请教下 flink-cdc同步到kafka怎么才能拿到完整热搜canal-json格式？

为了将Flink CDC同步到Kafka并获取完整的Canal JSON格式，你需要按照以下步骤操作：

1、配置Flink CDC Connector

2、配置Kafka Sink

3、使用Flink DataStream API处理数据

4、将处理后的数据写入Kafka

下面是详细的操作步骤和小标题：

1. 配置Flink CDC Connector

你需要配置Flink CDC Connector来连接MySQL数据库，在Flink的pom.xml文件中添加以下依赖：

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flinkconnectormysqlcdc</artifactId>
  <version>2.1.0</version>
</dependency>

创建一个Flink StreamExecutionEnvironment并添加CDC Source：

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.bridge.java.internal.StreamTableEnvironmentImpl;
public class FlinkCDCToKafka {
    public static void main(String[] args) throws Exception {
        // 创建Flink流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tableEnv = StreamTableEnvironmentImpl.create(env);
        // 配置CDC Source
        tableEnv.executeSql(
            "CREATE TABLE my_source (
" +
            "  id INT,
" +
            "  name STRING,
" +
            "  age INT
" +
            ") WITH (
" +
            "  'connector' = 'mysqlcdc',
" +
            "  'hostname' = 'localhost',
" +
            "  'port' = '3306',
" +
            "  'username' = 'root',
" +
            "  'password' = 'password',
" +
            "  'databasename' = 'my_database',
" +
            "  'tablename' = 'my_table'
" +
            ")"
        );
    }
}

2. 配置Kafka Sink

接下来，你需要配置Kafka Sink以便将处理后的数据写入Kafka，在Flink的pom.xml文件中添加以下依赖：

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flinkconnectorkafka</artifactId>
  <version>1.14.0</version>
</dependency>

在代码中添加Kafka Sink：

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.util.serialization.SimpleStringSchema;
// ...
// 创建Kafka Sink
FlinkKafkaProducer<String> kafkaSink = new FlinkKafkaProducer<>(
    "localhost:9092", // Kafka地址
    "my_topic", // 主题名称
    new SimpleStringSchema() // 序列化方式
);
// 将数据流连接到Kafka Sink
DataStream<String> dataStream = ...; // 你的数据流
dataStream.addSink(kafkaSink);

3. 使用Flink DataStream API处理数据

现在，你可以使用Flink DataStream API来处理从MySQL CDC源读取的数据，你可以对数据进行过滤、转换等操作：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
// ...
// 从CDC源读取数据
DataStream<Row> sourceStream = tableEnv.toRetractStream(tableEnv.sqlQuery("SELECT * FROM my_source"), Row.class);
// 对数据进行处理，例如过滤年龄大于30的记录
DataStream<Row> filteredStream = sourceStream.filter(new FilterFunction<Row>() {
    @Override
    public boolean filter(Row row) throws Exception {
        return row.getField(2).asInt() > 30;
    }
});

4. 将处理后的数据写入Kafka

将处理后的数据转换为JSON格式，并将其写入Kafka：

import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.util.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.StreamingFileSink;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.OnCheckpointRollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeRollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.SizeRollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.CompositeRollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TriggerType;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.RollingFilePolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.RollingFileAssigner;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.RollingFileSinkFactory;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.RollingFileSinkBuilder;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedRollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedRollingPolicyFactory;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContext;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContextFactory;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContextFactory;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContext;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolitricies.TimeBasedRollingPolicyFactory;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedRollingPolicy;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContextFactory;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.TimeBasedTriggerContext;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpoli