【问题标题】:Kafka streams records not forwarding after windowing/aggregation卡夫卡流记录在窗口/聚合后不转发
【发布时间】:2019-09-19 09:07:40
【问题描述】:

我正在使用带有 Tumbling Window 的 Kafka Streams,然后是聚合步骤。但是观察发出到聚合函数的元组数量正在下降。知道我哪里出错了吗?

代码:

  Properties props = new Properties();
  props.put(StreamsConfig.APPLICATION_ID_CONFIG, "events_streams_local");
  props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
  props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
  props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
  props.put(StreamsConfig.METRIC_REPORTER_CLASSES_CONFIG, Arrays.asList(JmxReporter.class));
  props.put(StreamsConfig.STATE_DIR_CONFIG, "/tmp/kafka-streams/data/");
  props.put(StreamsConfig.NUM_STREAM_THREADS_CONFIG, 20);

  props.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 60000);
  props.put(StreamsConfig.DEFAULT_TIMESTAMP_EXTRACTOR_CLASS_CONFIG, EventTimeExtractor.class);

  props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest");

  final StreamsBuilder builder = new StreamsBuilder();
  HashGenerator hashGenerator = new HashGenerator(1);
  builder
  .stream(inputTopics)
  .mapValues((key, value) -> {
    stats.incrInputRecords();
    Event event = jsonUtil.fromJson((String) value, Event.class);
    return event;
  })
  .filter(new UnifiedGAPingEventFilter(stats))
  .selectKey(new KeyValueMapper<Object, Event, String>() {

    @Override
    public String apply(Object key, Event event) {
      return (String) key;
    }
  })
  .groupByKey(Grouped.with(Serdes.String(), eventSerdes))
  .windowedBy(TimeWindows.of(Duration.ofSeconds(30)))
  .aggregate(new AggregateInitializer(), new UserStreamAggregator(), Materialized.with(Serdes.String(), aggrSerdes))
  .mapValues((k, v) -> {
    // update counter for aggregate records
    return v;
  })
  .toStream()
  .map(new RedisSink(stats));

  topology = builder.build();
  streams = new KafkaStreams(topology, props);

每秒的 Redis 操作数只是向下滑动。

【问题讨论】:

标签: java aggregation apache-kafka-streams windowing


【解决方案1】:

Kafka Streams 使用状态存储中的缓存来减少下游负载。如果您想将存储的每次更新都作为下游记录获取,您可以通过StreamsConfig#CACHE_MAX_BYTES_BUFFERING_CONFIG(全局用于所有存储)或通过将Materialized.as(...).withCachingDisabled() 传递给相应的运算符(例如,@987654324)将每个存储的缓存大小设置为零@)。

查看文档了解更多详情:https://docs.confluent.io/current/streams/developer-guide/memory-mgmt.html

【讨论】:

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