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# Copyright 2019 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
asyncio
import
itertools
import
os
import
warnings
from
datetime
import
datetime
,
timedelta
from
pathlib
import
Path
from
typing
import
(
Any
,
Callable
,
Dict
,
Iterable
,
List
,
Mapping
,
Optional
,
Sequence
,
Tuple
,
Union
,
cast
,
)
import
pandas
as
pd
import
pyarrow
as
pa
from
colorama
import
Fore
,
Style
from
fastapi
.
concurrency
import
run_in_threadpool
from
google
.
protobuf
.
timestamp_pb2
import
Timestamp
from
tqdm
import
tqdm
from
feast
import
feature_server
,
flags_helper
,
ui_server
,
utils
from
feast
.
base_feature_view
import
BaseFeatureView
from
feast
.
batch_feature_view
import
BatchFeatureView
from
feast
.
data_source
import
(
DataSource
,
KafkaSource
,
KinesisSource
,
PushMode
,
PushSource
,
)
from
feast
.
diff
.
infra_diff
import
InfraDiff
,
diff_infra_protos
from
feast
.
diff
.
registry_diff
import
RegistryDiff
,
apply_diff_to_registry
,
diff_between
from
feast
.
dqm
.
errors
import
ValidationFailed
from
feast
.
entity
import
Entity
from
feast
.
errors
import
(
DataFrameSerializationError
,
DataSourceRepeatNamesException
,
FeatureViewNotFoundException
,
PushSourceNotFoundException
,
RequestDataNotFoundInEntityDfException
,
)
from
feast
.
feast_object
import
FeastObject
from
feast
.
feature_service
import
FeatureService
from
feast
.
feature_view
import
DUMMY_ENTITY
,
DUMMY_ENTITY_NAME
,
FeatureView
from
feast
.
inference
import
(
update_data_sources_with_inferred_event_timestamp_col
,
update_feature_views_with_inferred_features_and_entities
,
)
from
feast
.
infra
.
infra_object
import
Infra
from
feast
.
infra
.
offline_stores
.
offline_utils
import
(
DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL
,
)
from
feast
.
infra
.
provider
import
Provider
,
RetrievalJob
,
get_provider
from
feast
.
infra
.
registry
.
base_registry
import
BaseRegistry
from
feast
.
infra
.
registry
.
registry
import
Registry
from
feast
.
infra
.
registry
.
sql
import
SqlRegistry
from
feast
.
on_demand_feature_view
import
OnDemandFeatureView
from
feast
.
online_response
import
OnlineResponse
from
feast
.
permissions
.
permission
import
Permission
from
feast
.
project
import
Project
from
feast
.
protos
.
feast
.
serving
.
ServingService_pb2
import
(
FieldStatus
,
GetOnlineFeaturesResponse
,
)
from
feast
.
protos
.
feast
.
types
.
EntityKey_pb2
import
EntityKey
from
feast
.
protos
.
feast
.
types
.
Value_pb2
import
RepeatedValue
,
Value
from
feast
.
protos
.
feast
.
types
.
Value_pb2
import
Value
as
ValueProto
from
feast
.
repo_config
import
RepoConfig
,
load_repo_config
from
feast
.
repo_contents
import
RepoContents
from
feast
.
saved_dataset
import
SavedDataset
,
SavedDatasetStorage
,
ValidationReference
from
feast
.
ssl_ca_trust_store_setup
import
configure_ca_trust_store_env_variables
from
feast
.
stream_feature_view
import
StreamFeatureView
from
feast
.
transformation
.
pandas_transformation
import
PandasTransformation
from
feast
.
transformation
.
python_transformation
import
PythonTransformation
from
feast
.
utils
import
_get_feature_view_vector_field_metadata
,
_utc_now
warnings
.
simplefilter
(
"once"
,
DeprecationWarning
)
class
FeatureStore
:
"""
A FeatureStore object is used to define, create, and retrieve features.
Attributes:
config: The config for the feature store.
repo_path: The path to the feature repo.
_registry: The registry for the feature store.
_provider: The provider for the feature store.
"""
config
:
RepoConfig
repo_path
:
Path
_registry
:
BaseRegistry
_provider
:
Provider
def
__init__
(
self
,
repo_path
:
Optional
[
str
]
=
None
,
config
:
Optional
[
RepoConfig
]
=
None
,
fs_yaml_file
:
Optional
[
Path
]
=
None
,
):
"""
Creates a FeatureStore object.
Args:
repo_path (optional): Path to the feature repo. Defaults to the current working directory.
config (optional): Configuration object used to configure the feature store.
fs_yaml_file (optional): Path to the `feature_store.yaml` file used to configure the feature store.
At most one of 'fs_yaml_file' and 'config' can be set.
Raises:
ValueError: If both or neither of repo_path and config are specified.
"""
if
fs_yaml_file
is
not
None
and
config
is
not
None
:
raise
ValueError
(
"You cannot specify both fs_yaml_file and config."
)
configure_ca_trust_store_env_variables
()
if
repo_path
:
self
.
repo_path
=
Path
(
repo_path
)
else
:
self
.
repo_path
=
Path
(
os
.
getcwd
())
# If config is specified, or fs_yaml_file is specified, those take precedence over
# the default feature_store.yaml location under repo_path.
if
config
is
not
None
:
self
.
config
=
config
elif
fs_yaml_file
is
not
None
:
self
.
config
=
load_repo_config
(
self
.
repo_path
,
fs_yaml_file
)
else
:
self
.
config
=
load_repo_config
(
self
.
repo_path
,
utils
.
get_default_yaml_file_path
(
self
.
repo_path
)
)
registry_config
=
self
.
config
.
registry
if
registry_config
.
registry_type
==
"sql"
:
self
.
_registry
=
SqlRegistry
(
registry_config
,
self
.
config
.
project
,
None
)
elif
registry_config
.
registry_type
==
"snowflake.registry"
:
from
feast
.
infra
.
registry
.
snowflake
import
SnowflakeRegistry
self
.
_registry
=
SnowflakeRegistry
(
registry_config
,
self
.
config
.
project
,
None
)
elif
registry_config
and
registry_config
.
registry_type
==
"remote"
:
from
feast
.
infra
.
registry
.
remote
import
RemoteRegistry
self
.
_registry
=
RemoteRegistry
(
registry_config
,
self
.
config
.
project
,
None
,
self
.
config
.
auth_config
)
else
:
self
.
_registry
=
Registry
(
self
.
config
.
project
,
registry_config
,
repo_path
=
self
.
repo_path
,
auth_config
=
self
.
config
.
auth_config
,
)
self
.
_provider
=
get_provider
(
self
.
config
)
def
__repr__
(
self
)
->
str
:
return
(
f"FeatureStore(
\n
"
f" repo_path=
{
self
.
repo_path
!r
}
,
\n
"
f" config=
{
self
.
config
!r
}
,
\n
"
f" registry=
{
self
.
_registry
!r
}
,
\n
"
f" provider=
{
self
.
_provider
!r
}
\n
"
f")"
)
@
property
def
registry
(
self
)
->
BaseRegistry
:
"""Gets the registry of this feature store."""
return
self
.
_registry
@
property
def
project
(
self
)
->
str
:
"""Gets the project of this feature store."""
return
self
.
config
.
project
def
_get_provider
(
self
)
->
Provider
:
# TODO: Bake self.repo_path into self.config so that we dont only have one interface to paths
return
self
.
_provider
def
refresh_registry
(
self
):
"""Fetches and caches a copy of the feature registry in memory.
Explicitly calling this method allows for direct control of the state of the registry cache. Every time this
method is called the complete registry state will be retrieved from the remote registry store backend
(e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features()
is called, then get_online_features() will use the cached registry instead of retrieving (and caching) the
registry itself.
Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that
refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value
greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be
downloaded synchronously, which may increase latencies if the triggering method is get_online_features().
"""
self
.
_registry
.
refresh
(
self
.
project
)
def
list_entities
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
Entity
]:
"""
Retrieves the list of entities from the registry.
Args:
allow_cache: Whether to allow returning entities from a cached registry.
tags: Filter by tags.
Returns:
A list of entities.
"""
return
self
.
_list_entities
(
allow_cache
,
tags
=
tags
)
def
_list_entities
(
self
,
allow_cache
:
bool
=
False
,
hide_dummy_entity
:
bool
=
True
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
,
)
->
List
[
Entity
]:
all_entities
=
self
.
_registry
.
list_entities
(
self
.
project
,
allow_cache
=
allow_cache
,
tags
=
tags
)
return
[
entity
for
entity
in
all_entities
if
entity
.
name
!=
DUMMY_ENTITY_NAME
or
not
hide_dummy_entity
]
def
list_feature_services
(
self
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
FeatureService
]:
"""
Retrieves the list of feature services from the registry.
Args:
tags: Filter by tags.
Returns:
A list of feature services.
"""
return
self
.
_registry
.
list_feature_services
(
self
.
project
,
tags
=
tags
)
def
_list_all_feature_views
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
BaseFeatureView
]:
feature_views
=
[]
for
fv
in
self
.
registry
.
list_all_feature_views
(
self
.
project
,
allow_cache
=
allow_cache
,
tags
=
tags
):
if
(
isinstance
(
fv
,
FeatureView
)
and
fv
.
entities
and
fv
.
entities
[
0
]
==
DUMMY_ENTITY_NAME
):
fv
.
entities
=
[]
fv
.
entity_columns
=
[]
feature_views
.
append
(
fv
)
return
feature_views
def
list_all_feature_views
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
BaseFeatureView
]:
"""
Retrieves the list of feature views from the registry.
Args:
allow_cache: Whether to allow returning entities from a cached registry.
Returns:
A list of feature views.
"""
return
self
.
_list_all_feature_views
(
allow_cache
,
tags
=
tags
)
def
list_feature_views
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
FeatureView
]:
"""
Retrieves the list of feature views from the registry.
Args:
allow_cache: Whether to allow returning entities from a cached registry.
tags: Filter by tags.
Returns:
A list of feature views.
"""
return
utils
.
_list_feature_views
(
self
.
_registry
,
self
.
project
,
allow_cache
,
tags
=
tags
)
def
list_batch_feature_views
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
FeatureView
]:
"""
Retrieves the list of feature views from the registry.
Args:
allow_cache: Whether to allow returning entities from a cached registry.
tags: Filter by tags.
Returns:
A list of feature views.
"""
return
self
.
_list_batch_feature_views
(
allow_cache
=
allow_cache
,
tags
=
tags
)
def
_list_batch_feature_views
(
self
,
allow_cache
:
bool
=
False
,
hide_dummy_entity
:
bool
=
True
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
,
)
->
List
[
FeatureView
]:
feature_views
=
[]
for
fv
in
self
.
_registry
.
list_feature_views
(
self
.
project
,
allow_cache
=
allow_cache
,
tags
=
tags
):
if
(
hide_dummy_entity
and
fv
.
entities
and
fv
.
entities
[
0
]
==
DUMMY_ENTITY_NAME
):
fv
.
entities
=
[]
fv
.
entity_columns
=
[]
feature_views
.
append
(
fv
)
return
feature_views
def
_list_stream_feature_views
(
self
,
allow_cache
:
bool
=
False
,
hide_dummy_entity
:
bool
=
True
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
,
)
->
List
[
StreamFeatureView
]:
stream_feature_views
=
[]
for
sfv
in
self
.
_registry
.
list_stream_feature_views
(
self
.
project
,
allow_cache
=
allow_cache
,
tags
=
tags
):
if
hide_dummy_entity
and
sfv
.
entities
[
0
]
==
DUMMY_ENTITY_NAME
:
sfv
.
entities
=
[]
sfv
.
entity_columns
=
[]
stream_feature_views
.
append
(
sfv
)
return
stream_feature_views
def
list_on_demand_feature_views
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
OnDemandFeatureView
]:
"""
Retrieves the list of on demand feature views from the registry.
Args:
allow_cache: Whether to allow returning entities from a cached registry.
tags: Filter by tags.
Returns:
A list of on demand feature views.
"""
return
self
.
_registry
.
list_on_demand_feature_views
(
self
.
project
,
allow_cache
=
allow_cache
,
tags
=
tags
)
def
list_stream_feature_views
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
StreamFeatureView
]:
"""
Retrieves the list of stream feature views from the registry.
Returns:
A list of stream feature views.
"""
return
self
.
_list_stream_feature_views
(
allow_cache
,
tags
=
tags
)
def
list_data_sources
(
self
,
allow_cache
:
bool
=
False
,
tags
:
Optional
[
dict
[
str
,
str
]]
=
None
)
->
List
[
DataSource
]:
"""
Retrieves the list of data sources from the registry.
Args:
allow_cache: Whether to allow returning data sources from a cached registry.
tags: Filter by tags.
Returns:
A list of data sources.
"""
return
self
.
_registry
.
list_data_sources
(
self
.
project
,
allow_cache
=
allow_cache
,
tags
=
tags
)
def
get_entity
(
self
,
name
:
str
,
allow_registry_cache
:
bool
=
False
)
->
Entity
:
"""
Retrieves an entity.
Args:
name: Name of entity.
allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry
Returns:
The specified entity.
Raises:
EntityNotFoundException: The entity could not be found.
"""
return
self
.
_registry
.
get_entity
(
name
,
self
.
project
,
allow_cache
=
allow_registry_cache
)
def
get_feature_service
(
self
,
name
:
str
,
allow_cache
:
bool
=
False
)
->
FeatureService
:
"""
Retrieves a feature service.
Args:
name: Name of feature service.
allow_cache: Whether to allow returning feature services from a cached registry.
Returns:
The specified feature service.
Raises:
FeatureServiceNotFoundException: The feature service could not be found.
"""
return
self
.
_registry
.
get_feature_service
(
name
,
self
.
project
,
allow_cache
)
def
get_feature_view
(
self
,
name
:
str
,
allow_registry_cache
:
bool
=
False
)
->
FeatureView
:
"""
Retrieves a feature view.
Args:
name: Name of feature view.
allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry
Returns:
The specified feature view.
Raises:
FeatureViewNotFoundException: The feature view could not be found.
"""
return
self
.
_get_feature_view
(
name
,
allow_registry_cache
=
allow_registry_cache
)
def
_get_feature_view
(
self
,
name
:
str
,
hide_dummy_entity
:
bool
=
True
,
allow_registry_cache
:
bool
=
False
,
)
->
FeatureView
:
feature_view
=
self
.
_registry
.
get_feature_view
(
name
,
self
.
project
,
allow_cache
=
allow_registry_cache
)
if
hide_dummy_entity
and
feature_view
.
entities
[
0
]
==
DUMMY_ENTITY_NAME
:
feature_view
.
entities
=
[]
return
feature_view
def
get_stream_feature_view
(
self
,
name
:
str
,
allow_registry_cache
:
bool
=
False
)
->
StreamFeatureView
:
"""
Retrieves a stream feature view.
Args:
name: Name of stream feature view.
allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry
Returns:
The specified stream feature view.
Raises:
FeatureViewNotFoundException: The feature view could not be found.
"""
return
self
.
_get_stream_feature_view
(
name
,
allow_registry_cache
=
allow_registry_cache
)
def
_get_stream_feature_view
(
self
,
name
:
str
,
hide_dummy_entity
:
bool
=
True
,
allow_registry_cache
:
bool
=
False
,
)
->
StreamFeatureView
:
stream_feature_view
=
self
.
_registry
.
get_stream_feature_view
(
name
,
self
.
project
,
allow_cache
=
allow_registry_cache
)
if
hide_dummy_entity
and
stream_feature_view
.
entities
[
0
]
==
DUMMY_ENTITY_NAME
:
stream_feature_view
.
entities
=
[]
return
stream_feature_view
def
get_on_demand_feature_view
(
self
,
name
:
str
,
allow_registry_cache
:
bool
=
False
)
->
OnDemandFeatureView
:
"""
Retrieves a feature view.
Args:
name: Name of feature view.
allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry
Returns:
The specified feature view.
Raises:
FeatureViewNotFoundException: The feature view could not be found.
"""
return
self
.
_registry
.
get_on_demand_feature_view
(
name
,
self
.
project
,
allow_cache
=
allow_registry_cache
)
def
get_data_source
(
self
,
name
:
str
)
->
DataSource
:
"""
Retrieves the list of data sources from the registry.
Args:
name: Name of the data source.
Returns:
The specified data source.
Raises:
DataSourceObjectNotFoundException: The data source could not be found.
"""
return
self
.
_registry
.
get_data_source
(
name
,
self
.
project
)
def
delete_feature_view
(
self
,
name
:
str
):
"""
Deletes a feature view of any kind (FeatureView, OnDemandFeatureView, StreamFeatureView).
Args:
name: Name of feature view.
Raises:
FeatureViewNotFoundException: The feature view could not be found.
"""
return
self
.
_registry
.
delete_feature_view
(
name
,
self
.
project
)
def
delete_feature_service
(
self
,
name
:
str
):
"""
Deletes a feature service.
Args:
name: Name of feature service.
Raises:
FeatureServiceNotFoundException: The feature view could not be found.
"""
return
self
.
_registry
.
delete_feature_service
(
name
,
self
.
project
)
def
_should_use_plan
(
self
):
"""Returns True if plan and _apply_diffs should be used, False otherwise."""
# Currently only the local provider with sqlite online store supports plan and _apply_diffs.
return
self
.
config
.
provider
==
"local"
and
(
self
.
config
.
online_store
and
self
.
config
.
online_store
.
type
==
"sqlite"
)
def
_validate_all_feature_views
(
self
,
views_to_update
:
List
[
FeatureView
],
odfvs_to_update
:
List
[
OnDemandFeatureView
],
sfvs_to_update
:
List
[
StreamFeatureView
],
):
"""Validates all feature views."""
if
len
(
odfvs_to_update
)
>
0
and
not
flags_helper
.
is_test
():
warnings
.
warn
(
"On demand feature view is an experimental feature. "
"This API is stable, but the functionality does not scale well for offline retrieval"
,
RuntimeWarning
,
)
_validate_feature_views
(
[
*
views_to_update
,
*
odfvs_to_update
,
*
sfvs_to_update
,
]
)
def
_make_inferences
(
self
,
data_sources_to_update
:
List
[
DataSource
],
entities_to_update
:
List
[
Entity
],
views_to_update
:
List
[
FeatureView
],
odfvs_to_update
:
List
[
OnDemandFeatureView
],
sfvs_to_update
:
List
[
StreamFeatureView
],
feature_services_to_update
:
List
[
FeatureService
],
):
"""Makes inferences for entities, feature views, odfvs, and feature services."""
update_data_sources_with_inferred_event_timestamp_col
(
data_sources_to_update
,
self
.
config
)
update_data_sources_with_inferred_event_timestamp_col
(
[
view
.
batch_source
for
view
in
views_to_update
],
self
.
config
)
update_data_sources_with_inferred_event_timestamp_col
(
[
view
.
batch_source
for
view
in
sfvs_to_update
],
self
.
config
)
# New feature views may reference previously applied entities.
entities
=
self
.
_list_entities
()
provider
=
self
.
_get_provider
()
update_feature_views_with_inferred_features_and_entities
(
provider
,
views_to_update
,
entities
+
entities_to_update
,
self
.
config
,
)
update_feature_views_with_inferred_features_and_entities
(
provider
,
sfvs_to_update
,
entities
+
entities_to_update
,
self
.
config
,
)
# We need to attach the time stamp fields to the underlying data sources
# and cascade the dependencies
update_feature_views_with_inferred_features_and_entities
(
provider
,
odfvs_to_update
,
entities
+
entities_to_update
,
self
.
config
)
# TODO(kevjumba): Update schema inference
for
sfv
in
sfvs_to_update
:
if
not
sfv
.
schema
:
raise
ValueError
(
f"schema inference not yet supported for stream feature views. please define schema for stream feature view:
{
sfv
.
name
}
"
)
for
odfv
in
odfvs_to_update
:
odfv
.
infer_features
()
odfvs_to_write
=
[
odfv
for
odfv
in
odfvs_to_update
if
odfv
.
write_to_online_store
]
# Update to include ODFVs with write to online store
fvs_to_update_map
=
{
view
.
name
:
view
for
view
in
[
*
views_to_update
,
*
sfvs_to_update
,
*
odfvs_to_write
]
}
for
feature_service
in
feature_services_to_update
:
feature_service
.
infer_features
(
fvs_to_update
=
fvs_to_update_map
)
def
_get_feature_views_to_materialize
(
self
,
feature_views
:
Optional
[
List
[
str
]],
)
->
List
[
Union
[
FeatureView
,
OnDemandFeatureView
]]:
"""
Returns the list of feature views that should be materialized.
If no feature views are specified, all feature views will be returned.
Args:
feature_views: List of names of feature views to materialize.
