You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2017/11/22 22:46:00 UTC
[jira] [Resolved] (SPARK-22584) dataframe write partitionBy out of
disk/java heap issues
[ https://issues.apache.org/jira/browse/SPARK-22584?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-22584.
-------------------------------
Resolution: Not A Problem
This doesn't sound like a bug. Running out of memory is 'normal' in that you'll often need to tune and pay attention to data distribution.
> dataframe write partitionBy out of disk/java heap issues
> --------------------------------------------------------
>
> Key: SPARK-22584
> URL: https://issues.apache.org/jira/browse/SPARK-22584
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Derek M Miller
>
> I have been seeing some issues with partitionBy for the dataframe writer. I currently have a file that is 6mb, just for testing, and it has around 1487 rows and 21 columns. There is nothing out of the ordinary with the columns, having either a DoubleType or StringType. The partitionBy calls two different partitions with verified low cardinality. One partition has 30 unique values and the other one has 2 unique values.
> ```scala
> df
> .write.partitionBy("first", "second")
> .mode(SaveMode.Overwrite)
> .parquet(s"$location$example/$corrId/")
> ```
> When running this example on Amazon's EMR with 5 r4.xlarges (30 gb of memory each), I am getting a java heap out of memory error. I have maximizeResourceAllocation set, and verified on the instances. I have even set it to false, explicitly set the driver and executor memory to 16g, but still had the same issue. Occasionally I get an error about disk space, and the job seems to work if I use an r3.xlarge (that has the ssd). But that seems weird that 6mb of data needs to spill to disk.
> The problem mainly seems to be centered around two + partitions vs 1. If I just use either of the partitions only, I have no problems. It's also worth noting that each of the partitions are evenly distributed.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org