文章目录
- Flink 系列文章
- 一、示例:按照分组规则进行图形匹配-KeyedBroadcastProcessFunction
- 1、maven依赖
- 2、实现
- 3、验证
- 1)、规则输入
- 2)、item输入
- 3)、控制台输出
- 二、示例:BroadcastProcessFunction将维表数据广播给其他流
- 1、maven依赖
- 2、实现
- 1)、BroadcastProcessFunction实现
- 2)、连接实现
- 3、验证
- 1)、输入user数据
- 2)、输入事实流订单数据
- 3)、观察程序控制台输出
本文详细的介绍了broadcast state的具体使用,并以两个例子分别介绍了BroadcastProcessFunction和KeyedBroadcastProcessFunction的具体实现。
本文除了maven依赖外,没有其他依赖。
一、示例:按照分组规则进行图形匹配-KeyedBroadcastProcessFunction
本示例是简单的应用broadcast state实现简单模式匹配,即实现:
1、按照相同颜色进行分组,在相同颜色组中按照规则进行匹配。
2、相同颜色的规则1:长方形后是三角形
3、相同颜色的规则2:正方形后是长方形
如匹配上述规则1或规则2则输出匹配成功。
1、maven依赖
<properties>
<encoding>UTF-8</encoding>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<java.version>1.8</java.version>
<scala.version>2.12</scala.version>
<flink.version>1.17.0</flink.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java</artifactId>
<version>${flink.version}</version>
<!-- <scope>provided</scope> -->
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-csv</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-json</artifactId>
<version>${flink.version}</version>
<scope>provided</scope>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.commons/commons-compress -->
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-compress</artifactId>
<version>1.24.0</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.2</version>
<!-- <scope>provided</scope> -->
</dependency>
</dependencies>
2、实现
package org.tablesql.join;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import org.apache.flink.api.common.state.MapState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.state.ReadOnlyBroadcastState;
import org.apache.flink.api.common.typeinfo.BasicTypeInfo;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.typeutils.ListTypeInfo;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.KeyedBroadcastProcessFunction;
import org.apache.flink.util.Collector;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
/*
* @Author: alanchan
*
* @LastEditors: alanchan
*
* @Description: 按照相同颜色进行分组,在相同颜色组中按照规则进行匹配。相同颜色的规则1:长方形后是三角形;规则2:正方形后是长方形
*/
public class TestJoinDimKeyedBroadcastProcessFunctionDemo {
@Data
@NoArgsConstructor
@AllArgsConstructor
static class Shape {
private String name;
private String desc;
}
@Data
@NoArgsConstructor
@AllArgsConstructor
static class Colour {
private String name;
private Long blue;
private Long red;
private Long green;
}
@Data
@NoArgsConstructor
@AllArgsConstructor
static class Item {
private Shape shape;
private Colour color;
}
@Data
@NoArgsConstructor
@AllArgsConstructor
static class Rule {
private String name;
private Shape first;
private Shape second;
}
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// item 实时流
DataStream<Item> itemStream = env.socketTextStream("192.168.10.42", 9999)
.map(o -> {
// 解析item流
// 数据结构:Item[shape(name,desc);color(name,blue,red,green)]
String[] lines = o.split(";");
String[] shapeString = lines[0].split(",");
String[] colorString = lines[1].split(",");
Shape shape = new Shape(shapeString[0],shapeString[1]);
Colour color = new Colour(colorString[0],Long.valueOf(colorString[1]),Long.valueOf(colorString[2]),Long.valueOf(colorString[3]));
return new Item(shape,color);
});
// rule 实时流
DataStream<Rule> ruleStream = env.socketTextStream("192.168.10.42", 8888)
.map(o -> {
// 解析rule流
// 数据结构:Rule[name;shape(name,desc);shape(name,desc)]
String[] lines = o.split(";");
String name = lines[0];