Raises:
FeatureViewNotFoundException: One of the specified feature views could not be found.
ValueError: One of the specified feature views is not configured for materialization.
"""
feature_views_to_materialize
:
List
[
Union
[
FeatureView
,
OnDemandFeatureView
]]
=
[]
if
feature_views
is
None
:
regular_feature_views
=
utils
.
_list_feature_views
(
self
.
_registry
,
self
.
project
,
hide_dummy_entity
=
False
)
feature_views_to_materialize
.
extend
(
[
fv
for
fv
in
regular_feature_views
if
fv
.
online
]
)
stream_feature_views_to_materialize
=
self
.
_list_stream_feature_views
(
hide_dummy_entity
=
False
)
feature_views_to_materialize
.
extend
(
[
sfv
for
sfv
in
stream_feature_views_to_materialize
if
sfv
.
online
]
)
on_demand_feature_views_to_materialize
=
self
.
list_on_demand_feature_views
()
feature_views_to_materialize
.
extend
(
[
odfv
for
odfv
in
on_demand_feature_views_to_materialize
if
odfv
.
write_to_online_store
]
)
else
:
for
name
in
feature_views
:
feature_view
:
Union
[
FeatureView
,
OnDemandFeatureView
]
try
:
feature_view
=
self
.
_get_feature_view
(
name
,
hide_dummy_entity
=
False
)
except
FeatureViewNotFoundException
:
try
:
feature_view
=
self
.
_get_stream_feature_view
(
name
,
hide_dummy_entity
=
False
)
except
FeatureViewNotFoundException
:
feature_view
=
self
.
get_on_demand_feature_view
(
name
)
if
hasattr
(
feature_view
,
"online"
)
and
not
feature_view
.
online
:
raise
ValueError
(
f"FeatureView
{
feature_view
.
name
}
is not configured to be served online."
)
elif
(
hasattr
(
feature_view
,
"write_to_online_store"
)
and
not
feature_view
.
write_to_online_store
):
raise
ValueError
(
f"OnDemandFeatureView
{
feature_view
.
name
}
is not configured for write_to_online_store."
)
feature_views_to_materialize
.
append
(
feature_view
)
return
feature_views_to_materialize
def
plan
(
self
,
desired_repo_contents
:
RepoContents
)
->
Tuple
[
RegistryDiff
,
InfraDiff
,
Infra
]:
"""Dry-run registering objects to metadata store.
The plan method dry-runs registering one or more definitions (e.g., Entity, FeatureView), and produces
a list of all the changes the that would be introduced in the feature repo. The changes computed by the plan
command are for informational purposes, and are not actually applied to the registry.
Args:
desired_repo_contents: The desired repo state.
Raises:
ValueError: The 'objects' parameter could not be parsed properly.
Examples:
Generate a plan adding an Entity and a FeatureView.
>>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
>>> from feast.feature_store import RepoContents
>>> from datetime import timedelta
>>> fs = FeatureStore(repo_path="project/feature_repo")
>>> driver = Entity(name="driver_id", description="driver id")
>>> driver_hourly_stats = FileSource(
... path="data/driver_stats.parquet",
... timestamp_field="event_timestamp",
... created_timestamp_column="created",
... )
>>> driver_hourly_stats_view = FeatureView(
... name="driver_hourly_stats",
... entities=[driver],
... ttl=timedelta(seconds=86400 * 1),
... source=driver_hourly_stats,
... )
>>> registry_diff, infra_diff, new_infra = fs.plan(RepoContents(
... projects=[Project(name="project")],
... data_sources=[driver_hourly_stats],
... feature_views=[driver_hourly_stats_view],
... on_demand_feature_views=list(),
... stream_feature_views=list(),
... entities=[driver],
... feature_services=list(),
... permissions=list())) # register entity and feature view
"""
# Validate and run inference on all the objects to be registered.
self
.
_validate_all_feature_views
(
desired_repo_contents
.
feature_views
,
desired_repo_contents
.
on_demand_feature_views
,
desired_repo_contents
.
stream_feature_views
,
)
_validate_data_sources
(
desired_repo_contents
.
data_sources
)
self
.
