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為了將(jiang)Flink CDC同步到Kafka并獲取完整的請教Canal JSON格式，你需要按照以下步驟操作：

1、整熱配置Flink┐(′?｀)┌ CDC Conne(′；ω；`)ctor

2、格式配置Kafka Sink

3、請教使用Flink DataSt??ream API處理數據

4、到完將處理后的整熱數據寫(xiě)入Kafka

下面是詳細的操作步驟和小標題：

1. 配置Flink CDC Connector

你需要配置Flink CDC Connector來(lái)連接My(′ω｀*)SQL數據庫，在Flink的格式pom.xml文件中添加以下依賴(lài)：

<dependency> <??groupId&g??t;org.apache.flink</grou??pId> <artifactId( ?ヮ?)>flinkconnectorm(′；ω；`)ysqlcdc</artifactId> <version>??;2.1.0</version></dependency>

創(chuàng )建一個(gè)Flink StreamExecutionEnvironment并添加CDC Source：

import org.apache.flink.streaming.api.environment.??StreamExecutionEnvironment;import or??g.apache.flink.table.api.bridge.java.Stream??TableEnvironment;import org.apache.flink.table.api.bridge.java.internal.StreamTableEnvironmentImpl;public class FlinkCDCToKafka {  public(′ω｀) static void main(String[] args) throws Exception(??ヮ?)?*:??? {  // 創(chuàng  )建??Flink流處理環(huán)境 StreamExecutionEnvironment env = StreamE??xecutionEnvironment.getExecutionEnvironm??ent(); StreamTa(°□°)bleEnvironment tableEnv = StreamTableEnvironmentImpl.create(env); // 配置CDC Source tableEnv.??executeSql( "CREATE TAB(′；д；`)LE my_sourc(′?｀)e (" + " id INT," + " name STRING," + " age INT" + ") WITH (" + " 'connヽ(′ー｀)ノector' = 'mysqlcdc'," + " 'hostname' = 'localhost'," + " 'port' = '3306'," + " 'username' = 'root'," + "??( ?ω?) 'password' = 'password'," + " 'databasename' = 'my_database'," + " 'tablename' = 'my_table'" + ")" ); }}

2. 配置Kafka Sink

接下來(lái)，你需(xu)要配置Kafka Sink以便將處理后的請教數據寫(xiě)入(ru)Kafka，在Fli(⊙_⊙)nk的到完pom.xml文件中添加以下依??賴(lài)：

<dependency> <groupId>org.apache.flink</groupId> <artifactId>flinkconn??ec??tヽ(′ー｀)ノorkafka</artifactId> <versi(′?ω?`)on>1(°ロ°) !.14.0</??version></dependency>

在代碼中添加Kafka?? Sink：

import org.apache.fli(′?｀)n(′▽?zhuān)?)k.s(°o°)treaming.api.datastream.D(′?_?`)ataStream;import org.apache.flink.streaming.connectorヾ(?■_■)ノs.kafka??.FlinkKafkaProducer;import org.apache.flink.streaming.util.serialization.SimpleStringSchema;// ...// 創(chuàng  )建Kafka( ?▽?) Sink??FlinkKafkaProducer<String> kafkaSink = new FlinkKafkaProducer<>( "localhost:9092", // Kafka地址 "my_topic", // 主題名稱(chēng) new SimpleStringSchema() // 序列化ヽ(′▽?zhuān)?ノ方式);// 將數據流連接到Kafka SinkDataStream<String> da??taStream = ...;┐(′д｀)┌ // 你的數據流dataStream.addSink(kafkaSink);??

現在，你可以使用Flink DataStream API來(lái)處理從MySQL CDC源讀取的整熱數據，你可以對數據進(jìn)行過(guò)濾、格式轉換等操作：

import org.apache.flink.api.comm??on.f??unctions.MapFunctioヾ(′▽?zhuān)??n;import or??g.a(╬ ò﹏ó)pache.flink.streaming.api.datastre(????)am.DataStream;import org.apache.flink.streaming.api.environment.StreamExecutionEnvi(????)ronment;// ...// 從CDC源讀取數據DataStream<Row> sourceStream = tableEnv.toRetractStream(tableEnv.sqlQu??ery("SELECT * FROM my_source"??;),請教 Row.class);// 對數( ???)據進(jìn)行處理，例如過(guò)濾年齡大于30的到完記錄DataStream<Row> filteredStream = sourceStream.filter(new FilterFunction<Row>() {  @Override public boolean filter(Row row) throw(′▽?zhuān)?s Exception {  return row.getField(2).asInt() > 30; }});

4. 將處理后的數據寫(xiě)入Kafka

import org.??apache.flink.api.java.tuple.Tuple2;import org.apache.flink.streaming.connectors.kafka.Flin(′▽?zhuān)?kKafkaProducer;import org.apache.flink.streaming.uti(′?｀*)l.serialization.SimpleStringSchema;import org.apache.??flink.streaming.api.datastream.DataStream;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.apache.flink.api.common.functions.MapFunction;import org.apache.flink.api.java.tuple(′?｀).Tuple2;import org.apache.flink.streaming.api.functions.sink.SinkFunction;import org.apache.flink.streaming.api.functions.sink.filesystem.StreamingFil??eSink;import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy;import org.apache.flink.strea(???)ming.api.functions.sink.filesystem.rollingpolicies.OnCheckpointRollingPolicy;import org.??apache.flink.st??reaming.api.functions.sink.filesystem.rollingpolicies.TimeRo??llingPolicy;impor??t org.apache.flink.streaming.api.functions.sink.filesystem.rollingpo??licies.SizeRollingPolicy;import org.apache.flink.streaming.a(′?_?`)pi.f??unctions.sink.filesystem.rol┐(′?｀)┌l(fā)ingpolici??es.CompositeRollingPolicy;import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TriggerType;import org.apache.flink.streaming.api.functions.sink.filesystem.rollingヾ(′ω｀)?policies.Rollˉ\_(ツ)_/ˉi(⊙_⊙)ngFilePolicy;import org.apache.flink.str( ?ω?)eaming.??api.functions.sink.filesystem.rollingpolicies.RollingFileAssigne(′ω｀)r;import org.apac??he.flink.streaming.api.function(′▽?zhuān)?s.sink.filesystem.rollingpolicies.RollingFヽ(′ー｀)ノileSinkFactory;import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.RollingFileSinkBuilder;import org.apache.flink.stre??aming.api.functions.sink.filesystem.rollingpolicies.TimeBasedRollingPolicy;import org.apache.f(//ω//)link.streaming.api.functions.sink.filesystem.??rollingpolicies.TimeBasedRollingPolicyFactory;imporヾ(＾-＾)ノt org.apache.flink.streaming.api.functions.sink.filesystem.rollingpo(◎_◎;)licies.TimeBasedTriggerContext;impo??rt org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContextFactory;import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBa??sedTriggerContextFactory;import org.apache.flink.streamin???g.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContext;import org.apache.flink.streaming.api.functions.sink.filesystem??.rollingpolitricie(?????)s.TimeBasedRollingPol??icyFactory;import org.apa??che.flink.streaming.api.functions.sink.fi??lesystem.rollingpolicies.TimeBasedRollingPolicy??;im(′?ω?`)port org.apache.flink.streaming.ap??i.functions.sink.filesystem.rollingpolic??i??es.TimeBasedTriggerContextFactory??;import org.apache.flink.streaming.api.functions.sink.filesys??tem.rollingpolicies.TimeBasedTriggerContext;im??port org.apache.flink.streaming.api.fu(╬?益?)nctions.sin??k.file(╬?益?)s??ystem.rollingpoli