Github user dongjoon-hyun commented on the issue:
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@gatorsmile , @hvanhovell . Yep. I knew the policy about new feature issues
and bug fix issues. So, I wish this PR is considered as bug fix issue from the
beginning. Ya, it's a new feature ind
Github user hvanhovell commented on the issue:
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@dongjoon-hyun the risk of this PR breaking something is quite low; so I am
open to backporting as long as long as it does not create too much issues. BTW:
did you open a backport? this was again
Github user gatorsmile commented on the issue:
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When implementing the native DDL support, we blocked a few DDL statements
due to the resource limit. Thus, this PR is not a bug fix. In theory, we should
not add new features into the previous re
Github user dongjoon-hyun commented on the issue:
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Thank you so much, @hvanhovell , @gatorsmile , and @skambha !
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Oh, I see. Does that mean it's not allowed to be backported due to the
policy although I made a patch for branch-2.0?
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Github user hvanhovell commented on the issue:
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@dongjoon-hyun we made the decision to regress here willingly. So I think
it is a new feature, also this is currently not backportable.
LGTM. Merging to master. Thanks.
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Github user dongjoon-hyun commented on the issue:
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Hi, @hvanhovell . I thought it's a regression bug fix.
I'm wondering if you think this is a new feature for Spark 2.1.
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Hi, @hvanhovell .
Could you review this again?
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Finally, it passed! @hvanhovell .
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Now, the PR description is also up-to-date.
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Never mind~. Thank you for coming back! I'll check the PR description again
and make up-to-date.
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Github user hvanhovell commented on the issue:
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@dongjoon-hyun sorry about that. Is the PR description (including all the
exceptions) up to date?
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Oh, conflicts occurs due to https://github.com/apache/spark/pull/15234 .
I'll resolve this soon.
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Hi, @hvanhovell .
Could you review this PR again, please?
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Hi, @hvanhovell .
Could you review this PR again when you have some time?
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I addressed those two comments. Thank you for thoughtful review,
@gatorsmile !
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LGTM except two minor comments and pending tests. cc @hvanhovell
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I see. I'll prevent that from those persistent views, too.
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Github user gatorsmile commented on the issue:
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I think we do not need an extra metastore call if we already know we do not
support partitions over views. You can check all the other DDL implementation.
We did the same thing.
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Github user dongjoon-hyun commented on the issue:
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For example, like the followings.
```
scala> sql("desc view1 partition (c='Us')").show
org.apache.spark.sql.AnalysisException: Partition spec is invalid. The spec
(c) must match the
Github user dongjoon-hyun commented on the issue:
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If we are talking about hive views (for non-partitioned persistent view),
there is no partition info because it looks like a normal table. So, my PR
handles that equally with normal tables.
Github user gatorsmile commented on the issue:
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We do not need to support partitioned views, I think. Your PR is unable to
handle it without a code change in `HiveClientImpl`.
For non-partitioned persistent view, you should also issue
Github user dongjoon-hyun commented on the issue:
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Do you mean PartitionedView as you mentioned at #15233 ?
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How about the persistent view? I think we do not support it too, right?
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Definitely! Thank you, @gatorsmile . I added the logic to cover that.
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Github user gatorsmile commented on the issue:
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This PR needs to cover more negative cases. Below is an example:
```Scala
spark.range(10).select('id as 'a, 'id as 'b).createTempView("view1")
sql("DESC view1 PARTITION (c='Us',
Github user dongjoon-hyun commented on the issue:
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Thank you for review @gatorsmile .
Hi, @hvanhovell .
Could you review this again?
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Yep. That's would be great. Thank you for guidance always!
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Github user gatorsmile commented on the issue:
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Thank you for your investigation! How about generating a user-friendly
error message in these unsupported cases? The error messages from Hive is very
confusing to the external users.
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Github user dongjoon-hyun commented on the issue:
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Hi, @gatorsmile . I investigated the related PRs.
* #12934 `[SPARK-14127][SQL] Makes 'DESC [EXTENDED|FORMATTED] '
support data source tables`
* #12844 `[SPARK-14127][SQL] Native
Github user gatorsmile commented on the issue:
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Partitioned View is another potential issue for this PR.
FYI, just submitted a related PR https://github.com/apache/spark/pull/15168
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Sure. I'll investigate from that. Thank you for the pointer, @gatorsmile .
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Github user gatorsmile commented on the issue:
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Yeah, please change the JIRA to improvement.
I did not check the code of Spark 1.6.2, but, since Spark 2.0, we provide a
native support for `DESC TABLE`. See the PR:
https://github.com/
Github user dongjoon-hyun commented on the issue:
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Actually, I think there was a reason not to do that in Spark 1.6.2. In
Spark 2.0, is there any new way to lookup the partition info from Parquet
Datasource through `SessionCatalog`? `SessionCa
Github user gatorsmile commented on the issue:
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Yeah, I knew. : ) This PR is to support `DESCRIBE PARTITION`, right? The
other `DESCRIBE` commands can support all the table types. Thus, I believe we
need to support all the cases for `DESCRIBE
Github user dongjoon-hyun commented on the issue:
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Hi, @gatorsmile .
It's the same behavior with Apache Spark 1.6.2. This PR doesn't make any
regression for Datasource tables. Actually, the error you mentioned is raised
by `HiveMetast
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Thank you, @gatorsmile . I'll fix that soon.
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It sounds like the implementation of this PR does not support data source
tables. Please try the following example.
```Scala
spark
.range(1).select('id as 'a, 'id as
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retest this please
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The failure seems to be irrelevant. Retest this please.
```
[info] - Naive Bayes Multinomial *** FAILED *** (137 milliseconds)
[info] Expected 0.7 and 0.6494565217391305 to be with
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Oh, my bad.
The only failure is `describe.sql`. We can not use `SQLQueryTestSuite`
since the last access time is always different.
```
describe.sql *** FAILED *** (272 milliseconds)
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https://github.com/apache/spark/pull/15168
Merged build finished. Test FAILed.
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/15168
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Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15168
Thank you, @gatorsmile .
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/15168
I also like this feature. The Hive-generated statistics for partitioned
tables are stored in partition-level properties. See the PR:
https://github.com/apache/spark/pull/15158
We definit
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15168
Hi, @hvanhovell . I updated the followings.
- Add 'EXTENDED' and 'FORMATTED' description for 'PARTITION'.
- Move the tests into SQLQueryTestSuite. `describe.sql`. Please note that
the s
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/15168
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Github user SparkQA commented on the issue:
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Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15168
Hi, @hvanhovell .
Currently, `DESC FORMATTED TABLE` and `DESC EXTENDED TABLE` shows a little
bit different from Apache Spark 1.6. I think it's improved a little. I'm
revising the DETAIL/EX
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15168
Thank you, @hvanhovell . Yes, currently, as you mentionedas `EXTEDNED` or
`FORMATTED` is not included in this PR since I wasn't sure that Spark 2.0 want
to fix this regression.
Like S
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15168
Hi, @hvanhovell .
Could you review this PR when you have some time?
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Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/15168
Hi, @rxin .
Could you review this PR to implement `DESCRIBE table PARTITION`?
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