Flink 維表 Join 實(shí)踐|附四種方式的源碼
常見的維表Join方式有四種：
預(yù)加載維表 熱存儲(chǔ)維表 廣播維表 Temporal table function join
下面分別使用這四種方式來(lái)實(shí)現(xiàn)一個(gè)join的需求，這個(gè)需求是：一個(gè)主流中數(shù)據(jù)是用戶信息，字段包括用戶姓名、城市id；維表是城市數(shù)據(jù)，字段包括城市ID、城市名稱。要求用戶表與城市表關(guān)聯(lián)，輸出為：用戶名稱、城市ID、城市名稱。
用戶表表結(jié)構(gòu)如下：
城市維表表結(jié)構(gòu)如下：
1、 預(yù)加載維表
通過(guò)定義一個(gè)類實(shí)現(xiàn)RichMapFunction，在open()中讀取維表數(shù)據(jù)加載到內(nèi)存中，在probe流map()方法中與維表數(shù)據(jù)進(jìn)行關(guān)聯(lián)。
RichMapFunction中open方法里加載維表數(shù)據(jù)到內(nèi)存的方式特點(diǎn)如下：
優(yōu)點(diǎn)：實(shí)現(xiàn)簡(jiǎn)單 缺點(diǎn)：因?yàn)閿?shù)據(jù)存于內(nèi)存，所以只適合小數(shù)據(jù)量并且維表數(shù)據(jù)更新頻率不高的情況下。雖然可以在open中定義一個(gè)定時(shí)器定時(shí)更新維表，但是還是存在維表更新不及時(shí)的情況。
下面是一個(gè)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 鳴宇淳 on 2020/6/1
* 這個(gè)例子是從socket中讀取的流，數(shù)據(jù)為用戶名稱和城市id，維表是城市id、城市名稱，
* 主流和維表關(guān)聯(lián)，得到用戶名稱、城市id、城市名稱
* 這個(gè)例子采用在RichMapfunction類的open方法中將維表數(shù)據(jù)加載到內(nèi)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 -> {
//輸入格式為：user,1000,分別是用戶名稱和城市編號(hào)
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>> {
//定義一個(gè)變量，用于保存維表數(shù)據(jù)在內(nèi)存
Map<Integer, String> dim;
@Override
public void open(Configuration parameters) throws Exception {
//在open方法中讀取維表數(shù)據(jù)，可以從數(shù)據(jù)中讀取、文件中讀取、接口中讀取等等。
dim = new HashMap<>();
dim.put(1001, "beijing");
dim.put(1002, "shanghai");
dim.put(1003, "wuhan");
dim.put(1004, "changsha");
}
@Override
public Tuple3<String, Integer, String> map(Tuple2<String, Integer> value) throws Exception {
//在map方法中進(jìn)行主流和維表的關(guān)聯(lián)
String cityName = "";
if (dim.containsKey(value.f1)) {
cityName = dim.get(value.f1);
}
return new Tuple3<>(value.f0, value.f1, cityName);
}
}
}
2、 熱存儲(chǔ)維表
這種方式是將維表數(shù)據(jù)存儲(chǔ)在Redis、HBase、MySQL等外部存儲(chǔ)中，實(shí)時(shí)流在關(guān)聯(lián)維表數(shù)據(jù)的時(shí)候?qū)崟r(shí)去外部存儲(chǔ)中查詢，這種方式特點(diǎn)如下：
優(yōu)點(diǎn)：維度數(shù)據(jù)量不受內(nèi)存限制，可以存儲(chǔ)很大的數(shù)據(jù)量。 缺點(diǎn)：因?yàn)榫S表數(shù)據(jù)在外部存儲(chǔ)中，讀取速度受制于外部存儲(chǔ)的讀取速度；另外維表的同步也有延遲。
(1) 使用cache來(lái)減輕訪問(wèn)壓力
可以使用緩存來(lái)存儲(chǔ)一部分常訪問(wèn)的維表數(shù)據(jù)，以減少訪問(wèn)外部系統(tǒng)的次數(shù)，比如使用guava Cache。
下面是一個(gè)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 鳴宇淳 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 -> {
//輸入格式為：user,1000,分別是用戶名稱和城市編號(hào)
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;
@Override
public void open(Configuration parameters) throws Exception {
//使用google LoadingCache來(lái)進(jìn)行緩存
dim = CacheBuilder.newBuilder()
//最多緩存?zhèn)€數(shù)，超過(guò)了就根據(jù)最近最少使用算法來(lái)移除緩存
.maximumSize(1000)
//在更新后的指定時(shí)間后就回收
.expireAfterWrite(10, TimeUnit.MINUTES)
//指定移除通知
.removalListener(new RemovalListener<Integer, String>() {
@Override
public void onRemoval(RemovalNotification<Integer, String> removalNotification) {
System.out.println(removalNotification.getKey() + "被移除了，值為：" + removalNotification.getValue());
}
})
.build(
//指定加載緩存的邏輯
new CacheLoader<Integer, String>() {
@Override
public String load(Integer cityId) throws Exception {
String cityName = readFromHbase(cityId);
return cityName;
}
}
);
}
private String readFromHbase(Integer cityId) {
//讀取hbase
//這里寫死，模擬從hbase讀取數(shù)據(jù)
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;
}
@Override
public Tuple3<String, Integer, String> map(Tuple2<String, Integer> value) throws Exception {
//在map方法中進(jìn)行主流和維表的關(guān)聯(lián)
String cityName = "";
if (dim.get(value.f1) != null) {
cityName = dim.get(value.f1);
}
return new Tuple3<>(value.f0, value.f1, cityName);
}
}
}
(2) 使用異步IO來(lái)提高訪問(wèn)吞吐量
Flink與外部存儲(chǔ)系統(tǒng)進(jìn)行讀寫操作的時(shí)候可以使用同步方式，也就是發(fā)送一個(gè)請(qǐng)求后等待外部系統(tǒng)響應(yīng)，然后再發(fā)送第二個(gè)讀寫請(qǐng)求，這樣的方式吞吐量比較低，可以用提高并行度的方式來(lái)提高吞吐量，但是并行度多了也就導(dǎo)致了進(jìn)程數(shù)量多了，占用了大量的資源。
Flink中可以使用異步IO來(lái)讀寫外部系統(tǒng)，這要求外部系統(tǒng)客戶端支持異步IO，不過(guò)目前很多系統(tǒng)都支持異步IO客戶端。但是如果使用異步就要涉及到三個(gè)問(wèn)題：
超時(shí)：如果查詢超時(shí)那么就認(rèn)為是讀寫失敗，需要按失敗處理； 并發(fā)數(shù)量：如果并發(fā)數(shù)量太多，就要觸發(fā)Flink的反壓機(jī)制來(lái)抑制上游的寫入。 返回順序錯(cuò)亂：順序錯(cuò)亂了要根據(jù)實(shí)際情況來(lái)處理，F(xiàn)link支持兩種方式：允許亂序、保證順序。
下面是一個(gè)實(shí)例，演示了試用異步IO來(lái)訪問(wèn)維表：
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 鳴宇淳 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 -> {
//輸入格式為：user,1000,分別是用戶名稱和城市編號(hào)
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
//保證順序：異步返回的結(jié)果保證順序，超時(shí)時(shí)間1秒，最大容量2，超出容量觸發(fā)反壓
.orderedWait(textStream, new JoinDemo3AyncFunction(), 1000L, TimeUnit.MILLISECONDS, 2)
.setParallelism(1);
DataStream<Tuple3<String,Integer, String>> unorderedResult = AsyncDataStream
//允許亂序：異步返回的結(jié)果允許亂序，超時(shí)時(shí)間1秒，最大容量2，超出容量觸發(fā)反壓
.unorderedWait(textStream, new JoinDemo3AyncFunction(), 1000L, TimeUnit.MILLISECONDS, 2)
.setParallelism(1);
orderedResult.print();
unorderedResult.print();
env.execute("joinDemo");
}
//定義個(gè)類，繼承RichAsyncFunction，實(shí)現(xiàn)異步查詢存儲(chǔ)在mysql里的維表
//輸入用戶名、城市ID，返回 Tuple3<用戶名、城市ID，城市名稱>
static class JoinDemo3AyncFunction extends RichAsyncFunction<Tuple2<String, Integer>, Tuple3<String, Integer, String>> {
// 鏈接
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;
@Override
public 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 = ?");
}
@Override
public void close() throws Exception {
super.close();
conn.close();
}
//異步查詢方法
@Override
public void asyncInvoke(Tuple2<String, Integer> input, ResultFuture<Tuple3<String,Integer, String>> resultFuture) throws Exception {
// 使用 city id 查詢
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);
}
//超時(shí)處理
@Override
public 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、 廣播維表
利用Flink的Broadcast State將維度數(shù)據(jù)流廣播到下游做join操作。特點(diǎn)如下：
優(yōu)點(diǎn)：維度數(shù)據(jù)變更后可以即時(shí)更新到結(jié)果中。 缺點(diǎn)：數(shù)據(jù)保存在內(nèi)存中，支持的維度數(shù)據(jù)量比較小。
下面是一個(gè)實(shí)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 鳴宇淳 on 2020/6/1
* 這個(gè)例子是從socket中讀取的流，數(shù)據(jù)為用戶名稱和城市id，維表是城市id、城市名稱，
* 主流和維表關(guān)聯(lián)，得到用戶名稱、城市id、城市名稱
* 這個(gè)例子采用 Flink 廣播流的方式來(lái)做為維度
**/
public class JoinDemo4 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//定義主流
