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[SPARK-25132][SQL][FOLLOW-UP] The behavior must be consistent to do the
conversion
## What changes were proposed in this pull request?
parquet data source tables and hive parquet tables have
Github user HeartSaVioR commented on a diff in the pull request:
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@@ -0,0 +1,241 @@
+/*
+ *
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Github user liyinan926 commented on a diff in the pull request:
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--- Diff: docs/running-on-kubernetes.md ---
@@ -215,6 +215,19 @@
spark.kubernetes.driver.volumes.persistentVolumeClaim.checkpointpvc.options.clai
Github user kiszk commented on the issue:
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Thank you for adding a description for the conf. Would it be possible to
add missing descriptions for other confs in `spark.network.crypto.*`?
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}
private[spark] object RDDInfo {
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Github user viirya commented on the issue:
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Yeah, it's interesting... Seems both `checkedCast` and `Platform.getByte`
are changed and performance gets gain. Any single change doesn't work.
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retest this please
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Github user tgravescs commented on the issue:
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yeah we should file a separate jira to look at the shuffle output.I'm
running a few stress tests and will let you know how those go.
could you file a jira for that and link to this
Github user mgaido91 commented on the issue:
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> While I say these performance differences, I do not understand why these
difference occurs completely. That is why I said "I have not found the root
cause".
Yes, I am in the same
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While I say these performance differences, I do not understand why these
difference occurs completely. That is why I said "I have not found the root
cause".
Let us narrow down the problem and
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I think it should be fine to make `ContextBarrierState` private[spark] to
test it, WDYT @mengxr ?
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retest this please
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you're right @kiszk. Let me update the PR accordingly in order to narrow
down the change/problem, thanks.
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+/*
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Github user jiangxb1987 commented on the issue:
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You can have `select * from (select a, a as c from table1 where a > 10) t
where a > c`
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LGTM
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>This does fix the regression, will we also lose the perf speed up too?
IIUC we did observe significant perf boost when introducing the memory block.
@cloud-fan I checked the original PR
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--- Diff:
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@@ -754,11 +754,14 @@ class Analyzer(
* a
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---
@@ -414,17 +468,37 @@ private[kafka010]
Github user kiszk commented on the issue:
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In addition to [your
commit](https://github.com/apache/spark/pull/22338/commits/91adce590461dda885d88319a700a775e63f9ce6),
I applied the following change, basically use `MemoryBlock` in
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---
@@ -23,12 +23,14 @@ package
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+/*
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@kiszk yes, I know. Moving `checkedCast` makes a big difference, but if you
just move it, without the other changes, there is no perf gain (at least this
is what I found in my experiments).
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gentle ping @maropu, could you help to review this? I'll keep follow up
this.
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2804,6 +2804,22 @@ def pandas_udf(f=None, returnType=None,
functionType=None):
| 1|1.5|
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@mgaido91 thanks, interestingly I did experiments with similar code in my
box.
While I am using the linux box, I can confirm the performance improvement
(or performance recover).
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Github user xuanyuanking commented on the issue:
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gental ping @cloud-fan @gatorsmile @kiszk, we still meet this in internal
folk, could you help to review? I'll resolve the conflict, great thanks.
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Do we have any compatibility issues here? Seems fine to me as we already
shaded kryo.
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--- Diff: build/mvn ---
@@ -91,15 +92,23 @@ install_mvn() {
# Install zinc under the build/ folder
install_zinc() {
- local
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gental ping @HyukjinKwon @BryanCutler
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GitHub user dongjinleekr opened a pull request:
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Feature/fix kafka sql trivials
## What changes were proposed in this pull request?
Fix unused imports & outdated comments on `kafka-0-10-sql` module. (Found
while I was working on
Github user xuanyuanking commented on the issue:
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```
Could I do the refactor of moving ContextBarrierState out of
BarrierCoordinator?
```
gental ping @jiangxb1987, I still follow up this. :)
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thanks, merging to master!
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This basically reverts the memory block in the hash computing, now the
memory block is just a holder of the base object and base offset. This does fix
the regression, will we also lose the perf
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Yeah same question, but I see why that could cause a problem. Is the point
here that while this is a test, the spark-submit run by the test should be run
'normally'? I am happy for a solution just
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