feat: Added Ray Compute Engine and Ray Offline Store Support - #5526
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Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
Signed-off-by: ntkathole <[email protected]>
| timestamp_col = self.column_info.timestamp_column | ||
| requested_feats = getattr(self.column_info, "feature_cols", []) | ||
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| # Check if the feature dataset contains aggregated features (from aggregation node) |
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this looks great some small nits but i think we're almost there. can you make some issues to track the follow up here so we don't forget and I'll merge 👍
| cls, | ||
| udf: Callable[[Any], Any], | ||
| udf_string: str, | ||
| udf: Optional[Callable[[Any], Any]] = None, |
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why make this optional?
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It's not optional in real use, we are still maintaining strict validation (lines 72-75 enforce that they can't actually be None). We have to make it optional to ensures that PandasTransformation objects can be properly reconstructed across Ray workers without constructor failures.
Signed-off-by: ntkathole <[email protected]>
franciscojavierarceo
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Thanks for this @ntkathole !!! |
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# [0.52.0](v0.51.0...v0.52.0) (2025-08-14) ### Bug Fixes * Correct entity value type mapping for aliased feature views ([#5492](#5492)) ([bdf20bb](bdf20bb)) * Correct namespace reference in remote Feast project setup for operator upgrade and previous version tests ([df391ec](df391ec)) * dell pydantic v1 ([1189512](1189512)) * Fixed the entity to on-demand feature view relationship ([1c59bba](1c59bba)) * Make transformers optional ([#5544](#5544)) ([a4eef38](a4eef38)) * Push Source inherits the timestamp fields from Data Source ([#5550](#5550)) ([b7ea5cc](b7ea5cc)) * Remove the devcontainer folder. ([a9815c2](a9815c2)) ### Features * Added API for discovering Feature Views by popular tags ([#5558](#5558)) ([2e5f564](2e5f564)) * Added filtering support for featureView and featureServices api ([#5552](#5552)) ([897b3f3](897b3f3)) * Added global search api and necessary unit tests ([#5532](#5532)) ([dd3061f](dd3061f)) * Added Ray Compute Engine and Ray Offline Store Support ([#5526](#5526)) ([72de088](72de088)) * Added recent visit logging api for registry server ([#5545](#5545)) ([2adcf2c](2adcf2c)) * **auth:** support client-credentials & static token for OIDC client auth ([fc44222](fc44222)) * **auth:** support client-credentials & static token for OIDC client auth ([795fc06](795fc06)) * Implement and enhance remote document retrieval functionality ([#5487](#5487)) ([d095b96](d095b96)) * Implemented consistent error handling ([7f10151](7f10151)) * Offline Store historical features retrieval without entity df, but based on datatime range ([#5527](#5527)) ([df942b9](df942b9))
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What this PR does / why we need it:
This PR introduces Ray as both a compute engine and an offline store in Feast. It enables scalable, distributed feature engineering and data access in Feast using Ray, making it easier to handle large datasets and complex feature pipelines.
Key Features
Ray Compute Engine
Ray Offline Store
Documentation added for both Ray compute engine and offline store, including configuration, resource management, and usage examples.