[umbrella][Feature] Improve the performance of querying paimon table on spark
- Dominant language
- Java
- Stars
- 3.4k
- Forks
- 1.4k
- Avg merge
- 1d 11h
- Merged PRs (30d)
- 396
Description
### Search before asking
- [X] I searched in the [issues](https://github.com/apache/incubator-paimon/issues) and found nothing similar.
### Motivation
Improve the performance of querying paimon table on spark.
### Solution
_No response_
### Anything else?
_No response_
### Are you willing to submit a PR?
- [ ] I'm willing to submit a PR!
Contributor guide
No contributing guide indexed for this repository
Research direction
The issue identifies Spark queries against Paimon tables as the target but names no files, tests, entry points, or specific bottleneck. Start by locating the Spark query implementation and existing performance tests, then define a measurable baseline and acceptance criteria before choosing a change. Done should mean a demonstrated query-performance improvement with coverage or benchmark evidence.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- java, spark
- Domain
- data-engineering
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100