Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/13773
@srowen Will submit more PRs about `JDBC`. The interface of
`DataFrameReader` and `DataFrameWriter` are not designed for `JDBC` data
sources. For Spark SQL beginners, they might hit various strange errors.
Anyway, will try to create less JIRAs, but, to be honest, in my previous
team, the JIRA-like defect tracking system is used to record the defects. We
always create multiple defects when they have different external impacts. It is
very bad for us to combine multiple issues into the same one. When each fixpack
or release is published, our customers, L2 and L3 might use it to know what are
included in the specific fixpack. Below is an example:
http://www-01.ibm.com/support/docview.wss?uid=swg21633303 There are a long
list. In Spark, all the JDBC related JIRAs can be classified into the same
group, but we should not combine multiple defects into the same one. In my
previous team, we always have to provide very clear titles for each
JIRA/defect. Users might not be patient to click the link to read the details.
I think the same logics is also applicable to Spark.
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