本文主要是介绍Flink 维表 Join 实践|附四种方式的源码，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
常见的维表Join方式有四种：
预加载维表
热存储维表
广播维表
Temporal table function join
下面分别使用这四种方式来实现一个join的需求，这个需求是：一个主流中数据是用户信息，字段包括用户姓名、城市id；维表是城市数据，字段包括城市ID、城市名称。要求用户表与城市表关联，输出为：用户名称、城市ID、城市名称。
用户表表结构如下：
城市维表表结构如下：
1、 预加载维表
通过定义一个类实现RichMapFunction，在open()中读取维表数据加载到内存中，在probe流map()方法中与维表数据进行关联。
RichMapFunction中open方法里加载维表数据到内存的方式特点如下：
优点：实现简单
缺点：因为数据存于内存，所以只适合小数据量并且维表数据更新频率不高的情况下。虽然可以在open中定义一个定时器定时更新维表，但是还是存在维表更新不及时的情况。
下面是一个例子：
package join;import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import java.util.HashMap;
import java.util.Map;/*** Create By 鸣宇淳 on 2020/6/1* 这个例子是从socket中读取的流，数据为用户名称和城市id，维表是城市id、城市名称，* 主流和维表关联，得到用户名称、城市id、城市名称* 这个例子采用在RichMapfunction类的open方法中将维表数据加载到内存**/
public class JoinDemo1 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<Tuple2<String, Integer>> textStream = env.socketTextStream("localhost", 9000, "\n").map(p -> {//输入格式为：user,1000,分别是用户名称和城市编号String[] list = p.split(",");return new Tuple2<String, Integer>(list[0], Integer.valueOf(list[1]));}).returns(new TypeHint<Tuple2<String, Integer>>() {});DataStream<Tuple3<String, Integer, String>> result = textStream.map(new MapJoinDemo1());result.print();env.execute("joinDemo1");}static class MapJoinDemo1 extends RichMapFunction<Tuple2<String, Integer>, Tuple3<String, Integer, String>> {//定义一个变量，用于保存维表数据在内存Map<Integer, String> dim;@Overridepublic void open(Configuration parameters) throws Exception {//在open方法中读取维表数据，可以从数据中读取、文件中读取、接口中读取等等。dim = new HashMap<>();dim.put(1001, "beijing");dim.put(1002, "shanghai");dim.put(1003, "wuhan");dim.put(1004, "changsha");}@Overridepublic Tuple3<String, Integer, String> map(Tuple2<String, Integer> value) throws Exception {//在map方法中进行主流和维表的关联String cityName = "";if (dim.containsKey(value.f1)) {cityName = dim.get(value.f1);}return new Tuple3<>(value.f0, value.f1, cityName);}}
}2、 热存储维表
这种方式是将维表数据存储在Redis、HBase、MySQL等外部存储中，实时流在关联维表数据的时候实时去外部存储中查询，这种方式特点如下：
优点：维度数据量不受内存限制，可以存储很大的数据量。
缺点：因为维表数据在外部存储中，读取速度受制于外部存储的读取速度；另外维表的同步也有延迟。
(1) 使用cache来减轻访问压力
可以使用缓存来存储一部分常访问的维表数据，以减少访问外部系统的次数，比如使用guava Cache。
下面是一个例子：
package join;import com.google.common.cache.*;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.TimeUnit;/*** Create By 鸣宇淳 on 2020/6/1**/
public class JoinDemo2 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<Tuple2<String, Integer>> textStream = env.socketTextStream("localhost", 9000, "\n").map(p -> {//输入格式为：user,1000,分别是用户名称和城市编号String[] list = p.split(",");return new Tuple2<String, Integer>(list[0], Integer.valueOf(list[1]));}).returns(new TypeHint<Tuple2<String, Integer>>() {});DataStream<Tuple3<String, Integer, String>> result = textStream.map(new MapJoinDemo1());result.print();env.execute("joinDemo1");}static class MapJoinDemo1 extends RichMapFunction<Tuple2<String, Integer>, Tuple3<String, Integer, String>> {LoadingCache<Integer, String> dim;@Overridepublic void open(Configuration parameters) throws Exception {//使用google LoadingCache来进行缓存dim = CacheBuilder.newBuilder()//最多缓存个数，超过了就根据最近最少使用算法来移除缓存.maximumSize(1000)//在更新后的指定时间后就回收.expireAfterWrite(10, TimeUnit.MINUTES)//指定移除通知.removalListener(new RemovalListener<Integer, String>() {@Overridepublic void