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import
copy
import
itertools
import
os
import
typing
import
warnings
from
collections
import
Counter
,
defaultdict
from
datetime
import
datetime
,
timezone
from
pathlib
import
Path
from
typing
import
(
Any
,
Dict
,
Iterable
,
List
,
Mapping
,
Optional
,
Sequence
,
Set
,
Tuple
,
Union
,
cast
,
)
import
pandas
as
pd
import
pyarrow
from
dateutil
.
tz
import
tzlocal
from
google
.
protobuf
.
timestamp_pb2
import
Timestamp
from
feast
.
constants
import
FEAST_FS_YAML_FILE_PATH_ENV_NAME
from
feast
.
entity
import
Entity
from
feast
.
errors
import
(
FeatureNameCollisionError
,
FeatureViewNotFoundException
,
RequestDataNotFoundInEntityRowsException
,
)
from
feast
.
infra
.
key_encoding_utils
import
deserialize_entity_key
from
feast
.
protos
.
feast
.
serving
.
ServingService_pb2
import
(
FieldStatus
,
GetOnlineFeaturesResponse
,
)
from
feast
.
protos
.
feast
.
types
.
EntityKey_pb2
import
EntityKey
as
EntityKeyProto
from
feast
.
protos
.
feast
.
types
.
Value_pb2
import
FloatList
as
FloatListProto
from
feast
.
protos
.
feast
.
types
.
Value_pb2
import
RepeatedValue
as
RepeatedValueProto
from
feast
.
protos
.
feast
.
types
.
Value_pb2
import
Value
as
ValueProto
from
feast
.
type_map
import
python_values_to_proto_values
from
feast
.
types
import
ComplexFeastType
,
PrimitiveFeastType
,
from_feast_to_pyarrow_type
from
feast
.
value_type
import
ValueType
from
feast
.
version
import
get_version
if
typing
.
TYPE_CHECKING
:
from
feast
.
base_feature_view
import
BaseFeatureView
from
feast
.
feature_service
import
FeatureService
from
feast
.
feature_view
import
FeatureView
from
feast
.
infra
.
registry
.
base_registry
import
BaseRegistry
from
feast
.
on_demand_feature_view
import
OnDemandFeatureView
APPLICATION_NAME
=
"feast-dev/feast"
USER_AGENT
=
"{}/{}"
.
format
(
APPLICATION_NAME
,
get_version
())
def
get_user_agent
():
return
USER_AGENT
def
make_tzaware
(
t
:
datetime
)
->
datetime
:
"""We assume tz-naive datetimes are UTC"""
if
t
.
tzinfo
is
None
:
return
t
.
replace
(
tzinfo
=
timezone
.
utc
)
else
:
return
t
def
make_df_tzaware
(
t
:
pd
.
DataFrame
)
->
pd
.
DataFrame
:
"""Make all datetime type columns tzaware; leave everything else intact."""
df
=
t
.
copy
()
# don't modify incoming dataframe inplace
for
column
in
df
.
columns
:
if
pd
.
api
.
types
.
is_datetime64_any_dtype
(
df
[
column
]):
df
[
column
]
=
pd
.
to_datetime
(
df
[
column
],
utc
=
True
)
return
df
def
to_naive_utc
(
ts
:
datetime
)
->
datetime
:
if
ts
.
tzinfo
is
None
:
return
ts
else
:
return
ts
.
astimezone
(
timezone
.
utc
).
replace
(
tzinfo
=
None
)
def
maybe_local_tz
(
t
:
datetime
)
->
datetime
:
if
t
.
tzinfo
is
None
:
return
t
.
replace
(
tzinfo
=
tzlocal
())
else
:
return
t
def
get_default_yaml_file_path
(
repo_path
:
Path
)
->
Path
:
if
FEAST_FS_YAML_FILE_PATH_ENV_NAME
in
os
.
environ
:
yaml_path
=
os
.
environ
[
FEAST_FS_YAML_FILE_PATH_ENV_NAME
]
return
Path
(
yaml_path
)
else
:
return
repo_path
/
"feature_store.yaml"
def
_get_requested_feature_views_to_features_dict
(
feature_refs
:
List
[
str
],
feature_views
:
List
[
"FeatureView"
],
on_demand_feature_views
:
List
[
"OnDemandFeatureView"
],
)
->
Tuple
[
Dict
[
"FeatureView"
,
List
[
str
]],
Dict
[
"OnDemandFeatureView"
,
List
[
str
]]]:
"""Create a dict of FeatureView -> List[Feature] for all requested features.
Set full_feature_names to True to have feature names prefixed by their feature view name.
