apache/beam · error · RuntimeError

Invalid feature store yaml file provided. Make sure the %s c

Error message

Invalid feature store yaml file provided. Make sure the %s contains the valid configuration for Feast feature store.

What it means

Raised in `__enter__` after `FeatureStore(fs_yaml_file=local_repo_path)` fails, meaning the downloaded yaml is present but its contents are not a valid Feast feature store configuration (bad registry/online store config, Feast version incompatibility, or malformed yaml). The original exception is replaced by this RuntimeError.

Source

Thrown at sdks/python/apache_beam/transforms/enrichment_handlers/feast_feature_store.py:151

    """
    self.entity_id = entity_id
    self.feature_store_yaml_path = feature_store_yaml_path
    self.feature_names = feature_names
    self.feature_service_name = feature_service_name
    self.full_feature_names = full_feature_names
    self.entity_row_fn = entity_row_fn
    self._exception_level = exception_level
    _validate_entity_key_exists(self.entity_id, self.entity_row_fn)
    _validate_feature_store_yaml_path_exists(self.feature_store_yaml_path)
    _validate_feature_names(self.feature_names, self.feature_service_name)

  def __enter__(self):
    """Connect with the Feast feature store."""
    local_repo_path = download_fs_yaml_file(self.feature_store_yaml_path)
    try:
      self.store = FeatureStore(fs_yaml_file=local_repo_path)
    except Exception:
      raise RuntimeError(
          'Invalid feature store yaml file provided. Make sure '
          'the %s contains the valid configuration for Feast feature store.' %
          self.feature_store_yaml_path)
    if self.feature_service_name:
      try:
        self.features = self.store.get_feature_service(
            self.feature_service_name)
      except Exception:
        raise RuntimeError(
            'Could not find the feature service %s for the feature '
            'store configured in %s.' %
            (self.feature_service_name, self.feature_store_yaml_path))
    else:
      self.features = self.feature_names

  def __call__(self, request: beam.Row, *args, **kwargs):
    """Fetches feature values for an entity-id from the Feast feature store.

View on GitHub (pinned to 12126d8942)

Solutions

Validate the yaml locally: `FeatureStore(fs_yaml_file='feature_store.yaml')` in the same feast version as the pipeline.
Pin the pipeline container's feast version to match the one used to generate the config.
Check required keys (project, registry, provider, online_store) exist in the yaml.
Catch the original exception during development (temporarily bypass) to see the underlying parse error.

Example fix

# before (incomplete yaml)
project: my_project
# after
project: my_project
registry: gs://my-bucket/registry.db
provider: gcp
online_store:
  type: datastore
entity_key_serialization_version: 2

Defensive patterns

Strategy: try-catch

Validate before calling

from feast import FeatureStore
FeatureStore(fs_yaml_file='feature_store.yaml')  # validate locally with same feast version

Try / catch

try:
    store = FeatureStore(fs_yaml_file=local_path)
except Exception as e:
    raise RuntimeError(f'Invalid feast yaml {yaml_path}: {e!r}') from e

Prevention

Pin feast version in the pipeline container to match config generation.
Validate the yaml in CI with the same feast release.
Keep required keys (project, registry, provider, online_store) in a template.

When it happens

Trigger: `__enter__` is invoked as the pipeline starts running; Feast's FeatureStore constructor raises because the yaml lacks required sections (registry, entity_key_serialization_version), uses an unsupported provider, or the installed feast version cannot parse the config format.

Common situations: Hand-edited feature_store.yaml with missing fields; Feast major version upgrade changing config schema; yaml pointing to a repo config not a feature store config.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/7e8fc86a0ca35d06. Report an issue: GitHub.