Speed up the loading of large tables#2026
Merged
Merged
Conversation
evetion
approved these changes
Feb 3, 2025
Member
evetion
left a comment
evetion
left a comment
There was a problem hiding this comment.
I like these kinds of improvements 🎉
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
2 participants
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
This fixes a performance issue that @rbruijnshkv encountered trying to initialize a model with a
Basin / timecolumn of 6 million rows, spread over 1000 Basin nodes. It spent around 1-2 seconds per Basin node on this line.time is a StructVector, which stores columns as vectors. By broadcasting getfield we iterated over rows generating BasinTime structs and then taking one field, which works but is much slower than just taking out the field that is already a vector.The general recommendation for such large tables is to not store them in the model database but a separate Arrow file like here: https://github.com/Deltares/Ribasim/blob/v2025.1.0/python/ribasim_testmodels/ribasim_testmodels/basic.py#L210. Doing this shrank the database from 400 to 100 MB, and also sped up initialization. This should help both formats though.