Bump connector upper bound version #3835
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…nd native Series constructor switching bugs (#3498) SNOW-2157873 occurs because upstream modin does not implement __array_function__, instead converting to numpy ndarrays via __array__ when a numpy function is called on it. The presence of the extension wrapper for __array_function__ introduced by Snowpark pandas confuses numpy dispatch, causing unexpected AttributeErrors. This is fixed upstream with modin-project/modin#7617, and will presumably become available in the next modin release. On the Snowpark side, this PR adds relevant tests, and adds a version-guarded flag to remove the extension function and push it down to the query compiler. SNOW-2173644 occurs in specific circumstances when determining switching conditions for the DataFrame constructor. Series objects are treated as dict-like, but Series.values is a property rather than a function. We thus skip over native_pd.Series objects in the dict-like check in move_to_me_cost.
…al column names (#3501)
Signed-off-by: sfc-gh-mvashishtha <mahesh.vashishtha@snowflake.com>
Signed-off-by: sfc-gh-mvashishtha <mahesh.vashishtha@snowflake.com> Co-authored-by: Jonathan Shi <149419494+sfc-gh-joshi@users.noreply.github.com>
…#3488) Co-authored-by: Jonathan Shi <149419494+sfc-gh-joshi@users.noreply.github.com> Co-authored-by: Hazem Elmeleegy <hazem.elmeleegy@snowflake.com>
…ndas methods (#3512) When AutoSwitchBackend (hybrid execution PrPr) is enabled, the client will automatically switch to the pandas backend when a method registered via a register_*_not_implemented annotation is called.
Co-authored-by: Jonathan Shi <149419494+sfc-gh-joshi@users.noreply.github.com>
…ctions ( part 1) (#3811) Co-authored-by: Jamison Rose <Jamison.Rose@snowflake.com>
…e-op switching for joins (#3818)
Add some changes that we need to support Modin 0.37.0: - add a Snowflake extension for `Series.to_json` that raises `NotImplementedError`. - `test_inplace_false_with_assignment_does_not_mutate_df` now passes - change some expectations relating to merges Signed-off-by: sfc-gh-mvashishtha <mahesh.vashishtha@snowflake.com> Co-authored-by: Jonathan Shi <jonathan.shi@snowflake.com>
…t. (#3820) To implement to_snowflake() for a Snowpark pandas dataframe on the pandas backend with large enough data, upload data via a parquet file instead of via a Snowpark dataframe. The Snowpark dataframe typically inserts values through parametrized SQL queries. Benchmarking showed that a good threshold to switch to parquet was roughly 3 MB, so I've set that as the configurable default switching threshold. Performance of this approach seems to improve with dataset size. Exporting an 800 MB dataframe took about 55 seconds via parquet versus about 429 seconds via the old method, so we get over 7x speedup. We can take a similar approach to speed up pandas_backend_df.move_to('snowflake'). Here are benchmark results with a 3XL warehouse: <img width="571" height="432" alt="to_snowflake_timing_2" src="https://github.com/user-attachments/assets/0f652f6e-4510-4a94-9a75-028f5e09f2b0" /> Signed-off-by: sfc-gh-mvashishtha <mahesh.vashishtha@snowflake.com> Co-authored-by: Jonathan Shi <149419494+sfc-gh-joshi@users.noreply.github.com>
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