apache-spark - Spark Structured Streaming with Kafka 不支持startingOffset ="earliest"

Question

我已经设置了 Spark Structured Streaming (Spark 2.3.2) 来读取 Kafka (2.0.0)。如果消息在 Spark 流作业开始之前进入主题，我将无法从主题的开头开始消费。 Spark 流的这种预期行为是否会忽略在 Spark Stream 作业初始运行之前生成的 Kafka 消息(即使使用 .option("stratingOffsets","earliest"))？

重现步骤

在开始流式作业之前，创建 test主题(单个代理，单个分区)并为该主题生成消息(在我的示例中为 3 条消息)。

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/

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()

预期与实际输出

我希望流从 offset=1 开始。但是，它从 offset=3 开始读取。可以看到kafka客户端实际上是在重置起始偏移量: 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 流处理我在启动流作业后生成的消息。

Spark 流的这种预期行为是否会忽略在 Spark Stream 作业初始运行之前生成的 Kafka 消息(即使使用 .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}}

Spark 批处理模式

我能够确认批处理模式从头开始读取 - 所以 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

Answer 1

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