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结构化流媒体赢得了't write DF to file sink citing /_spark_metadata/9.compact doesn't存在

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我正在EMR 5.11.1,Spark 2.2.1中构建一个Kafka摄取模块 . 我的目的是使用结构化流来消费Kafka主题,进行一些处理,并以镶木地板格式存储到EMRFS / S3 .

控制台接收器按预期工作,文件接收器不起作用 .

spark-shell

val event = spark.readStream.format("kafka")
.option("kafka.bootstrap.servers", <server list>)
.option("subscribe", <topic>)
.load()

val eventdf = event.select($"value" cast "string" as "json")
.select(from_json($"json", readSchema) as "data")
.select("data.*")

val outputdf = <some processing on eventdf>

这有效:

val console_query = outputdf.writeStream.format("console")
.trigger(Trigger.ProcessingTime(10.seconds))
.outputMode(OutputMode.Append)
.start

这不是:

val filesink_query = outputdf.writeStream
.partitionBy(<some column>)
.format("parquet")
.option("path", <some path in EMRFS>)
.option("checkpointLocation", "/tmp/ingestcheckpoint")
.trigger(Trigger.ProcessingTime(10.seconds))
.outputMode(OutputMode.Append)
.start //fails

我试过的东西不起作用:

  • sc.hadoopConfiguration.set("parquet.enable.summary-metadata","false")

  • 将格式更改为CSV而不是镶木地板

  • 将输出模式更改为完成(仅支持追加)

  • 不同的触发间隔
    readStream上

  • .option("failOnDataLoss",false)

一些挖掘源代码的人把我带到了这里:https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/CompactibleFileStreamLog.scala它说缺少.compact文件应该触发默认值 .

因此尝试:spark.conf.set(“spark.sql.streaming.fileSink.log.cleanupDelay”,60000)以确保在新批处理创建组合元数据文件之前未删除旧批处理元数据

使这个错误烦人的原因是它并不总是可重现的 . 在不更改代码中的单个字符的情况下,写入镶木地板有时会起作用,或者不起作用 . 我已经尝试清理检查点位置,spark / hdfs日志等,以防火花内部的“状态”导致此问题 .

这是错误堆栈跟踪:

query: org.apache.spark.sql.streaming.StreamingQuery = org.apache.spark.sql.execution.streaming.StreamingQueryWrapper@56122c1

18/04/09 20:20:04 ERROR FileFormatWriter: Aborting job null.
java.lang.IllegalStateException: history/1523305060336/_spark_metadata/9.compact doesn't exist when compacting batch 19 (compactInterval: 10)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
        at scala.Option.getOrElse(Option.scala:121)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:173)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:172)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
        at scala.collection.immutable.NumericRange.foreach(NumericRange.scala:73)
        at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
        at scala.collection.AbstractTraversable.map(Traversable.scala:104)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.compact(CompactibleFileStreamLog.scala:172)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.add(CompactibleFileStreamLog.scala:156)
        at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol.commitJob(ManifestFileCommitProtocol.scala:64)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:207)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)
        at org.apache.spark.sql.execution.streaming.FileStreamSink.addBatch(FileStreamSink.scala:123)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$1.apply$mcV$sp(StreamExecution.scala:666)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$1.apply(StreamExecution.scala:666)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$1.apply(StreamExecution.scala:666)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch(StreamExecution.scala:665)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(StreamExecution.scala:306)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:294)
        at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:290)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:206)
18/04/09 20:20:04 ERROR StreamExecution: Query [id = 5251fe93-2b6b-4dff-bec3-7801dc7e6417, runId = 083547c1-69b7-40e7-8bf9-3c3af11d4c31] terminated with error
org.apache.spark.SparkException: Job aborted.
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:213)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:166)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:166)
        at org.apache.spark.sql.execution.streaming.FileStreamSink.addBatch(FileStreamSink.scala:123)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$1.apply$mcV$sp(StreamExecution.scala:666)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$1.apply(StreamExecution.scala:666)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch$1.apply(StreamExecution.scala:666)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch(StreamExecution.scala:665)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(StreamExecution.scala:306)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1$$anonfun$apply$mcZ$sp$1.apply(StreamExecution.scala:294)
        at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:279)
        at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:294)
        at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
        at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:290)
        at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:206)
Caused by: java.lang.IllegalStateException: history/1523305060336/_spark_metadata/9.compact doesn't exist when compacting batch 19 (compactInterval: 10)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
        at scala.Option.getOrElse(Option.scala:121)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:173)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:172)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
        at scala.collection.immutable.NumericRange.foreach(NumericRange.scala:73)
        at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
        at scala.collection.AbstractTraversable.map(Traversable.scala:104)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.compact(CompactibleFileStreamLog.scala:172)
        at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.add(CompactibleFileStreamLog.scala:156)
        at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol.commitJob(ManifestFileCommitProtocol.scala:64)
        at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply$mcV$sp(FileFormatWriter.scala:207)
        ... 20 more

2 回答

  • 0

    事实证明,S3不支持Spark检查点所需的read-after-write语义 .

    This article建议使用AWS EFS进行检查点 .

    S3仍然是从中摄取数据或从中获取数据的好地方 .

  • 0

    我通过清除检查点路径解决了这个问题:

    • 删除检查点路径:

    sudo -u hdfs hdfs dfs -rmr ${your_checkpoint_path}

    • 重新提交你的火花工作 .

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