJoinFunction。在当前滑动窗口中，一个流的元素没有来自另一个流的元素，则不会发射！请注意，某些元素可能会连接到一个滑动窗口中，但不会连接到另一个滑动窗口中！

        在本例中，我们使用大小为2毫秒的滑动窗口，并将其滑动1毫秒，从而产生滑动窗口[-1，0]，[0,1]，[1,2]，[2,3]…。x轴下方的连接元素是传递给每个滑动窗口的JoinFunction的元素。在这里，您还可以看到，例如，在窗口[2,3]中，橙色②与绿色③连接，但在窗口[1,2]中没有与任何对象连接。

import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
...
DataStream<Integer> orangeStream = ...DataStream<Integer> greenStream = ...
orangeStream.join(greenStream).where(<KeySelector>).equalTo(<KeySelector>).window(SlidingEventTimeWindows.of(Time.milliseconds(2) /* size */, Time.milliseconds(1) /* slide */)).apply (new JoinFunction<Integer, Integer, String> (){@Overridepublic String join(Integer first, Integer second) {return first + "," + second;}});

2.3. Session Window Join

        在执行会话窗口联接时，具有相同键（当“组合”时满足会话条件）的所有元素以成对组合方式联接，并传递给JoinFunction或FlatJoinFunction。同样，这执行一个内部连接，所以如果有一个会话窗口只包含来自一个流的元素，则不会发出任何输出！

        在这里，我们定义了一个会话窗口连接，其中每个会话被至少1ms的间隔分割。有三个会话，在前两个会话中，来自两个流的连接元素被传递给JoinFunction。在第三个会话中，绿色流中没有元素，所以⑧和⑨没有连接！

import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.windowing.assigners.EventTimeSessionWindows;
import org.apache.flink.streaming.api.windowing.time.Time;...
DataStream<Integer> orangeStream = ...DataStream<Integer> greenStream = ...
orangeStream.join(greenStream).where(<KeySelector>).equalTo(<KeySelector>).window(EventTimeSessionWindows.withGap(Time.milliseconds(1))).apply (new JoinFunction<Integer, Integer, String> (){@Overridepublic String join(Integer first, Integer second) {return first + "," + second;}});

3. Interval Join

        前面学习的Window Join必须要在一个Window中进行JOIN，那如果没有Window如何处理呢？interval join也是使用相同的key来join两个流（流A、流B），并且流B中的元素中的时间戳，和流A元素的时间戳，有一个时间间隔。

b.timestamp ∈ [a.timestamp + lowerBound; a.timestamp + upperBound] 
or 
a.timestamp + lowerBound <= b.timestamp <= a.timestamp + upperBound

也就是：流B的元素的时间戳 ≥ 流A的元素时间戳 + 下界，且，流B的元素的时间戳 ≤ 流A的元素时间戳 + 上界。

        在上面的示例中，我们将两个流“orange”和“green”连接起来，其下限为-2毫秒，上限为+1毫秒。默认情况下，这些边界是包含的，但是可以应用.lowerBoundExclusive（）和.upperBoundExclusive来更改行为orangeElem.ts + lowerBound <= greenElem.ts <= orangeElem.ts + upperBound

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(){@Overridepublic void processElement(Integer left, Integer right, Context ctx, Collector<String> out) {out.collect(first + "," + second);}});

