2021年大数据Flink（四十五）：扩展阅读 双流Join
目录
扩展阅读 双流Join
介绍
Window Join
Interval Join
代码演示1
代码演示2
重点注意
扩展阅读 双流Join
介绍
https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/stream/operators/joining.html
https://zhuanlan.zhihu.com/p/340560908
https://blog.csdn.net/andyonlines/article/details/108173259
双流Join是Flink面试的高频问题。一般情况下说明以下几点就可以hold了：
- 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；
Window Join
- 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 orangeStream = ...DataStream greenStream = ...
orangeStream.join(greenStream)
.where()
.equalTo()
.window(TumblingEventTimeWindows.of(Time.milliseconds(2)))
.apply (new JoinFunction (){
@Override
public String join(Integer first, Integer second) {
return first + "," + second;
}
}); - 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 orangeStream = ...DataStream greenStream = ...
orangeStream.join(greenStream)
.where()
.equalTo()
.window(SlidingEventTimeWindows.of(Time.milliseconds(2) /* size */, Time.milliseconds(1) /* slide */))
.apply (new JoinFunction (){
@Override
public String join(Integer first, Integer second) {
return first + "," + second;
}
}); - 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 orangeStream = ...DataStream greenStream = ...
orangeStream.join(greenStream)
.where()
.equalTo()
.window(EventTimeSessionWindows.withGap(Time.milliseconds(1)))
.apply (new JoinFunction (){
@Override
public String join(Integer first, Integer second) {
return first + "," + second;
}
});
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 orangeStream = ...DataStream greenStream = ...
orangeStream
.keyBy()
.intervalJoin(greenStream.keyBy())
.between(Time.milliseconds(-2), Time.milliseconds(1))
.process (new ProcessJoinFunction out) {
out.collect(first + "," + second);
}
});
代码演示1
- 需求
来做个案例：
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。
package cn.lanson.extend;
import com.alibaba.fastjson.JSON;
import lombok.Data;
import org.apache.flink.api.common.eventtime.*;
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;
/**
* Author lanson
* Desc
*/
public class JoinDemo01 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 构建商品数据流
DataStream goodsDS = env.addSource(new GoodsSource())
.assignTimestampsAndWatermarks(new GoodsWatermark());
// 构建订单明细数据流
DataStream orderItemDS = env.addSource(new OrderItemSource())
.assignTimestampsAndWatermarks(new OrderItemWatermark());
// 进行关联查询
DataStream factOrderItemDS = orderItemDS.join(goodsDS)
// join条件:第一个流orderItemDS的GoodsId == 第二个流goodsDS的GoodsId
.where(OrderItem::getGoodsId)
.equalTo(Goods::getGoodsId)
//指定窗口
.window(TumblingEventTimeWindows.of(Time.seconds(5)))
//处理join结果
.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
@Data
public static class Goods {
private String goodsId;
private String goodsName;
private BigDecimal goodsPrice;
public static List 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;
}
@Override
public String toString() {
return JSON.toJSONString(this);
}
}
//订单明细类
@Data
public static class OrderItem {
private String itemId;
private String goodsId;
private Integer count;
@Override
public String toString() {
return JSON.toJSONString(this);
}
}
//关联结果
@Data
public static class FactOrderItem {
private String goodsId;
private String goodsName;
private BigDecimal count;
private BigDecimal totalMoney;
@Override
public 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 GoodsSource extends RichSourceFunction {
private Boolean isCancel;
@Override
public void open(Configuration parameters) throws Exception {
isCancel = false;
}
@Override
public void run(SourceContext sourceContext) throws Exception {
while(!isCancel) {
Goods.GOODS_LIST.stream().forEach(goods -> sourceContext.collect(goods));
TimeUnit.SECONDS.sleep(1);
}
}
@Override
public void cancel() {
isCancel = true;
}
}
//构建订单明细Stream源
public static class OrderItemSource extends RichSourceFunction {
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();
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);
}
}
@Override
public void cancel() {
isCancel = true;
}
}
//构建水印分配器，学习测试直接使用系统时间了
public static class GoodsWatermark implements WatermarkStrategy {
@Override
public TimestampAssigner createTimestampAssigner(TimestampAssignerSupplier.Context context) {
return (element, recordTimestamp) -> System.currentTimeMillis();
}
@Override
public WatermarkGenerator createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
return new WatermarkGenerator() {
@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()));
}