DataStream<Tuple2<String, Integer>> textStream = env.socketTextStream("localhost", 9000, "\n")
.map(p -> {
//輸入格式為：user,1000,分別是用戶名稱和城市編號(hào)
String[] list = p.split(",");
return new Tuple2<String, Integer>(list[0], Integer.valueOf(list[1]));
})
.returns(new TypeHint<Tuple2<String, Integer>>() {
});
//定義城市流
DataStream<Tuple2<Integer, String>> cityStream = env.socketTextStream("localhost", 9001, "\n")
.map(p -> {
//輸入格式為：城市ID,城市名稱
String[] list = p.split(",");
return new Tuple2<Integer, String>(Integer.valueOf(list[0]), list[1]);
})
.returns(new TypeHint<Tuple2<Integer, String>>() {
});
//將城市流定義為廣播流
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>>() {
//處理非廣播流，關(guān)聯(lián)維度
@Override
public 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));
}
@Override
public void processBroadcastElement(Tuple2<Integer, String> value, Context ctx, Collector<Tuple3<String, Integer, String>> out) throws Exception {
System.out.println("收到廣播數(shù)據(jù)：" + value);
ctx.getBroadcastState(broadcastDesc).put(value.f0, value.f1);
}
});
result.print();
env.execute("joinDemo");
}
}
4、 Temporal table function join
Temporal table是持續(xù)變化表上某一時(shí)刻的視圖，Temporal table function是一個(gè)表函數(shù)，傳遞一個(gè)時(shí)間參數(shù)，返回Temporal table這一指定時(shí)刻的視圖。
可以將維度數(shù)據(jù)流映射為Temporal table，主流與這個(gè)Temporal table進(jìn)行關(guān)聯(lián)，可以關(guān)聯(lián)到某一個(gè)版本（歷史上某一個(gè)時(shí)刻）的維度數(shù)據(jù)。
Temporal table function join的特點(diǎn)如下：
優(yōu)點(diǎn)：維度數(shù)據(jù)量可以很大，維度數(shù)據(jù)更新及時(shí)，不依賴外部存儲(chǔ)，可以關(guān)聯(lián)不同版本的維度數(shù)據(jù)。 缺點(diǎn)：只支持在Flink SQL API中使用。
(1) ProcessingTime的一個(gè)實(shí)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 鳴宇淳 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);
//定義主流
DataStream<Tuple2<String, Integer>> textStream = env.socketTextStream("localhost", 9000, "\n")
.map(p -> {
//輸入格式為：user,1000,分別是用戶名稱和城市編號(hào)
String[] list = p.split(",");
return new Tuple2<String, Integer>(list[0], Integer.valueOf(list[1]));
})
.returns(new TypeHint<Tuple2<String, Integer>>() {
});
//定義城市流
DataStream<Tuple2<Integer, String>> cityStream = env.socketTextStream("localhost", 9001, "\n")
.map(p -> {
//輸入格式為：城市ID,城市名稱
String[] list = p.split(",");
return new Tuple2<Integer, String>(Integer.valueOf(list[0]), list[1]);
})
.returns(new TypeHint<Tuple2<Integer, String>>() {
});
//轉(zhuǎn)變?yōu)門able
Table userTable = tableEnv.fromDataStream(textStream, "user_name,city_id,ps.proctime");
Table cityTable = tableEnv.fromDataStream(cityStream, "city_id,city_name,ps.proctime");
//定義一個(gè)TemporalTableFunction
TemporalTableFunction dimCity = cityTable.createTemporalTableFunction("ps", "city_id");
//注冊(cè)表函數(shù)
tableEnv.registerFunction("dimCity", dimCity);
//關(guān)聯(lián)查詢
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");
//打印輸出
DataStream resultDs = tableEnv.toAppendStream(result, Row.class);
resultDs.print();
env.execute("joinDemo");
}
}
(2) EventTime的一個(gè)實(shí)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 鳴宇淳 on 2020/6/1