String[] firstShapeString = lines[1].split(",");
String[] secondShapeString = lines[2].split(",");
Shape firstShape = new Shape(firstShapeString[0],firstShapeString[1]);
Shape secondShape = new Shape(secondShapeString[0],secondShapeString[1]);
return new Rule(name,firstShape,secondShape);
}).setParallelism(1);
// 将图形使用颜色进行划分
KeyedStream<Item, Colour> colorPartitionedStream = itemStream
.keyBy(new KeySelector<Item, Colour>() {
@Override
public Colour getKey(Item value) throws Exception {
return value.getColor();// 实现分组
}
});
colorPartitionedStream.print("colorPartitionedStream:---->");
// 一个 map descriptor,它描述了用于存储规则名称与规则本身的 map 存储结构
MapStateDescriptor<String, Rule> ruleStateDescriptor = new MapStateDescriptor<>(
"RulesBroadcastState",
BasicTypeInfo.STRING_TYPE_INFO,
TypeInformation.of(new TypeHint<Rule>() {
}));
// 将rule定义为广播流,广播规则并且创建 broadcast state
BroadcastStream<Rule> ruleBroadcastStream = ruleStream.broadcast(ruleStateDescriptor);
// 连接,输出流,connect() 方法需要由非广播流来进行调用,BroadcastStream 作为参数传入。
DataStream<String> output = colorPartitionedStream
.connect(ruleBroadcastStream)
.process(
// KeyedBroadcastProcessFunction 中的类型参数表示:
// 1. key stream 中的 key 类型
// 2. 非广播流中的元素类型
// 3. 广播流中的元素类型
// 4. 结果的类型,在这里是 string
new KeyedBroadcastProcessFunction<Colour, Item, Rule, String>() {
// 存储部分匹配的结果,即匹配了一个元素,正在等待第二个元素
// 用一个数组来存储,因为同时可能有很多第一个元素正在等待
private final MapStateDescriptor<String, List<Item>> itemMapStateDesc = new MapStateDescriptor<>(
"items",
BasicTypeInfo.STRING_TYPE_INFO,
new ListTypeInfo<>(Item.class));
// 与之前的 ruleStateDescriptor 相同,用于存储规则名称与规则本身的 map 存储结构
private final MapStateDescriptor<String, Rule> ruleStateDescriptor = new MapStateDescriptor<>(
"RulesBroadcastState",
BasicTypeInfo.STRING_TYPE_INFO,
TypeInformation.of(new TypeHint<Rule>() {
}));
// 负责处理广播流的元素
@Override
public void processBroadcastElement(Rule ruleValue,
KeyedBroadcastProcessFunction<Colour, Item, Rule, String>.Context ctx,
Collector<String> out) throws Exception {
// 得到广播流的存储状态:ctx.getBroadcastState(MapStateDescriptor<K, V> stateDescriptor)
// 查询元素的时间戳:ctx.timestamp()
// 查询目前的Watermark:ctx.currentWatermark()
// 目前的处理时间(processing time):ctx.currentProcessingTime()
// 产生旁路输出:ctx.output(OutputTag<X> outputTag, X value)
// 在 getBroadcastState() 方法中传入的 stateDescriptor 应该与调用 .broadcast(ruleStateDescriptor) 的参数相同
ctx.getBroadcastState(ruleStateDescriptor).put(ruleValue.getName(), ruleValue);
}
// 负责处理另一个流的元素
@Override
public void processElement(Item itemValue,
KeyedBroadcastProcessFunction<Colour, Item, Rule, String>.ReadOnlyContext ctx,
Collector<String> out) throws Exception {
final MapState<String, List<Item>> itemMapState = getRuntimeContext().getMapState(itemMapStateDesc);
final Shape shape = itemValue.getShape();
System.out.println("shape:"+shape);
// 在 getBroadcastState() 方法中传入的 stateDescriptor 应该与调用 .broadcast(ruleStateDescriptor) 的参数相同
ReadOnlyBroadcastState<String, Rule> readOnlyBroadcastState = ctx.getBroadcastState(ruleStateDescriptor);
Iterable<Entry<String, Rule>> iterableRule = readOnlyBroadcastState.immutableEntries();
for (Entry<String, Rule> entry : iterableRule) {
final String ruleName = entry.getKey();
final Rule rule = entry.getValue();
// 初始化
List<Item> itemStoredList = itemMapState.get(ruleName);
if (itemStoredList == null) {
itemStoredList = new ArrayList<>();
}
// 比较 shape
if (shape.getName().equals(rule.second.getName()) && !itemStoredList.isEmpty()) {
