GitHub user falaki opened a pull request:
https://github.com/apache/spark/pull/15375
[SPARK-17790] Support for parallelizing R data.frame larger than 2GB
## What changes were proposed in this pull request?
If the R data structure that is being parallelized is larger than `INT_MAX`
we use files to transfer data to JVM. The serialization protocol mimics Python
pickling. This allows us to simply call `PythonRDD.readRDDFromFile` to create
the RDD.
I tested this on my MacBook. Following code works with this patch:
```R
intMax <- .Machine$integer.max
largeVec <- 1:intMax
rdd <- SparkR:::parallelize(sc, largeVec, 2)
```
## How was this patch tested?
* [ ] Unit tests
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/falaki/spark SPARK-17790
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/15375.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 #15375
----
commit 140755c5934e49870bc0ee4e44149db1a2fcda73
Author: Hossein <[email protected]>
Date: 2016-10-06T05:28:59Z
Using temp file for prallelizing large R objects
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