GitHub user zero323 opened a pull request:
https://github.com/apache/spark/pull/9099
[SPARK-11086][SPARKR] Use dropFactors column-wise instead of nested loop
when createDataFrame
Use `dropFactors` column-wise instead of nested loop when `createDataFrame`
from a `data.frame`
At this moment SparkR createDataFrame is using nested loop to convert
factors to character when called on a local data.frame. It works but is
incredibly slow especially with data.table (~ 2 orders of magnitude compared to
PySpark / Pandas version on a DateFrame of size 1M rows x 2 columns).
A simple improvement is to apply `dropFactor `column-wise and then reshape
output list.
It should at least partially address
[SPARK-8277](https://issues.apache.org/jira/browse/SPARK-8277).
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/zero323/spark SPARK-11086
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/9099.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #9099
----
commit 91f1bb35bce6298e9fc30f5affb057d12aac6c26
Author: zero323 <[email protected]>
Date: 2015-10-13T17:35:24Z
Use dropFactors column-wise instead of nested loop when createDataFrame
from a data.frame
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