**/
public class JoinDemo9 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//指定是EventTime
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, bsSettings);
env.setParallelism(1);
//主流，用戶流, 格式為：user_name、city_id、ts
List<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(
//指定水位線、時(shí)間戳
new BoundedOutOfOrdernessTimestampExtractor<Tuple3<String, Integer, Long>>(Time.seconds(10)) {
@Override
public long extractTimestamp(Tuple3<String, Integer, Long> element) {
return element.f2;
}
}
);
//定義城市流,格式為：city_id、city_name、ts
List<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(
//指定水位線、時(shí)間戳
new BoundedOutOfOrdernessTimestampExtractor<Tuple3<Integer, String, Long>>(Time.seconds(10)) {
@Override
public long extractTimestamp(Tuple3<Integer, String, Long> element) {
return element.f2;
}
});
//轉(zhuǎn)變?yōu)門able
Table 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);
//定義一個(gè)TemporalTableFunction
TemporalTableFunction dimCity = cityTable.createTemporalTableFunction("ts", "city_id");
//注冊(cè)表函數(shù)
tableEnv.registerFunction("dimCity", dimCity);
//關(guān)聯(lián)查詢
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");
//打印輸出
DataStream resultDs = tableEnv.toAppendStream(result, Row.class);
resultDs.print();
env.execute("joinDemo");
}
}
結(jié)果輸出為：
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
通過(guò)結(jié)果可以看到，根據(jù)主流中的EventTime的時(shí)間，去維表流中取響應(yīng)時(shí)間版本的數(shù)據(jù)。
(3) Kafka Source的EventTime實(shí)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 鳴宇淳 on 2020/6/1
**/
public class JoinDemo10 {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//指定是EventTime
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
EnvironmentSettings bsSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, bsSettings);
env.setParallelism(1);
//Kafka的ip和要消費(fèi)的topic,//Kafka設(shè)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");
//讀取用戶信息Kafka
FlinkKafkaConsumer<UserInfo> userConsumer = new FlinkKafkaConsumer<UserInfo>("user", new UserInfoSchema(), props);
userConsumer.setStartFromEarliest();
userConsumer.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<UserInfo>(Time.seconds(0)) {
@Override
public long extractTimestamp(UserInfo userInfo) {
return userInfo.getTs();
}
});
//讀取城市維度信息Kafka
FlinkKafkaConsumer<CityInfo> cityConsumer = new FlinkKafkaConsumer<CityInfo>("city", new CityInfoSchema(), props);
cityConsumer.setStartFromEarliest();
cityConsumer.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<CityInfo>(Time.seconds(0)) {
@Override
public long extractTimestamp(CityInfo cityInfo) {
return cityInfo.getTs();
}
});
//主流，用戶流, 格式為：user_name、city_id、ts
Table userTable = tableEnv.fromDataStream(env.addSource(userConsumer),"userName,cityId,ts.rowtime" );
//定義城市維度流,格式為：city_id、city_name、ts
Table cityTable = tableEnv.fromDataStream(env.addSource(cityConsumer),"cityId,cityName,ts.rowtime");
tableEnv.createTemporaryView("userTable", userTable);
tableEnv.createTemporaryView("cityTable", cityTable);
//定義一個(gè)TemporalTableFunction