_make_inferences
(
desired_repo_contents
.
data_sources
,
desired_repo_contents
.
entities
,
desired_repo_contents
.
feature_views
,
desired_repo_contents
.
on_demand_feature_views
,
desired_repo_contents
.
stream_feature_views
,
desired_repo_contents
.
feature_services
,
)
# Compute the desired difference between the current objects in the registry and
# the desired repo state.
registry_diff
=
diff_between
(
self
.
_registry
,
self
.
project
,
desired_repo_contents
)
# Compute the desired difference between the current infra, as stored in the registry,
# and the desired infra.
self
.
_registry
.
refresh
(
project
=
self
.
project
)
current_infra_proto
=
self
.
_registry
.
get_infra
(
self
.
project
).
to_proto
()
desired_registry_proto
=
desired_repo_contents
.
to_registry_proto
()
new_infra
=
self
.
_provider
.
plan_infra
(
self
.
config
,
desired_registry_proto
)
new_infra_proto
=
new_infra
.
to_proto
()
infra_diff
=
diff_infra_protos
(
current_infra_proto
,
new_infra_proto
,
project
=
self
.
project
)
return
registry_diff
,
infra_diff
,
new_infra
def
_apply_diffs
(
self
,
registry_diff
:
RegistryDiff
,
infra_diff
:
InfraDiff
,
new_infra
:
Infra
):
"""Applies the given diffs to the metadata store and infrastructure.
Args:
registry_diff: The diff between the current registry and the desired registry.
infra_diff: The diff between the current infra and the desired infra.
new_infra: The desired infra.
"""
infra_diff
.
update
()
apply_diff_to_registry
(
self
.
_registry
,
registry_diff
,
self
.
project
,
commit
=
False
)
self
.
_registry
.
update_infra
(
new_infra
,
self
.
project
,
commit
=
True
)
def
apply
(
self
,
objects
:
Union
[
Project
,
DataSource
,
Entity
,
FeatureView
,
OnDemandFeatureView
,
BatchFeatureView
,
StreamFeatureView
,
FeatureService
,
ValidationReference
,
Permission
,
List
[
FeastObject
],
],
objects_to_delete
:
Optional
[
List
[
FeastObject
]]
=
None
,
partial
:
bool
=
True
,
):
"""Register objects to metadata store and update related infrastructure.
The apply method registers one or more definitions (e.g., Entity, FeatureView) and registers or updates these
objects in the Feast registry. Once the apply method has updated the infrastructure (e.g., create tables in
an online store), it will commit the updated registry. All operations are idempotent, meaning they can safely
be rerun.
Args:
objects: A single object, or a list of objects that should be registered with the Feature Store.
objects_to_delete: A list of objects to be deleted from the registry and removed from the
provider's infrastructure. This deletion will only be performed if partial is set to False.
partial: If True, apply will only handle the specified objects; if False, apply will also delete
all the objects in objects_to_delete, and tear down any associated cloud resources.
Raises:
ValueError: The 'objects' parameter could not be parsed properly.
Examples:
Register an Entity and a FeatureView.
>>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
>>> from datetime import timedelta
>>> fs = FeatureStore(repo_path="project/feature_repo")
>>> driver = Entity(name="driver_id", description="driver id")
>>> driver_hourly_stats = FileSource(
... path="data/driver_stats.parquet",
... timestamp_field="event_timestamp",
... created_timestamp_column="created",
... )
>>> driver_hourly_stats_view = FeatureView(
... name="driver_hourly_stats",
... entities=[driver],
... ttl=timedelta(seconds=86400 * 1),
... source=driver_hourly_stats,
... )
>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view
"""
# TODO: Add locking
if
not
isinstance
(
objects
,
Iterable
):
objects
=
[
objects
]
assert
isinstance
(
objects
,
list
)
if
not
objects_to_delete
:
objects_to_delete
=
[]
# Separate all objects into entities, feature services, and different feature view types.
projects_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
Project
)]
if
len
(
projects_to_update
)
>
1
:
raise
ValueError
(
"Only one project can be applied at a time."
