Spark结构化流式Kafka偏移管理

Question

我正在研究为Spark结构化流在kafka中存储kafka偏移量，就像它为DStreamsStream.AsInstanceOf[CanCommitOffsets].CommitAsync(offsetRanges)工作一样，除了结构化流，我也在研究同样的情况。是否支持结构化流？如果是，我如何实现？

我知道使用.option（“checkpointLocation”，checkpointLocation)进行hdfs检查点，但我对内置的偏移量管理感兴趣。

我期待Kafka存储偏移量只在内部没有火花hdfs检查点。

Answer 1

我正在使用在某处找到的这段代码。

public class OffsetManager {

    private String storagePrefix;

    public OffsetManager(String storagePrefix) {
        this.storagePrefix = storagePrefix;
    }

    /**
     * Overwrite the offset for the topic in an external storage.
     *
     * @param topic     - Topic name.
     * @param partition - Partition of the topic.
     * @param offset    - offset to be stored.
     */
    void saveOffsetInExternalStore(String topic, int partition, long offset) {

        try {

            FileWriter writer = new FileWriter(storageName(topic, partition), false);

            BufferedWriter bufferedWriter = new BufferedWriter(writer);
            bufferedWriter.write(offset + "");
            bufferedWriter.flush();
            bufferedWriter.close();

        } catch (Exception e) {
            e.printStackTrace();
            throw new RuntimeException(e);
        }
    }

    /**
     * @return he last offset + 1 for the provided topic and partition.
     */
    long readOffsetFromExternalStore(String topic, int partition) {

        try {

            Stream<String> stream = Files.lines(Paths.get(storageName(topic, partition)));

            return Long.parseLong(stream.collect(Collectors.toList()).get(0)) + 1;

        } catch (Exception e) {
            e.printStackTrace();
        }

        return 0;
    }

    private String storageName(String topic, int partition) {
        return "Offsets\\" + storagePrefix + "-" + topic + "-" + partition;
    }

}

SaveOffset...在记录处理成功后调用，否则不存储偏移量。我使用Kafka主题作为源，所以我指定startingoffsets作为从readoffsets中检索到的偏移量...