4. 案例一

需求：        

        使用两个指定Source模拟数据，一个Source是订单明细，一个Source是商品数据。我们通过window join，将数据关联到一起。

思路：

1、Window Join首先需要使用where和equalTo指定使用哪个key来进行关联，此处我们通过应用方法，基于GoodsId来关联两个流中的元素。

2、设置5秒的滚动窗口，流的元素关联都会在这个5秒的窗口中进行关联。

3、apply方法中实现将两个不同类型的元素关联并生成一个新类型的元素。

import com.alibaba.fastjson.JSON;
import lombok.Data;
import org.apache.flink.api.common.eventtime.*;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;import java.math.BigDecimal;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import java.util.UUID;
import java.util.concurrent.TimeUnit;public class JoinDemo01 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 构建商品数据流DataStream<Goods> goodsDS = env.addSource(new GoodsSource11(), TypeInformation.of(Goods.class)).assignTimestampsAndWatermarks(new GoodsWatermark());// 构建订单明细数据流DataStream<OrderItem> orderItemDS = env.addSource(new OrderItemSource(), TypeInformation.of(OrderItem.class)).assignTimestampsAndWatermarks(new OrderItemWatermark());// 进行关联查询DataStream<FactOrderItem> factOrderItemDS = orderItemDS.join(goodsDS)// 第一个流orderItemDS.where(OrderItem::getGoodsId)// 第二流goodsDS.equalTo(Goods::getGoodsId).window(TumblingEventTimeWindows.of(Time.seconds(5))).apply((OrderItem item, Goods goods) -> {FactOrderItem factOrderItem = new FactOrderItem();factOrderItem.setGoodsId(goods.getGoodsId());factOrderItem.setGoodsName(goods.getGoodsName());factOrderItem.setCount(new BigDecimal(item.getCount()));factOrderItem.setTotalMoney(goods.getGoodsPrice().multiply(new BigDecimal(item.getCount())));return factOrderItem;});factOrderItemDS.print();env.execute("滚动窗口JOIN");}//商品类@Datapublic static class Goods {private String goodsId;private String goodsName;private BigDecimal goodsPrice;public static List<Goods> GOODS_LIST;public static Random r;static  {r = new Random();GOODS_LIST = new ArrayList<>();GOODS_LIST.add(new Goods("1", "小米12", new BigDecimal(4890)));GOODS_LIST.add(new Goods("2", "iphone12", new BigDecimal(12000)));GOODS_LIST.add(new Goods("3", "MacBookPro", new BigDecimal(15000)));GOODS_LIST.add(new Goods("4", "Thinkpad X1", new BigDecimal(9800)));GOODS_LIST.add(new Goods("5", "MeiZu One", new BigDecimal(3200)));GOODS_LIST.add(new Goods("6", "Mate 40", new BigDecimal(6500)));}public static Goods randomGoods() {int rIndex = r.nextInt(GOODS_LIST.size());return GOODS_LIST.get(rIndex);}public Goods() {}public Goods(String goodsId, String goodsName, BigDecimal goodsPrice) {this.goodsId = goodsId;this.goodsName = goodsName;this.goodsPrice = goodsPrice;}@Overridepublic String toString() {return JSON.toJSONString(this);}}//订单明细类@Datapublic static class OrderItem {private String itemId;private String goodsId;private Integer count;@Overridepublic String toString() {return JSON.toJSONString(this);}}//关联结果@Datapublic static class FactOrderItem {private String goodsId;private String goodsName;private BigDecimal count;private BigDecimal totalMoney;@Overridepublic String toString() {return JSON.toJSONString(this);}}//构建一个商品Stream源（这个好比就是维表）public static class GoodsSource11 extends RichSourceFunction {private Boolean isCancel;@Overridepublic void open(Configuration parameters) throws Exception {isCancel = false;}@Overridepublic void run(SourceContext sourceContext) throws Exception {while(!isCancel) {Goods.GOODS_LIST.stream().forEach(goods -> sourceContext.collect(goods));TimeUnit.SECONDS.sleep(1);}}@Overridepublic void cancel() {isCancel = true;}}//构建订单明细Stream源public static class OrderItemSource extends RichSourceFunction {private Boolean isCancel;private Random r;@Overridepublic void open(Configuration parameters) throws Exception {isCancel = false;r = new Random();}@Overridepublic void run(SourceContext sourceContext) throws Exception {while(!isCancel) {Goods goods = Goods.randomGoods();OrderItem orderItem = new OrderItem();orderItem.setGoodsId(goods.getGoodsId());orderItem.setCount(r.nextInt(10) + 1);orderItem.setItemId(UUID.randomUUID().toString());sourceContext.collect(orderItem);orderItem.setGoodsId("111");sourceContext.collect(orderItem);TimeUnit.SECONDS.sleep(1);}}@Overridepublic void cancel() {isCancel = true;}}//构建水印分配器（此处为了简单），直接使用系统时间了public static class GoodsWatermark implements WatermarkStrategy<Goods> {@Overridepublic TimestampAssigner<Goods> createTimestampAssigner(TimestampAssignerSupplier.Context context) {return (element, recordTimestamp) -> System.currentTimeMillis();}@Overridepublic WatermarkGenerator<Goods> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {return new WatermarkGenerator<Goods>() {@Overridepublic void onEvent(Goods event, long eventTimestamp, WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}@Overridepublic void onPeriodicEmit(WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}};}}public static class OrderItemWatermark implements WatermarkStrategy<OrderItem> {@Overridepublic TimestampAssigner<OrderItem> createTimestampAssigner(TimestampAssignerSupplier.Context context) {return (element, recordTimestamp) -> System.currentTimeMillis();}@Overridepublic WatermarkGenerator<OrderItem> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {return new WatermarkGenerator<OrderItem>() {@Overridepublic void onEvent(OrderItem event, long eventTimestamp, WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}@Overridepublic void onPeriodicEmit(WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}};}}
}

