Flink高级特性和新特性(八)

BroadcastState 状态管理

• broadcast state 广播变量状态

• 应用场景

  关联更新的规则，获取指定的数据（给ip得到经度纬度）=> 地图 API 获取到 省市区街道位置

• 需求

  实时Flink DataStream 过滤出配置中（数据库）的用户，并在事件流中补全这批用户的基础信息。

• 需求流程

• 开发步骤

package cn.itcast.flink.broadcast;import org.apache.flink.api.common.state.BroadcastState;import org.apache.flink.api.common.state.MapStateDescriptor;import org.apache.flink.api.common.state.ReadOnlyBroadcastState;import org.apache.flink.api.common.typeinfo.Types;import org.apache.flink.api.java.tuple.Tuple;import org.apache.flink.api.java.tuple.Tuple2;import org.apache.flink.api.java.tuple.Tuple4;import org.apache.flink.api.java.tuple.Tuple6;import org.apache.flink.configuration.Configuration;import org.apache.flink.streaming.api.datastream.BroadcastStream;import org.apache.flink.streaming.api.datastream.DataStreamSource;import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction;import org.apache.flink.streaming.api.functions.source.RichSourceFunction;import org.apache.flink.streaming.api.functions.source.SourceFunction;import org.apache.flink.util.Collector;import java.sql.Connection;import java.sql.DriverManager;import java.sql.PreparedStatement;import java.sql.ResultSet;import java.text.SimpleDateFormat;import java.util.Date;import java.util.HashMap;import java.util.Map;import java.util.Random;/** * Author itcast * Date 2021/6/24 8:29 * 两个数据流 1.事件流 2.用户配置流 3.connect关联操作 4.打印输出 5.执行任务 * <String,String,String,Integer></> * {"userID": "user_3", "eventTime": "2019-08-17 12:19:47", "eventType": "browse", "productID": 1} * {"userID": "user_2", "eventTime": "2019-08-17 12:19:48", "eventType": "click", "productID": 1} * <String, String, Integer ></> * 'user_2', '李四', 20 * 最终的数据流 6个 Tuple6<String,String,String,Integer,String,Integer></> * (user_3,2019-08-17 12:19:47,browse,1,王五,33) * (user_2,2019-08-17 12:19:48,click,1,李四,20) */public class BroadcastStateDemo {    public static void main(String[] args) throws Exception {        //1.env        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();        //设置并行度        env.setParallelism(1);        //2.source        //-1.构建实时数据事件流-自定义随机        //<userID, eventTime, eventType, productID>        DataStreamSource<Tuple4<String, String, String, Integer>> clickSource = env.addSource(new MySource());        //-2.构建配置流-从MySQL        //<用户id,<姓名,年龄>>        DataStreamSource<Map<String, Tuple2<String, Integer>>> configSource = env.addSource(new MySQLSource());        //3.transformation        //-1.定义状态描述器        //MapStateDescriptor<Void, Map<String, Tuple2<String, Integer>>> descriptor =        //new MapStateDescriptor<>("config",Types.VOID, Types.MAP(Types.STRING, Types.TUPLE(Types.STRING, Types.INT)));        MapStateDescriptor<Void, Map<String, Tuple2<String,Integer>>> broadcastDesc = new MapStateDescriptor<>("config", Types.VOID, Types.MAP(Types.STRING, Types.TUPLE(Types.STRING, Types.INT)));        //-2.广播配置流        //BroadcastStream<Map<String, Tuple2<String, Integer>>> broadcastDS = configDS.broadcast(descriptor);        BroadcastStream<Map<String, Tuple2<String, Integer>>> broadcastDS = configSource.broadcast(broadcastDesc);        //-3.将事件流和广播流进行连接        //BroadcastConnectedStream<Tuple4<String, String, String, Integer>, Map<String, Tuple2<String, Integer>>> connectDS =eventDS.connect(broadcastDS);        SingleOutputStreamOperator<Tuple6<String, String, String, Integer, String, Integer>> result = clickSource.connect(broadcastDS)                //-4.处理连接后的流-根据配置流补全事件流中的用户的信息                .process(new BroadcastProcessFunction<Tuple4<String, String, String, Integer>, Map<String, Tuple2<String, Integer>>, Tuple6<String, String, String, Integer, String, Integer>>() {                    @Override                    public void processElement(Tuple4<String, String, String, Integer> value, ReadOnlyContext ctx, Collector<Tuple6<String, String, String, Integer, String, Integer>> out) throws Exception {                        //读取出来 f0 为 userId                        //事件流中读取用户 userId                        String userId = value.f0;                        //从ctx环境变量中通过 desc 读取出来广播状态                        ReadOnlyBroadcastState<Void, Map<String, Tuple2<String, Integer>>> broadcastState = ctx.getBroadcastState(broadcastDesc);                        //如果广播状态不为空，get(null) 获取出来 配置数据Tuple2                        if (broadcastState != null) {                            Map<String, Tuple2<String, Integer>> map = broadcastState.get(null);                            //判断 map 不为空则                            if (map != null) {                                Tuple2<String, Integer> stringIntegerTuple2 = map.get(userId);                                //取出姓名和年龄                                //collect 收集 Tuple6                                //3-4.