onRemoval(RemovalNotification<Integer, String> removalNotification) {System.out.println(removalNotification.getKey() + "被移除了，值为：" + removalNotification.getValue());}}).build(//指定加载缓存的逻辑new CacheLoader<Integer, String>() {@Overridepublic String load(Integer cityId) throws Exception {String cityName = readFromHbase(cityId);return cityName;}});}private String readFromHbase(Integer cityId) {//读取hbase//这里写死，模拟从hbase读取数据Map<Integer, String> temp = new HashMap<>();temp.put(1001, "beijing");temp.put(1002, "shanghai");temp.put(1003, "wuhan");temp.put(1004, "changsha");String cityName = "";if (temp.containsKey(cityId)) {cityName = temp.get(cityId);}return cityName;}@Overridepublic Tuple3<String, Integer, String> map(Tuple2<String, Integer> value) throws Exception {//在map方法中进行主流和维表的关联String cityName = "";if (dim.get(value.f1) != null) {cityName = dim.get(value.f1);}return new Tuple3<>(value.f0, value.f1, cityName);}}
}(2) 使用异步IO来提高访问吞吐量
Flink与外部存储系统进行读写操作的时候可以使用同步方式，也就是发送一个请求后等待外部系统响应，然后再发送第二个读写请求，这样的方式吞吐量比较低，可以用提高并行度的方式来提高吞吐量，但是并行度多了也就导致了进程数量多了，占用了大量的资源。
Flink中可以使用异步IO来读写外部系统，这要求外部系统客户端支持异步IO，不过目前很多系统都支持异步IO客户端。但是如果使用异步就要涉及到三个问题：
超时：如果查询超时那么就认为是读写失败，需要按失败处理；
并发数量：如果并发数量太多，就要触发Flink的反压机制来抑制上游的写入。
返回顺序错乱：顺序错乱了要根据实际情况来处理，Flink支持两种方式：允许乱序、保证顺序。
下面是一个实例，演示了试用异步IO来访问维表：
package join;import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.AsyncDataStream;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.async.ResultFuture;
import org.apache.flink.streaming.api.functions.async.RichAsyncFunction;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.TimeUnit;/*** Create By 鸣宇淳 on 2020/6/1**/
public class JoinDemo3 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStream<Tuple2<String, Integer>> textStream = env.socketTextStream("localhost", 9000, "\n").map(p -> {//输入格式为：user,1000,分别是用户名称和城市编号String[] list = p.split(",");return new Tuple2<String, Integer>(list[0], Integer.valueOf(list[1]));}).returns(new TypeHint<Tuple2<String, Integer>>() {});DataStream<Tuple3<String,Integer, String>> orderedResult = AsyncDataStream//保证顺序：异步返回的结果保证顺序，超时时间1秒，最大容量2，超出容量触发反压.orderedWait(textStream, new JoinDemo3AyncFunction(), 1000L, TimeUnit.MILLISECONDS, 2).setParallelism(1);DataStream<Tuple3<String,Integer, String>> unorderedResult = AsyncDataStream//允许乱序：异步返回的结果允许乱序，超时时间1秒，最大容量2，超出容量触发反压.unorderedWait(textStream, new JoinDemo3AyncFunction(), 1000L, TimeUnit.MILLISECONDS, 2).setParallelism(1);orderedResult.print();unorderedResult.print();env.execute("joinDemo");}//定义个类，继承RichAsyncFunction，实现异步查询存储在mysql里的维表//输入用户名、城市ID，返回 Tuple3<用户名、城市ID，城市名称>static class JoinDemo3AyncFunction extends RichAsyncFunction<Tuple2<String, Integer>, Tuple3<String, Integer, String>> {// 链接private static String jdbcUrl = "jdbc:mysql://192.168.145.1:3306?useSSL=false";private static String username = "root";private static String password = "123";private static String driverName = "com.mysql.jdbc.Driver";java.sql.Connection conn;PreparedStatement ps;@Overridepublic void open(Configuration parameters) throws Exception {super.open(parameters);Class.forName(driverName);conn = DriverManager.getConnection(jdbcUrl, username, password);ps = conn.prepareStatement("select city_name from tmp.city_info where id = ?");}@Overridepublic void close() throws Exception {super.close();conn.close();}//异步查询方法@Overridepublic void