"""
feature_views_to_feature_map
:
Dict
[
"FeatureView"
,
List
[
str
]]
=
defaultdict
(
list
)
on_demand_feature_views_to_feature_map
:
Dict
[
"OnDemandFeatureView"
,
List
[
str
]]
=
(
defaultdict
(
list
)
)
for
ref
in
feature_refs
:
ref_parts
=
ref
.
split
(
":"
)
feature_view_from_ref
=
ref_parts
[
0
]
feature_from_ref
=
ref_parts
[
1
]
found
=
False
for
fv
in
feature_views
:
if
fv
.
projection
.
name_to_use
()
==
feature_view_from_ref
:
found
=
True
feature_views_to_feature_map
[
fv
].
append
(
feature_from_ref
)
for
odfv
in
on_demand_feature_views
:
if
odfv
.
projection
.
name_to_use
()
==
feature_view_from_ref
:
found
=
True
on_demand_feature_views_to_feature_map
[
odfv
].
append
(
feature_from_ref
)
if
not
found
:
raise
ValueError
(
f"Could not find feature view from reference
{
ref
}
"
)
return
feature_views_to_feature_map
,
on_demand_feature_views_to_feature_map
def
_get_column_names
(
feature_view
:
"FeatureView"
,
entities
:
List
[
Entity
]
)
->
Tuple
[
List
[
str
],
List
[
str
],
str
,
Optional
[
str
]]:
"""
If a field mapping exists, run it in reverse on the join keys,
feature names, event timestamp column, and created timestamp column
to get the names of the relevant columns in the offline feature store table.
Returns:
Tuple containing the list of reverse-mapped join_keys,
reverse-mapped feature names, reverse-mapped event timestamp column,
and reverse-mapped created timestamp column that will be passed into
the query to the offline store.
"""
# if we have mapped fields, use the original field names in the call to the offline store
timestamp_field
=
feature_view
.
batch_source
.
timestamp_field
feature_names
=
[
feature
.
name
for
feature
in
feature_view
.
features
]
created_timestamp_column
=
feature_view
.
batch_source
.
created_timestamp_column
from
feast
.
feature_view
import
DUMMY_ENTITY_ID
join_keys
=
[
entity
.
join_key
for
entity
in
entities
if
entity
.
join_key
!=
DUMMY_ENTITY_ID
]
if
feature_view
.
batch_source
.
field_mapping
is
not
None
:
reverse_field_mapping
=
{
v
:
k
for
k
,
v
in
feature_view
.
batch_source
.
field_mapping
.
items
()
}
timestamp_field
=
(
reverse_field_mapping
[
timestamp_field
]
if
timestamp_field
in
reverse_field_mapping
.
keys
()
else
timestamp_field
)
created_timestamp_column
=
(
reverse_field_mapping
[
created_timestamp_column
]
if
created_timestamp_column
and
created_timestamp_column
in
reverse_field_mapping
.
keys
()
else
created_timestamp_column
)
join_keys
=
[
reverse_field_mapping
[
col
]
if
col
in
reverse_field_mapping
.
keys
()
else
col
for
col
in
join_keys
]
feature_names
=
[
reverse_field_mapping
[
col
]
if
col
in
reverse_field_mapping
.
keys
()
else
col
for
col
in
feature_names
]
# We need to exclude join keys and timestamp columns from the list of features, after they are mapped to
# their final column names via the `field_mapping` field of the source.
feature_names
=
[
name
for
name
in
feature_names
if
name
not
in
join_keys
and
name
!=
timestamp_field
and
name
!=
created_timestamp_column
]
return
(
join_keys
,
feature_names
,
timestamp_field
,
created_timestamp_column
,
)
def
_run_pyarrow_field_mapping
(
table
:
pyarrow
.
Table
,
field_mapping
:
Dict
[
str
,
str
],
)
->
pyarrow
.
Table
:
# run field mapping in the forward direction
cols
=
table
.
column_names
mapped_cols
=
[
field_mapping
[
col
]
if
col
in
field_mapping
.
keys
()
else
col
for
col
in
cols
]
table
=
table
.
rename_columns
(
mapped_cols
)
return
table
def
_get_fields_with_aliases
(
fields
:
List
[
str
],
field_mappings
:
Dict
[
str
,
str
],
)
->
Tuple
[
List
[
str
],
List
[
str
]]:
"""
Get a list of fields with aliases based on the field mappings.
"""
for
field
in
fields
:
if
"."
in
field
and
field
not
in
field_mappings
:
raise
ValueError
(
f"Feature
{
field
}
contains a '.' character, which is not allowed in field names. Use field mappings to rename fields."
)
fields_with_aliases
=
[
f"
{
field
}
AS
{
field_mappings
[
field
]
}
"
if
field
in
field_mappings
else
field
for
field
in
fields
]
aliases
=
[
field_mappings
[
field
]
if
field
in
field_mappings
else
field
for
field
in
fields
]
return
(
fields_with_aliases
,
aliases
)
def
_coerce_datetime
(
ts
):
"""
Depending on underlying time resolution, arrow to_pydict() sometimes returns pd
timestamp type (for nanosecond resolution), and sometimes you get standard python datetime
(for microsecond resolution).
While pd timestamp class is a subclass of python datetime, it doesn't always behave the
same way. We convert it to normal datetime so that consumers downstream don't have to deal
with these quirks.
"""
if
isinstance
(
ts
,
pd
.
Timestamp
):
return
ts
.
to_pydatetime
()
else
:
return
ts
def
_convert_arrow_to_proto
(
table
:
Union
[
pyarrow
.
Table
,
pyarrow
.
RecordBatch
],
feature_view
:
Union
[
"FeatureView"
,
"BaseFeatureView"
,
"OnDemandFeatureView"
],
join_keys
:
Dict
[
str
,
ValueType
],
)
->
List
[
Tuple
[
EntityKeyProto
,
Dict
[
str
,
ValueProto
],
datetime
,
Optional
[
datetime
]]]:
# This is a workaround for isinstance(feature_view, OnDemandFeatureView), which triggers a circular import
if
getattr
(
feature_view
,
"source_request_sources"
,
None
):
return
_convert_arrow_odfv_to_proto
(
table
,
feature_view
,
join_keys
)
# type: ignore[arg-type]
else
:
return
_convert_arrow_fv_to_proto
(
table
,
feature_view
,
join_keys
)
# type: ignore[arg-type]
def
_convert_arrow_fv_to_proto
(
table
:
Union
[
pyarrow
.