5. 案例二

1、通过keyBy将两个流join到一起

2、interval join需要设置流A去关联哪个时间范围的流B中的元素。此处，我设置的下界为-1、上界为0，且上界是一个开区间。表达的意思就是流A中某个元素的时间，对应上一秒的流B中的元素。

3、process中将两个key一样的元素，关联在一起，并加载到一个新的FactOrderItem对象中

import com.alibaba.fastjson.JSON;
import lombok.Data;
import org.apache.flink.api.common.eventtime.*;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.configuration.Configuration;
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.functions.source.RichSourceFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;import java.math.BigDecimal;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import java.util.UUID;
import java.util.concurrent.TimeUnit;public class JoinDemo02 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 构建商品数据流DataStream<Goods> goodsDS = env.addSource(new GoodsSource11(), TypeInformation.of(Goods.class)).assignTimestampsAndWatermarks(new GoodsWatermark());// 构建订单明细数据流DataStream<OrderItem> orderItemDS = env.addSource(new OrderItemSource(), TypeInformation.of(OrderItem.class)).assignTimestampsAndWatermarks(new OrderItemWatermark());// 进行关联查询SingleOutputStreamOperator<FactOrderItem> factOrderItemDS = orderItemDS.keyBy(item -> item.getGoodsId()).intervalJoin(goodsDS.keyBy(goods -> goods.getGoodsId())).between(Time.seconds(-1), Time.seconds(0)).upperBoundExclusive().process(new ProcessJoinFunction<OrderItem, Goods, FactOrderItem>() {@Overridepublic void processElement(OrderItem left, Goods right, Context ctx, Collector<FactOrderItem> out) throws Exception {FactOrderItem factOrderItem = new FactOrderItem();factOrderItem.setGoodsId(right.getGoodsId());factOrderItem.setGoodsName(right.getGoodsName());factOrderItem.setCount(new BigDecimal(left.getCount()));factOrderItem.setTotalMoney(right.getGoodsPrice().multiply(new BigDecimal(left.getCount())));out.collect(factOrderItem);}});factOrderItemDS.print();env.execute("Interval JOIN");}//商品类@Datapublic static class Goods {private String goodsId;private String goodsName;private BigDecimal goodsPrice;public static List<Goods> GOODS_LIST;public static Random r;static  {r = new Random();GOODS_LIST = new ArrayList<>();GOODS_LIST.add(new Goods("1", "小米12", new BigDecimal(4890)));GOODS_LIST.add(new Goods("2", "iphone12", new BigDecimal(12000)));GOODS_LIST.add(new Goods("3", "MacBookPro", new BigDecimal(15000)));GOODS_LIST.add(new Goods("4", "Thinkpad X1", new BigDecimal(9800)));GOODS_LIST.add(new Goods("5", "MeiZu One", new BigDecimal(3200)));GOODS_LIST.add(new Goods("6", "Mate 40", new BigDecimal(6500)));}public static Goods randomGoods() {int rIndex = r.nextInt(GOODS_LIST.size());return GOODS_LIST.get(rIndex);}public Goods() {}public Goods(String goodsId, String goodsName, BigDecimal goodsPrice) {this.goodsId = goodsId;this.goodsName = goodsName;this.goodsPrice = goodsPrice;}@Overridepublic String toString() {return JSON.toJSONString(this);}}//订单明细类@Datapublic static class OrderItem {private String itemId;private String goodsId;private Integer count;@Overridepublic String toString() {return JSON.toJSONString(this);}}//关联结果@Datapublic static class FactOrderItem {private String goodsId;private String goodsName;private BigDecimal count;private BigDecimal totalMoney;@Overridepublic String toString() {return JSON.toJSONString(this);}}//构建一个商品Stream源（这个好比就是维表）public static class GoodsSource11 extends RichSourceFunction {private Boolean isCancel;@Overridepublic void open(Configuration parameters) throws Exception {isCancel = false;}@Overridepublic void run(SourceContext sourceContext) throws Exception {while(!isCancel) {Goods.GOODS_LIST.stream().forEach(goods -> sourceContext.collect(goods));TimeUnit.SECONDS.sleep(1);}}@Overridepublic void cancel() {isCancel = true;}}//构建订单明细Stream源public static class OrderItemSource extends RichSourceFunction {private Boolean isCancel;private Random r;@Overridepublic void open(Configuration parameters) throws Exception {isCancel = false;r = new Random();}@Overridepublic void run(SourceContext sourceContext) throws Exception {while(!isCancel) {Goods goods = Goods.randomGoods();OrderItem orderItem = new OrderItem();orderItem.setGoodsId(goods.getGoodsId());orderItem.setCount(r.nextInt(10) + 1);orderItem.setItemId(UUID.randomUUID().toString());sourceContext.collect(orderItem);orderItem.setGoodsId("111");sourceContext.collect(orderItem);TimeUnit.SECONDS.sleep(1);}}@Overridepublic void cancel() {isCancel = true;}}//构建水印分配器（此处为了简单），直接使用系统时间了public static class GoodsWatermark implements WatermarkStrategy<Goods> {@Overridepublic TimestampAssigner<Goods> createTimestampAssigner(TimestampAssignerSupplier.Context context) {return (element, recordTimestamp) -> System.currentTimeMillis();}@Overridepublic WatermarkGenerator<Goods> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {return new WatermarkGenerator<Goods>() {@Overridepublic void onEvent(Goods event, long eventTimestamp, WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}@Overridepublic void onPeriodicEmit(WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}};}}public static class OrderItemWatermark implements WatermarkStrategy<OrderItem> {@Overridepublic TimestampAssigner<OrderItem> createTimestampAssigner(TimestampAssignerSupplier.Context context) {return (element, recordTimestamp) -> System.currentTimeMillis();}@Overridepublic WatermarkGenerator<OrderItem> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {return new WatermarkGenerator<OrderItem>() {@Overridepublic void onEvent(OrderItem event, long eventTimestamp, WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}@Overridepublic void onPeriodicEmit(WatermarkOutput output) {output.emitWatermark(new Watermark(System.currentTimeMillis()));}};}}
}

---

注：此博客根据某马2020年贺岁视频改编而来 -> B站网址

注：其他相关文章链接由此进 -> Flink文章汇总

---