fix(sql): Further optimize GetForecastAsTimeseries#141
Merged
Conversation
Benchmark ResultsBenchmark resultsBenchmark vs base branch |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Contribution Checklist
make lintwith your changes locally?make testwith your changes locally?Warning
PRs may be closed if all the above boxes are not checked.
Changes in this Pull Request
Uses the LATERAL join trick to reduce the number of joins to the
predicted_generation_valuestable.The previous query was joining every possible forecast to the forecast values table in order to then filter
Whereas, with the LIMIT 1 LATERAL join, the latest forecast for each location is found prior to joining
Sorting these old overlapping forecasts was being done by writing them to disk
Which is entirely unecessary after the new LATERL/LIMIT process - postgres can simply join each individual forecast in memory via the pre-sorted index.
This reduces CPU usage by around 80% for this query. It is still constrained by disk I/O.