Question

目前，我有一个Flink集群，希望按一种模式使用Kafka主题，通过这种方式，我们不需要维护一个硬代码Kafka主题列表。

import java.util.regex.Pattern;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010;
...
private static final Pattern topicPattern = Pattern.compile("(DC_TEST_([A-Z0-9_]+)");
...
FlinkKafkaConsumer010<KafkaMessage> kafkaConsumer = new FlinkKafkaConsumer010<>(
          topicPattern, deserializerClazz.newInstance(), kafkaConsumerProps);
DataStream<KafkaMessage> input = env.addSource(kafkaConsumer);

我只想通过上述方式知道，如何在处理过程中知道真实的Kafka主题名称？谢谢。

-更新- 我之所以需要了解主题信息，是因为我们需要此主题名称作为即将在Flink接收器部件中使用的参数。

Answer 1

您可以实现自己的自定义KafkaDeserializationSchema，如下所示：

  public class CustomKafkaDeserializationSchema implements KafkaDeserializationSchema<Tuple2<String, String>> {
    @Override
    public boolean isEndOfStream(Tuple2<String, String> nextElement) {
        return false;
    }

    @Override
    public Tuple2<String, String> deserialize(ConsumerRecord<byte[], byte[]> record) throws Exception {
        return new Tuple2<>(record.topic(), new String(record.value(), "UTF-8"));
    }

    @Override
    public TypeInformation<Tuple2<String, String>> getProducedType() {
        return new TupleTypeInfo<>(BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.STRING_TYPE_INFO);
    }
  }

使用自定义的KafkaDeserializationSchema，可以创建其元素包含主题信息的DataStream。在我的演示案例中，元素类型为Tuple2<String, String>，因此您可以通过Tuple2#f0访问主题名称。

FlinkKafkaConsumer010<Tuple2<String, String>> kafkaConsumer = new FlinkKafkaConsumer010<>(
          topicPattern, new CustomKafkaDeserializationSchema, kafkaConsumerProps);
DataStream<Tuple2<String, String>> input = env.addSource(kafkaConsumer);

input.process(new ProcessFunction<Tuple2<String,String>, String>() {
            @Override
            public void processElement(Tuple2<String, String> value, Context ctx, Collector<String> out) throws Exception {
                String topicName = value.f0;
                // your processing logic here.
                out.collect(value.f1);
            }
        });

Answer 2

有两种方法可以做到这一点。

选项1：

您可以使用Kafka-clients库访问Kafka元数据，获取主题列表。添加Maven依赖项或等效项。

<!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka-clients -->
<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>2.3.0</version>
</dependency>

您可以从Kafka集群中获取主题，并使用正则表达式进行过滤，如下所示：

 private static final Pattern topicPattern = Pattern.compile("(DC_TEST_([A-Z0-9_]+)");

  Properties properties = new Properties();
  properties.put("bootstrap.servers","localhost:9092");
  properties.put("client.id","java-admin-client");
  try (AdminClient client = AdminClient.create(properties)) {
     ListTopicsOptions options = new ListTopicsOptions();
     options.listInternal(false);
      Collection<TopicListing> listing =  client.listTopics(options).listings().get();
      List<String> allTopicsList = listings.stream().map(TopicListing::name)
      .collect(Collectors.toList());
      List<String> matchedTopics = allTopicsList.stream()
                            .filter(topicPattern.asPredicate())
                            .collect(Collectors.toList());
    }catch (Exception e) {
      e.printStackTrace();
    }
}

一旦您匹配了主题列表，就可以将其传递给FlinkKafkaConsumer。

选项2：

Flink版本1.8中的

FlinkKafkaConsumer011支持基于模式动态地进行主题和分区发现。下面是示例：

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
 private static final Pattern topicPattern = Pattern.compile("(DC_TEST_([A-Z0-9_]+)");
Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
properties.setProperty("group.id", "test");

FlinkKafkaConsumer011<String> myConsumer = new FlinkKafkaConsumer011<>(
    topicPattern ,
    new SimpleStringSchema(),
    properties);

链接：https://ci.apache.org/projects/flink/flink-docs-release-1.8/dev/connectors/kafka.html#kafka-consumers-topic-and-partition-discovery

对于您而言，选项2最合适。

由于您要作为KafkaMessage的一部分访问主题元数据，因此需要实现KafkaDeserializationSchema接口，如下所示：

public class CustomKafkaDeserializationSchema extends KafkaDeserializationSchema<KafkaMessage> {
    /**
     * Deserializes the byte message.
     *
     * @param messageKey the key as a byte array (null if no key has been set).
     * @param message The message, as a byte array (null if the message was empty or deleted).
     * @param partition The partition the message has originated from.
     * @param offset the offset of the message in the original source (for example the Kafka offset).
     *
     * @return The deserialized message as an object (null if the message cannot be deserialized).
     */
    @Override
    public KafkaMessage deserialize(ConsumerRecord<byte[], byte[]> record) throws IOException {
        //You can access record.key(), record.value(), record.topic(), record.partition(), record.offset() to get topic information.
         KafkaMessage kafkaMessage = new KafkaMessage();
         kafkaMessage.setTopic(record.topic());
         // Make your kafka message here and assign the values like above.
        return kafkaMessage ;
    }

    @Override
    public boolean isEndOfStream(Long nextElement) {
        return false;
    }       
}

然后致电：

FlinkKafkaConsumer010<Tuple2<String, String>> kafkaConsumer = new FlinkKafkaConsumer010<>(
          topicPattern, new CustomKafkaDeserializationSchema, kafkaConsumerProps);

如何在Flink Kafka Consumer中动态获取处理中的Kafka主题名称？

2 个答案: