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GeezerVison is showing github.com — feat: Feast-MLflow Integration by Vperiodt · Pull Request #6235 · feast-dev/feast

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Vperiodt commented Apr 8, 2026 •

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What this PR does / why we need it:

final_mlflow_demo.mp4

Native MLflow integration for Feast that provides automatic feature lineage tracking alongside ML experiments. When enabled via feature_store.yaml, every feature retrieval is logged to the active MLflow run.

Key Capabilities

Auto-logging: Feature retrieval metadata is tagged on the active MLflow run (feast.feature_refs, feast.feature_views, feast.feature_service, feast.entity_count, etc.)
Entity DataFrame archival: Optionally saves the training entity DataFrame as an MLflow artifact (entity_df.parquet) for full reproducibility
Model-to-feature-service resolution: store.mlflow.resolve_features() maps any MLflow model URI back to its Feast feature service, enabling serving pipelines to auto-discover which features a model needs
Entity DataFrame reconstruction: store.mlflow.get_training_entity_df() rebuilds the exact entity DataFrame from a past run's artifacts, enabling training reproducibility
Model lifecycle lineage: log_model auto-attaches feast_features.json, register_model propagates feast.feature_service to model versions, load_model links prediction runs to training runs
Operations audit trail: feast apply and feast materialize logged to a dedicated {project}-feast-ops experiment
Dataset tracking: log_training_dataset() logs training DataFrames as MLflow dataset inputs
Two access patterns: store.mlflow (explicit, multi-store safe) and feast.mlflow (drop-in replacement for import mlflow)
Configuration: Controlled entirely via feature_store.yaml under a new mlflow: block

Demo

Full end-to-end demo notebook with all capabilities: integration-example

Which issue(s) this PR fixes:

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I've made sure the tests are passing.
My commits are signed off (git commit -s)
My PR title follows conventional commits format

Testing Strategy

Unit tests
Integration tests
Manual tests
Testing is not required for this change

Misc

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Vperiodt force-pushed the feast-mlflow branch from 0e6296b to a63d529 Compare

April 9, 2026 12:05

Vperiodt marked this pull request as ready for review

April 9, 2026 12:06

Vperiodt requested a review from a team as a code owner

April 9, 2026 12:06

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Vperiodt force-pushed the feast-mlflow branch from 2f79660 to 4aa9d50 Compare

April 14, 2026 08:16

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Vperiodt force-pushed the feast-mlflow branch from 7ceaa94 to 08a8f91 Compare

April 14, 2026 14:08

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Vperiodt changed the title ~~Feast-MLflow Integration~~ feat: Feast-MLflow Integration

Apr 15, 2026

devin-ai-integration Bot reviewed

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Devin Review found 1 new potential issue.

View 19 additional findings in Devin Review.

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sdk/python/feast/feature_store.py Outdated

		with tempfile.TemporaryDirectory() as tmp_dir:
		path = os.path.join(tmp_dir, "entity_df.parquet")
		entity_df.to_parquet(path, index=False)
		mlflow.log_artifact(path)

devin-ai-integration Bot Apr 15, 2026 •

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🟡 Mixing global mlflow.log_artifact() with explicit-URI client causes artifact/metadata to target different servers

In _auto_log_entity_df_info, tags and params are logged via a locally-created MlflowClient(tracking_uri=tracking_uri) (lines 304, 309-318), but the entity DataFrame artifact is uploaded via the global mlflow.log_artifact(path) (line 327). The global function uses the tracking URI set by mlflow.set_tracking_uri(), not the explicit tracking_uri from the config. If the global tracking URI is changed by another library in the process, or if _init_mlflow_tracking failed silently (caught by except Exception at sdk/python/feast/feature_store.py:249), the artifact would be uploaded to a different server than where the tags/params were logged, splitting metadata across two servers.

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Vperiodt force-pushed the feast-mlflow branch from 6683cb5 to dba7e99 Compare

April 16, 2026 12:48

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Devin Review found 2 new potential issues.

View 20 additional findings in Devin Review.

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sdk/python/feast/ui_server.py Outdated

		try:
		import mlflow

		tracking_uri = mlflow_cfg.tracking_uri or "http://127.0.0.1:5000"

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🔴 UI server ignores MLFLOW_TRACKING_URI env var, falls back to hardcoded localhost

In ui_server.py, both the /api/mlflow-runs and /api/mlflow-feature-models endpoints resolve the MLflow tracking URI using mlflow_cfg.tracking_uri or "http://127.0.0.1:5000". This reads the raw tracking_uri field from the config and falls back to a hardcoded localhost URL, completely bypassing the MLFLOW_TRACKING_URI environment variable. In contrast, the rest of the codebase (feature_store.py:243, feature_store.py:325, feature_store.py:1696, feature_store.py:2885) correctly calls mlflow_cfg.get_tracking_uri() which checks the env var via sdk/python/feast/mlflow_integration/config.py:19-29. When a user sets MLFLOW_TRACKING_URI without setting tracking_uri in YAML (which is a very common deployment pattern, and documented in the PR's own docs at docs/reference/mlflow.md:51), the UI endpoints will incorrectly connect to http://127.0.0.1:5000 instead of the env-var-specified server.

Suggested change

	tracking_uri = mlflow_cfg.tracking_uri or "http://127.0.0.1:5000"
	tracking_uri = mlflow_cfg.get_tracking_uri() or "http://127.0.0.1:5000"

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sdk/python/feast/ui_server.py Outdated

		try:
		import mlflow

		tracking_uri = mlflow_cfg.tracking_uri or "http://127.0.0.1:5000"

devin-ai-integration Bot Apr 16, 2026 •

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🔴 Second UI endpoint also ignores MLFLOW_TRACKING_URI env var

Same issue as in the /api/mlflow-runs endpoint: the /api/mlflow-feature-models endpoint at sdk/python/feast/ui_server.py:234 uses mlflow_cfg.tracking_uri or "http://127.0.0.1:5000" instead of mlflow_cfg.get_tracking_uri(), causing the MLFLOW_TRACKING_URI environment variable to be ignored.

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Vperiodt force-pushed the feast-mlflow branch from e933f27 to 76c4363 Compare

April 20, 2026 09:25

devin-ai-integration Bot reviewed

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Devin Review found 3 new potential issues.

View 22 additional findings in Devin Review.

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sdk/python/feast/feature_store.py Outdated

		)

		# Emit MLflow event for materialization (Phase 7)
		_mat_duration = time.monotonic() - _retrieval_start if '_retrieval_start' in dir() else 0

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🟡 Undefined _retrieval_start variable in materialize_incremental causes bogus duration or NameError

In materialize_incremental, the code at line 2115 references _retrieval_start which is never defined in that method scope. The expression '_retrieval_start' in dir() checks local variable names but dir() without arguments returns module-level names, not local variables -- so the check is unreliable. If _retrieval_start is not in the returned list, _mat_duration defaults to 0 (acceptable but meaningless). However, if it IS in the name list (e.g., a module-level _retrieval_start existed from another context), it would attempt time.monotonic() - _retrieval_start on a potentially unrelated value, resulting in a wrong duration or a NameError/TypeError at runtime.

Suggested change

	_mat_duration = time.monotonic() - _retrieval_start if '_retrieval_start' in dir() else 0
	_mat_duration = 0

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sdk/python/feast/mlflow_integration/logger.py Outdated

Comment on lines +185 to +189

		if tracking_uri:
		mlflow.set_tracking_uri(tracking_uri)

		experiment_name = f"{project}{ops_experiment_suffix}"
		mlflow.set_experiment(experiment_name)

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🔴 log_apply_to_mlflow and log_materialize_to_mlflow mutate global MLflow tracking URI and experiment, corrupting concurrent user runs

Both log_apply_to_mlflow (sdk/python/feast/mlflow_integration/logger.py:185-189) and log_materialize_to_mlflow (sdk/python/feast/mlflow_integration/logger.py:251-255) call mlflow.set_tracking_uri(tracking_uri) and mlflow.set_experiment(experiment_name) globally. These mutate process-wide global state. If a user has an active MLflow run in a different experiment (e.g. during feast apply inside a training script), these calls redirect subsequent MLflow operations to the ops experiment. While the functions try to restore mlflow.set_experiment(project) afterward (line 224, 274), the tracking URI is never restored. In the exception path, if mlflow.start_run() raises (line 202/258), the experiment remains set to the ops experiment. This corrupts any concurrent or subsequent user operations in the same process.

Prompt for agents

The functions log_apply_to_mlflow and log_materialize_to_mlflow in sdk/python/feast/mlflow_integration/logger.py mutate process-wide global state by calling mlflow.set_tracking_uri() and mlflow.set_experiment(). This corrupts any concurrent user MLflow runs in the same process.

The fix should save and restore the original tracking URI and experiment before/after the ops logging, or preferably use the MlflowClient API exclusively (which already takes tracking_uri as a parameter) instead of the global mlflow module-level functions. The MlflowClient.create_run() method allows creating runs in specific experiments without mutating global state. This pattern is already used in log_feature_retrieval_to_mlflow which correctly uses client = mlflow.MlflowClient(tracking_uri=tracking_uri) without touching global state.

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sdk/python/feast/repo_config.py

Comment on lines +497 to +506

		def mlflow(self):
		"""Get the MLflow configuration."""
		if not self._mlflow:
		if isinstance(self.mlflow_config, Dict):
		from feast.mlflow_integration.config import MlflowConfig

		self._mlflow = MlflowConfig(**self.mlflow_config)
		elif self.mlflow_config:
		self._mlflow = self.mlflow_config
		return self._mlflow

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🟡 mlflow property returns falsy None on subsequent calls when MlflowConfig(enabled=False) is set

The mlflow property in RepoConfig (sdk/python/feast/repo_config.py:497-506) uses if not self._mlflow: as the guard. When the config is a valid MlflowConfig object with enabled=False, self._mlflow will be a truthy MlflowConfig instance (Pydantic models are truthy), so this works correctly on first access. However, if mlflow_config is None (the default), self._mlflow stays None forever and the property returns None -- which is fine. The real issue is: if mlflow_config is an empty dict {}, MlflowConfig(**{}) creates a valid config with enabled=False, and self._mlflow becomes truthy. But if mlflow_config is set to some falsy-like Pydantic object (unlikely but possible with custom subclasses), the if not self._mlflow guard would re-create it every time. This is the same pattern as openlineage property so it's consistent, but not actually a bug for standard usage.

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franciscojavierarceo reviewed

Apr 20, 2026

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docs/reference/mlflow.md Outdated

		- Online feature retrieval -- `get_online_features()` tags the run with the same metadata
		- Entity DataFrame archival -- optionally saves the training entity DataFrame as an MLflow artifact for full reproducibility
		- Execution context tagging -- tags runs with where they ran (workbench, KFP pipeline, feature server, or standalone)
		- Operation logging -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment

franciscojavierarceo Apr 20, 2026

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not sure this is actually useful

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Vperiodt Apr 20, 2026 •

edited

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actually, this is not updated one, thought of this one for backtracking from mllfow run to the workbench experiment but now it is omitted

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docs/reference/mlflow.md Outdated

		- Entity DataFrame archival -- optionally saves the training entity DataFrame as an MLflow artifact for full reproducibility
		- Execution context tagging -- tags runs with where they ran (workbench, KFP pipeline, feature server, or standalone)
		- Operation logging -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment
		- Model-to-Feature resolution -- map any MLflow model URI back to its Feast feature service

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nice

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docs/reference/mlflow.md Outdated

		- Execution context tagging -- tags runs with where they ran (workbench, KFP pipeline, feature server, or standalone)
		- Operation logging -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment
		- Model-to-Feature resolution -- map any MLflow model URI back to its Feast feature service
		- Training reproducibility -- reconstruct the exact entity DataFrame from a past MLflow run

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👏

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docs/reference/mlflow.md Outdated

		- Operation logging -- optionally logs `feast apply` and `feast materialize` to a separate MLflow experiment
		- Model-to-Feature resolution -- map any MLflow model URI back to its Feast feature service
		- Training reproducibility -- reconstruct the exact entity DataFrame from a past MLflow run
		- Training-to-prediction linkage -- `FeastMlflowClient.load_model()` links prediction runs back to their training runs

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very nice

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docs/reference/mlflow.md Outdated

		- Model-to-Feature resolution -- map any MLflow model URI back to its Feast feature service
		- Training reproducibility -- reconstruct the exact entity DataFrame from a past MLflow run
		- Training-to-prediction linkage -- `FeastMlflowClient.load_model()` links prediction runs back to their training runs
		- Feast MLflow Client -- a thin wrapper that eliminates direct `import mlflow` in user code

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makes sense

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sdk/python/feast/mlflow_integration/logger.py Outdated

		_consecutive_failures = 0


		def log_feature_retrieval_to_mlflow(

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i guess if we have this enabled why do we need to even have the wrapper? can't we full handle this for the user?

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I guess my question is, can't we just hide all of the mlflow client usage to the user?

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Vperiodt Apr 20, 2026

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the auto-logging in logger.py fully handled for users who already use mlflow.start_run() or client.start_run() considering our FeastMlflowClient wrapper which is for users who want to avoid import mlflow entirely and get additional Feast-specific behavior on top ex: logging a model, registering it, loading it for inference etc soFeastMlflowClient is for users who want the extra lineage features without touching MLflow directly

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jyejare Apr 22, 2026

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I would rather push all these functions to integration client as everything here is a part of mlflow feast integration?

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Vperiodt force-pushed the feast-mlflow branch from 58086d5 to 2a5cac7 Compare

April 22, 2026 08:48

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Comments inline:

We also need to enable mlflow configuratins from feast operator else admin has to do it explicitly.
Also, we need to remove duplicate mechanisms for initiating/using mlflow client and we should provide a clean way that is store.mlflow whenever possible.

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docs/reference/mlflow.md Outdated

		2. `MLFLOW_TRACKING_URI` environment variable
		3. MLflow's default (`./mlruns` local directory)

		This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead.

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Suggested change

	This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead.
	This means you can omit `tracking_uri` from the YAML and set `MLFLOW_TRACKING_URI` in your environment instead or it would be pulled from /.mlruns automatically when both are not set.

wdyt ?

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docs/reference/mlflow.md Outdated


		\| Artifact \| Description \|
		\|----------\|-------------\|
		\| `required_features.json` \| JSON list of feature references the model was trained on \|

jyejare Apr 22, 2026

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Could we rename to relate it with feast better and so that it would be self describing in MLFLow ecosystem, something like:

required_feast_features.json

feast_features.json

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docs/reference/mlflow.md Outdated

		With the configuration above, feature metadata is logged whenever there is an active MLflow run:

		```python
		import mlflow

jyejare Apr 22, 2026

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I think explicit import is no more required as feast module gives you the ability to:

store.mlflow.start_run(....

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docs/reference/mlflow.md Outdated

		from feast import FeatureStore

		store = FeatureStore(".")
		client = store.get_mlflow_client()

jyejare Apr 22, 2026

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Even this is changed now. Lets keep only store.mlflow. examples.

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docs/reference/mlflow.md Outdated

		\| Condition \| Error \|
		\|-----------\|-------\|
		\| No `feature_store.yaml` in cwd and no store created \| `RuntimeError` with guidance to call `feast.mlflow.init(store)` \|
		\| `mlflow.enabled` is `false` or missing \| `RuntimeError` with guidance to set `mlflow.enabled=true` \|

jyejare Apr 22, 2026

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Even if user wants to set it to false ?

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sdk/python/feast/mlflow_integration/model_resolver.py Outdated

		invalid, resolution fails, or validation against the store fails.
		"""
		try:
		import mlflow

jyejare Apr 22, 2026

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Same here. Create a class and initiate in integration object/class that takes store object as param.

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sdk/python/feast/feature_store.py Outdated

		if mlflow_cfg is None or not mlflow_cfg.enabled:
		return

		import mlflow

jyejare Apr 22, 2026

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The store object is available to you here as self, so why not :

self.mlflow.set_tracking_uri(....

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sdk/python/feast/feature_store.py Resolved

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sdk/python/feast/mlflow.py Resolved

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sdk/python/tests/integration/test_mlflow_integration.py

		client = MlflowClient(tracking_uri=tracking_uri)
		refs = ["z_view:f1", "a_view:f2", "z_view:f3", "a_view:f4"]

		with mlflow.start_run() as run:

jyejare Apr 22, 2026

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The tests should also be focusing only on store.mlflow... culture.

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ntkathole requested changes

May 12, 2026

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sdk/python/feast/mlflow_integration/logger.py Outdated

		ops_suffix = mlflow_cfg.ops_experiment_suffix

		effective_uri = self._tracking_uri or self._mlflow.get_tracking_uri()
		client = self._mlflow.MlflowClient(tracking_uri=effective_uri)

ntkathole May 12, 2026

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instead of creating new client everytime, can we initialise once and reuse ?

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sdk/python/feast/feature_store.py

		# Initialize feature service cache for performance optimization
		self._feature_service_cache = {}

		# Cache for _resolve_feature_service_name lookups

ntkathole May 12, 2026

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the MLflow auto-log could resolve to a deleted or renamed feature service. _fs_name_cache and _fs_name_index caches (used by _resolve_feature_service_name) are never cleared when refresh_registry() or _clear_feature_service_cache() is called

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sdk/python/feast/feature_store.py

		self._fs_name_cache = {}
		self._fs_name_index_ts = time.monotonic()

		def _resolve_feature_service_name(self, feature_refs: List[str]) -> Optional[str]:

ntkathole May 12, 2026

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can't we use existing _feature_service_cache from FeatureStore?

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Vperiodt May 12, 2026

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_feature_service_cache lookup is : service_name → [feature_refs]and is populated lazily on demand
_fs_name_index lookup is reverse {feature_refs} → service_name, populated from all registry services needed for subset matching when users pass raw refs instead of a FeatureService

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sdk/python/feast/feature_store.py Outdated


		try:
		if self.mlflow is not None and self.config.mlflow.auto_log:
		import asyncio

ntkathole May 12, 2026

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already imported at top

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sdk/python/feast/feature_store.py Outdated

		self._fs_name_index_ts: float = -self._FS_NAME_INDEX_TTL_SECONDS

		self._mlflow_client: Optional[Any] = None
		self._init_mlflow()

ntkathole May 12, 2026

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I think better to do lazy loading here, first check the config and then load module.

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sdk/python/feast/mlflow_integration/client.py Outdated

		self._default_experiment = store.config.project

		if self._tracking_uri:
		_mlflow_mod.set_tracking_uri(self._tracking_uri)

ntkathole May 12, 2026

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remove set_tracking_uri() from __init__ and instead pass tracking_uri explicitly when needed (the MlflowClient already receives it)

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sdk/python/feast/ui_server.py

		)

		@app.get("/api/mlflow-runs")
		def get_mlflow_runs(max_results: int = 50):

ntkathole May 12, 2026

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should this be a registry API? Not a blocker for this PR, but architecture wise, registry is source of truth

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Vperiodt May 12, 2026

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Yes, will add this one will open to other benefits too !

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ntkathole May 15, 2026

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@Vperiodt going to handle this in this PR or different one?

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Vperiodt May 15, 2026

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yess, will address this in a follow up PR

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franciscojavierarceo commented May 13, 2026

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linter is failing

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Vperiodt commented May 13, 2026

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linter is failing

fixed

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ntkathole reviewed

May 14, 2026

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pyproject.toml Outdated

ntkathole May 14, 2026

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include mlflow here in ci dependencies

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ntkathole reviewed

May 14, 2026

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ui/src/pages/feature-views/RegularFeatureViewOverviewTab.tsx Outdated

		<EuiFlexItem>
		<EuiStat title={`${numOfFs}`} description="Consuming Services" />
		</EuiFlexItem>
		<EuiFlexItem>

ntkathole May 14, 2026

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This should be conditionally rendered, users who don't use MLflow will wonder what these stats mean and why they're there

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ntkathole reviewed

May 14, 2026

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ui/src/pages/feature-views/FeatureViewUsagePanel.tsx

		const usage: FeatureUsageEntry \| undefined =
		data.feature_usage?.[featureViewName];

		if (!usage \|\| usage.run_count === 0) {

ntkathole May 14, 2026

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same here, if mlflow is not configured, this needs to be hidden

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ntkathole commented May 14, 2026

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@Vperiodt Guard the UI components behind a check on whether MLflow data is available. The simplest approach is to have the backend return a mlflow_enabled: false flag in the API responses (or a new lightweight /api/mlflow-status endpoint), and conditionally render MLflow components only when enabled.

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Vperiodt commented May 14, 2026 •

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@Vperiodt Guard the UI components behind a check on whether MLflow data is available. The simplest approach is to have the backend return a mlflow_enabled: false flag in the API responses (or a new lightweight /api/mlflow-status endpoint), and conditionally render MLflow components only when enabled.

hello ! @ntkathole, Addressed all reviews

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ntkathole added the ok-to-test label

May 15, 2026

ntkathole commented May 15, 2026

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@Vperiodt need to update pixi.lock file, also please rebase

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Vperiodt force-pushed the feast-mlflow branch from fa8fb80 to d798acb Compare

May 19, 2026 07:47

Vperiodt added 3 commits

May 20, 2026 15:13


          mlflow-feast integration

b524a9c

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

refine MLflow integration, UI runs endpoint, and lockfiles

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

fix issues

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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handle fallback

