yaooqinn opened a new pull request #32819:
URL: https://github.com/apache/spark/pull/32819
<!--
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 message, please read the
guideline first:
https://spark.apache.org/error-message-guidelines.html
-->
### What changes were proposed in this pull request?
Currently, Spark allows users to set scalability within a Spark application
using dynamic allocation. `spark.dynamicAllocation.minExecutors` &
`spark.dynamicAllocation.maxExecutors` are used for scaling up and down. Within
an application，Spark tactfully use them to request executors from cluster
manager according to the real-time workload. Once set, the range is fixed
through the whole application lifecycle. This is not very convenient for
long-running application when the range should be changeable for some cases,
such as:
1. the cluster manager itself or the queue will scale up and down, which
looks very likely to happen in modern cloud platforms
2. the application is long-running, but the timeliness, priority, e.t.c are
not only determined by the workload with the application, but also by the
traffic across the cluster manager or just different moments
3. e.t.c.
### 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.
-->
make the dynamic allocation for long term Spark applications
### 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'.
-->
Configs below are changeable:
spark.dynamicAllocation.maxExecutors
spark.dynamicAllocation.minExecutors
### 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.
-->
new tests
--
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.
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