)
entities_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
Entity
)]
views_to_update
=
[
ob
for
ob
in
objects
if
(
# BFVs are not handled separately from FVs right now.
(
isinstance
(
ob
,
FeatureView
)
or
isinstance
(
ob
,
BatchFeatureView
))
and
not
isinstance
(
ob
,
StreamFeatureView
)
)
]
sfvs_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
StreamFeatureView
)]
odfvs_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
OnDemandFeatureView
)]
odfvs_with_writes_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
OnDemandFeatureView
)
and
ob
.
write_to_online_store
]
services_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
FeatureService
)]
data_sources_set_to_update
=
{
ob
for
ob
in
objects
if
isinstance
(
ob
,
DataSource
)
}
validation_references_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
ValidationReference
)
]
permissions_to_update
=
[
ob
for
ob
in
objects
if
isinstance
(
ob
,
Permission
)]
batch_sources_to_add
:
List
[
DataSource
]
=
[]
for
data_source
in
data_sources_set_to_update
:
if
(
isinstance
(
data_source
,
PushSource
)
or
isinstance
(
data_source
,
KafkaSource
)
or
isinstance
(
data_source
,
KinesisSource
)
):
assert
data_source
.
batch_source
batch_sources_to_add
.
append
(
data_source
.
batch_source
)
for
batch_source
in
batch_sources_to_add
:
data_sources_set_to_update
.
add
(
batch_source
)
for
fv
in
itertools
.
chain
(
views_to_update
,
sfvs_to_update
,
odfvs_with_writes_to_update
):
if
isinstance
(
fv
,
FeatureView
)
and
fv
.
batch_source
:
data_sources_set_to_update
.
add
(
fv
.
batch_source
)
if
hasattr
(
fv
,
"stream_source"
):
if
fv
.
stream_source
:
data_sources_set_to_update
.
add
(
fv
.
stream_source
)
if
isinstance
(
fv
,
OnDemandFeatureView
):
for
source_fvp
in
fv
.
source_feature_view_projections
:
odfv_batch_source
:
Optional
[
DataSource
]
=
(
fv
.
source_feature_view_projections
[
source_fvp
].
batch_source
)
if
odfv_batch_source
is
not
None
:
data_sources_set_to_update
.
add
(
odfv_batch_source
)
else
:
pass
for
odfv
in
odfvs_to_update
:
for
v
in
odfv
.
source_request_sources
.
values
():
data_sources_set_to_update
.
add
(
v
)
data_sources_to_update
=
list
(
data_sources_set_to_update
)
# Handle all entityless feature views by using DUMMY_ENTITY as a placeholder entity.
entities_to_update
.
append
(
DUMMY_ENTITY
)
# Validate all feature views and make inferences.
self
.
_validate_all_feature_views
(
views_to_update
,
odfvs_to_update
,
sfvs_to_update
,
)
self
.
_make_inferences
(
data_sources_to_update
,
entities_to_update
,
views_to_update
,
odfvs_to_update
,
sfvs_to_update
,
services_to_update
,
)
# Add all objects to the registry and update the provider's infrastructure.
for
project
in
projects_to_update
:
self
.
_registry
.
apply_project
(
project
,
commit
=
False
)
for
ds
in
data_sources_to_update
:
self
.
_registry
.
apply_data_source
(
ds
,
project
=
self
.
project
,
commit
=
False
)
for
view
in
itertools
.
chain
(
views_to_update
,
odfvs_to_update
,
sfvs_to_update
):
self
.
_registry
.
apply_feature_view
(
view
,
project
=
self
.
project
,
commit
=
False
)
for
ent
in
entities_to_update
:
self
.
_registry
.
apply_entity
(
ent
,
project
=
self
.
project
,
commit
=
False
)
for
feature_service
in
services_to_update
:
self
.
_registry
.
apply_feature_service
(
feature_service
,
project
=
self
.
project
,
commit
=
False
)
for
validation_references
in
validation_references_to_update
:
self
.
_registry
.
apply_validation_reference
(
validation_references
,
project
=
self
.
project
,
commit
=
False
)
for
permission
in
permissions_to_update
:
self
.
_registry
.
apply_permission
(
permission
,
project
=
self
.
project
,
commit
=
False
)
entities_to_delete
=
[]
views_to_delete
=
[]
sfvs_to_delete
=
[]
permissions_to_delete
=
[]
if
not
partial
:
# Delete all registry objects that should not exist.
entities_to_delete
=
[
ob
for
ob
in
objects_to_delete
if
isinstance
(
ob
,
Entity
)
]
views_to_delete
=
[
ob
for
ob
in
objects_to_delete
View remainder of file in raw view
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