ulysses-you opened a new pull request, #38756:
URL: https://github.com/apache/spark/pull/38756
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines:
https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR:
https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g.,
'[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a
faster review.
7. If you want to add a new configuration, please read the guideline first
for naming configurations in
'core/src/main/scala/org/apache/spark/internal/config/ConfigEntry.scala'.
8. If you want to add or modify an error type or message, please read the
guideline first in
'core/src/main/resources/error/README.md'.
-->
### What changes were proposed in this pull request?
<!--
Please clarify what changes you are proposing. The purpose of this section
is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR. See the examples below.
1. If you refactor some codes with changing classes, showing the class
hierarchy will help reviewers.
2. If you fix some SQL features, you can provide some references of other
DBMSes.
3. If there is design documentation, please add the link.
4. If there is a discussion in the mailing list, please add the link.
-->
Add a new transform to optimize global sort only. Global sort only mean
there is no addition shuffle exchange except sort itself.
```
Optimizer Strategy Preparations
Sort -> GlobalSortOnly -> GlobalSortOnlyExec ->
GlobalSortOnlyExec
planForSample
rddForSample
```
Make `OrderedDistribution` and `RangePartitioning` take a new parameter
`rddForSample: Option[() => RDD[InternalRow]]`, so we can pass the
rddForSample to `ShuffleExchangeExec`
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
When do a global sort, firstly we do sample to get range bounds, then we use
the range partitioner to do shuffle exchange.
The issue is, the sample plan is coupled with the shuffle plan that causes
we can not optimize the sample plan. What we need for sample plan is the
columns for sort order but the shuffle plan contains all data columns. So at
least, we can do column pruning for the sample plan to only fetch the ordering
columns.
A common example is: `OPTIMIZE table ZORDER BY columns`
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such as
the documentation fix.
If yes, please clarify the previous behavior and the change this PR proposes
- provide the console output, description and/or an example to show the
behavior difference if possible.
If possible, please also clarify if this is a user-facing change compared to
the released Spark versions or within the unreleased branches such as master.
If no, write 'No'.
-->
for user, no it's improve performance
for developer, yes it provides a custom way to do sample for range partition
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some
test cases that check the changes thoroughly including negative and positive
cases if possible.
If it was tested in a way different from regular unit tests, please clarify
how you tested step by step, ideally copy and paste-able, so that other
reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why
it was difficult to add.
If benchmark tests were added, please run the benchmarks in GitHub Actions
for the consistent environment, and the instructions could accord to:
https://spark.apache.org/developer-tools.html#github-workflow-benchmarks.
-->
add tests
performace test:
```
val rows = 1000000
val columns = (0 until(200)).map(i => s"uuid() c$i")
spark.range(rows).selectExpr(columns:
_*).repartition(30).write.format("parquet").saveAsTable("t")
spark.sql("SELECT * FROM t ORDER BY c0, c1,
c2").write.format("noop").mode("overwrite").save()
```
after do column pruning, the stage for sample from 7s -> 0.4s, the whole
query 1.4X
<img width="1050" alt="image"
src="https://user-images.githubusercontent.com/12025282/203262222-9099cd3c-27f1-46e5-8fc9-e38bf3bd79e5.png";>
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
![https://user-images.githubusercontent.com/12025282/203262222-9099cd3c-27f1-46e5-8fc9-e38bf3bd79e5.png"](https://user-images.githubusercontent.com/12025282/203262222-9099cd3c-27f1-46e5-8fc9-e38bf3bd79e5.png"){kind=link}