Table
,
pyarrow
.
RecordBatch
],
feature_view
:
"FeatureView"
,
join_keys
:
Dict
[
str
,
ValueType
],
)
->
List
[
Tuple
[
EntityKeyProto
,
Dict
[
str
,
ValueProto
],
datetime
,
Optional
[
datetime
]]]:
# Avoid ChunkedArrays which guarantees `zero_copy_only` available.
if
isinstance
(
table
,
pyarrow
.
Table
):
table
=
table
.
to_batches
()[
0
]
columns
=
[
(
field
.
name
,
field
.
dtype
.
to_value_type
())
for
field
in
feature_view
.
features
]
+
list
(
join_keys
.
items
())
proto_values_by_column
=
{
column
:
python_values_to_proto_values
(
table
.
column
(
column
).
to_numpy
(
zero_copy_only
=
False
),
value_type
)
for
column
,
value_type
in
columns
}
entity_keys
=
[
EntityKeyProto
(
join_keys
=
join_keys
,
entity_values
=
[
proto_values_by_column
[
k
][
idx
]
for
k
in
join_keys
],
)
for
idx
in
range
(
table
.
num_rows
)
]
# Serialize the features per row
feature_dict
=
{
feature
.
name
:
proto_values_by_column
[
feature
.
name
]
for
feature
in
feature_view
.
features
}
features
=
[
dict
(
zip
(
feature_dict
,
vars
))
for
vars
in
zip
(
*
feature_dict
.
values
())]
# Convert event_timestamps
event_timestamps
=
[
_coerce_datetime
(
val
)
for
val
in
pd
.
to_datetime
(
table
.
column
(
feature_view
.
batch_source
.
timestamp_field
).
to_numpy
(
zero_copy_only
=
False
)
)
]
# Convert created_timestamps if they exist
if
feature_view
.
batch_source
.
created_timestamp_column
:
created_timestamps
=
[
_coerce_datetime
(
val
)
for
val
in
pd
.
to_datetime
(
table
.
column
(
feature_view
.
batch_source
.
created_timestamp_column
).
to_numpy
(
zero_copy_only
=
False
)
)
]
else
:
created_timestamps
=
[
None
]
*
table
.
num_rows
return
list
(
zip
(
entity_keys
,
features
,
event_timestamps
,
created_timestamps
))
def
_convert_arrow_odfv_to_proto
(
table
:
Union
[
pyarrow
.
Table
,
pyarrow
.
RecordBatch
],
feature_view
:
"OnDemandFeatureView"
,
join_keys
:
Dict
[
str
,
ValueType
],
)
->
List
[
Tuple
[
EntityKeyProto
,
Dict
[
str
,
ValueProto
],
datetime
,
Optional
[
datetime
]]]:
# Avoid ChunkedArrays which guarantees `zero_copy_only` available.
if
isinstance
(
table
,
pyarrow
.
Table
):
table
=
table
.
to_batches
()[
0
]
columns
=
[
(
field
.
name
,
field
.
dtype
.
to_value_type
())
for
field
in
feature_view
.
features
]
+
list
(
join_keys
.
items
())
proto_values_by_column
=
{
column
:
python_values_to_proto_values
(
table
.
column
(
column
).
to_numpy
(
zero_copy_only
=
False
),
value_type
)
for
column
,
value_type
in
columns
if
column
in
table
.
column_names
}
# Ensure join keys are included in proto_values_by_column, but check if they exist first
for
join_key
,
value_type
in
join_keys
.
items
():
if
join_key
not
in
proto_values_by_column
:
# Check if the join key exists in the table before trying to access it
if
join_key
in
table
.
column_names
:
proto_values_by_column
[
join_key
]
=
python_values_to_proto_values
(
table
.
column
(
join_key
).
to_numpy
(
zero_copy_only
=
False
),
value_type
)
else
:
# Create null/default values if the join key isn't in the table
null_column
=
[
None
]
*
table
.
num_rows
proto_values_by_column
[
join_key
]
=
python_values_to_proto_values
(
null_column
,
value_type
)
# Adding On Demand Features
for
feature
in
feature_view
.
features
:
if
(
feature
.
name
in
[
c
[
0
]
for
c
in
columns
]
and
feature
.
name
not
in
proto_values_by_column
):
# initializing the column as null
null_column
=
pyarrow
.
array
(
[
None
]
*
table
.
num_rows
,
type
=
from_feast_to_pyarrow_type
(
feature
.
dtype
),
)
updated_table
=
pyarrow
.
