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
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