Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Reducing informer-reflector's memory footprint #594

Closed
Iceber opened this issue Nov 17, 2023 · 1 comment · Fixed by #591
Closed

Reducing informer-reflector's memory footprint #594

Iceber opened this issue Nov 17, 2023 · 1 comment · Fixed by #591
Labels
kind/feature New feature

Comments

@Iceber
Copy link
Member

Iceber commented Nov 17, 2023

What would you like to be added?

Reflector waits for all data to be fetched in List before sending it to DeltaFIFO and notifying ResourceHandler to process it.

This leads to large memory consumption, and in List phase, the data is not written to the storage layer, so the processing efficiency is not maximized.

Why is this needed?

The synchronizer now causes a large amount of resources to pile up in memory when it starts, and only processes them after all resources have been pulled into memory, saving them to the storage component.
If we can avoid this huge memory buildup at List time and can quickly process the resources that have been pulled, then not only does it reduce memory, but it also speeds up the processing of the resources (writing to the storage component)

@Iceber Iceber added the kind/feature New feature label Nov 17, 2023
@clusterpedia-bot
Copy link

Hi @Iceber,
Thanks for opening an issue!
We will look into it as soon as possible.

Details

Instructions for interacting with me using comments are available here.
If you have questions or suggestions related to my behavior, please file an issue against the gh-ci-bot repository.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
kind/feature New feature
Projects
None yet
Development

Successfully merging a pull request may close this issue.

2 participants