FLINK CDC 结合hadoop checkpoint 代码

以下是一个简单的FLINK CDC结合Hadoop Checkpoint的代码示例：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.util.serialization.JSONKeyValueDeserializationSchema;
import org.apache.flink.util.Collector;

import java.util.Properties;

public class FlinkCDCWithHadoopCheckpoint {

    public static void main(String[] args) throws Exception {

        // parse input arguments
        final ParameterTool params = ParameterTool.fromArgs(args);

        // set up the execution environment
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // enable checkpointing
        env.enableCheckpointing(5000, CheckpointingMode.EXACTLY_ONCE);

        // set checkpoint directory
        env.getCheckpointConfig().setCheckpointDir(params.get("checkpointDir"));

        // create a Kafka consumer
        Properties props = new Properties();
        props.setProperty("bootstrap.servers", params.get("bootstrap.servers"));
        props.setProperty("group.id", params.get("group.id"));

        FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>(params.get("topic"), new SimpleStringSchema(), props);

        // add Kafka consumer as source
        DataStream<String> stream = env.addSource(consumer);

        // parse JSON messages and extract values
        DataStream<String> values = stream
                .map(new MapFunction<String, String>() {
                    @Override
                    public String map(String value) throws Exception {
                        // parse JSON message and extract value
                        return value;
                    }
                });

        // write values to Hadoop file system
        values.writeAsText(params.get("outputPath"));

        // execute program
        env.execute("Flink CDC with Hadoop Checkpoint");
    }
}

在这个示例中，我们使用了FLINK的Kafka Consumer将数据从Kafka Topic中读取，然后使用FLINK的MapFunction将JSON消息解析为字符串，并将其写入Hadoop文件系统中。我们还使用FLINK的Checkpointing功能来保证数据的Exactly-Once语义。我们设置了checkpoint目录和checkpoint间隔，并将其传递给FLINK环境。最后，我们执行FLINK程序并将结果写入Hadoop文件系统。