asyncInvoke(Tuple2<String, Integer> input, ResultFuture<Tuple3<String,Integer, String>> resultFuture) throws Exception {// 使用 city id 查询ps.setInt(1, input.f1);ResultSet rs = ps.executeQuery();String cityName = null;if (rs.next()) {cityName = rs.getString(1);}List list = new ArrayList<Tuple2<Integer, String>>();list.add(new Tuple3<>(input.f0,input.f1, cityName));resultFuture.complete(list);}//超时处理@Overridepublic void timeout(Tuple2<String, Integer> input, ResultFuture<Tuple3<String,Integer, String>> resultFuture) throws Exception {List list = new ArrayList<Tuple2<Integer, String>>();list.add(new Tuple3<>(input.f0,input.f1, ""));resultFuture.complete(list);}}
}3、 广播维表
利用Flink的Broadcast State将维度数据流广播到下游做join操作。特点如下：
优点：维度数据变更后可以即时更新到结果中。
缺点：数据保存在内存中，支持的维度数据量比较小。
下面是一个实例：
package join;import org.apache.flink.api.common.functions.RichMapFunction;
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.TypeHint;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
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.BroadcastProcessFunction;
import org.apache.flink.util.Collector;import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;/*** Create By 鸣宇淳 on 2020/6/1* 这个例子是从socket中读取的流，数据为用户名称和城市id，维表是城市id、城市名称，* 主流和维表关联，得到用户名称、城市id、城市名称* 这个例子采用 Flink 广播流的方式来做为维度**/
public class JoinDemo4 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//定义主流DataStream<Tuple2<String, Integer>> textStream = env.socketTextStream("localhost", 9000, "\n").map(p -> {//输入格式为：user,1000,分别是用户名称和城市编号String[] list = p.split(",");return new Tuple2<String, Integer>(list[0], Integer.valueOf(list[1]));}).returns(new TypeHint<Tuple2<String, Integer>>() {});//定义城市流DataStream<Tuple2<Integer, String>> cityStream = env.socketTextStream("localhost", 9001, "\n").map(p -> {//输入格式为：城市ID,城市名称String[] list = p.split(",");return new Tuple2<Integer, String>(Integer.valueOf(list[0]), list[1]);}).returns(new TypeHint<Tuple2<Integer, String>>() {});//将城市流定义为广播流final MapStateDescriptor<Integer, String> broadcastDesc = new MapStateDescriptor("broad1", Integer.class, String.class);BroadcastStream<Tuple2<Integer, String>> broadcastStream = cityStream.broadcast(broadcastDesc);DataStream result = textStream.connect(broadcastStream).process(new BroadcastProcessFunction<Tuple2<String, Integer>, Tuple2<Integer, String>, Tuple3<String, Integer, String>>() {//处理非广播流，关联维度@Overridepublic void processElement(Tuple2<String, Integer> value, ReadOnlyContext ctx, Collector<Tuple3<String, Integer, String>> out) throws Exception {ReadOnlyBroadcastState<Integer, String> state = ctx.getBroadcastState(broadcastDesc);String cityName = "";if (state.contains(value.f1)) {cityName = state.get(value.f1);}out.collect(new Tuple3<>(value.f0, value.f1, cityName));}@Overridepublic void processBroadcastElement(Tuple2<Integer, String> value, Context ctx, Collector<Tuple3<String, Integer, String>> out) throws Exception {System.out.println("收到广播数据：" + value);ctx.getBroadcastState(broadcastDesc).put(value.f0, value.f1);}});result.print();env.execute("joinDemo");}
}4、 Temporal table function join
Temporal table是持续变化表上某一时刻的视图，Temporal table function是一个表函数，传递一个时间参数，返回Temporal table这一指定时刻的视图。
可以将维度数据流映射为Temporal table，主流与这个Temporal table进行关联，可以关联到某一个版本（历史上某一个时刻）的维度数据。
Temporal table function join的特点如下：
优点：维度数据量可以很大，维度数据更新及时，不依赖外部存储，可以关联不同版本的维度数据。