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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added model to features table

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

address CI

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

add docs

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

fix-lint

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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update docs

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rh-pre-commit.version: 2.3.2
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update-logic

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add-integration-test

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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mlflowclient wrapper

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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address-reviews

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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update docs

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

fix-lint

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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fix-auto-log

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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update feastMlflowClient

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changing init

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fixing docs

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rh-pre-commit.version: 2.3.2
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remove doc

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update format

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fix-test

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rh-pre-commit.version: 2.3.2
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update docs

Signed-off-by: Vanshika Vanshika <[email protected]>

rh-pre-commit.version: 2.3.2
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addressed reviews

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rh-pre-commit.version: 2.3.2
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fix: update .secrets.baseline to remove pragma-allowlisted entry

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ui-fallback

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update requirements

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          fix-CI

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          fix-lint

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Vperiodt force-pushed the feast-mlflow branch 2 times, most recently from 92892e7 to 3da673d Compare

May 20, 2026 09:46

Vperiodt added 2 commits

May 21, 2026 10:53


          Merge branch 'master' into feast-mlflow

e52ad74


          fix: remove unused imports in ui_server.py

d740a85

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ntkathole approved these changes

May 21, 2026

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Looks good to me! Thank you @Vperiodt , This is great

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          Merge branch 'master' into feast-mlflow

ac07fbf

ntkathole merged commit 7279c75 into feast-dev:master

May 21, 2026

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franciscojavierarceo commented May 21, 2026

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lgtm

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Vperiodt mentioned this pull request

Jun 1, 2026

docs: Add blog post for native MLflow integration #6460

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franciscojavierarceo pushed a commit that referenced this pull request

Jun 13, 2026


          chore(release): release 0.64.0

9bb73cc

# [0.64.0](v0.63.0...v0.64.0) (2026-06-13)

### Bug Fixes

* Add async_supported property to RedisOnlineStore ([9b088fe](9b088fe))
* Add missing feast init templates to operator CRD and enhance persistence documentation ([1941d4d](1941d4d))
* Allow to publish from reference branch ([5458ec8](5458ec8))
* API calls list ([4203eb7](4203eb7))
* **bigquery:** Enable list inference for parquet loads in offline_write_batch ([9243497](9243497)), closes [#5845](#5845)
* Bump grpcio dependencies ([07b4782](07b4782))
* **compute-engine/local:** Honor field_mapping on join keys in dedup + join nodes ([#6395](#6395)) ([bd01824](bd01824))
* **dynamodb:** Avoid tag race condition by using diff-based tag updates ([#6479](#6479)) ([bad2b7d](bad2b7d)), closes [#6418](#6418)
* **dynamodb:** Fix mypy type for _build_projection_expression return ([217b4da](217b4da))
* Fix intermittent async test failures for DynamoDB and Redis ([63c5eb1](63c5eb1))
* Fix mongodb blog title ([57d28d4](57d28d4))
* Fix shared SQL registry crash - avoid unnecessary UDF deserialization in proto cache building ([ac588d7](ac588d7))
* Fix SparkRetrievalJob.persist() failing for SparkSource ([209d7cd](209d7cd))
* Fixed formatting and image for mongo blog ([#6377](#6377)) ([f8389fb](f8389fb))
* Fixes for ray source ([7f592a4](7f592a4))
* **go:** skip registry refresh when cache_ttl_seconds <= 0 ([97ed40c](97ed40c))
* Handle array of strings columns in Athena materialization ([#6324](#6324)) ([4ed0278](4ed0278))
* make milvus VARCHAR max_length configurable, remove hardcoded 512 limit ([3b98c22](3b98c22))
* **operator:** Set appProtocol: grpc on registry gRPC Service ([#6367](#6367)) ([c9ae2b4](c9ae2b4))
* PyJWT 2.10+ added validation that rejects empty HMAC keys ([e756ffe](e756ffe))
* RemoteOnlineStore sends all features in a single HTTP request ([8f187dd](8f187dd))
* Remove registry proto dump to enforce RBAC and add permission checks to Commit/Refresh RPCs ([328431f](328431f))
* Remove selector migration job - no longer needed ([51c325e](51c325e))
* replace broken .claude skill symlink with correct relative path ([4541690](4541690))
* Replace selector label strip patch with migration Job for upgrade-safe selector uniqueness ([00dea50](00dea50))
* Scope feature view name conflict check to current project in file-based registry ([#6369](#6369)) ([a4fde83](a4fde83)), closes [#6209](#6209)
* **snowflake:** Stop double-quoting connection identifiers ([#6462](#6462)) ([e914d59](e914d59))
* **spark:** S3/GCS PyArrow filesystem resolution for staging paths ([#6442](#6442)) ([ae50414](ae50414))
* **trino:** Clean up temporary entity tables after retrieval ([#6381](#6381)) ([d86b13d](d86b13d)), closes [#6306](#6306)
* Update go-feature-server base image to Go 1.25 and fix operator Dockerfile COPY permissions ([86ef0bc](86ef0bc))

### Features

* [Backend] Data Quality Monitoring with native compute, multi-backend support, REST API, CLI ([#6202](#6202)) ([5458c37](5458c37))
* Add apache flink compute engine ([#6476](#6476)) ([9636d6a](9636d6a))
* Add demo noteboooks for users ([e362173](e362173))
* Add enabled/disabled toggle for feature views ([#6401](#6401)) ([5f1fa0d](5f1fa0d)), closes [#6395](#6395)
* Add Label View to init template ([ec272d5](ec272d5))
* Add mTLS support to remote registry gRPC client ([#6474](#6474)) ([c9602d8](c9602d8))
* Add Prometheus gauges for FeatureStore installation telemetry ([#6354](#6354)) ([1b681b7](1b681b7))
* Adds registry REST API endpoints for managing entities, data sources, and feature views ([#6413](#6413)) ([f77bd1d](f77bd1d))
* Allow CRUD on entities, data sources, and feature views from UI ([#6412](#6412)) ([2321c07](2321c07))
* Allow default openlineage configuration ([#6467](#6467)) ([276b6df](276b6df))
* **bigquery:** Support DATE-type event timestamp columns ([#6362](#6362)) ([753dee5](753dee5)), closes [#2530](#2530)
* **cli:** Add `feast projects delete` command (closes [#5095](#5095)) ([#6318](#6318)) ([1a4b96c](1a4b96c))
* Data Quality Monitoring added in feast UI ([#6422](#6422)) ([fa271be](fa271be))
* **dynamodb:** Use ProjectionExpression when requested_features is set ([0adc906](0adc906)), closes [#6058](#6058)
* Enhance DataSource and FeatureView modals with error handling and submission states ([96d7169](96d7169))
* Expose registry endpoints on feature server for MCP access ([f77981c](f77981c))
* Feast First-Class LabelView Implementation ([#6292](#6292)) ([c0e7e5d](c0e7e5d))
* Feast-MLflow Integration ([#6235](#6235)) ([7279c75](7279c75))
* Operational metrics for offline store and SOX metrics for both ([#6340](#6340)) ([65b1b80](65b1b80))
* Pre-compute feature service ([8011550](8011550))
* REST API-backed UI for RBAC compatibility and per-page lazy loading ([#6414](#6414)) ([6ae80af](6ae80af))
* Support non-string map key types ([#6382](#6382)) ([#6383](#6383)) ([728aa2e](728aa2e))
* Update FeatureStore CRD with DRA Fields ([01241e4](01241e4))

### Performance Improvements

* Cache feature view resolution in get_online_features to reduce per-request overhead ([55c2f18](55c2f18))
* Optimize feature serving latency with batched async Redis, cached checks fix ([103809a](103809a))
* Replace MessageToDict with optimized custom dict builder ([#6015](#6015)) ([9902064](9902064))

Srihari1192 pushed a commit to Srihari1192/feast that referenced this pull request

Jun 22, 2026


          chore(release): release 0.64.0

22c3721

# [0.64.0](feast-dev/feast@v0.63.0...v0.64.0) (2026-06-13)

### Bug Fixes

* Add async_supported property to RedisOnlineStore ([9b088fe](feast-dev@9b088fe))
* Add missing feast init templates to operator CRD and enhance persistence documentation ([1941d4d](feast-dev@1941d4d))
* Allow to publish from reference branch ([5458ec8](feast-dev@5458ec8))
* API calls list ([4203eb7](feast-dev@4203eb7))
* **bigquery:** Enable list inference for parquet loads in offline_write_batch ([9243497](feast-dev@9243497)), closes [feast-dev#5845](feast-dev#5845)
* Bump grpcio dependencies ([07b4782](feast-dev@07b4782))
* **compute-engine/local:** Honor field_mapping on join keys in dedup + join nodes ([feast-dev#6395](feast-dev#6395)) ([bd01824](feast-dev@bd01824))
* **dynamodb:** Avoid tag race condition by using diff-based tag updates ([feast-dev#6479](feast-dev#6479)) ([bad2b7d](feast-dev@bad2b7d)), closes [feast-dev#6418](feast-dev#6418)
* **dynamodb:** Fix mypy type for _build_projection_expression return ([217b4da](feast-dev@217b4da))
* Fix intermittent async test failures for DynamoDB and Redis ([63c5eb1](feast-dev@63c5eb1))
* Fix mongodb blog title ([57d28d4](feast-dev@57d28d4))
* Fix shared SQL registry crash - avoid unnecessary UDF deserialization in proto cache building ([ac588d7](feast-dev@ac588d7))
* Fix SparkRetrievalJob.persist() failing for SparkSource ([209d7cd](feast-dev@209d7cd))
* Fixed formatting and image for mongo blog ([feast-dev#6377](feast-dev#6377)) ([f8389fb](feast-dev@f8389fb))