RecordBatch
.
from_arrays
(
table
.
columns
+
[
null_column
],
schema
=
table
.
schema
.
append
(
pyarrow
.
field
(
feature
.
name
,
null_column
.
type
)
# type: ignore[attr-defined]
),
)
proto_values_by_column
[
feature
.
name
]
=
python_values_to_proto_values
(
updated_table
.
column
(
feature
.
name
).
to_numpy
(
zero_copy_only
=
False
),
feature
.
dtype
.
to_value_type
(),
)
entity_keys
=
[
EntityKeyProto
(
join_keys
=
join_keys
,
entity_values
=
[
proto_values_by_column
[
k
][
idx
]
for
k
in
join_keys
if
k
in
proto_values_by_column
],
)
for
idx
in
range
(
table
.
num_rows
)
]
# Serialize the features per row
feature_dict
=
{
feature
.
name
:
proto_values_by_column
[
feature
.
name
]
for
feature
in
feature_view
.
features
}
if
feature_view
.
write_to_online_store
:
table_columns
=
[
col
.
name
for
col
in
table
.
schema
]
for
feature
in
feature_view
.
schema
:
if
feature
.
name
not
in
feature_dict
and
feature
.
name
in
table_columns
:
feature_dict
[
feature
.
name
]
=
proto_values_by_column
[
feature
.
name
]
features
=
[
dict
(
zip
(
feature_dict
,
vars
))
for
vars
in
zip
(
*
feature_dict
.
values
())]
# We need to artificially add event_timestamps and created_timestamps
event_timestamps
=
[]
timestamp_values
=
pd
.
to_datetime
([
_utc_now
()
for
i
in
range
(
table
.
num_rows
)])
for
val
in
timestamp_values
:
event_timestamps
.
append
(
_coerce_datetime
(
val
))
# setting them equivalent
created_timestamps
=
event_timestamps
return
list
(
zip
(
entity_keys
,
features
,
event_timestamps
,
created_timestamps
))
def
_validate_entity_values
(
join_key_values
:
Dict
[
str
,
List
[
ValueProto
]]):
set_of_row_lengths
=
{
len
(
v
)
for
v
in
join_key_values
.
values
()}
if
len
(
set_of_row_lengths
)
>
1
:
raise
ValueError
(
"All entity rows must have the same columns."
)
return
set_of_row_lengths
.
pop
()
def
_validate_feature_refs
(
feature_refs
:
List
[
str
],
full_feature_names
:
bool
=
False
):
"""
Validates that there are no collisions among the feature references.
Args:
feature_refs: List of feature references to validate. Feature references must have format
"feature_view:feature", e.g. "customer_fv:daily_transactions".
full_feature_names: If True, the full feature references are compared for collisions; if False,
only the feature names are compared.
Raises:
FeatureNameCollisionError: There is a collision among the feature references.
"""
collided_feature_refs
=
[]
if
full_feature_names
:
collided_feature_refs
=
[
ref
for
ref
,
occurrences
in
Counter
(
feature_refs
).
items
()
if
occurrences
>
1
]
else
:
feature_names
=
[
ref
.
split
(
":"
)[
1
]
for
ref
in
feature_refs
]
collided_feature_names
=
[
ref
for
ref
,
occurrences
in
Counter
(
feature_names
).
items
()
if
occurrences
>
1
]
for
feature_name
in
collided_feature_names
:
collided_feature_refs
.
extend
(
[
ref
for
ref
in
feature_refs
if
ref
.
endswith
(
":"
+
feature_name
)]
)
if
len
(
collided_feature_refs
)
>
0
:
raise
FeatureNameCollisionError
(
collided_feature_refs
,
full_feature_names
)
def
_group_feature_refs
(
features
:
List
[
str
],
all_feature_views
:
List
[
"FeatureView"
],
all_on_demand_feature_views
:
List
[
"OnDemandFeatureView"
],
)
->
Tuple
[
List
[
Tuple
[
Union
[
"FeatureView"
,
"OnDemandFeatureView"
],
List
[
str
]]],
List
[
Tuple
[
"OnDemandFeatureView"
,
List
[
str
]]],
]:
"""Get list of feature views and corresponding feature names based on feature references"""
# view name to view proto
view_index
:
Dict
[
str
,
Union
[
"FeatureView"
,
"OnDemandFeatureView"
]]
=
{
view
.
projection
.
name_to_use
():
view
for
view
in
all_feature_views
}
# on demand view to on demand view proto
on_demand_view_index
:
Dict
[
str
,
"OnDemandFeatureView"
]
=
{}
for
view
in
all_on_demand_feature_views
:
if
view
.
projection
and
not
view
.
write_to_online_store
:
on_demand_view_index
[
view
.
projection
.
name_to_use
()]
=
view
elif
view
.
projection
and
view
.
write_to_online_store
:
# we insert the ODFV view to FVs for ones that are written to the online store
view_index
[
view
.
projection
.
name_to_use
()]
=
view
# view name to feature names
views_features
=
defaultdict
(
set
)
# on demand view name to feature names
on_demand_view_features
=
defaultdict
(
set
)
for
ref
in
features
:
view_name
,
feat_name
=
ref
.
split
(
":"
)
if
view_name
in
view_index
:
if
hasattr
(
view_index
[
view_name
],
"write_to_online_store"
):
tmp_feat_name
=
[
f
for
f
in
view_index
[
view_name
].
schema
if
f
.
name
==
feat_name
]
if
len
(
tmp_feat_name
)
>
0
:
feat_name
=
tmp_feat_name
[
0
].
name
else
:
view_index
[
view_name
].
projection
.
get_feature
(
feat_name
)
# For validation
views_features
[
view_name
].
add
(
feat_name
)
elif
view_name
in
on_demand_view_index
:
on_demand_view_index
[
view_name
].
projection
.
