GitHub user nongli opened a pull request:
https://github.com/apache/spark/pull/12243
[SPARK-14467][SQL] Interleave CPU and IO better in FileScanRDD.
## What changes were proposed in this pull request?
This patch updates FileScanRDD to start reading from the next file while
the current file
is being processed. The goal is to have better interleaving of CPU and IO.
It does this
by launching a future which will asynchronously start preparing the next
file to be read.
The expectation is that the async task is IO intensive and the current file
(which
includes all the computation for the query plan) is CPU intensive. For some
file formats,
this would just mean opening the file and the initial setup. For file
formats like
parquet, this would mean doing all the IO for all the columns.
## How was this patch tested?
Good coverage from existing tests. Added a new one to test the flag.
Cluster testing on tpcds queries.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/nongli/spark interleave
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/12243.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 #12243
----
commit cc6d98a17f6fa4249951802f981c2224d354e651
Author: Nong Li <[email protected]>
Date: 2016-04-05T20:36:34Z
[SPARK-14467][SQL] Interleave CPU and IO better in FileScanRDD.
This patch updates FileScanRDD to start reading from the next file while
the current file
is being processed. The goal is to have better interleaving of CPU and IO.
It does this
by launching a future which will asynchronously start preparing the next
file to be read.
The expectation is that the async task is IO intensive and the current file
(which
includes all the computation for the query plan) is CPU intensive. For some
file formats,
this would just mean opening the file and the initial setup. For file
formats like
parquet, this would mean doing all the IO for all the columns.
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