Parquet error when saving from Spark

Question

After repartitioning a DataFrame in Spark 1.3.0 I get a .parquet exception when saving to Amazon's S3.

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.

Answer 1

I can actually reproduce this problem with Spark 1.3.1 on EMR, when saving to S3.

However, saving to HDFS works fine. You could save to HDFS first, and then use e.g. s3distcp to move the files to S3.

Answer 2

I faced with this error when saveAsParquetFile into HDFS. It was because datanode socket write timeout, therefore I change it to a longer one in Hadoop Settings:

<property>
  <name>dfs.datanode.socket.write.timeout</name>
  <value>3000000</value>
</property>
<property>
  <name>dfs.socket.timeout</name>
  <value>3000000</value>
</property> 

Hope this helps if you could set S3 like this.