get_feature
(
feat_name
)
# For validation
on_demand_view_features
[
view_name
].
add
(
feat_name
)
# Let's also add in any FV Feature dependencies here.
for
input_fv_projection
in
on_demand_view_index
[
view_name
].
source_feature_view_projections
.
values
():
for
input_feat
in
input_fv_projection
.
features
:
views_features
[
input_fv_projection
.
name
].
add
(
input_feat
.
name
)
else
:
raise
FeatureViewNotFoundException
(
view_name
)
fvs_result
:
List
[
Tuple
[
Union
[
"FeatureView"
,
"OnDemandFeatureView"
],
List
[
str
]]]
=
[]
odfvs_result
:
List
[
Tuple
[
"OnDemandFeatureView"
,
List
[
str
]]]
=
[]
for
view_name
,
feature_names
in
views_features
.
items
():
fvs_result
.
append
((
view_index
[
view_name
],
list
(
feature_names
)))
for
view_name
,
feature_names
in
on_demand_view_features
.
items
():
odfvs_result
.
append
((
on_demand_view_index
[
view_name
],
list
(
feature_names
)))
return
fvs_result
,
odfvs_result
def
construct_response_feature_vector
(
values_vector
:
Iterable
[
Any
],
statuses_vector
:
Iterable
[
Any
],
timestamp_vector
:
Iterable
[
Any
],
mapping_indexes
:
Iterable
[
List
[
int
]],
output_len
:
int
,
)
->
GetOnlineFeaturesResponse
.
FeatureVector
:
values_output
:
Iterable
[
Any
]
=
[
None
]
*
output_len
statuses_output
:
Iterable
[
Any
]
=
[
None
]
*
output_len
timestamp_output
:
Iterable
[
Any
]
=
[
None
]
*
output_len
for
i
,
destinations
in
enumerate
(
mapping_indexes
):
for
idx
in
destinations
:
values_output
[
idx
]
=
values_vector
[
i
]
# type: ignore[index]
statuses_output
[
idx
]
=
statuses_vector
[
i
]
# type: ignore[index]
timestamp_output
[
idx
]
=
timestamp_vector
[
i
]
# type: ignore[index]
return
GetOnlineFeaturesResponse
.
FeatureVector
(
values
=
values_output
,
statuses
=
statuses_output
,
event_timestamps
=
timestamp_output
,
)
def
_augment_response_with_on_demand_transforms
(
online_features_response
:
GetOnlineFeaturesResponse
,
feature_refs
:
List
[
str
],
requested_on_demand_feature_views
:
List
[
"OnDemandFeatureView"
],
full_feature_names
:
bool
,
):
"""Computes on demand feature values and adds them to the result rows.
Assumes that 'online_features_response' already contains the necessary request data and input feature
views for the on demand feature views. Unneeded feature values such as request data and
unrequested input feature views will be removed from 'online_features_response'.
Args:
online_features_response: Protobuf object to populate
feature_refs: List of all feature references to be returned.
requested_on_demand_feature_views: List of all odfvs that have been requested.
full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,
changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to
"customer_fv__daily_transactions").
"""
from
feast
.
online_response
import
OnlineResponse
requested_odfv_map
=
{
odfv
.
name
:
odfv
for
odfv
in
requested_on_demand_feature_views
}
requested_odfv_feature_names
=
requested_odfv_map
.
keys
()
odfv_feature_refs
=
defaultdict
(
list
)
for
feature_ref
in
feature_refs
:
view_name
,
feature_name
=
feature_ref
.
split
(
":"
)
if
view_name
in
requested_odfv_feature_names
:
odfv_feature_refs
[
view_name
].
append
(
f"
{
requested_odfv_map
[
view_name
].
projection
.
name_to_use
()
}
__
{
feature_name
}
"
if
full_feature_names
else
feature_name
)
initial_response
=
OnlineResponse
(
online_features_response
)
initial_response_arrow
:
Optional
[
pyarrow
.
Table
]
=
None
initial_response_dict
:
Optional
[
Dict
[
str
,
List
[
Any
]]]
=
None
# Apply on demand transformations and augment the result rows
odfv_result_names
=
set
()
for
odfv_name
,
_feature_refs
in
odfv_feature_refs
.
items
():
odfv
=
requested_odfv_map
[
odfv_name
]
if
not
odfv
.
write_to_online_store
:
if
odfv
.
mode
==
"python"
:
if
initial_response_dict
is
None
:
initial_response_dict
=
initial_response
.
to_dict
()
transformed_features_dict
:
Dict
[
str
,
List
[
Any
]]
=
odfv
.
transform_dict
(
initial_response_dict
)
elif
odfv
.
mode
in
{
"pandas"
,
"substrait"
}:
if
initial_response_arrow
is
None
:
initial_response_arrow
=
initial_response
.
to_arrow
()
transformed_features_arrow
=
odfv
.
transform_arrow
(
initial_response_arrow
,
full_feature_names
)
else
:
raise
Exception
(
f"Invalid OnDemandFeatureMode:
{
odfv
.
mode
}
. Expected one of 'pandas', 'python', or 'substrait'."
)
transformed_features
=
(
transformed_features_dict
if
odfv
.
mode
==
"python"
else
transformed_features_arrow
)
transformed_columns
=
(
transformed_features
.
column_names
if
isinstance
(
transformed_features
,
pyarrow
.
