After repartitioning a DataFrame in Spark 1.3.0 I get a .parquet exception
when saving toAmazon's S3. The data that I try to write is 10G.
logsForDate
.repartition(10)
.saveAsParquetFile(destination) // <-- Exception here
The exception I receive is:
java.io.IOException: The file being written is in an invalid state. Probably
caused by an error thrown previously. Current state: COLUMN
at parquet.hadoop.ParquetFileWriter$STATE.error(ParquetFileWriter.java:137)
at
parquet.hadoop.ParquetFileWriter$STATE.startBlock(ParquetFileWriter.java:129)
at parquet.hadoop.ParquetFileWriter.startBlock(ParquetFileWriter.java:173)
at
parquet.hadoop.InternalParquetRecordWriter.flushRowGroupToStore(InternalParquetRecordWriter.java:152)
at
parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:112)
at parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:73)
at
org.apache.spark.sql.parquet.ParquetRelation2.org$apache$spark$sql$parquet$ParquetRelation2$$writeShard$1(newParquet.scala:635)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:649)
at
org.apache.spark.sql.parquet.ParquetRelation2$$anonfun$insert$2.apply(newParquet.scala:649)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
at org.apache.spark.scheduler.Task.run(Task.scala:64)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
I would like to know what is the problem and how to solve it.
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