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https://issues.apache.org/jira/browse/SPARK-18227?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Michael Armbrust resolved SPARK-18227.
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Resolution: Not A Problem
> Parquet file stream sink create a hidden directory "_spark_metadata" cause
> the DataFrame read from directory failed
> -------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-18227
> URL: https://issues.apache.org/jira/browse/SPARK-18227
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.0.1
> Reporter: Lantao Jin
>
> When we set an out directory as a streaming sink with parquet format in
> structured streaming, as the streaming job running, all output parquet files
> will be written to this out directory. However, it also creates a hidden
> directory called "_spark_metadata" in the out directory. If we load the
> parquet files from the out directory by "load", it will throw
> RuntimeException and task failed.
> {code:java}
> val stream = modifiedData.writeStream.format("parquet")
> .option("checkpointLocation", "/path/ck/")
> .start("/path/out/")
> val df1 = spark.read.format("parquet").load("/path/out/*")
> {code}
> {panel}
> 16/11/02 03:49:40 WARN TaskSetManager: Lost task 1.0 in stage 110.0 (TID
> 3131, cupid044.stratus.phx.ebay.com): java.lang.Ru
> ntimeException: hdfs:///path/out/_spark_metadata/0 is not a Parquet file (too
> s
> mall)
> at
> org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:412)
> at
> org.apache.parquet.hadoop.ParquetFileReader.readFooter(ParquetFileReader.java:385)
> at
> org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase.initialize(SpecificParquetRec
> ordReaderBase.java:107)
> at
> org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initialize(VectorizedParquetRec
> ordReader.java:109)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFor
> mat.scala:367)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFor
> mat.scala:341)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:116)
> at
> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:91)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithKeys$(Unknown
> Sour
> ce)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at
> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
> {panel}
> That's because the ParquetFileReader reads the metadata file as a parquet
> format.
> I thought the smooth way to fix it is moving the metadata directory to
> another path, but from the code DataSource.scala, it has less path
> information except out directory path to store into. So maybe skipping hidden
> files and paths could be a better way. But from the stack trace above, it
> failed in initialize() in SpecificParquetRecordReaderBase. It means that
> metadata files in hidden directory have been traversed in upper
> invocation(FileScanRDD). But in there, no format info can be known to skip a
> hidden directory(or over authority).
> So, what is the best way to fix it?
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