Table
)
else
transformed_features
)
selected_subset
=
[
f
for
f
in
transformed_columns
if
f
in
_feature_refs
]
proto_values
=
[]
schema_dict
=
{
k
.
name
:
k
.
dtype
for
k
in
odfv
.
schema
}
for
selected_feature
in
selected_subset
:
feature_vector
=
transformed_features
[
selected_feature
]
selected_feature_type
=
schema_dict
.
get
(
selected_feature
,
None
)
feature_type
:
ValueType
=
ValueType
.
UNKNOWN
if
selected_feature_type
is
not
None
:
if
isinstance
(
selected_feature_type
, (
ComplexFeastType
,
PrimitiveFeastType
)
):
feature_type
=
selected_feature_type
.
to_value_type
()
elif
not
isinstance
(
selected_feature_type
,
ValueType
):
raise
TypeError
(
f"Unexpected type for feature_type:
{
type
(
feature_type
)
}
"
)
proto_values
.
append
(
python_values_to_proto_values
(
feature_vector
if
isinstance
(
feature_vector
,
list
)
else
[
feature_vector
]
if
odfv
.
mode
==
"python"
else
feature_vector
.
to_numpy
(),
feature_type
,
)
)
odfv_result_names
|=
set
(
selected_subset
)
online_features_response
.
metadata
.
feature_names
.
val
.
extend
(
selected_subset
)
for
feature_idx
in
range
(
len
(
selected_subset
)):
online_features_response
.
results
.
append
(
GetOnlineFeaturesResponse
.
FeatureVector
(
values
=
proto_values
[
feature_idx
],
statuses
=
[
FieldStatus
.
PRESENT
]
*
len
(
proto_values
[
feature_idx
]),
event_timestamps
=
[
Timestamp
()]
*
len
(
proto_values
[
feature_idx
]),
)
)
def
_get_entity_maps
(
registry
,
project
,
feature_views
,
)
->
Tuple
[
Dict
[
str
,
str
],
Dict
[
str
,
ValueType
],
Set
[
str
]]:
# TODO(felixwang9817): Support entities that have different types for different feature views.
entities
=
registry
.
list_entities
(
project
,
allow_cache
=
True
)
entity_name_to_join_key_map
:
Dict
[
str
,
str
]
=
{}
entity_type_map
:
Dict
[
str
,
ValueType
]
=
{}
for
entity
in
entities
:
entity_name_to_join_key_map
[
entity
.
name
]
=
entity
.
join_key
for
feature_view
in
feature_views
:
for
entity_name
in
feature_view
.
entities
:
entity
=
registry
.
get_entity
(
entity_name
,
project
,
allow_cache
=
True
)
# User directly uses join_key as the entity reference in the entity_rows for the
# entity mapping case.
entity_name
=
feature_view
.
projection
.
join_key_map
.
get
(
entity
.
join_key
,
entity
.
name
)
join_key
=
feature_view
.
projection
.
join_key_map
.
get
(
entity
.
join_key
,
entity
.
join_key
)
entity_name_to_join_key_map
[
entity_name
]
=
join_key
for
entity_column
in
feature_view
.
entity_columns
:
entity_type_map
[
entity_column
.
name
]
=
entity_column
.
dtype
.
to_value_type
()
return
(
entity_name_to_join_key_map
,
entity_type_map
,
set
(
entity_name_to_join_key_map
.
values
()),
)
def
_get_table_entity_values
(
table
:
"FeatureView"
,
entity_name_to_join_key_map
:
Dict
[
str
,
str
],
join_key_proto_values
:
Dict
[
str
,
List
[
ValueProto
]],
)
->
Dict
[
str
,
List
[
ValueProto
]]:
# The correct join_keys expected by the OnlineStore for this Feature View.
table_join_keys
=
[
entity_name_to_join_key_map
[
entity_name
]
for
entity_name
in
table
.
entities
]
# If the FeatureView has a Projection then the join keys may be aliased.
alias_to_join_key_map
=
{
v
:
k
for
k
,
v
in
table
.
projection
.
join_key_map
.
items
()}
# Subset to columns which are relevant to this FeatureView and
# give them the correct names.
entity_values
=
{
alias_to_join_key_map
.
get
(
k
,
k
):
v
for
k
,
v
in
join_key_proto_values
.
items
()
if
alias_to_join_key_map
.
get
(
k
,
k
)
in
table_join_keys
}
return
entity_values
def
_get_unique_entities
(
table
:
"FeatureView"
,
join_key_values
:
Dict
[
str
,
List
[
ValueProto
]],
entity_name_to_join_key_map
:
Dict
[
str
,
str
],
)
->
Tuple
[
Tuple
[
Dict
[
str
,
ValueProto
], ...],
Tuple
[
List
[
int
], ...],
int
]:
"""Return the set of unique composite Entities for a Feature View and the indexes at which they appear.
This method allows us to query the OnlineStore for data we need only once
rather than requesting and processing data for the same combination of
Entities multiple times.