for (Item item : itemStoredList) {
// 符合规则,收集匹配结果
out.collect("匹配成功: " + item + " - " + itemValue);
}
itemStoredList.clear();
}
// 规则连续性设置
if (shape.getName().equals(rule.first.getName())) {
itemStoredList.add(itemValue);
}
//
if (itemStoredList.isEmpty()) {
itemMapState.remove(ruleName);
} else {
itemMapState.put(ruleName, itemStoredList);
}
}
}
});
output.print("output:------->");
env.execute();
}
}
3、验证
在netcat中启动两个端口,分别是8888和9999,8888输入规则,9999输入item,然后关键控制台输出。
1)、规则输入
red;rectangle,is a rectangle;tripe,is a tripe
green;square,is a square;rectangle,is a rectangle
2)、item输入
# 匹配成功
rectangle,is a rectangle;red,100,100,100
tripe,is a tripe;red,100,100,100
# 匹配成功
square,is square;green,150,150,150
rectangle,is a rectangle;green,150,150,150
# 匹配不成功
tripe,is tripe;blue,200,200,200
# 匹配成功
rectangle,is a rectangle;blue,100,100,100
tripe,is a tripe;blue,100,100,100
# 匹配不成功
tripe,is a tripe;blue,100,100,100
rectangle,is a rectangle;blue,100,100,100
3)、控制台输出
colorPartitionedStream:---->:9> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=red, blue=100, red=100, green=100))shape:TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle)
colorPartitionedStream:---->:9> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=tripe, desc=is a tripe), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=red, blue=100, red=100, green=100))shape:TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=tripe, desc=is a tripe)
output:------->:9> 匹配成功: TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=red, blue=100, red=100, green=100)) - TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=tripe, desc=is a tripe), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=red, blue=100, red=100, green=100))
colorPartitionedStream:---->:9> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=green, blue=150, red=150, green=150))
output:------->:9> 匹配成功: TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=square, desc=is square), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=green, blue=150, red=150, green=150)) - TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=green, blue=150, red=150, green=150))
colorPartitionedStream:---->:3> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=tripe, desc=is tripe), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=blue, blue=200, red=200, green=200))
colorPartitionedStream:---->:1> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=blue, blue=100, red=100, green=100))
colorPartitionedStream:---->:1> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=tripe, desc=is a tripe), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=blue, blue=100, red=100, green=100))
output:------->:1> 匹配成功: TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=blue, blue=100, red=100, green=100)) - TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=tripe, desc=is a tripe), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=blue, blue=100, red=100, green=100))
colorPartitionedStream:---->:1> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=tripe, desc=is a tripe), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=blue, blue=100, red=100, green=100))
colorPartitionedStream:---->:1> TestJoinDimKeyedBroadcastProcessFunctionDemo.Item(shape=TestJoinDimKeyedBroadcastProcessFunctionDemo.Shape(name=rectangle, desc=is a rectangle), color=TestJoinDimKeyedBroadcastProcessFunctionDemo.Colour(name=blue, blue=100, red=100, green=100))