处理(process)连接后的流-根据配置流补全事件流中的用户的信息,Tuple4和Tuple2合并                                //处理每一条元素，processElement                                out.collect(Tuple6.of(                                        userId,                                        value.f1,                                        value.f2,                                        value.f3,                                        stringIntegerTuple2.f0,                                        stringIntegerTuple2.f1                                ));                            }                        }                    }                    //value就是MySQLSource中每隔一段时间获取到的最新的map数据                    //先根据状态描述器获取历史的广播状态 ctx.getBroadcastState(desc)                    @Override                    public void processBroadcastElement(Map<String, Tuple2<String, Integer>> value, Context ctx, Collector<Tuple6<String, String, String, Integer, String, Integer>> out) throws Exception {                        //再清空历史状态 broadcastState 数据                        BroadcastState<Void, Map<String, Tuple2<String, Integer>>> broadcastState = ctx.getBroadcastState(broadcastDesc);                        //最后将最新的广播流数据放到 state 中（更新状态数据） broadcastState.put(null,value)                        broadcastState.clear();                        broadcastState.put(null, value);                    }                });        //处理广播中的元素        //4.sinks        result.print();        //5.execute        env.execute();    }    /** * <userID, eventTime, eventType, productID> */    public static class MySource implements SourceFunction<Tuple4<String, String, String, Integer>> {        private boolean isRunning = true;        @Override        public void run(SourceContext<Tuple4<String, String, String, Integer>> ctx) throws Exception {            Random random = new Random();            SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");            while (isRunning){                int id = random.nextInt(4) + 1;                String user_id = "user_" + id;                String eventTime = df.format(new Date());                String eventType = "type_" + random.nextInt(3);                int productId = random.nextInt(4);                ctx.collect(Tuple4.of(user_id,eventTime,eventType,productId));                Thread.sleep(500);            }        }        @Override        public void cancel() {            isRunning = false;        }    }    /** * <用户id,<姓名,年龄>> */    public static class MySQLSource extends RichSourceFunction<Map<String, Tuple2<String, Integer>>> {        private boolean flag = true;        private Connection conn = null;        private PreparedStatement ps = null;        private ResultSet rs = null;        @Override        public void open(Configuration parameters) throws Exception {            conn = DriverManager.getConnection("jdbc:mysql://node3:3306/bigdata?useSSL=false",                    "root",                    "123456");            String sql = "select `userID`, `userName`, `userAge` from `user_info`";            ps = conn.prepareStatement(sql);        }        @Override        public void run(SourceContext<Map<String, Tuple2<String, Integer>>> ctx) throws Exception {            while (flag){                Map<String, Tuple2<String, Integer>> map = new HashMap<>();                ResultSet rs = ps.executeQuery();                while (rs.next()){                    String userID = rs.getString("userID");                    String userName = rs.getString("userName");                    int userAge = rs.getInt("userAge");                    //Map<String, Tuple2<String, Integer>>                    map.put(userID, Tuple2.of(userName,userAge));                }                ctx.collect(map);                Thread.sleep(5000);//每隔5s更新一下用户的配置信息!            }        }        @Override        public void cancel() {            flag = false;        }        @Override        public void close() throws Exception {            if (conn != null) conn.close();            if (ps != null) ps.close();            if (rs != null) rs.close();        }    }}

• 实时的数据流和 动态变化的数据库中的配置流 进行 connect 操作， 打印输出