使用Kafka进行Spark结构化流式传输时，不建议使用startOffset =“ earliest”

时间：2019-06-19 02:31:10

标签： apache-spark spark-streaming spark-structured-streaming spark-streaming-kafka

我已经设置了Spark结构化流（Spark 2.3.2）以从Kafka（2.0.0）中读取。如果消息在启动Spark流作业之前进入主题，则无法从主题的开头开始进行消费。是Spark流的这种预期行为，它忽略了在Spark Stream作业的初始运行之前生成的Kafka消息（即使使用.option（“ stratingOffsets”，“最早”））？

复制步骤

  1. 在开始流工作之前，创建test主题（单个代理，单个分区）并生成与此主题相关的消息（在我的示例中为3条消息）。

  2. 使用以下命令启动spark-shell：spark-shell --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.3.2.3.1.0.0-78 --repositories http://repo.hortonworks.com/content/repositories/releases/

  3. 执行下面的spark scala代码。

// Local
val df = spark.readStream.format("kafka")
  .option("kafka.bootstrap.servers", "localhost:9097")
  .option("failOnDataLoss","false")
  .option("stratingOffsets","earliest")
  .option("subscribe", "test")
  .load()

// Sink Console
val ds = df.writeStream.format("console").queryName("Write to console")
  .trigger(org.apache.spark.sql.streaming.Trigger.ProcessingTime("10 second"))
  .start()

预期与实际产量

我希望流从offset = 1开始。但是，它从offset = 3开始读取。您可以看到kafka客户端实际上正在重置起始偏移量：2019-06-18 21:22:57 INFO Fetcher:583 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] Resetting offset for partition test-0 to offset 3.

我可以看到spark流处理了我在开始流工作后生成的消息。

Spark流式传输的这种预期行为是否忽略了在首次运行Spark Stream作业之前生成的Kafka消息（甚至使用.option("stratingOffsets","earliest")）？

2019-06-18 21:22:57 INFO  AppInfoParser:109 - Kafka version : 2.0.0.3.1.0.0-78
2019-06-18 21:22:57 INFO  AppInfoParser:110 - Kafka commitId : 0f47b27cde30d177
2019-06-18 21:22:57 INFO  MicroBatchExecution:54 - Starting new streaming query.
2019-06-18 21:22:57 INFO  Metadata:273 - Cluster ID: LqofSZfjTu29BhZm6hsgsg
2019-06-18 21:22:57 INFO  AbstractCoordinator:677 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] Discovered group coordinator localhost:9097 (id: 2147483647 rack: null)
2019-06-18 21:22:57 INFO  ConsumerCoordinator:462 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] Revoking previously assigned partitions []
2019-06-18 21:22:57 INFO  AbstractCoordinator:509 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] (Re-)joining group
2019-06-18 21:22:57 INFO  AbstractCoordinator:473 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] Successfully joined group with generation 1
2019-06-18 21:22:57 INFO  ConsumerCoordinator:280 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] Setting newly assigned partitions [test-0]
2019-06-18 21:22:57 INFO  Fetcher:583 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] Resetting offset for partition test-0 to offset 3.
2019-06-18 21:22:58 INFO  KafkaSource:54 - Initial offsets: {"test":{"0":3}}
2019-06-18 21:22:58 INFO  Fetcher:583 - [Consumer clientId=consumer-2, groupId=spark-kafka-source-e948eee9-3024-4f14-bcb8-75b80d43cbb1--181544888-driver-0] Resetting offset for partition test-0 to offset 3.
2019-06-18 21:22:58 INFO  MicroBatchExecution:54 - Committed offsets for batch 0. Metadata OffsetSeqMetadata(0,1560910978083,Map(spark.sql.shuffle.partitions -> 200, spark.sql.streaming.stateStore.providerClass -> org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider))
2019-06-18 21:22:58 INFO  KafkaSource:54 - GetBatch called with start = None, end = {"test":{"0":3}}

火花批处理模式

我能够确认批处理模式从头开始读取-因此，Kafka保留配置没有问题

val df = spark
  .read
  .format("kafka")
  .option("kafka.bootstrap.servers", "localhost:9097")
  .option("subscribe", "test")
  .load()

df.count // Long = 3

2 个答案:

答案 0 :(得分：0)

您可以通过两种方式执行此操作。将数据从kafka加载到流数据帧或将数据从kafka加载到静态数据帧（用于测试）。

我认为您由于group-id而看不到数据。 kafka将提交消费者组并抵消内部主题。确保每次读取的组名都是唯一的。

这是两个选项。

选项1：从kafka读取数据到流数据帧 

// spark streaming with kafka 

import org.apache.spark.sql.streaming.ProcessingTime

val ds1 = spark.readStream.format("kafka")
.option("kafka.bootstrap.servers","app01.app.test.net:9097,app02.app.test.net:9097")
.option("subscribe", "kafka-testing-topic")
.option("kafka.security.protocol", "SASL_PLAINTEXT")
.option("startingOffsets","earliest")
.option("maxOffsetsPerTrigger","6000")
.load()

val ds2 = ds1.select(from_json($"value".cast(StringType), dataSchema).as("data")).select("data.*")
val ds3 = ds2.groupBy("TABLE_NAME").count()
ds3.writeStream
.trigger(ProcessingTime("10 seconds"))
.queryName("query1").format("console")
.outputMode("complete")
.start()
.awaitTermination()

选项2：将数据从kafka读取到静态数据帧（以进行测试，将从头开始加载） 


// Subscribe to 1 topic defaults to the earliest and latest offsets
val ds1 = spark.read.format("kafka")
.option("kafka.bootstrap.servers","app01.app.test.net:9097,app02.app.test.net:9097")
.option("subscribe", "kafka-testing-topic")
.option("kafka.security.protocol", "SASL_PLAINTEXT")
.option("spark.streaming.kafka.consumer.cache.enabled","false")
.load()

val ds2 = ds1.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)","topic","partition","offset","timestamp")
val ds3 = ds2.select("value").rdd.map(x => x.toString)
ds3.count()

答案 1 :(得分：0)

哈哈，这是一个简单的错字：“ stratingOffsets”应该是“ startingOffsets”

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