Github user viirya commented on the issue:
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@WeichenXu123 Sure. And I must point out that I ran this benchmark in
spark-shell under local mode. It is great if you can run the benchmark too to
verify the numbers.
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Github user viirya commented on the issue:
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Updated test codes:
import org.apache.spark.ml.feature._
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.types._
import spark.implicits._
Github user tgravescs commented on the issue:
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It looks like zstd-jni has now been updated to pull 1.3.1
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New numbers:
numColums | Old Mean | Old Median | New Mean | New Median
-- | -- | -- | -- | --
1 | 0.1727832915997 | 0.1537169693 | 0.1687325048997 | 0.1521283075
10 |
Github user viirya commented on the issue:
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@WeichenXu123 I'm ok for that but I think adding an interface doesn't break
binary compatibility?
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@viirya It is possible I think. A similar example is, `HasRegParam` trait,
do not put `setRegParam` in trait but moved into concrete estimator/transformer
class, should be the same reason.
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CC @maropu
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@viirya Yes. But if there is some better design I will be happy to listen.
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--- Diff: sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
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Great! That's it. thanks!
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I don't think re-using shuffle is the reason behind the numbers. If you
looked at the previous comments, you will find that I ran the test before
without `count` after `model.transform`. Namely the
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[SPARK-22033][CORE] BufferHolder, other size checks should account for the
specific VM array size limitations
## What changes were proposed in this pull request?
Try to avoid allocating an
Github user WeichenXu123 commented on the issue:
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@viirya I guess the reason is, the old PR version:
`df.withColumn(..).withColumn(..).withColumn(..)`, the long df chain
prevent the shuffle re-using... but now you merge them into one
Github user viirya commented on the issue:
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Btw, I don't see any hint about
`df.withColumn(..).withColumn(..).withColumn(..)` can prevent the shuffle
re-using.
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@WeichenXu123 Btw, the test is basically re-using the codes from
https://github.com/apache/spark/pull/18902#issuecomment-321727416. Is your
concern is specified for this?
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retest this please
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@gatorsmile Dunno, but the logical operator does.
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[SPARK-22026][SQL][WIP] data source v2 write path
## What changes were proposed in this pull request?
A working prototype for data source v2 write path.
TODO: doc.
##
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@WeichenXu123, thank you for your prompt reply!
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Sure, we all agree there is a mechanism for avoiding overhead. However,
performance tests are very tricky things, 5% is not a huge improvement, and
hard-coding the aggregation depth to `2` limits
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+/*
+ * Licensed to
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Will do. Delete this pull afterwards?
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--- Diff:
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Github user srowen commented on the issue:
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No way to make the change without a PR, so no leave it.
http://spark.apache.org/contributing.html
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- You can get it from the table metadata `table: CatalogTable`
- `Insertintohadoopfsrelationcommand.scala ` is for data source tables. We
only have the issues for Hive table writing,
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2142,18 +2159,26 @@ def udf(f=None, returnType=StringType()):
| 8| JOHN DOE|
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Can one of the admins verify this patch?
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private final long length;
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HashMapGrowthStrategy DOUBLING = new
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Thanks! Merged to master.
Could you resolve the above comments in the follow-up PR? Thanks!
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Github user ajbozarth commented on the issue:
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It looks like you have a bunch of unrelated code in this PR, this seems to
be caused by how you're doing development. You've opened this PR from your
master branch and it includes work on 3 other
Github user dhruve commented on the issue:
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@tgraves I have addressed the comments and tried to cover the possible
cases in the existing test for job groups and speculation. Kindly let me know
if we need to add or address more use cases.
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* has
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Thanks, I'll try to review this by EOD tomorrow
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@zhengruifeng Can you please update the PR description so it describes the
actual functionality being added?
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@WeichenXu123 Thanks for finding that bug! Can you please separate out
your bugfix? It's good to get fixes in, rather than attaching them to PRs
which may require discussion, so that we make
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[SPARK-21809] : Change Stage Page to use datatables to support sorting
columns and searching
Support column sort and search for Stage Server using jQuery DataTable and
REST API. Before this
Github user kevinyu98 commented on the issue:
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Hello Sean : Thank so much for the help on this PR, appreciated all the
help from you and all the reviewers.
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These are fair arguments. I guess it makes sense to throw an exception;
that's fine with me.
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This has ended up being more complex than we envisioned. It would be
valuable to describe the design succinctly so that people can debate it on
JIRA. Could you please describe your solution on
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