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广播流是什么？
将一条数据广播到所有的节点。使用 dataStream.broadCast()
广播流使用场景？
一般用于动态加载配置项。比如lol，每天不断有人再投诉举报，客服根本忙不过来，腾讯内部做了一个判断，只有vip3以上的客户的投诉才会有人工一对一回复，过了一段时间大家都发现vip3才有人工，都开始充钱到vip3，此时人还是很多，于是只有vip4上的客户才能人工回复
vip3->vip4 这种判断标准在不断的变化。此时就需要广播流。因为此时数据只有1条，需要多个节点都收到这个变化的数据。
广播流怎么用？
一般通过connect合流去操作 a connect b.broadcast 。a是主流也就是数据流，b是配置变化流
不多说直接上demo，开箱即用
package com.chenchi.broadcast;import org.apache.flink.api.common.state.BroadcastState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
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 org.apache.flink.streaming.api.functions.co.CoProcessFunction;
import org.apache.flink.streaming.api.functions.co.KeyedBroadcastProcessFunction;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.util.Collector;import java.util.HashMap;
import java.util.Random;public class BroadCastStreamDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<Pattern> patternDataStream = env.addSource(new ChangeSource());DataStream<User> userDataStream = env.addSource(new CustomerSource());userDataStream.print("user");patternDataStream.print("pattern");//test1 直接合流 不广播。只会在一个节点更新。 用于特殊需求？
// userDataStream
// .keyBy(user -> user.userId)
// .connect(patternDataStream)
// .process(new CustomerSimpleProcess())
// .print();//test2// 定义广播状态的描述器，创建广播流 如何保存需要的广播数据呢 这个案例是通过map保留变化数据
// userDataStream
// .keyBy(user -> user.userId)
// .connect(patternDataStream.broadcast())
// .process(new CustomerSimpleProcess())
// .print();//test3MapStateDescriptor<Void, Pattern> bcStateDescriptor = new MapStateDescriptor<>("patterns", Types.VOID, Types.POJO(Pattern.class));//通过描述器 更新BroadcastStream<Pattern> broadcast = patternDataStream.broadcast(bcStateDescriptor);userDataStream.keyBy(user -> user.userId).connect(broadcast).process(new CustomerBroadCastProcess()).print();env.execute();}private static class CustomerBroadCastProcess extends KeyedBroadcastProcessFunction<Integer, User, Pattern, String> {@Overridepublic void processElement(User user, KeyedBroadcastProcessFunction<Integer, User, Pattern, String>.ReadOnlyContext readOnlyContext, Collector<String> collector) throws Exception {Integer userVip = user.getVip();//获取广播流的数据 不是通过map保存Pattern pattern = readOnlyContext.getBroadcastState(new MapStateDescriptor<>("patterns", Types.VOID, Types.POJO(Pattern.class))).get(null);if (pattern!=null){Integer patternVip = pattern.vip;String result = "当前系统需要的vip等级=" + patternVip + ",用户id=" + user.userId + ",vip=" + userVip;if (userVip>= patternVip){result=result+"符合要求";}else {result=result+"不符合要求";}collector.collect(result);}else {System.out.println("pattern is null ");}}@Overridepublic void processBroadcastElement(Pattern pattern, KeyedBroadcastProcessFunction<Integer,User, Pattern, String>.Context context, Collector<String> collector) throws Exception {BroadcastState<Void, Pattern> bcState = context.getBroadcastState(new MapStateDescriptor<>("patterns", Types.VOID, Types.POJO(Pattern.class)));// 将广播状态更新为当前的patternbcState.put(null, pattern);}}public static class CustomerSimpleProcess extends CoProcessFunction<User, Pattern, String> {ValueState<Integer> vip; //这个是保留主流的state的。 不是保留广播流的stateHashMap<String,Integer> vipMap;@Overridepublic void open(Configuration parameters) throws Exception {vip = getRuntimeContext().getState(new ValueStateDescriptor<>("vip", Integer.class));vipMap=new HashMap<String,Integer>();super.open(parameters);}@Overridepublic void processElement1(User user, CoProcessFunction<User, Pattern, String>.Context context, Collector<String> collector) throws Exception {Integer userVip = user.getVip();Integer patternVip = vipMap.getOrDefault("vip", 0);String result = "当前系统需要的vip等级=" + patternVip + ",用户id=" + user.userId + ",vip=" + userVip;if (userVip>=patternVip){result=result+"符合要求";}else {result=result+"不符合要求";}collector.collect(result);}@Overridepublic void processElement2(Pattern pattern, CoProcessFunction<User, Pattern, String>.Context context, Collector<String> collector) throws Exception {vipMap.put("vip",pattern.vip);}}public static class User {public Integer userId;public Integer vip;public User() {}public User(Integer userId, Integer vip) {this.userId = userId;this.vip = vip;}public Integer getUserId() {return userId;}public void setUserId(Integer userId) {this.userId = userId;}public Integer getVip() {return vip;}public void setVip(Integer vip) {this.vip = vip;}@Overridepublic String toString() {return "Action{" +"userId=" + userId +", vip='" + vip + '\'' +'}';}}// 定义行为模式POJO类，包含先后发生的两个行为public static class Pattern {public Integer vip;public Pattern() {}public Pattern(Integer vip) {this.vip = vip;}@Overridepublic String toString() {return "Pattern{" +"vip='" + vip + '\'' +'}';}}private static class CustomerSource implements SourceFunction<User> {boolean run = true;@Overridepublic void run(SourceContext<User> sourceContext) throws Exception {while (true) {Integer userId = new Random().nextInt(1000);Integer vip = new Random().nextInt(10);sourceContext.collect(new User(userId, vip));Thread.sleep(1000);}}@Overridepublic void cancel() {run = false;}}private static class ChangeSource implements SourceFunction<Pattern> {boolean run = true;@Overridepublic void run(SourceContext<Pattern> sourceContext) throws Exception {int i = 1;while (true) {sourceContext.collect(new Pattern(i++));Thread.sleep(5000);}}@Overridepublic void cancel() {run = false;}}}
demo思想：以上述vip做例子，获取用户不断投诉的id和vip等级， 数据库保存可以享受人工服务的vip等级，该等级可以自行调整(我是随着时间变化主键增大)。
test1 不广播
注意看pattern:4 print vip=2的消息但是不代表是task4收到的消息，我们看到>1输出了vip=2
但是task10 task9都还是vip=0 ，说明流没有广播，除非此处并行度设置为1
test2 map保存变化数据
test3通过描述器获取数据
和test2 一样，不过要注意因为两个流的数据有先后，可能还没有pattern就来了user信息，所以建议先初始化，或者先添加pattern流。