二、示例:BroadcastProcessFunction将维表数据广播给其他流
本示例是将用户信息作为维表通过流进行广播,在事实表订单流中进行连接匹配输出。
1、maven依赖
参考上述示例中的依赖。
2、实现
实现方式可以使用匿名内部类或内部类实现,本示例为了清楚其中的逻辑关系,特意以一个具体class来实现。
1)、BroadcastProcessFunction实现
/*
* @Author: alanchan
* @LastEditors: alanchan
* @Description:
*/
package org.tablesql.join;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.state.ReadOnlyBroadcastState;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction;
import org.apache.flink.util.Collector;
import org.tablesql.join.TestJoinDimFromBroadcastDataStreamDemo.Order;
import org.tablesql.join.TestJoinDimFromBroadcastDataStreamDemo.User;
// final BroadcastProcessFunction<IN1, IN2, OUT> function)
public class JoinBroadcastProcessFunctionImpl extends BroadcastProcessFunction<Order, User, Tuple2<Order, String>> {
// 用于存储规则名称与规则本身的 map 存储结构
MapStateDescriptor<Integer, User> broadcastDesc;
JoinBroadcastProcessFunctionImpl(MapStateDescriptor<Integer, User> broadcastDesc) {
this.broadcastDesc = broadcastDesc;
}
// 负责处理广播流的元素
@Override
public void processBroadcastElement(User value,
BroadcastProcessFunction<Order, User, Tuple2<Order, String>>.Context ctx,
Collector<Tuple2<Order, String>> out) throws Exception {
System.out.println("收到广播数据:" + value);
// 得到广播流的存储状态
ctx.getBroadcastState(broadcastDesc).put(value.getId(), value);
}
// 处理非广播流,关联维度
@Override
public void processElement(Order value,
BroadcastProcessFunction<Order, User, Tuple2<Order, String>>.ReadOnlyContext ctx,
Collector<Tuple2<Order, String>> out) throws Exception {
// 得到广播流的存储状态
ReadOnlyBroadcastState<Integer, User> state = ctx.getBroadcastState(broadcastDesc);
out.collect(new Tuple2<>(value, state.get(value.getUId()).getName()));
}
}
2)、连接实现
/*
* @Author: alanchan
* @LastEditors: alanchan
* @Description:
*/
package org.tablesql.join;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
public class TestJoinDimFromBroadcastDataStreamDemo {
// 维表
@Data
@NoArgsConstructor
@AllArgsConstructor
static class User {
private Integer id;
private String name;
private Double balance;
private Integer age;
private String email;
}
// 事实表
@Data
@NoArgsConstructor
@AllArgsConstructor
static class Order {
private Integer id;
private Integer uId;
private Double total;
}
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// order 实时流
DataStream<Order> orderDs = env.socketTextStream("192.168.10.42", 9999)
.map(o -> {
String[] lines = o.split(",");
return new Order(Integer.valueOf(lines[0]), Integer.valueOf(lines[1]), Double.valueOf(lines[2]));
});
// user 实时流
DataStream<User> userDs = env.socketTextStream("192.168.10.42", 8888)
.map(o -> {
String[] lines = o.split(",");
return new User(Integer.valueOf(lines[0]), lines[1], Double.valueOf(lines[2]), Integer.valueOf(lines[3]), lines[4]);
}).setParallelism(1);
// 一个 map descriptor,它描述了用于存储规则名称与规则本身的 map 存储结构
// MapStateDescriptor<String, Rule> ruleStateDescriptor = new MapStateDescriptor<>(
// "RulesBroadcastState",
// BasicTypeInfo.STRING_TYPE_INFO,
// TypeInformation.of(new TypeHint<Rule>() {
// }));
// 广播流,广播规则并且创建 broadcast state
// BroadcastStream<Rule> ruleBroadcastStream = ruleStream.broadcast(ruleStateDescriptor);
// 将user流(维表)定义为广播流
final MapStateDescriptor<Integer, User> broadcastDesc = new MapStateDescriptor("Alan_RulesBroadcastState",
Integer.class,
User.class);
BroadcastStream<User> broadcastStream = userDs.broadcast(broadcastDesc);
// 需要由非广播流来进行调用
DataStream result = orderDs.connect(broadcastStream)
