GitHub user yhuai opened a pull request:
https://github.com/apache/spark/pull/6252
[SPARK-7713] [SQL] Use shared broadcast hadoop conf for partitioned table
scan.
https://issues.apache.org/jira/browse/SPARK-7713
I tested the performance with the following code:
```scala
import sqlContext._
import sqlContext.implicits._
(1 to 5000).foreach { i =>
val df = (1 to 1000).map(j => (j, s"str$j")).toDF("a",
"b").save(s"/tmp/partitioned/i=$i")
}
sqlContext.sql("""
CREATE TEMPORARY TABLE partitionedParquet
USING org.apache.spark.sql.parquet
OPTIONS (
path '/tmp/partitioned'
)""")
table("partitionedParquet").explain(true)
```
In our master `explain` takes 40s in my laptop. With this PR, `explain`
takes 14s.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/yhuai/spark broadcastHadoopConf
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/6252.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 #6252
----
commit 9e7c3cd1984bcee9e5538c72f556ffd8d91f3f23
Author: Yin Huai <[email protected]>
Date: 2015-05-19T02:19:41Z
Use a shared broadcast Hadoop Configuration for partitioned
HadoopFsRelations.
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