"""
# Get the correct set of entity values with the correct join keys.
table_entity_values
=
_get_table_entity_values
(
table
,
entity_name_to_join_key_map
,
join_key_values
,
)
# Validate that all expected join keys exist and have non-empty values.
expected_keys
=
set
(
entity_name_to_join_key_map
.
values
())
expected_keys
.
discard
(
"__dummy_id"
)
missing_keys
=
sorted
(
list
(
set
([
key
for
key
in
expected_keys
if
key
not
in
table_entity_values
]))
)
empty_keys
=
sorted
(
list
(
set
([
key
for
key
in
expected_keys
if
not
table_entity_values
.
get
(
key
)]))
)
if
missing_keys
or
empty_keys
:
if
not
any
(
table_entity_values
.
values
()):
raise
KeyError
(
f"Missing join key values for keys:
{
missing_keys
}
. "
f"No values provided for keys:
{
empty_keys
}
. "
f"Provided join_key_values:
{
list
(
join_key_values
.
keys
())
}
"
)
# Convert the column-oriented table_entity_values into row-wise data.
keys
=
list
(
table_entity_values
.
keys
())
# Each row is a tuple of ValueProto objects corresponding to the join keys.
rowise
=
list
(
enumerate
(
zip
(
*
table_entity_values
.
values
())))
# If there are no rows, return empty tuples.
if
not
rowise
:
return
(), (),
0
# Sort rowise so that rows with the same join key values are adjacent.
rowise
.
sort
(
key
=
lambda
row
:
tuple
(
getattr
(
x
,
x
.
WhichOneof
(
"val"
))
for
x
in
row
[
1
]))
# Group rows by their composite join key value.
groups
=
[
(
dict
(
zip
(
keys
,
key_tuple
)), [
idx
for
idx
,
_
in
group
])
for
key_tuple
,
group
in
itertools
.
groupby
(
rowise
,
key
=
lambda
row
:
row
[
1
])
]
# If no groups were formed (should not happen for valid input), return empty tuples.
if
not
groups
:
return
(), (),
0
# Unpack the unique entities and their original row indexes.
unique_entities
,
indexes
=
tuple
(
zip
(
*
groups
))
return
unique_entities
,
indexes
,
len
(
rowise
)
def
_get_unique_entities_from_values
(
table_entity_values
:
Dict
[
str
,
List
[
ValueProto
]],
)
->
Tuple
[
Tuple
[
Dict
[
str
,
ValueProto
], ...],
Tuple
[
List
[
int
], ...],
int
]:
"""Return the set of unique composite Entities for a Feature View and the indexes at which they appear.
This method allows us to query the OnlineStore for data we need only once
rather than requesting and processing data for the same combination of
Entities multiple times.
"""
keys
=
table_entity_values
.
keys
()
# Sort the rowise data to allow for grouping but keep original index. This lambda is
# sufficient as Entity types cannot be complex (ie. lists).
rowise
=
list
(
enumerate
(
zip
(
*
table_entity_values
.
values
())))
rowise
.
sort
(
key
=
lambda
row
:
tuple
(
getattr
(
x
,
x
.
WhichOneof
(
"val"
))
for
x
in
row
[
1
]))
# Identify unique entities and the indexes at which they occur.
unique_entities
:
Tuple
[
Dict
[
str
,
ValueProto
], ...]
indexes
:
Tuple
[
List
[
int
], ...]
unique_entities
,
indexes
=
tuple
(
zip
(
*
[
(
dict
(
zip
(
keys
,
k
)), [
_
[
0
]
for
_
in
g
])
for
k
,
g
in
itertools
.
groupby
(
rowise
,
key
=
lambda
x
:
x
[
1
])
]
)
)
return
unique_entities
,
indexes
,
len
(
rowise
)
def
_drop_unneeded_columns
(
online_features_response
:
GetOnlineFeaturesResponse
,
requested_result_row_names
:
Set
[
str
],
):
"""
Unneeded feature values such as request data and unrequested input feature views will
be removed from 'online_features_response'.
Args:
online_features_response: Protobuf object to populate
requested_result_row_names: Fields from 'result_rows' that have been requested, and
therefore should not be dropped.
"""
# Drop values that aren't needed
unneeded_feature_indices
=
[
idx
for
idx
,
val
in
enumerate
(
online_features_response
.
metadata
.
feature_names
.
val
)
if
val
not
in
requested_result_row_names
]
for
idx
in
reversed
(
unneeded_feature_indices
):
del
online_features_response
.
metadata
.
feature_names
.
val
[
idx
]
del
online_features_response
.
results
[
idx
]
def
_populate_result_rows_from_columnar
(
online_features_response
:
GetOnlineFeaturesResponse
,
data
:
Dict
[
str
,
List
[
ValueProto
]],
):
timestamp
=
Timestamp
()
# Only initialize this timestamp once.
# Add more values to the existing result rows
for
feature_name
,
feature_values
in
data
.
items
():
online_features_response
.
metadata
.
feature_names
.
val
.
append
(
feature_name
)
online_features_response
.
results
.
append
(
GetOnlineFeaturesResponse
.
FeatureVector
(
values
=
feature_values
,
statuses
=
[
FieldStatus
.
