GitHub user gatorsmile opened a pull request:
https://github.com/apache/spark/pull/15975
Fix Concurrent Table Fetching Using DataFrameReader JDBC APIs
### What changes were proposed in this pull request?
The following two `DataFrameReader` JDBC APIs ignore the user-specified
parameters of parallelism degree.
```Scala
def jdbc(
url: String,
table: String,
columnName: String,
lowerBound: Long,
upperBound: Long,
numPartitions: Int,
connectionProperties: Properties): DataFrame
```
```Scala
def jdbc(
url: String,
table: String,
predicates: Array[String],
connectionProperties: Properties): DataFrame
```
This PR is to fix the issues. To verify the behavior correctness, we
improve the plan output of `EXPLAIN` command by adding `numPartitions` in the
`JDBCRelation` node.
Before the fix,
```
== Physical Plan ==
*Scan JDBCRelation(TEST.PEOPLE) [NAME#1896,THEID#1897] ReadSchema:
struct<NAME:string,THEID:int>
```
After the fix,
```
== Physical Plan ==
*Scan JDBCRelation(TEST.PEOPLE) [numPartitions=3] [NAME#1896,THEID#1897]
ReadSchema: struct<NAME:string,THEID:int>
```
### How was this patch tested?
Added the verification logics on all the test cases for JDBC concurrent
fetching.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/gatorsmile/spark jdbc
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/15975.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 #15975
----
commit bcc86c0395ddc24cb629f46af9f985bdff0387a6
Author: gatorsmile <[email protected]>
Date: 2016-11-22T05:49:42Z
fix.
----
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