使用 Spark 从 Kafka 主题中的特定分区流式传输数据

Question

我已经看到与

类似的问题

但我仍然想知道是否无法从特定分区流式传输数据？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)

P.S: cdc-classic 是17分区的主题名

Answer 1

Kafka的分区是Spark的并行化单元。因此，即使从技术上讲它在某种程度上是可能的，但它没有意义，因为所有数据都将由单个执行程序处理。您可以简单地以 KafkaConsumer:

启动进程，而不是使用 Spark

 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)

如果您想利用 Spark 自动重试，您可以简单地创建一个 Docker 图像，然后使用具有适当重试配置的 Kubernetes 启动它。

关于Spark，如果你真的要用它，你应该检查一下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消息，它 returns 您 "out of range" 偏移消息（可能以 0 开头？）。

Answer 2

在此行指定分区号和分区的起始偏移量以流式传输数据，

Map(new TopicPartition(topic, partition) -> 2L)

其中，

partition为分区号
2L指分区的起始偏移量

然后我们可以从选定的分区流式传输数据。

使用 Spark 从 Kafka 主题中的特定分区流式传输数据

Stream data using Spark from a partiticular partition within Kafka topics

apache-kafka

apache-spark

spark-streaming

apache-spark-sql

kafka-consumer-api