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apache-spark - 使用 Spark 从 Kafka 主题中的特定分区流式传输数据

问题描述

我已经看到了与clickhere类似的问题

但我仍然想知道来自特定分区的流数据是否不可能？我在 Spark Streaming 订阅方法中使用了 Kafka Consumer Strategies。

ConsumerStrategies.Subscribe[String, String](topics, kafkaParams, offsets)

这是我尝试订阅主题和分区的代码片段，

val topics = Array("cdc-classic")
val topic="cdc-classic"
val partition=2;
val offsets= 
Map(new TopicPartition(topic, partition) -> 2L)//I am not clear with this line, (I tried to set topic and partition number as 2)
val stream = KafkaUtils.createDirectStream[String, String](
      ssc,
      PreferConsistent,
      Subscribe[String, String](topics, kafkaParams,offsets))

但是当我运行这段代码时，我得到以下异常，

     Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 0.0 failed 1 times, most recent failure: Lost task 5.0 in stage 0.0 (TID 5, localhost, executor driver): org.apache.kafka.clients.consumer.OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions: {cdc-classic-2=2}
    at org.apache.kafka.clients.consumer.internals.Fetcher.parseCompletedFetch(Fetcher.java:878)
    at org.apache.kafka.clients.consumer.internals.Fetcher.fetchedRecords(Fetcher.java:525)
    at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1110)
    at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1043)
    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.poll(CachedKafkaConsumer.scala:99)
    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.get(CachedKafkaConsumer.scala:70)
Caused by: org.apache.kafka.clients.consumer.OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions: {cdc-classic-2=2}
    at org.apache.kafka.clients.consumer.internals.Fetcher.parseCompletedFetch(Fetcher.java:878)
    at org.apache.kafka.clients.consumer.internals.Fetcher.fetchedRecords(Fetcher.java:525)
    at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1110)
    at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:1043)
    at org.apache.spark.streaming.kafka010.CachedKafkaConsumer.poll(CachedKafkaConsumer.scala:99)

PS：cdc-classic是17个分区的主题名

标签： apache-sparkapache-kafkaapache-spark-sqlspark-streamingkafka-consumer-api

解决方案

Kafka 的分区是 Spark 的并行化单元。因此，即使从技术上讲它是可能的，它也没有任何意义，因为所有数据都将由单个执行程序处理。您可以简单地启动您的流程，而不是使用 Spark KafkaConsumer：

 String topic = "foo";
 TopicPartition partition0 = new TopicPartition(topic, 0);
 TopicPartition partition1 = new TopicPartition(topic, 1);
 consumer.assign(Arrays.asList(partition0, partition1));

（https://kafka.apache.org/0110/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html）

如果你想从 Spark 自动重试中获益，你可以简单地用它创建一个 Docker 镜像，然后使用 Kubernetes 启动它，并使用适当的重试配置。

关于 Spark，如果你真的想使用它，你应该检查你读取的分区的偏移量是多少。可能您提供了一个不正确的信息，它会返回“超出范围”偏移消息（可能以 0 开头？）。

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