};
}
}
//构建水印分配器，学习测试直接使用系统时间了
public static class OrderItemWatermark implements WatermarkStrategy {
@Override
public TimestampAssigner createTimestampAssigner(TimestampAssignerSupplier.Context context) {
return (element, recordTimestamp) -> System.currentTimeMillis();
}
@Override
public WatermarkGenerator createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
return new WatermarkGenerator() {
@Override
public void onEvent(OrderItem event, long eventTimestamp, WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
@Override
public void onPeriodicEmit(WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
};
}
}
}
代码演示2
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
package cn.lanson.extend;
import com.alibaba.fastjson.JSON;
import lombok.Data;
import org.apache.flink.api.common.eventtime.*;
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;
/**
* Author lanson
* Desc
*/
public class JoinDemo02 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// 构建商品数据流
DataStream goodsDS = env.addSource(new GoodsSource())
.assignTimestampsAndWatermarks(new GoodsWatermark());
// 构建订单明细数据流
DataStream orderItemDS = env.addSource(new OrderItemSource())
.assignTimestampsAndWatermarks(new OrderItemWatermark());
// 进行关联查询
SingleOutputStreamOperator factOrderItemDS = orderItemDS.keyBy(OrderItem::getGoodsId)
.intervalJoin(goodsDS.keyBy(Goods::getGoodsId))
.between(Time.seconds(-1), Time.seconds(0))
//.upperBoundExclusive()
.process(new ProcessJoinFunction() {
@Override
public void processElement(OrderItem left, Goods right, Context ctx, Collector 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
@Data
public static class Goods {
private String goodsId;
private String goodsName;
private BigDecimal goodsPrice;
public static List 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;
}
@Override
public String toString() {
return JSON.toJSONString(this);
}
}
//订单明细类
@Data
public static class OrderItem {
private String itemId;
private String goodsId;
private Integer count;
@Override
public String toString() {
return JSON.toJSONString(this);
}
}
//关联结果
@Data
public static class FactOrderItem {
private String goodsId;
private String goodsName;
private BigDecimal count;
private BigDecimal totalMoney;
@Override
public 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 GoodsSource extends RichSourceFunction {
private Boolean isCancel;
@Override
public void open(Configuration parameters) throws Exception {
isCancel = false;
}
@Override
public void run(SourceContext sourceContext) throws Exception {
while (!isCancel) {
Goods.GOODS_LIST.stream().forEach(goods -> sourceContext.collect(goods));
TimeUnit.SECONDS.sleep(1);
}
}
@Override
public void cancel() {
isCancel = true;
}
}
//构建订单明细Stream源
public static class OrderItemSource extends RichSourceFunction {
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();
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);
}
}
@Override
public void cancel() {
isCancel = true;
}
}
//构建水印分配器，学习测试直接使用系统时间了
public static class GoodsWatermark implements WatermarkStrategy {
@Override
public TimestampAssigner createTimestampAssigner(TimestampAssignerSupplier.Context context) {
return (element, recordTimestamp) -> System.currentTimeMillis();
}
@Override
public WatermarkGenerator createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
return new WatermarkGenerator() {
@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()));
}
};
}
}
//构建水印分配器，学习测试直接使用系统时间了
public static class OrderItemWatermark implements WatermarkStrategy {
@Override
public TimestampAssigner createTimestampAssigner(TimestampAssignerSupplier.Context context) {
return (element, recordTimestamp) -> System.currentTimeMillis();
}
@Override
public WatermarkGenerator createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
return new WatermarkGenerator() {
@Override
public void onEvent(OrderItem event, long eventTimestamp, WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
@Override
public void onPeriodicEmit(WatermarkOutput output) {
output.emitWatermark(new Watermark(System.currentTimeMillis()));
}
};
}
}
} 重点注意
注意:后面项目中涉及到双流
接下来的内容面试常问
双流Join是Flink面试的高频问题。一般情况下说明以下几点就可以hold了： 1.Join大体分类只有两种：Window Join和Interval Join。 2.Window Join又可以根据Window的类型细分出3种： Tumbling 、Sliding 、Session Widnow Join。 3.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； 4.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；
看官网示例说明
https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/stream/operators/joining.html