.process(new JoinBroadcastProcessFunctionImpl(broadcastDesc));
result.print();
env.execute();
}
// final BroadcastProcessFunction<IN1, IN2, OUT> function)
// static class JoinBroadcastProcessFunctionImpl extends BroadcastProcessFunction<Order, User, Tuple2<Order, String>> {
// // 用于存储规则名称与规则本身的 map 存储结构
// MapStateDescriptor<Integer, User> broadcastDesc;
// JoinBroadcastProcessFunctionImpl(MapStateDescriptor<Integer, User> broadcastDesc) {
// this.broadcastDesc = broadcastDesc;
// }
// // 负责处理广播流的元素
// @Override
// public void processBroadcastElement(User value,
// BroadcastProcessFunction<Order, User, Tuple2<Order, String>>.Context ctx,
// Collector<Tuple2<Order, String>> out) throws Exception {
// System.out.println("收到广播数据:" + value);
// // 得到广播流的存储状态
// ctx.getBroadcastState(broadcastDesc).put(value.getId(), value);
// }
// // 处理非广播流,关联维度
// @Override
// public void processElement(Order value,
// BroadcastProcessFunction<Order, User, Tuple2<Order, String>>.ReadOnlyContext ctx,
// Collector<Tuple2<Order, String>> out) throws Exception {
// // 得到广播流的存储状态
// ReadOnlyBroadcastState<Integer, User> state = ctx.getBroadcastState(broadcastDesc);
// out.collect(new Tuple2<>(value, state.get(value.getUId()).getName()));
// }
// }
}
3、验证
本示例使用的是两个socket数据源,通过netcat进行模拟。
1)、输入user数据
“192.168.10.42”, 8888
// user 流数据(维度表),由于未做容错处理,需要先广播维度数据,否则会出现空指针异常
// 1001,alan,18,20,alan.chan.chn@163.com
// 1002,alanchan,19,25,alan.chan.chn@163.com
// 1003,alanchanchn,20,30,alan.chan.chn@163.com
// 1004,alan_chan,27,20,alan.chan.chn@163.com
// 1005,alan_chan_chn,36,10,alan.chan.chn@163.com
2)、输入事实流订单数据
“192.168.10.42”, 9999
// order 流数据
// 16,1002,211
// 17,1004,234
// 18,1005,175
3)、观察程序控制台输出
// 控制台输出
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1001, name=alan, balance=18.0, age=20, email=alan.chan.chn@163.com)
// ......
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1001, name=alan, balance=18.0, age=20, email=alan.chan.chn@163.com)
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1002, name=alanchan, balance=19.0, age=25, email=alan.chan.chn@163.com)
// ......
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1002, name=alanchan, balance=19.0, age=25, email=alan.chan.chn@163.com)
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1003, name=alanchanchn, balance=20.0, age=30, email=alan.chan.chn@163.com)
// ......
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1003, name=alanchanchn, balance=20.0, age=30, email=alan.chan.chn@163.com)
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1004, name=alan_chan, balance=27.0, age=20, email=alan.chan.chn@163.com)
// ......
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1004, name=alan_chan, balance=27.0, age=20, email=alan.chan.chn@163.com)
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1005, name=alan_chan_chn, balance=36.0, age=10, email=alan.chan.chn@163.com)
// ......
// 收到广播数据:TestJoinDimFromBroadcastDataStreamDemo.User(id=1005, name=alan_chan_chn, balance=36.0, age=10, email=alan.chan.chn@163.com)
// 7> (TestJoinDimFromBroadcastDataStreamDemo.Order(id=16, uId=1002, total=211.0),alanchan)
// 8> (TestJoinDimFromBroadcastDataStreamDemo.Order(id=17, uId=1004, total=234.0),alan_chan)
// 9> (TestJoinDimFromBroadcastDataStreamDemo.Order(id=18, uId=1005, total=175.0),alan_chan_chn)
以上,本文详细的介绍了broadcast state的具体使用,并以两个例子分别介绍了BroadcastProcessFunction和KeyedBroadcastProcessFunction的具体实现。