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Github user cloud-fan commented on the issue:
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@dongjoon-hyun please also update the title of the JIRA ticket, thanks!
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thanks, merging to master!
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Github user rxin commented on the issue:
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BTW I think this is probably SPIP-worthy. At the very least we should write
a design doc on this, similar to the other docs for dsv2 sub-components. We
should really think about whether it'd be possibl
Github user mhamilton723 commented on the issue:
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@WeichenXu123. Awesome work! I have not had a chance to go through this in
depth but I did this in the originating project, [MMLSpark](www.aka.ms/spark),
a while back and have been meaning to s
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cc @gatorsmile @cloud-fan
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--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -3237,6 +3238,28 @@ class Dataset[T] private[sql](
files.toSet.toArray
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--- Diff: build/mvn ---
@@ -91,15 +92,23 @@ install_mvn() {
# Install zinc under the build/ folder
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- local zinc_path="zi
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retest this please
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Github user cloud-fan commented on the issue:
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@tgravescs yes you are right about the problem here. Instead of asking
executors to remove old committed shuffle data, I prefer #6648 , which just
write new shuffle data with a different file name
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--- Diff:
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Github user hindog commented on the issue:
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I believe another performance impact related to this may be attributed to
the `cast` operator failing to match during filter-pushdown, meaning that the
filter on the timestamp will NOT get pushed dow
Github user fangshil commented on the issue:
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To summarize our discussion in this pr:
Spark-avro is now merged into Spark as a built-in data source. Upstream
community is not merging the AvroEncoder to support Avro types in Dataset,
inste
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ping @srowen @HyukjinKwon
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Ideally the last test should have 50 partitions? is it because we really
need the test data to be at least 50 bytes? ideally a multiple of 50, I guess.
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Github user bomeng commented on the issue:
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Here is the test code, not sure it is right or not ---
```
test("Number of partitions") {
sc = new SparkContext(new
SparkConf().setAppName("test").setMaster("local")
.set(
Github user wangyum commented on a diff in the pull request:
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--- Diff:
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Github user tigerquoll commented on the issue:
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So Kudu range partitions support arbitrary sized partition intervals, like
the example below, where the first and last range partition are six months in
size, but the middle partition is one year
Github user tigerquoll commented on the issue:
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Sure,
I am looking at the point of view of supporting Kudu. Check out
https://kudu.apache.org/docs/schema_design.html#partitioning for some of the
details. In particular
https://kudu.apach
Github user maropu commented on the issue:
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I also can't find a strong reason to append a new API in `Dataset`... btw,
to add a new API there, you'd be better to discuss in jira before making a pr,
I think. cc: @rxin @cloud-fan @HyukjinKwon
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retest this please
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Github user rdblue commented on the issue:
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@tigerquoll, what we come up with needs to work across a variety of data
sources, including those like JDBC that can delete at a lower granularity than
partition.
For Hive tables, the partit
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--- Diff:
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Github user gatorsmile commented on the issue:
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Did we introduce any behavior change in
https://github.com/apache/spark/pull/21273? Does this PR resolve it?
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I am assuming this API was intended to support the "drop partition"
use-case. I'm arguing that adding and deleting partitions deal with a concept
that is a slightly higher concept than just a bu
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Github user ifilonenko commented on the issue:
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@felixcheung @holdenk I have moved the PySpark example files to a more
appropriate location. Any other comments before merge?
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@vinodkc Could you answer the question from @cloud-fan ?
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Retest this please.
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At this time, R failure.
```
DONE
===
Had test warnings or failures; see logs.
```
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Github user HeartSaVioR commented on the issue:
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@zsxwing
If it means code freeze for 2.4 is just around the corner then sure! We can
focus on blockers for releasing 2.4, and revisit this again. Let me reflect
@gaborgsomogyi review commen
Github user dongjoon-hyun commented on the issue:
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Oh, I assumed that it's already dockerized. Sorry, never mind about that
@shaneknapp . And, thanks!
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add @jerryshao for more feedback. Thanks.
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GitHub user zsxwing opened a pull request:
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[SPARK-25336][SS]Revert SPARK-24863 and SPARK 24748
## What changes were proposed in this pull request?
Revert SPARK-24863 and SPARK 24748 as per discussion in #21721. We will
revisit them
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Any more comments? @MLnick @jkbradley
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@mgaido91 Thank you for your suggestion, I updated the PR name, description
and sources with a new version using a parameter `atPosition` instead of a flag
`atTheEnd`. Let me know what you think a
Github user shaneknapp commented on the issue:
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moving any parts of the spark build infrastructure to use docker is a big
project and not happening in the next few months.
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Github user tgravescs commented on the issue:
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yeah you would have to be able to handle network partitioning somehow. I
don't know how difficult it is but its definitely work we may not want to do
here. I was trying to clarify and make sure
Github user gatorsmile commented on a diff in the pull request:
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--- Diff: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
---
@@ -1513,37 +1513,34 @@ private[spark] class DAGScheduler(
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Github user dongjoon-hyun commented on the issue:
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Hi, @shaneknapp and @srowen .
Can we build and use the zinc-installed docker images in our build system?
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this PR is waiting on @shaneknapp to migrate to ubuntu and have R setup in
the node responsible for distribution building. This was planning on being done
right after the 2.4 cut.
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[SPARK-25335][BUILD] Skip Zinc downloading if it's installed in the system
## What changes were proposed in this pull request?
Zinc is 23.5MB.
```
$ curl -LO https://downloads
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Github user gatorsmile commented on the issue:
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> So in order to fix that we would need a way to tell the executors to
remove that older committed shuffle data
@tgravescs It is also hard to implement such a robust solution for removing
Github user ifilonenko commented on the issue:
https://github.com/apache/spark/pull/21669
This PR has been tested and passed on a local cluster with an integration
test that will be merged in a follow-up PR. It passes all three configuration
options. It is now in a state that is ready
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/21669
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/22192
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Github user bersprockets commented on the issue:
https://github.com/apache/spark/pull/22192
retest this please.
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Github user ifilonenko commented on a diff in the pull request:
https://github.com/apache/spark/pull/21669#discussion_r215057079
--- Diff: core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala ---
@@ -164,7 +164,15 @@ private[spark] class SparkSubmit extends Logging {
Github user ankuriitg commented on a diff in the pull request:
https://github.com/apache/spark/pull/22209#discussion_r215056633
--- Diff:
core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala ---
@@ -1190,6 +1190,61 @@ class AppStatusListenerSuite extends SparkFu
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