PRESENT
]
*
len
(
feature_values
),
event_timestamps
=
[
timestamp
]
*
len
(
feature_values
),
)
)
def
get_needed_request_data
(
grouped_odfv_refs
:
List
[
Tuple
[
"OnDemandFeatureView"
,
List
[
str
]]],
)
->
Set
[
str
]:
needed_request_data
:
Set
[
str
]
=
set
()
for
odfv
,
_
in
grouped_odfv_refs
:
odfv_request_data_schema
=
odfv
.
get_request_data_schema
()
# if odfv.write_to_online_store, we should not pass in the request data
needed_request_data
.
update
(
odfv_request_data_schema
.
keys
())
return
needed_request_data
def
ensure_request_data_values_exist
(
needed_request_data
:
Set
[
str
],
request_data_features
:
Dict
[
str
,
List
[
Any
]],
):
if
len
(
needed_request_data
)
!=
len
(
request_data_features
.
keys
()):
missing_features
=
[
x
for
x
in
needed_request_data
if
x
not
in
request_data_features
]
raise
RequestDataNotFoundInEntityRowsException
(
feature_names
=
missing_features
)
def
_populate_response_from_feature_data
(
feature_data
:
Iterable
[
Tuple
[
Iterable
[
Timestamp
],
Iterable
[
"FieldStatus.ValueType"
],
Iterable
[
ValueProto
]
]
],
indexes
:
Iterable
[
List
[
int
]],
online_features_response
:
GetOnlineFeaturesResponse
,
full_feature_names
:
bool
,
requested_features
:
Iterable
[
str
],
table
:
"FeatureView"
,
output_len
:
int
,
):
"""Populate the GetOnlineFeaturesResponse with feature data.
This method assumes that `_read_from_online_store` returns data for each
combination of Entities in `entity_rows` in the same order as they
are provided.
Args:
feature_data: A list of data in Protobuf form which was retrieved from the OnlineStore.
indexes: A list of indexes which should be the same length as `feature_data`. Each list
of indexes corresponds to a set of result rows in `online_features_response`.
online_features_response: The object to populate.
full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,
changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to
"customer_fv__daily_transactions").
requested_features: The names of the features in `feature_data`. This should be ordered in the same way as the
data in `feature_data`.
table: The FeatureView that `feature_data` was retrieved from.
output_len: The number of result rows in `online_features_response`.
"""
# Add the feature names to the response.
table_name
=
table
.
projection
.
name_to_use
()
requested_feature_refs
=
[
f"
{
table_name
}
__
{
feature_name
}
"
if
full_feature_names
else
feature_name
for
feature_name
in
requested_features
]
online_features_response
.
metadata
.
feature_names
.
val
.
extend
(
requested_feature_refs
)
# Process each feature vector in a single pass
for
timestamp_vector
,
statuses_vector
,
values_vector
in
feature_data
:
response_vector
=
construct_response_feature_vector
(
values_vector
,
statuses_vector
,
timestamp_vector
,
indexes
,
output_len
)
online_features_response
.
results
.
append
(
response_vector
)
def
_populate_response_from_feature_data_v2
(
feature_data
:
Iterable
[
Tuple
[
Iterable
[
Timestamp
],
Iterable
[
"FieldStatus.ValueType"
],
Iterable
[
ValueProto
]
]
],
indexes
:
Iterable
[
List
[
int
]],
online_features_response
:
GetOnlineFeaturesResponse
,
requested_features
:
Iterable
[
str
],
output_len
:
int
,
):
"""Populate the GetOnlineFeaturesResponse with feature data.
This method assumes that `_read_from_online_store` returns data for each
combination of Entities in `entity_rows` in the same order as they
are provided.
Args:
feature_data: A list of data in Protobuf form which was retrieved from the OnlineStore.
indexes: A list of indexes which should be the same length as `feature_data`. Each list
of indexes corresponds to a set of result rows in `online_features_response`.
online_features_response: The object to populate.
full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names,
changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to
"customer_fv__daily_transactions").
requested_features: The names of the features in `feature_data`. This should be ordered in the same way as the
data in `feature_data`.
output_len: The number of result rows in `online_features_response`.
"""
# Add the feature names to the response.
requested_feature_refs
=
[(
feature_name
)
for
feature_name
in
requested_features
]
online_features_response
.
metadata
.
feature_names
.
val
.
extend
(
requested_feature_refs
)
timestamps
,
statuses
,
values
=
zip
(
*
feature_data
)
# Populate the result with data fetched from the OnlineStore
# which is guaranteed to be aligned with `requested_features`.
for
timestamp_vector
,
statuses_vector
,
values_vector
in
feature_data
:
response_vector
=
construct_response_feature_vector
(
values_vector
,
statuses_vector
,
timestamp_vector
,
indexes
,
output_len
)
online_features_response
.
results
.
append
(
response_vector
)
def
_convert_entity_key_to_proto_to_dict
(
entity_key_vals
:
List
[
EntityKeyProto
],
)
->
Dict
[
str
,
List
[
ValueProto
]]:
entity_dict
:
Dict
[
str
,
List
[
ValueProto
]]
=
{}
for
entity_key_val
in
entity_key_vals
:
if
entity_key_val
is
not
None
:
for
join_key
,
entity_value
in
zip
(
entity_key_val
.
join_keys
,
entity_key_val
.
entity_values
):
if
join_key
not
in
entity_dict
:
entity_dict
[
join_key
]
=
[]
# python_entity_value = _proto_value_to_value_type(entity_value)
entity_dict
[
join_key
].
append
(
entity_value
)
return
entity_dict
def
_get_features
(
registry
,
project
,
features
:
Union
[
List
[
str
],
"FeatureService"
],
allow_cache
:
bool
=
False
,
)
->
List
[
str
]:
from
feast
.
feature_service
import
FeatureService
_features
=
features
if
not
_features
:
raise
ValueError
(
"No features specified for retrieval"
)
View remainder of file in raw view
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