Flink消費kafka如何獲取每條消息對應的topic

1.首先自定義個 KafkaDeserializationSchema

public class CustomKafkaDeserializationSchema implements KafkaDeserializationSchema<Tuple2<String, String>> {
	@Override
	//nextElement 是否表示流的最後一條元素，我們要設置爲 false ,因爲我們需要 msg 源源不斷的被消費
	public boolean isEndOfStream(Tuple2<String, String> nextElement) {
		return false;
	}
	
	@Override
	// 反序列化 kafka 的 record，我們直接返回一個 tuple2<kafkaTopicName,kafkaMsgValue>
	public Tuple2<String, String> deserialize(ConsumerRecord<byte[], byte[]> record) throws Exception {
		return new Tuple2<>(record.topic(), new String(record.value(), "UTF-8"));
	}
	
	@Override
	//告訴 Flink 我輸入的數據類型, 方便 Flink 的類型推斷
	public TypeInformation<Tuple2<String, String>> getProducedType() {
		return new TupleTypeInfo<>(BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.STRING_TYPE_INFO);
	}
}

2.使用自定義的 KafkaDeserializationSchema 進行消費

public static void main(String[] args) throws Exception {
		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
				
		Properties properties = new Properties();
		properties.setProperty("bootstrap.servers", "localhost:9092");
		properties.setProperty("group.id", "test");
		
		FlinkKafkaConsumer<Tuple2<String, String>> kafkaConsumer = new FlinkKafkaConsumer<>("test", new CustomKafkaDeserializationSchema(), properties);
		kafkaConsumer.setStartFromEarliest();
		env.addSource(kafkaConsumer).flatMap(new FlatMapFunction<Tuple2<String, String>, Object>() {
			@Override
			public void flatMap(Tuple2<String, String> value, Collector<Object> out) throws Exception {
				System.out.println("topic==== " + value.f0);
			}
		});
		
		// execute program
		env.execute("Flink Streaming Java API Skeleton");
	}