Convert nested join in Vector Queries to Pandas Merge. #1298
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2 tasks
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For 20% speedup, how many rows does the table contain? |
100k |
jiashenC
reviewed
Oct 19, 2023
| for col_name in column_list: | ||
| res_row[col_name] = row[col_name] | ||
| res_row_list[idx] = res_row | ||
| result_df = pd.merge( |
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Instead of doing O(n) of merging, will we get better performance if get all batches from the child and do merging only once?
There was a problem hiding this comment.
Instead of doing O(n) of merging, will we get better performance if get all batches from the child and do merging only once?
Thanks for the suggestion, I have also made changes to not add child frames into the result df before merging to avoid unnecessary processing. The speedup is 2X now.
jiashenC
approved these changes
Oct 26, 2023
| left_index=True, | ||
| right_index=True, | ||
| how="left", | ||
| # sort=False |
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Profiling on Vector Scan showed that we are spending a lot of time in the post-processing logic doing a Nested Join. This is an initial commit to change that into a Join using Pandas. Change showed ~50% improvement in Similarity Queries.