缺点：只支持在Flink SQL API中使用。
(1) ProcessingTime的一个实例
package join;import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.table.functions.TemporalTableFunction;
import org.apache.flink.types.Row;/*** Create By 鸣宇淳 on 2020/6/1**/
public class JoinDemo5 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, bsSettings);//定义主流DataStream<Tuple2<String, Integer>> textStream = env.socketTextStream("localhost", 9000, "\n").map(p -> {//输入格式为：user,1000,分别是用户名称和城市编号String[] list = p.split(",");return new Tuple2<String, Integer>(list[0], Integer.valueOf(list[1]));}).returns(new TypeHint<Tuple2<String, Integer>>() {});//定义城市流DataStream<Tuple2<Integer, String>> cityStream = env.socketTextStream("localhost", 9001, "\n").map(p -> {//输入格式为：城市ID,城市名称String[] list = p.split(",");return new Tuple2<Integer, String>(Integer.valueOf(list[0]), list[1]);}).returns(new TypeHint<Tuple2<Integer, String>>() {});//转变为TableTable userTable = tableEnv.fromDataStream(textStream, "user_name,city_id,ps.proctime");Table cityTable = tableEnv.fromDataStream(cityStream, "city_id,city_name,ps.proctime");//定义一个TemporalTableFunctionTemporalTableFunction dimCity = cityTable.createTemporalTableFunction("ps", "city_id");//注册表函数tableEnv.registerFunction("dimCity", dimCity);//关联查询Table result = tableEnv.sqlQuery("select u.user_name,u.city_id,d.city_name from " + userTable + " as u " +", Lateral table (dimCity(u.ps)) d " +"where u.city_id=d.city_id");//打印输出DataStream resultDs = tableEnv.toAppendStream(result, Row.class);resultDs.print();env.execute("joinDemo");}
}(2) EventTime的一个实例
package join;import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.table.functions.TemporalTableFunction;
import org.apache.flink.types.Row;import java.sql.Timestamp;
import java.util.ArrayList;
import java.util.List;/*** Create By 鸣宇淳 on 2020/6/1**/
public class JoinDemo9 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//指定是EventTimeenv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, bsSettings);env.setParallelism(1);//主流，用户流, 格式为：user_name、city_id、tsList<Tuple3<String, Integer, Long>> list1 = new ArrayList<>();list1.add(new Tuple3<>("user1", 1001, 1L));list1.add(new Tuple3<>("user1", 1001, 10L));list1.add(new Tuple3<>("user2", 1002, 2L));list1.add(new Tuple3<>("user2", 1002, 15L));DataStream<Tuple3<String, Integer, Long>> textStream = env.fromCollection(list1).assignTimestampsAndWatermarks(//指定水位线、时间戳new BoundedOutOfOrdernessTimestampExtractor<Tuple3<String, Integer, Long>>(Time.seconds(10)) {@Overridepublic long extractTimestamp(Tuple3<String, Integer, Long> element) {return element.f2;}});//定义城市流,格式为：city_id、city_name、tsList<Tuple3<Integer, String, Long>> list2 = new ArrayList<>();list2.add(new Tuple3<>(1001, "beijing", 1L));list2.add(new Tuple3<>(1001, "beijing2", 10L));list2.add(new Tuple3<>(1002, "shanghai", 1L));list2.add(new Tuple3<>(1002, "shanghai2", 5L));DataStream<Tuple3<Integer, String, Long>> cityStream = env.fromCollection(list2).assignTimestampsAndWatermarks(//指定水位线、时间戳new BoundedOutOfOrdernessTimestampExtractor<Tuple3<Integer, String, Long>>(Time.seconds(10)) {@Overridepublic long extractTimestamp(Tuple3<Integer, String, Long> element) {return element.f2;}});//转变为TableTable userTable = tableEnv.fromDataStream(textStream, "user_name,city_id,ts.rowtime");Table cityTable = tableEnv.fromDataStream(cityStream, "city_id,city_name,ts.rowtime");tableEnv.createTemporaryView("userTable", userTable);tableEnv.createTemporaryView("cityTable", cityTable);//定义一个TemporalTableFunctionTemporalTableFunction dimCity = cityTable.createTemporalTableFunction("ts", "city_id");//注册表函数tableEnv.registerFunction("dimCity", dimCity);//关联查询Table result = tableEnv.sqlQuery("select u.user_name,u.city_id,d.city_name,u.ts from userTable as u " +", Lateral table (dimCity(u.ts)) d " +"where u.city_id=d.city_id");//打印输出DataStream resultDs = tableEnv.toAppendStream(result, Row.class);resultDs.print();env.execute("joinDemo");}
}结果输出为：
user1,1001,beijing,1970-01-01T00:00:00.001
user1,1001,beijing2,1970-01-01T00:00:00.010
user2,1002,shanghai,1970-01-01T00:00:00.002
user2,1002,shanghai2,1970-01-01T00:00:00.015
通过结果可以看到，根据主流中的EventTime的时间，去维表流中取响应时间版本的数据。
(3) Kafka Source的EventTime实例
package join.temporaltablefunctionjoin;import lombok.Data;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.StreamTableEnvironment;
import org.apache.flink.table.functions.TemporalTableFunction;
import org.apache.flink.types.Row;import java.io.Serializable;
import java.util.Properties;/*** Create By 鸣宇淳 on 2020/6/1**/
public class JoinDemo10 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//指定是EventTimeenv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, bsSettings);env.setParallelism(1);//Kafka的ip和要消费的topic,//Kafka设置String kafkaIPs = "192.168.***.**1:9092,192.168.***.**2:9092,192.168.***.**3:9092";Properties props = new Properties();props.setProperty("bootstrap.servers", kafkaIPs);props.setProperty("group.id", "group.cyb.2");//读取用户信息KafkaFlinkKafkaConsumer<UserInfo> userConsumer = new FlinkKafkaConsumer<UserInfo>("user", new UserInfoSchema(), props);userConsumer.setStartFromEarliest();userConsumer.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<UserInfo>(Time.seconds(0)) {@Overridepublic long extractTimestamp(UserInfo userInfo) {return userInfo.getTs();}});//读取城市维度信息KafkaFlinkKafkaConsumer<CityInfo> cityConsumer = new FlinkKafkaConsumer<CityInfo>("city", new CityInfoSchema(), props);cityConsumer.setStartFromEarliest();cityConsumer.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<CityInfo>(Time.seconds(0)) {@Overridepublic long extractTimestamp(CityInfo cityInfo) {return cityInfo.getTs();}});//主流，用户流, 格式为：user_name、city_id、tsTable userTable = tableEnv.fromDataStream(env.addSource(userConsumer),"userName,cityId,ts.rowtime" );//定义城市维度流,格式为：city_id、city_name、tsTable cityTable = tableEnv.fromDataStream(env.addSource(cityConsumer),"cityId,cityName,ts.rowtime");tableEnv.createTemporaryView("userTable", userTable);tableEnv.createTemporaryView("cityTable", cityTable);//定义一个TemporalTableFunctionTemporalTableFunction dimCity = cityTable.createTemporalTableFunction("ts", "cityId");//注册表函数tableEnv.registerFunction("dimCity", dimCity);Table u = tableEnv.sqlQuery("select * from userTable");u.printSchema();tableEnv.toAppendStream(u, Row.class).print("用户流接收到：");Table c = tableEnv.sqlQuery("select * from cityTable");c.printSchema();tableEnv.toAppendStream(c, Row.class).print("城市流接收到：");//关联查询Table result = tableEnv.sqlQuery("select u.userName,u.cityId,d.cityName,u.ts " +"from userTable as u " +", Lateral table (dimCity(u.ts)) d " +"where u.cityId=d.cityId");//打印输出DataStream resultDs = tableEnv.toAppendStream(result, Row.class);resultDs.print("\t\t关联输出：");env.execute("joinDemo");}
