Closed shamb0 closed 1 month ago
Hi @philippemnoel,
Haha, I definitely understand the challenge of reviewing large PRs! You're right—this PR is a combination of changes from multiple sources, which has made it more complex, breaking these down will significantly improve the review process.
Based on your suggestions, here's my proposed approach to restructure the work:
Component | Action Plan |
---|---|
Benchmarking code | Move to cargo-paradedb , use PG DB URL instead of direct Postgres dependency |
New pga_fixtures crate |
Create a separate PR with rationale for new test fixtures |
Changes from PR #30 | Split into a separate PR for easier review |
Remaining changes (PR #115) | Keep in current PR, but streamline for focused review |
This breakdown should make each PR more manageable and easier to review. I'll start implementing these changes right away.
Do you think this approach addresses your concerns?
Hi @philippemnoel,
Haha, I definitely understand the challenge of reviewing large PRs! You're right—this PR is a combination of changes from multiple sources, which has made it more complex, breaking these down will significantly improve the review process.
Based on your suggestions, here's my proposed approach to restructure the work:
Component Action Plan Benchmarking code Move to
cargo-paradedb
, use PG DB URL instead of direct Postgres dependency Newpga_fixtures
crate Create a separate PR with rationale for new test fixtures Changes from PR #30 Split into a separate PR for easier review Remaining changes (PR #115) Keep in current PR, but streamline for focused review This breakdown should make each PR more manageable and easier to review. I'll start implementing these changes right away.Do you think this approach addresses your concerns?
Sounds promising!
Going to close this until a more scoped PR is raised.
Ticket(s) Closed
This PR is part of a pair; please consider both for review and merge.
https://github.com/paradedb/paradedb/pull/1703 https://github.com/paradedb/pg_analytics/pull/131
What
This PR implements benchmarking functionality to analyze query performance under different caching conditions across various data sources supported by
pg_analytics
.Why
To evaluate how different cache configurations impact query performance, ensuring that the system optimally handles various data sources and caching scenarios.
How
The test function follows a structured flow:
criterion
framework.criterion
.The SQL command below is used to toggle Parquet metadata caching (In-memory):
Where
cache_setting
can be either "true" or "false", depending on the test scenario.Benchmarking
To run the benchmarking, use the following command:
Integration Notes
The diagram below outlines key components and their interactions, providing a high-level overview of the prototype design: