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Thanks! Merging to master/2.2
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Really appreciate your patience and your work! This is how we work in Spark
SQL. Thanks!
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I learned a lot from you, thanks all.
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LGTM except a few minor comments. The fix looks great now! Thanks everyone!
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retest this please.
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@10110346 Thanks for working this! Sorry I've confused you in previous
comments. Current changes looks good to me.
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LGTM. cc @gatorsmile for final check.
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LGTM
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Github user 10110346 commented on the issue:
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ok,thanks @viirya
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Github user viirya commented on the issue:
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@10110346 As using `resolveOperators` can solve the whole bug, let's do it
and simplify the whole change. Sorry for confusing.
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Github user maropu commented on the issue:
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We need to backport this issue to branch-2.2? I think the opinion depends
on the backport decision. If no, I'm with your suggestion (keep this issue as a
blocker for branch-2.3).
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Github user viirya commented on the issue:
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Well, maybe we should revisit this after #17770 gets merged. Because after
that, we won't go through analyzed plans anymore.
At that time, we can simply solve all the issues by making
Github user viirya commented on the issue:
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@10110346 I deleted the previous comment because I find I forgot that
`resolveOperators` will be removed by #17770 in near future.
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Sorry @10110346 would you mind to try and see if
https://github.com/apache/spark/pull/18779#discussion_r131287607 works to solve
all the issues.
If it could, that maybe more simple.
Github user maropu commented on the issue:
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It might help to document this in the Dataset `groupBy` comment.
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Github user gatorsmile commented on the issue:
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Unfortunately, our Dataset APIs support it. We have to keep the support.
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Github user maropu commented on the issue:
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Aha, I see... I'm not sure this is an expected behaviour though, yea, we
might have the compatibility issue as you said. I feel, if we explicitly
support `group-by-ordinal` in dataset apis, the
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@maropu Because we already support it in Dataset API as the example
https://github.com/apache/spark/pull/18779#issuecomment-319880753 shows, I'm
afraid that there are users using this feature. If we
Github user maropu commented on the issue:
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@viirya btw, we still need to support group/sort-by-ordinal in Dataset? If
this pr merged, the behaviour seems to change. I feel the syntax in Dataset is
a little confusing..
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@viirya great! thanks!
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retest this please
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@maropu The PR is at #17770.
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ok, thanks!
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Github user viirya commented on the issue:
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@maropu I have a PR before to solve that. Due to some reasons it will be
merged on 2.3. I am out of laptop, will refer it once I can access laptop.
On Aug 3, 2017 5:07 PM,
Github user maropu commented on the issue:
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@gatorsmile @viirya I looked into why it applied some analyzer rules into
already-analyzed plans and I noticed that some rules used
`transform/transformUp` instead of `resolveOperators` in `apply`.
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@gatorsmile
scala> df.groupBy(lit(2)).agg(col("a")).queryExecution.logical
res6: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
'Aggregate [2], [2 AS 2#51,
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When using Dataset groupBy API, if you use int literals as grouping
expressions, do we filter this case out for substituting `UnresolvedOrdinals`?
Seems there is no related logic to prevent it.
Github user gatorsmile commented on the issue:
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Our Dataset APIs support `conf.groupByOrdinal`? If so, this might surprise
me. `conf.groupByOrdinal` was introduced for SQL APIs only.
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Let's remove the rule `SubstituteUnresolvedOrdinals` and move all the
`UnresolvedOrdinal` stuffs (order by and group by) into parser.
Btw, also don't forget Dataset API, please see
Github user gatorsmile commented on the issue:
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Thank you for finding the root cause. @maropu
Moving them to the parser sounds reasonable to me, but we also should avoid
analyzing the analyzed plan again. Thanks for your works!
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I think this will fail the test case in
`SubstituteUnresolvedOrdinalsSuite`.
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I think it is a perfect solution,thank you very much. @viirya @maropu
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oh, yea. I feel it's ok to do so.
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Github user viirya commented on the issue:
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`AstBuilder` can access conf. So it can only replace int literals with
`UnresolvedOrdinal` when the config is enabled. Otherwise, leave it as it's.
In Analyzer, we remove the rule
Github user maropu commented on the issue:
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yea, it is like; it adds `UnresolvedOrdinal` in `AstBuilder` and, if
`conf.groupByOrdinal`=false, analyzer drops `UnresolvedOrdinal`. Since
`conf.groupByOrdinal`=true by defualt, dropping
Github user viirya commented on the issue:
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Well, I think we can not avoid the various cases bringing int literals into
grouping expressions.
To fix it, I think we should not replace any int literals with
`UnresolvedOrdinal` in
Github user maropu commented on the issue:
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@10110346 Thanks, I looked into this failure and I could easily fix this
like:
https://github.com/apache/spark/compare/master...maropu:SPARK-21580#diff-57b3d87be744b7d79a9beacf8e5e5eb2R2287.
But, I
Github user viirya commented on the issue:
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@10110346 Yeah, thanks. Please confirm it. I think running
`SubstituteUnresolvedOrdinals` with `Once` should work.
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@maropu `select 3 as c, 4 as d, sum(b) from data group by c, d`
This test case still has exeception using your modification:GROUP BY
position 4 is not in select list (valid range is [1, 3]);
Github user maropu commented on the issue:
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ok, fixed:
https://github.com/apache/spark/compare/master...maropu:SPARK-21580#diff-a0f2e45a5da747e9ec483f3557aa1b8bR210
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I have ran `SubstituteUnresolvedOrdinals` rule with `Once `, it still
looks like some problems, i will confirm it
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oh, I missed. I'll re-check.
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@maropu Could you not put the test cases in the end of
`group-by-ordinal.sql `?
because it has set `spark.sql.groupByOrdinal=false; ` in the end
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@viirya yea, it does:
https://github.com/apache/spark/compare/master...maropu:SPARK-21580#diff-a0f2e45a5da747e9ec483f3557aa1b8bR210.
I also think `SubstituteUnresolvedOrdinals` should be applied
Github user viirya commented on the issue:
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After rethinking this, do we have any chance to have a group by ordinal or
order by ordinal which is not coming from user input and produced by analysis?
If the answer is no, we can simply
Github user viirya commented on the issue:
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@maropu does it solve @10110346's issue at
https://github.com/apache/spark/pull/18779#issuecomment-319601312?
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Another way to fix this is something like
https://github.com/apache/spark/compare/master...maropu:SPARK-21580.
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Actually I don't think `SubstituteUnresolvedOrdinals` rule should run with
`fixedPoint`. It should be run with `Once`.
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`k` is resolved to `4` in `ResolveAggAliasInGroupBy`,and then `4` is
resolved to `ResolvedOrdinal(4)`
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@10110346 Why?
In the query `select a, 4 AS k, count(b) from data group by k, 1`, `1` is
resolved to `ResolvedOrdinal(1)` first. Then at the beginning of optimization,
`ResolvedOrdinal(1)`
Github user 10110346 commented on the issue:
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I have updated this PR, please help to review it again. @viirya also cc
@cloud-fan @gatorsmile
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@viirya Maybe adding `ResolvedOrdinal` is not very well.
I have another problem:
`select a, **4 AS k**, count(b) from data group by k, 1;`
This test case has the same exception:
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@10110346 Can't we also do the same on order by ordinal?
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@viirya Only to `group-by ordinal`, i think this is a good idea.
but this will also result in inconsistent processing between `order-by
ordinal` and `group-by ordinal`.
and i feel
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18779
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18779
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https://github.com/apache/spark/pull/18779
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