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--- Diff: core/src/main/scala/org/apache/spark/status/LiveEntity.scala ---
@@ -254,6 +255,7 @@ private class LiveExecutor(val executorId: String,
Github user foxish commented on the issue:
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Makes sense. The change LGTM.
On Jan 29, 2018 10:23 AM, "Jiang Xingbo" wrote:
> IIUC there was a issue in launching Thrift Server on YARN cluster mode,
Github user icexelloss commented on the issue:
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@gatorsmile I don't think any change of naming (group map, group agg, etc)
has been agreed upon yet. We can certainly open an PR to discuss it.
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ok to test
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[SPARK-23261] [PySpark] Rename Pandas UDFs
## What changes were proposed in this pull request?
Rename the public APIs and names of pandas udfs.
- `PANDAS SCALAR UDF` -> `SCALAR
Github user BryanCutler commented on the issue:
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Thanks @ueshin and @HyukjinKwon , those fixes look reasonable to backport
so sounds good to me. I'll run some tests too and then add to this PR.
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* `PANDAS SCALAR UDF` -> `SCALAR PANDAS UDF`
This doesn't really change the API so +1
* `PANDAS GROUP MAP UDF` -> `GROUPED MAP PANDAS UDF`
The API changes from
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Github user vanzin commented on the issue:
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Let's try different people. @zsxwing @squito
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retest this please
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LGTM
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Github user gatorsmile commented on the issue:
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Had an offline discussion with @sameeragarwal and @cloud-fan . To be
consistent with the other APIs, we would propose to make the above changes.
The major question is about the current
Github user squito commented on the issue:
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I am merging this now to master & 2.3
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Github user squito commented on the issue:
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code looks good to me, but lets what @tgravescs @ajbozarth say.
@ajbozarth it is wordy, but I think `Active (Blacklisted in Stages: [...])`
is probably the best of the options so far.
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@ajbozarth Ok. For tomorrow I can update both the label and the screenshots.
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--- Diff: core/src/main/scala/org/apache/spark/status/LiveEntity.scala ---
@@ -254,6 +255,7 @@ private class LiveExecutor(val executorId: String,
_addTime:
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@@ -594,12 +606,24 @@ private[spark] class AppStatusListener(
Github user gatorsmile commented on the issue:
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@icexelloss Thanks for your reply. Welcome your comment in my PR
https://github.com/apache/spark/pull/20428
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Github user ajbozarth commented on the issue:
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@jiangxb1987 I agree that this is a behavior change, but it's a behavior
change back to how it was before we switched the page from scala to js. This
LGTM
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GitHub user henryr opened a pull request:
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[SPARK-23157][SQL] Explain restriction on column expression in withColumn()
## What changes were proposed in this pull request?
It's not obvious from the comments that any added column must
Github user squito commented on the issue:
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lgtm
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--- Diff:
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@@ -25,7 +25,7 @@
* session.
*/
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Github user ajbozarth commented on the issue:
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I agree that despite being wordy `Active (Blacklisted in Stages: [...])`
looks best, could you update it and post screenshots to confirm?
And if @squito says the code looks good I trust
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Github user mridulm commented on the issue:
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@shivaram Thinking more, this might affect everything which does a zip (or
variants/similar idioms like limit K, etc) on partition should be affected -
with random + index in coalesce +
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Github user mridulm commented on the issue:
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@sameeragarwal Interesting, this is still assuming that shuffle (after
fetch) is stable, right ? Is this gauranteed in face of memory pressure/spills ?
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Github user rednaxelafx commented on the issue:
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@kiszk SGTM and LGTM. Let's ship it!
One more question on the side: with the `forceComment = true`, are we fully
sure that won't affect the equality of `CodeAndComment`?
The whole
Github user vanzin commented on a diff in the pull request:
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Github user cloud-fan commented on the issue:
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Actually, aggregation can only be executed on grouped data, so
`SQL_PANDAS_GROUPED_AGG_UDF` doesn't seem to be very concise. How about
`SQL_PANDAS_UDAF`? My only concern is how to support partial
Github user rdblue commented on the issue:
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> I think the renaming is worth to remove future confusions.
What future confusion?
