一、Flink 专栏
Flink 专栏系统介绍某一知识点，并辅以具体的示例进行说明。
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
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。
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。
<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>
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();
}
}
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。
red;rectangle,is a rectangle;tripe,is a tripe
green;square,is a square;rectangle,is a rectangle
# 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
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))
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。
参考上述示例中的依赖。
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。
/*
* @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()));
}
}
/*
* @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()));
// }
// }
}
本示例使用的是两个socket数据源，通过netcat进行模拟。
“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
“192.168.10.42”, 9999
// order 流数据
// 16,1002,211
// 17,1004,234
// 18,1005,175
// 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。