TemporalTableFunction dimCity = cityTable.createTemporalTableFunction("ts", "cityId");
//注冊(cè)表函數(shù)
tableEnv.registerFunction("dimCity", dimCity);
Table u = tableEnv.sqlQuery("select * from userTable");
u.printSchema();
tableEnv.toAppendStream(u, Row.class).print("用戶流接收到：");
Table c = tableEnv.sqlQuery("select * from cityTable");
c.printSchema();
tableEnv.toAppendStream(c, Row.class).print("城市流接收到：");
//關(guān)聯(lián)查詢
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");
//打印輸出
DataStream resultDs = tableEnv.toAppendStream(result, Row.class);
resultDs.print("\t\t關(guān)聯(lián)輸出：");
env.execute("joinDemo");
}
}
package join.temporaltablefunctionjoin;
import java.io.Serializable;
/**
* Create By 鳴宇淳 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 鳴宇淳 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 鳴宇淳 on 2020/6/4
**/
public class UserInfoSchema implements DeserializationSchema<UserInfo> {
@Override
public UserInfo deserialize(byte[] message) throws IOException {
String jsonStr = new String(message, StandardCharsets.UTF_8);
UserInfo data = JSON.parseObject(jsonStr, new TypeReference<UserInfo>() {});
return data;
}
@Override
public boolean isEndOfStream(UserInfo nextElement) {
return false;
}
@Override
public 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 鳴宇淳 on 2020/6/4
**/
public class CityInfoSchema implements DeserializationSchema<CityInfo> {
@Override
public CityInfo deserialize(byte[] message) throws IOException {
String jsonStr = new String(message, StandardCharsets.UTF_8);
CityInfo data = JSON.parseObject(jsonStr, new TypeReference<CityInfo>() {});
return data;
}
@Override
public boolean isEndOfStream(CityInfo nextElement) {
return false;
}
@Override
public TypeInformation<CityInfo> getProducedType() {
return TypeInformation.of(new TypeHint<CityInfo>() {
});
}
}
依次向user和city兩個(gè)topic中寫入數(shù)據(jù)，
用戶信息格式：
{“userName”:“user1”,“cityId”:1,“ts”:11}
城市維度格式：
{“cityId”:1,“cityName”:“nanjing”,“ts”:15}
測(cè)試得到的輸出如下：
城市流接收到：> 1,beijing,1970-01-01T00:00
用戶流接收到：> user1,1,1970-01-01T00:00
關(guān)聯(lián)輸出：> user1,1,beijing,1970-01-01T00:00
城市流接收到：> 1,shanghai,1970-01-01T00:00:00.005
用戶流接收到：> user1,1,1970-01-01T00:00:00.001
關(guān)聯(lián)輸出：> user1,1,beijing,1970-01-01T00:00:00.001
用戶流接收到：> user1,1,1970-01-01T00:00:00.004
關(guān)聯(lián)輸出：> user1,1,beijing,1970-01-01T00:00:00.004
用戶流接收到：> user1,1,1970-01-01T00:00:00.005
關(guān)聯(lián)輸出：> user1,1,shanghai,1970-01-01T00:00:00.005
用戶流接收到：> user1,1,1970-01-01T00:00:00.007
用戶流接收到：> user1,1,1970-01-01T00:00:00.009
城市流接收到：> 1,shanghai,1970-01-01T00:00:00.007
關(guān)聯(lián)輸出：> user1,1,shanghai,1970-01-01T00:00:00.007
城市流接收到：> 1,wuhan,1970-01-01T00:00:00.010
關(guān)聯(lián)輸出：> user1,1,shanghai,1970-01-01T00:00:00.009
用戶流接收到：> user1,1,1970-01-01T00:00:00.011
城市流接收到：> 1,nanjing,1970-01-01T00:00:00.015
關(guān)聯(lián)輸出：> user1,1,wuhan,1970-01-01T00:00:00.011
5、四種維表關(guān)聯(lián)方式比較
本文轉(zhuǎn)載自：https://blog.csdn.net/chybin500/article/details/106482620
評(píng)論
圖片
表情