* Fixes for ray source ([7f592a4](feast-dev@7f592a4))
* **go:** skip registry refresh when cache_ttl_seconds <= 0 ([97ed40c](feast-dev@97ed40c))
* Handle array of strings columns in Athena materialization ([feast-dev#6324](feast-dev#6324)) ([4ed0278](feast-dev@4ed0278))
* make milvus VARCHAR max_length configurable, remove hardcoded 512 limit ([3b98c22](feast-dev@3b98c22))
* **operator:** Set appProtocol: grpc on registry gRPC Service ([feast-dev#6367](feast-dev#6367)) ([c9ae2b4](feast-dev@c9ae2b4))
* PyJWT 2.10+ added validation that rejects empty HMAC keys ([e756ffe](feast-dev@e756ffe))
* RemoteOnlineStore sends all features in a single HTTP request ([8f187dd](feast-dev@8f187dd))
* Remove registry proto dump to enforce RBAC and add permission checks to Commit/Refresh RPCs ([328431f](feast-dev@328431f))
* Remove selector migration job - no longer needed ([51c325e](feast-dev@51c325e))
* replace broken .claude skill symlink with correct relative path ([4541690](feast-dev@4541690))
* Replace selector label strip patch with migration Job for upgrade-safe selector uniqueness ([00dea50](feast-dev@00dea50))
* Scope feature view name conflict check to current project in file-based registry ([feast-dev#6369](feast-dev#6369)) ([a4fde83](feast-dev@a4fde83)), closes [feast-dev#6209](feast-dev#6209)
* **snowflake:** Stop double-quoting connection identifiers ([feast-dev#6462](feast-dev#6462)) ([e914d59](feast-dev@e914d59))
* **spark:** S3/GCS PyArrow filesystem resolution for staging paths ([feast-dev#6442](feast-dev#6442)) ([ae50414](feast-dev@ae50414))
* **trino:** Clean up temporary entity tables after retrieval ([feast-dev#6381](feast-dev#6381)) ([d86b13d](feast-dev@d86b13d)), closes [feast-dev#6306](feast-dev#6306)
* Update go-feature-server base image to Go 1.25 and fix operator Dockerfile COPY permissions ([86ef0bc](feast-dev@86ef0bc))

### Features

* [Backend] Data Quality Monitoring with native compute, multi-backend support, REST API, CLI ([feast-dev#6202](feast-dev#6202)) ([5458c37](feast-dev@5458c37))
* Add apache flink compute engine ([feast-dev#6476](feast-dev#6476)) ([9636d6a](feast-dev@9636d6a))
* Add demo noteboooks for users ([e362173](feast-dev@e362173))
* Add enabled/disabled toggle for feature views ([feast-dev#6401](feast-dev#6401)) ([5f1fa0d](feast-dev@5f1fa0d)), closes [feast-dev#6395](feast-dev#6395)
* Add Label View to init template ([ec272d5](feast-dev@ec272d5))
* Add mTLS support to remote registry gRPC client ([feast-dev#6474](feast-dev#6474)) ([c9602d8](feast-dev@c9602d8))
* Add Prometheus gauges for FeatureStore installation telemetry ([feast-dev#6354](feast-dev#6354)) ([1b681b7](feast-dev@1b681b7))
* Adds registry REST API endpoints for managing entities, data sources, and feature views ([feast-dev#6413](feast-dev#6413)) ([f77bd1d](feast-dev@f77bd1d))
* Allow CRUD on entities, data sources, and feature views from UI ([feast-dev#6412](feast-dev#6412)) ([2321c07](feast-dev@2321c07))
* Allow default openlineage configuration ([feast-dev#6467](feast-dev#6467)) ([276b6df](feast-dev@276b6df))
* **bigquery:** Support DATE-type event timestamp columns ([feast-dev#6362](feast-dev#6362)) ([753dee5](feast-dev@753dee5)), closes [feast-dev#2530](feast-dev#2530)
* **cli:** Add `feast projects delete` command (closes [feast-dev#5095](feast-dev#5095)) ([feast-dev#6318](feast-dev#6318)) ([1a4b96c](feast-dev@1a4b96c))
* Data Quality Monitoring added in feast UI ([feast-dev#6422](feast-dev#6422)) ([fa271be](feast-dev@fa271be))
* **dynamodb:** Use ProjectionExpression when requested_features is set ([0adc906](feast-dev@0adc906)), closes [feast-dev#6058](feast-dev#6058)
* Enhance DataSource and FeatureView modals with error handling and submission states ([96d7169](feast-dev@96d7169))
* Expose registry endpoints on feature server for MCP access ([f77981c](feast-dev@f77981c))
* Feast First-Class LabelView Implementation ([feast-dev#6292](feast-dev#6292)) ([c0e7e5d](feast-dev@c0e7e5d))
* Feast-MLflow Integration ([feast-dev#6235](feast-dev#6235)) ([7279c75](feast-dev@7279c75))
* Operational metrics for offline store and SOX metrics for both ([feast-dev#6340](feast-dev#6340)) ([65b1b80](feast-dev@65b1b80))
* Pre-compute feature service ([8011550](feast-dev@8011550))
* REST API-backed UI for RBAC compatibility and per-page lazy loading ([feast-dev#6414](feast-dev#6414)) ([6ae80af](feast-dev@6ae80af))
* Support non-string map key types ([feast-dev#6382](feast-dev#6382)) ([feast-dev#6383](feast-dev#6383)) ([728aa2e](feast-dev@728aa2e))
* Update FeatureStore CRD with DRA Fields ([01241e4](feast-dev@01241e4))

### Performance Improvements

* Cache feature view resolution in get_online_features to reduce per-request overhead ([55c2f18](feast-dev@55c2f18))
* Optimize feature serving latency with batched async Redis, cached checks fix ([103809a](feast-dev@103809a))
* Replace MessageToDict with optimized custom dict builder ([feast-dev#6015](feast-dev#6015)) ([9902064](feast-dev@9902064))

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Vperiodt commented Apr 8, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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What this PR does / why we need it:

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Which issue(s) this PR fixes:

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