Cache ADBC Arrow logical type metadata#26
Merged
adsharma merged 1 commit intoJul 5, 2026
Conversation
Contributor
|
CI error will be fixed when LadybugDB/ladybug#653 lands. |
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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 PR updates the ADBC extension to preserve Arrow logical type metadata discovered at bind/query
setup time and reuse it during scan materialization.
The main motivation is Snowflake-style logical decimal metadata carried in Arrow schema metadata. ADBC
drivers can expose decimal semantics through metadata even when the physical Arrow storage is integer-
or float-backed. Before this change, the ADBC extension relied on the generic Arrow type conversion path
alone, which could lose those logical decimal annotations during scan conversion.
At query setup time, the ADBC connector reads the Arrow stream schema once and resolves logical type
metadata for each column. That metadata is stored alongside the query result and later passed into the
scan path for each batch.
This keeps the hot scan loop simple and avoids reparsing schema metadata per batch while preserving vendor-specific logical annotations such as Snowflake decimal metadata.