I understand that the difference isn't obvious, but making the names less
accurate isn't a good
Github user BryanCutler commented on the issue:
https://github.com/apache/spark/pull/19575
Thanks @gatorsmile , I made
https://issues.apache.org/jira/browse/SPARK-23258 to track changing the
`maxRecordsPerBatch` conf and I will externalize it in this PR.
> group map ->
Github user gatorsmile commented on the issue:
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Thanks! Merged to master/2.3.
This PR is just to reflect the latest changes in the join selection rule.
We can continue the improvement in the comment.
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Thanks to everyone for contributing and reviewing!
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Can one of the admins verify this patch?
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cc @cloud-fan @sameeragarwal
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Github user cloud-fan commented on the issue:
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About the renaming, a lot of people complained to me about why the namings
are not consistent, including @rxin . I named it `ReadTask` at the beginning
because it really works like a task. But I
Github user gatorsmile commented on the issue:
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Thanks! I will submit a follow-up PR to rename it.
Merged to 2.3 and master.
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IIUC there was a issue in launching Thrift Server on YARN cluster mode, and
I'm not sure whether it has been fixed (maybe @jerryshao can kindly check
that?) Anyway that is not a problem on
Github user gatorsmile commented on the issue:
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Thanks for submitting this follow-up PR. Originally, I planned to do it
with the other renaming work.
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Github user gerashegalov commented on the issue:
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thanks for feedback, Marcelo. I was also thinking back and forth where to
put this logic. Ideally YARN should provide a permalink similar to AM proxy for
logs as well. However, this was faster
Github user rdblue commented on a diff in the pull request:
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--- Diff:
sql/core/src/main/java/org/apache/spark/sql/sources/v2/ReadSupport.java ---
@@ -18,23 +18,23 @@
package org.apache.spark.sql.sources.v2;
Github user debugger87 commented on the issue:
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https://github.com/apache/spark/pull/19721 Fixed the same issue, i will
close it.
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[SPARK-1][SQL][FOLLOWUP] Externalize
spark.sql.execution.arrow.maxRecordsPerBatch
## What changes were proposed in this pull request?
This is a followup to #19575 which added a
Github user jiangxb1987 commented on the issue:
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@mridulm I also agree we should follow @sameeragarwal 's suggestion to let
shuffle fetch produce deterministic output, and only do this for a few
operations (e.g. repartition/zipWithIndex, do
Github user mridulm commented on the issue:
https://github.com/apache/spark/pull/20414
@jiangxb1987 Unfortunately I am unable to analyze this in detail; but
hopefully can give some pointers, which I hope, helps !
One example I can think of is, for shuffle which uses
Github user rdblue commented on the issue:
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> I'd suggest that we just propogate the paths parameter to options, and
data source implementations are free to interprete the path option to whatever
they want, e.g. table and database names.
Github user rdblue commented on the issue:
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> [The push-down rule may be run more than once if filters are not pushed
through projections] looks weird, do you have a query to reproduce this issue?
One of the DataSourceV2 tests hit
Github user sameeragarwal commented on the issue:
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@vanzin and reviewers -- is this ready to go? We're waiting on RC3 for
this. Thanks!
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--- Diff: docs/sql-programming-guide.md ---
@@ -1640,6 +1640,133 @@ Configuration of Hive is done by placing your
`hive-site.xml`, `core-site.xml` a
Github user cloud-fan commented on the issue:
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I dig into the commit history and recalled why I made these decisions:
* having an mutable `DataSourceV2Relation`. This is mostly to avoid to keep
adding more constructor parameters to
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Github user vanzin commented on the issue:
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I was hoping that one of the other committers who +1'ed the patch would
push it instead of me. (Ignoring the info vs. debug discussion.)
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Github user cloud-fan commented on the issue:
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My point is, `ReadTask` is more precise, but `DataReaderFactor` also
works(Spark serialize and send it to executors, and ask it to create data
reader, it's reasonable to call it a factory). If we
Github user BryanCutler commented on the issue:
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cc @gatorsmile
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GitHub user bersprockets opened a pull request:
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[Spark 23240][python] Better error message when extraneous data in
pyspark.daemon's stdout
## What changes were proposed in this pull request?
Print more helpful message when daemon
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