flink自定义kafka sink

要自定义 Flink 的 Kafka Sink，您可以通过实现 `org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema` 接口来实现自定义的序列化逻辑。以下是一个简单的示例：

import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.kafka.clients.producer.ProducerRecord;

public class CustomKafkaSerializationSchema implements KafkaSerializationSchema<String> {

    private final String topic;

    public CustomKafkaSerializationSchema(String topic) {
        this.topic = topic;
    }

    @Override
    public ProducerRecord<byte[], byte[]> serialize(String element, Long timestamp) {
        // 将 String 类型的数据序列化为字节数组，可以根据需要进行自定义序列化逻辑
        byte[] serializedValue = element.getBytes();
        return new ProducerRecord<>(topic, serializedValue);
    }
}

然后，您可以在 Flink 程序中使用这个自定义的 KafkaSink，示例如下：

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;

public class CustomKafkaSinkExample {

    public static void main(String[] args) throws Exception {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 创建数据流
        DataStream<String> stream = ...

        // 创建自定义的 KafkaSink
        String topic = "your-topic";
        FlinkKafkaProducer<String> kafkaSink = new FlinkKafkaProducer<>(
                topic,
                new CustomKafkaSerializationSchema(topic),
                properties);

        // 将数据流写入 Kafka
        stream.addSink(kafkaSink);

        env.execute("Custom Kafka Sink Example");
    }
}