My Structured Streaming application is writing to parquet and i want to get rid of the _spark_metadata folder its creating. I used below property and it seems fine
--conf "spark.hadoop.parquet.enable.summary-metadata=false"
When the application starts no _spark_metadata folder is generated. But once it moves to RUNNING status and starts processing messages, it's failing with the below error saying _spark_metadata folder doesn't exist. Seems structured stream is relying on this folder without which we can't run. Just wondering if disabling metadata property makes any sense in this context. Is this a bug that the stream is not referring to the conf?
Caused by: java.io.FileNotFoundException: File /_spark_metadata does not exist.
at org.apache.hadoop.fs.Hdfs.listStatus(Hdfs.java:261)
at org.apache.hadoop.fs.FileContext$Util$1.next(FileContext.java:1765)
at org.apache.hadoop.fs.FileContext$Util$1.next(FileContext.java:1761)
at org.apache.hadoop.fs.FSLinkResolver.resolve(FSLinkResolver.java:90)
at org.apache.hadoop.fs.FileContext$Util.listStatus(FileContext.java:1761)
at org.apache.hadoop.fs.FileContext$Util.listStatus(FileContext.java:1726)
at org.apache.hadoop.fs.FileContext$Util.listStatus(FileContext.java:1685)
at org.apache.spark.sql.execution.streaming.HDFSMetadataLog$FileContextManager.list(HDFSMetadataLog.scala:370)
at org.apache.spark.sql.execution.streaming.HDFSMetadataLog.getLatest(HDFSMetadataLog.scala:231)
at org.apache.spark.sql.execution.streaming.FileStreamSink.addBatch(FileStreamSink.scala:99)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:477)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:475)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
the reason this was happening is that the kafkacheckpoint folder was not cleanedup. the files inside the kafka checkpointing was cross referencing the spark metadata files and failing .once i removed both it started working