pan3793 opened a new pull request, #50765:
URL: https://github.com/apache/spark/pull/50765
<!--
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
'common/utils/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.
-->
On a busy Hadoop cluster, the `GetFileInfo` and `GetBlockLocations`
contribute the most RPCs to the HDFS NameNode. After investigating the Spark
Parquet vectorized reader, I think 3/4 RPCs can be reduced.
<img width="1719" alt="Xnip2025-04-30_16-23-27"
src="https://github.com/user-attachments/assets/e201ac8e-e414-4fec-96ef-173ff2cb5b14";
/>
Currently, the Parquet vectorized reader produces 4 NameNode RPCs on reading
each file (or split):
1. Read the footer - one `GetFileInfo` and one `GetBlockLocations`
2. Read the data (row groups) - one `GetFileInfo` and one `GetBlockLocations`
The key idea of this PR is:
1. Driver already knows the `FileStatus` for each Parquet file during the
planning phase, we can transfer the `FileStatus` from the driver to the
executor via `PartitionFile`, so that the task doesn't need to ask the NameNode
again, this saves two `GetFileInfo` RPCs.
2. Reuse the `SeekableInputStream` on reading footer and row groups, this
saves one `GetBlockLocations` RPC.
<img width="911" height="321" alt="image"
src="https://github.com/user-attachments/assets/b419d82b-911e-4751-9a46-bc59c118ad64";
/>
The PR requires some changes on the Parquet side first. (Changes are already
included in Parquet 1.16.0)
- https://github.com/apache/parquet-java/pull/3208
- https://github.com/apache/parquet-java/pull/3262
### 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.
3. If you fix a bug, you can clarify why it is a bug.
-->
Reduce unnecessary RPCs of NameNode to improve performance and stability for
large Hadoop clusters.
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such as
new features, bug fixes, or other behavior changes. Documentation-only updates
are not considered user-facing changes.
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'.
-->
No.
### 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.
-->
### Pass UT
A few UTs are tuned to adapt to the change.
### Manual test with TPC-H query.
Manually tested on a small Hadoop cluster, the test uses TPC-H Q4, based on
sf3000 Parquet tables.
HDFS NameNode metrics (master VS. this PR)
<img width="1716" alt="Xnip2025-04-30_16-43-13"
src="https://github.com/user-attachments/assets/e872d19e-6ee9-481e-93f6-e06c70e834d1";
/>
<img width="1717" alt="Xnip2025-04-30_16-43-31"
src="https://github.com/user-attachments/assets/ec7bab48-a2b9-4de9-805a-7992b94b940f";
/>
### Production Verification
The patch has also been deployed to a production cluster for 4 months, where
95% of workloads are Spark jobs.
Before
<img width="1382" alt="image"
src="https://github.com/user-attachments/assets/3e58a9e6-ef3d-4924-b58d-f616531177e4";
/>
After
<img width="1388" alt="image"
src="https://github.com/user-attachments/assets/a4233822-729b-4278-9eac-0d979efe9345";
/>
### Was this patch authored or co-authored using generative AI tooling?
<!--
If generative AI tooling has been used in the process of authoring this
patch, please include the
phrase: 'Generated-by: ' followed by the name of the tool and its version.
If no, write 'No'.
Please refer to the [ASF Generative Tooling
Guidance](https://www.apache.org/legal/generative-tooling.html) for details.
-->
No.
--
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]
