Question

我已经在4个分区的Kafka主题上部署了一个包含4名工作人员的结构化流。

我假设将为4个分区部署4个工作人员，并在worker＆lt; - ＆gt;分区之间进行一对一映射。

但事实并非如此。所有分区都由同一个Executor提供服务。我通过检查thread-id并记录执行程序来确认这一点。

是否有任何文档显示Kafka分区和Spark Structured Streams之间的相关性。另外，有没有我们可以调整的旋钮。

Answer 1

如果您使用的是DirectStream API，则相关性为1：1（sparkcore：partition）。来自spark streaming guide，

Kafka 0.10的Spark Streaming集成在设计上类似于 0.8直流方法。它提供简单的并行性，1：1 Kafka分区和Spark分区之间的对应关系访问抵消和元数据

Answer 2

相关性为“ 1：n（executor：partitions）”：一个Kafka分区只能由一个执行者使用，一个执行者可以使用多个Kafka分区。

这与Spark Streaming一致。

对于结构化流，默认模型为“微批处理模型”，“连续处理模型”仍处于“实验”状态。

对于“微批处理模型”，在“ KafkaSource.scala”中有

 *   - The DF returned is based on [[KafkaSourceRDD]] which is constructed such that the
 *     data from Kafka topic + partition is consistently read by the same executors across
 *     batches, and cached KafkaConsumers in the executors can be reused efficiently. See the
 *     docs on [[KafkaSourceRDD]] for more details.

在“ KafkaSourceRDD”中

/**
 * An RDD that reads data from Kafka based on offset ranges across multiple partitions.
 * Additionally, it allows preferred locations to be set for each topic + partition, so that
 * the [[KafkaSource]] can ensure the same executor always reads the same topic + partition
 * and cached KafkaConsumers (see [[KafkaDataConsumer]] can be used read data efficiently.
 *
 * ...
 */
private[kafka010] class KafkaSourceRDD(

我们知道默认位置策略为LocationStrategies.PreferConsistent。

对于“连续处理模型”，在“ KafkaContinuousReader.scala”中

  override def createUnsafeRowReaderFactories(): ju.List[DataReaderFactory[UnsafeRow]] = {
    ...
    startOffsets.toSeq.map {
      case (topicPartition, start) =>
        KafkaContinuousDataReaderFactory(
          topicPartition, start, kafkaParams, pollTimeoutMs, failOnDataLoss)
          .asInstanceOf[DataReaderFactory[UnsafeRow]]
    }.asJava
  }

/**
 * A data reader factory for continuous Kafka processing. This will be serialized and transformed
 * into a full reader on executors.
 *
 * @param topicPartition The (topic, partition) pair this task is responsible for.
 * ...
 */
case class KafkaContinuousDataReaderFactory(
    topicPartition: TopicPartition,
    startOffset: Long,
    kafkaParams: ju.Map[String, Object],
    pollTimeoutMs: Long,
    failOnDataLoss: Boolean) extends DataReaderFactory[UnsafeRow] {
  override def createDataReader(): KafkaContinuousDataReader = {
    new KafkaContinuousDataReader(
      topicPartition, startOffset, kafkaParams, pollTimeoutMs, failOnDataLoss)
  }
}

我们知道每个(topic, partition)将包含在一个工厂中，然后将包含在一个执行程序中。

解释Spark Structured Streaming执行程序和Kafka分区之间的映射

2 个答案: