Derek M Miller created SPARK-22584:
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Summary: 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 String 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),
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.
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