}
package join.temporaltablefunctionjoin;
import java.io.Serializable;/*** Create By 鸣宇淳 on 2020/6/4**/@Data
public class UserInfo implements Serializable {private String userName;private Integer cityId;private Long ts;
}
package join.temporaltablefunctionjoin;
import java.io.Serializable;/*** Create By 鸣宇淳 on 2020/6/4**/
@Data
public class CityInfo implements Serializable {private Integer cityId;private String cityName;private Long ts;
}
package join.temporaltablefunctionjoin;import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.TypeReference;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.serialization.DeserializationSchema;import java.io.IOException;
import java.nio.charset.StandardCharsets;/*** Create By 鸣宇淳 on 2020/6/4**/
public class UserInfoSchema implements DeserializationSchema<UserInfo> {@Overridepublic UserInfo deserialize(byte[] message) throws IOException {String jsonStr = new String(message, StandardCharsets.UTF_8);UserInfo data = JSON.parseObject(jsonStr, new TypeReference<UserInfo>() {});return data;}@Overridepublic boolean isEndOfStream(UserInfo nextElement) {return false;}@Overridepublic TypeInformation<UserInfo> getProducedType() {return TypeInformation.of(new TypeHint<UserInfo>() {});}
}package join.temporaltablefunctionjoin;import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.TypeReference;
import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.typeinfo.TypeHint;
import org.apache.flink.api.common.typeinfo.TypeInformation;import java.io.IOException;
import java.nio.charset.StandardCharsets;/*** Create By 鸣宇淳 on 2020/6/4**/
public class CityInfoSchema implements DeserializationSchema<CityInfo> {@Overridepublic CityInfo deserialize(byte[] message) throws IOException {String jsonStr = new String(message, StandardCharsets.UTF_8);CityInfo data = JSON.parseObject(jsonStr, new TypeReference<CityInfo>() {});return data;}@Overridepublic boolean isEndOfStream(CityInfo nextElement) {return false;}@Overridepublic TypeInformation<CityInfo> getProducedType() {return TypeInformation.of(new TypeHint<CityInfo>() {});}
}依次向user和city两个topic中写入数据，
用户信息格式：
{“userName”:“user1”,“cityId”:1,“ts”:11}
城市维度格式：
{“cityId”:1,“cityName”:“nanjing”,“ts”:15}
测试得到的输出如下：
城市流接收到：> 1,beijing,1970-01-01T00:00
用户流接收到：> user1,1,1970-01-01T00:00关联输出：> user1,1,beijing,1970-01-01T00:00
城市流接收到：> 1,shanghai,1970-01-01T00:00:00.005
用户流接收到：> user1,1,1970-01-01T00:00:00.001关联输出：> user1,1,beijing,1970-01-01T00:00:00.001
用户流接收到：> user1,1,1970-01-01T00:00:00.004关联输出：> user1,1,beijing,1970-01-01T00:00:00.004
用户流接收到：> user1,1,1970-01-01T00:00:00.005关联输出：> user1,1,shanghai,1970-01-01T00:00:00.005
用户流接收到：> user1,1,1970-01-01T00:00:00.007
用户流接收到：> user1,1,1970-01-01T00:00:00.009
城市流接收到：> 1,shanghai,1970-01-01T00:00:00.007关联输出：> user1,1,shanghai,1970-01-01T00:00:00.007
城市流接收到：> 1,wuhan,1970-01-01T00:00:00.010关联输出：> user1,1,shanghai,1970-01-01T00:00:00.009
用户流接收到：> user1,1,1970-01-01T00:00:00.011
城市流接收到：> 1,nanjing,1970-01-01T00:00:00.015关联输出：> user1,1,wuhan,1970-01-01T00:00:00.0115、四种维表关联方式比较
本文转载自：https://blog.csdn.net/chybin500/article/details/106482620
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