Shuffle use pyarrow more broadly #8596
Open
Conversation
This file contains 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
Unit Test ResultsSee test report for an extended history of previous test failures. This is useful for diagnosing flaky tests. 29 files ± 0 29 suites ±0 11h 19m 23s ⏱️ + 7m 11s For more details on these failures and errors, see this check. Results for commit 9826d68. ± Comparison against base commit 8927bfd. ♻️ This comment has been updated with latest results. |
|
|
||
| partitions = t.select([column]).to_pandas()[column].unique() | ||
| partitions.sort() | ||
| partitions = np.array(pa.compute.unique(t[column]).sort()) |
There was a problem hiding this comment.
Can we avoid the remainder of the np conversion as well by leveraging pa.compute.(not)_equal and pa.compute.indices_nonzero?
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Outdated suggestions cannot be applied.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.
These are a couple of very minor fixes that in my very limited small scale testing turned out to speed up things.
I can speak at least to the
uniquecomputation that I saw this flare up in profiles as well and benchmarking this on toy examples shows that this is about 20x faster than on main but that depends of course on the kind of data so in general this is likely not as impactful