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fix: Bugfix for grabbing historical data from Snowflake with array type features. #3964

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Feb 22, 2024
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fix: Bugfix for grabbing historical data from Snowflake with array type features. #3964

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20 changes: 10 additions & 10 deletions docs/reference/data-sources/overview.md
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Expand Up @@ -19,13 +19,13 @@ Details for each specific data source can be found [here](README.md).
Below is a matrix indicating which data sources support which types.

| | File | BigQuery | Snowflake | Redshift | Postgres | Spark | Trino |
| :-------------------------------- | :-- | :-- | :-- | :-- | :-- | :-- | :-- |
| `bytes` | yes | yes | yes | yes | yes | yes | yes |
| `string` | yes | yes | yes | yes | yes | yes | yes |
| `int32` | yes | yes | yes | yes | yes | yes | yes |
| `int64` | yes | yes | yes | yes | yes | yes | yes |
| `float32` | yes | yes | yes | yes | yes | yes | yes |
| `float64` | yes | yes | yes | yes | yes | yes | yes |
| `bool` | yes | yes | yes | yes | yes | yes | yes |
| `timestamp` | yes | yes | yes | yes | yes | yes | yes |
| array types | yes | yes | no | no | yes | yes | no |
| :-------------------------------- | :-- | :-- |:----------| :-- | :-- | :-- | :-- |
| `bytes` | yes | yes | yes | yes | yes | yes | yes |
| `string` | yes | yes | yes | yes | yes | yes | yes |
| `int32` | yes | yes | yes | yes | yes | yes | yes |
| `int64` | yes | yes | yes | yes | yes | yes | yes |
| `float32` | yes | yes | yes | yes | yes | yes | yes |
| `float64` | yes | yes | yes | yes | yes | yes | yes |
| `bool` | yes | yes | yes | yes | yes | yes | yes |
| `timestamp` | yes | yes | yes | yes | yes | yes | yes |
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I marked this as yes to keep it simple. But the full truth is that it does not work when inferring features. That note is included in the other edited document.

| array types | yes | yes | yes | no | yes | yes | no |
2 changes: 1 addition & 1 deletion docs/reference/data-sources/snowflake.md
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Expand Up @@ -46,5 +46,5 @@ The full set of configuration options is available [here](https://rtd.feast.dev/

## Supported Types

Snowflake data sources support all eight primitive types, but currently do not support array types.
Snowflake data sources support all eight primitive types. Array types are also supported but not with type inference.
For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
4 changes: 3 additions & 1 deletion sdk/python/feast/infra/offline_stores/snowflake.py
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Expand Up @@ -463,7 +463,9 @@ def _to_df_internal(self, timeout: Optional[int] = None) -> pd.DataFrame:
Array(Float32),
Array(Bool),
]:
df[feature.name] = [json.loads(x) for x in df[feature.name]]
df[feature.name] = [
json.loads(x) if x else None for x in df[feature.name]
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nice

]

return df

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26 changes: 26 additions & 0 deletions sdk/python/tests/unit/infra/offline_stores/test_snowflake.py
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@@ -1,14 +1,18 @@
import re
from unittest.mock import ANY, MagicMock, patch

import pandas as pd
import pytest
from pytest_mock import MockFixture

from feast import FeatureView, Field, FileSource
from feast.infra.offline_stores.snowflake import (
SnowflakeOfflineStoreConfig,
SnowflakeRetrievalJob,
)
from feast.infra.online_stores.sqlite import SqliteOnlineStoreConfig
from feast.repo_config import RepoConfig
from feast.types import Array, String


@pytest.fixture(params=["s3", "s3gov"])
Expand Down Expand Up @@ -55,3 +59,25 @@ def test_to_remote_storage(retrieval_job):
mock_get_file_names_from_copy.assert_called_once_with(ANY, ANY)
native_path = mock_get_file_names_from_copy.call_args[0][1]
assert re.match("^s3://.*", native_path), "path should be s3://*"


def test_snowflake_to_df_internal(
retrieval_job: SnowflakeRetrievalJob, mocker: MockFixture
):
mock_execute = mocker.patch(
"feast.infra.offline_stores.snowflake.execute_snowflake_statement"
)
mock_execute.return_value.fetch_pandas_all.return_value = pd.DataFrame.from_dict(
{"feature1": ['["1", "2", "3"]', None, "[]"]} # For Valid, Null, and Empty
)

feature_view = FeatureView(
name="my-feature-view",
entities=[],
schema=[
Field(name="feature1", dtype=Array(String)),
],
source=FileSource(path="dummy.path"), # Dummy value
)
retrieval_job._feature_views = [feature_view]
retrieval_job._to_df_internal()
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