java - 我在使用 readStream().format ("kafka"时遇到问题)

Question

请帮助修复错误:

20/04/09 18:38:44 ERROR MicroBatchExecution: Query [id = 9f3cbbf6-85a8-4aed-89c6-f5d3ff9c40fa, runId = 73c071c6-e222-4760-a750-393666a298af] terminated with error
java.lang.ClassCastException: org.apache.spark.sql.execution.streaming.SerializedOffset cannot be cast to org.apache.spark.sql.sources.v2.reader.streaming.Offset
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:405)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:390)
        at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
        at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
        at scala.collection.Iterator$class.foreach(Iterator.scala:891)
        at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
        at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
        at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
        at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
        at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:389)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
        at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
        at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
20/04/09 18:38:44 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!

我的代码是:

   SQLContext sqlContext = new SQLContext(HadoopWork.sc);
    sqlContext
            .readStream()
            .format("kafka")
            .option("kafka.bootstrap.servers", IKafkaConstants.KAFKA_BROKERS)
            .option("subscribe", IKafkaConstants.TOPIC_NAME)
            .option("startingOffsets",  "earliest")
            .option("enable.auto.commit", false)
            .option("checkpointLocation", "/tmp/checkpoint/1")
            .load()
            .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
            .writeStream()
            .format("parquet")
            .outputMode("append")
            .option("checkpointLocation", "/tmp/checkpoint/2")
            .option("compression", "gzip")
            .option("path", "/user/test/")
            .option("enable.auto.commit", false)
            .option("startingOffsets",  "earliest")
            .start();

Answer 1

这似乎是一个较旧的已知 bug在结构化流媒体中。

<小时/>

升级到 Spark 2.3.2+ 应该可以解决问题