flink cdc代码

当使用Flink CDC（Change Data Capture）时，可以通过以下代码来实现：

1. 首先，需要添加Flink CDC的依赖包到你的项目中。可以在pom.xml文件中添加以下依赖项：

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka_2.12</artifactId>
    <version>${flink.version}</version>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-elasticsearch7_2.12</artifactId>
    <version>${flink.version}</version>
</dependency>

请确保`${flink.version}`的值与你使用的Flink版本匹配。

2. 创建一个Flink Job来消费变更数据，并将其发送到Elasticsearch。

import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.elasticsearch.ElasticsearchSink;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaTopicPartition;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionAssigner;
import org.apache.flink.streaming.util.serialization.KeyedSerializationSchema;
import org.apache.flink.types.Row;

import java.util.*;

public class FlinkCDCJob {
    public static void main(String[] args) throws Exception {
        // 解析命令行参数
        ParameterTool parameterTool = ParameterTool.fromArgs(args);

        // 创建Flink执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置并行度
        env.setParallelism(parameterTool.getInt("parallelism", 1));

        // 设置重启策略
        env.setRestartStrategy(RestartStrategies.fixedDelayRestart(
                parameterTool.getInt("restart-attempts", 3),
                parameterTool.getLong("delay-between-attempts", 1000L)
        ));

        // 创建Kafka消费者
        Properties props = new Properties();
        props.setProperty("bootstrap.servers", parameterTool.getRequired("bootstrap-servers"));
        props.setProperty("group.id", parameterTool.getRequired("group-id"));

        FlinkKafkaConsumer<Row> kafkaConsumer = new FlinkKafkaConsumer<>(
                parameterTool.getRequired("topic"),
                (KafkaDeserializationSchema<Row>) (record, timestamp) -> {
                    // 解析Kafka记录，将其转换为Flink的Row对象
                    // 这里需要根据你的数据格式进行相应的解析操作
                    // 返回的Row对象中包含了变更前后的数据信息
                    return null;
                },
                props
        );

        // 从指定的offset开始消费数据
        if (parameterTool.has("from-offset")) {
            String[] offsets = parameterTool.getRequired("from-offset").split(",");
            Map<KafkaTopicPartition, Long> specificOffsets = new HashMap<>();
            for (String offset : offsets) {
                String[] parts = offset.split(":");
                specificOffsets.put(new KafkaTopicPartition(parts[0], Integer.parseInt(parts[1])), Long.parseLong(parts[2]));
            }
            kafkaConsumer.setStartFromSpecificOffsets(specificOffsets);
        }

        // 创建Kafka消费数据流
        DataStreamSource<Row> stream = env.addSource(kafkaConsumer);

        // 将数据流发送到Elasticsearch
        List<HttpHost> httpHosts = new ArrayList<>();
        httpHosts.add(new HttpHost(parameterTool.getRequired("es-host"), parameterTool.getInt("es-port", 9200)));

        ElasticsearchSink.Builder<Row> esSinkBuilder = new ElasticsearchSink.Builder<>(
                httpHosts,
                (KafkaSerializationSchema<Row>) (element, timestamp) -> {
                    // 将Flink的Row对象转换为要发送到Elasticsearch的数据格式
                    // 这里需要根据你的数据格式进行相应的转换操作
                    return null;
                }
        );

        // 批量写入配置
        esSinkBuilder.setBulkFlushMaxActions(parameterTool.getInt("bulk-flush-max-actions", 100));

        stream.addSink(esSinkBuilder.build());

        // 执行Flink Job
        env.execute("Flink CDC Job");
    }
}

请根据你的实际情况修改上述代码中的配置参数和数据转换逻辑。

这段代码使用Flink从Kafka消费变更数据，并将其发送到Elasticsearch。你需要根据你的数据格式和业务逻辑来实现`KafkaDeserializationSchema`和`KafkaSerializationSchema`接口中的方法。同时，你还需要配置Kafka和Elasticsearch的连接信息、消费组ID、并设置适当的并行度和重启策略等。

希望对你有帮助！