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fix: Fix NumPy - PyArrow array type mapping in Trino offline store by ebolblga · Pull Request #5393 · feast-dev/feast · GitHub

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fix: Fix NumPy - PyArrow array type mapping in Trino offline store #5393

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franciscojavierarceo merged 3 commits into from
Jun 4, 2025
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fix: Fix NumPy - PyArrow array type mapping in Trino offline store #5393

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6610a75
fix: remove forced schema when converting pandas to pyarrow table
ebolblga May 27, 2025
dd118d6
fix: add arrays to trino supported types in docs
ebolblga May 27, 2025
cd1f210
chore: linting
ebolblga May 27, 2025
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chore: linting 
Signed-off-by: ebolblga <kkochanovskiy@gmail.com>
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ebolblga committed Jun 3, 2025
commit cd1f210da69a8479a51fb9341f16c03f9195a376
4 changes: 1 addition & 3 deletions sdk/python/feast/infra/offline_stores/contrib/trino_offline_store/trino.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,9 +207,7 @@ def _to_df_internal(self, timeout: Optional[int] = None) -> pd.DataFrame:

def _to_arrow_internal(self, timeout: Optional[int] = None) -> pyarrow.Table:
"""Return payrrow dataset as synchronously including on demand transforms"""
return pyarrow.Table.from_pandas(
self._to_df_internal(timeout=timeout)
)
return pyarrow.Table.from_pandas(self._to_df_internal(timeout=timeout))

franciscojavierarceo May 27, 2025

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Choose a reason Spam Abuse Off Topic Outdated Duplicate Resolved Low Quality

why remove the explicit schema declaration?

ebolblga May 28, 2025

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Choose a reason Spam Abuse Off Topic Outdated Duplicate Resolved Low Quality

Let's say I have Pandas DataFrame like this:

feature_x feature_y
[1] None
[3, 4] [54, 38]
[75, 1, 12] [40, 0]

If I output column dtypes then feature_x would still be object (array(int)) and feature_y would be converted to float by underlying NumPy because of the Null.

By default, pandas uses NumPy data types, which do not support missing values in integer arrays. If you create a Series or DataFrame column with integers and include a null value (e.g., None or np.nan), pandas will upcast the column to a floating-point type (float64) to accommodate the missing value.

If I later pass this dataframe WITH forced schema, PyArrow will see that I want to cast float to array(int) and throw an error. If I don't pass the schema though, it will infer type itself and work as expected.

def to_sql(self) -> str:
"""Returns the SQL query that will be executed in Trino to build the historical feature table"""
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