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feast/sdk/python/feast/utils.py at master · encyc/feast · GitHub
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import
copy
import
itertools
import
os
import
typing
import
warnings
from
collections
import
Counter
,
defaultdict
from
datetime
import
datetime
,
timedelta
,
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
.
aggregation
import
aggregation_specs_to_agg_ops
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
.
field
import
Field
from
feast
.
infra
.
compute_engines
.
backends
.
pandas_backend
import
PandasBackend
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
.
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
_parse_feature_ref
(
ref
:
str
)
->
Tuple
[
str
,
Optional
[
int
],
str
]:
"""Parse 'fv_name@version:feature' into (fv_name, version_number, feature_name).
If no @version is present, version_number is None (meaning 'latest').
Examples:
'driver_stats:trips' -> ('driver_stats', None, 'trips')
'driver_stats@v2:trips' -> ('driver_stats', 2, 'trips')
'driver_stats@latest:trips' -> ('driver_stats', None, 'trips')
"""
import
re
colon_idx
=
ref
.
find
(
":"
)
if
colon_idx
<
0
:
raise
ValueError
(
f"Invalid feature reference '
{
ref
}
'. Expected format: '<feature_view>:<feature>' "
f"or '<feature_view>@<version>:<feature>'"
)
fv_part
=
ref
[:
colon_idx
]
feature_name
=
ref
[
colon_idx
+
1
:]
at_idx
=
fv_part
.
find
(
"@"
)
if
at_idx
<
0
:
return
(
fv_part
,
None
,
feature_name
)
fv_name
=
fv_part
[:
at_idx
]
version_str
=
fv_part
[
at_idx
+
1
:]
if
not
version_str
or
version_str
.
lower
()
==
"latest"
:
return
(
fv_name
,
None
,
feature_name
)
# Parse version number from formats like "v2", "V2"
match
=
re
.
match
(
r"^[vV](\d+)$"
,
version_str
)
if
not
match
:
# Not a recognized version format — treat entire fv_part as the name
return
(
fv_part
,
None
,
feature_name
)
return
(
fv_name
,
int
(
match
.
group
(
1
)),
feature_name
)
def
_strip_version_from_ref
(
ref
:
str
)
->
str
:
"""Strip @version from a feature reference, returning 'fv_name:feature'.
Used to produce clean refs for output column naming.
"""
fv_name
,
_
,
feature_name
=
_parse_feature_ref
(
ref
)
return
f"
{
fv_name
}
:
{
feature_name
}
"
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
compute_non_entity_date_range
(
feature_views
:
List
[
"FeatureView"
],
start_date
:
Optional
[
datetime
]
=
None
,
end_date
:
Optional
[
datetime
]
=
None
,
default_window_days
:
int
=
30
,
)
->
Tuple
[
datetime
,
datetime
]:
if
end_date
is
None
:
end_date
=
datetime
.
now
(
tz
=
timezone
.
utc
)
else
:
end_date
=
make_tzaware
(
end_date
)
if
start_date
is
None
:
max_ttl_seconds
=
max
(
(
int
(
fv
.
ttl
.
total_seconds
())
for
fv
in
feature_views
if
fv
.
ttl
and
isinstance
(
fv
.
ttl
,
timedelta
)
),
default
=
0
,
)
start_date
=
end_date
-
timedelta
(
seconds
=
max_ttl_seconds
if
max_ttl_seconds
>
0
else
default_window_days
*
86400
)
else
:
start_date
=
make_tzaware
(
start_date
)
return
start_date
,
end_date
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
:
fv_name
,
version_num
,
feature_from_ref
=
_parse_feature_ref
(
ref
)
# Build the key that matches projection.name_to_use()
if
version_num
is
not
None
:
feature_view_from_ref
=
f"
{
fv_name
}
@v
{
version_num
}
"
else
:
feature_view_from_ref
=
fv_name
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
feature_view
.
batch_source
is
None
:
raise
ValueError
(
f"Feature view '
{
feature_view
.
name
}
' has no batch_source and cannot be used for offline retrieval."
)
# 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
# For feature views with aggregations, read INPUT columns from aggregations.
# This applies to StreamFeatureView, BatchFeatureView,
# or any FeatureView that has aggregations.
if
hasattr
(
feature_view
,
"aggregations"
)
and
feature_view
.
aggregations
:
# Extract unique input columns from aggregations, preserving order
feature_names
=
list
(
dict
.
fromkeys
(
agg
.
column
for
agg
in
feature_view
.
aggregations
)
)
else
:
# For regular feature views, use the feature names
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
_columns_to_proto_values
(
table
:
pyarrow
.
RecordBatch
,
columns
:
List
[
Tuple
[
str
,
ValueType
]],
allow_missing
:
bool
=
False
,
)
->
Dict
[
str
,
List
[
ValueProto
]]:
"""Convert table columns to proto values dict.
Args:
table: PyArrow RecordBatch containing the data.
columns: List of (column_name, value_type) tuples to convert.
allow_missing: If True, skip columns not found in table. If False, raise ValueError.
Returns:
Dict mapping column names to lists of ValueProto.
"""
result
:
Dict
[
str
,
List
[
ValueProto
]]
=
{}
for
column
,
value_type
in
columns
:
if
column
in
table
.
column_names
:
result
[
column
]
=
python_values_to_proto_values
(
table
.
column
(
column
).
to_numpy
(
zero_copy_only
=
False
),
value_type
)
elif
not
allow_missing
:
raise
ValueError
(
f"Column
{
column
}
not found in table"
)
return
result
def
_build_entity_keys
(
num_rows
:
int
,
join_keys
:
Dict
[
str
,
ValueType
],
proto_values
:
Dict
[
str
,
List
[
ValueProto
]],
)
->
List
[
EntityKeyProto
]:
"""Build entity key protos for each row.
Args:
num_rows: Number of rows to generate entity keys for.
join_keys: Dict mapping join key names to their value types.
proto_values: Dict mapping column names to lists of ValueProto values.
Returns:
List of EntityKeyProto, one per row.
"""
return
[
EntityKeyProto
(
join_keys
=
list
(
join_keys
.
keys
()),
entity_values
=
[
proto_values
[
k
][
idx
]
for
k
in
join_keys
if
k
in
proto_values
],
)
for
idx
in
range
(
num_rows
)
]
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
# Check for source_request_sources or source_feature_view_projections attributes to identify ODFVs
if
(
getattr
(
feature_view
,
"source_request_sources"
,
None
)
is
not
None
or
getattr
(
feature_view
,
"source_feature_view_projections"
,
None
)
is
not
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
]
if
feature_view
.
batch_source
is
None
:
raise
ValueError
(
f"Feature view '
{
feature_view
.
name
}
' has no batch_source and cannot be converted to proto."
)
# TODO: This will break if the feature view has aggregations or transformations
columns
=
[
(
field
.
name
,
field
.
dtype
.
to_value_type
())
for
field
in
feature_view
.
features
]
+
list
(
join_keys
.
items
())
proto_values_by_column
=
_columns_to_proto_values
(
table
,
columns
,
allow_missing
=
False
)
entity_keys
=
_build_entity_keys
(
table
.
num_rows
,
join_keys
,
proto_values_by_column
)
# 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
())
# Convert columns that exist in the table
proto_values_by_column
=
_columns_to_proto_values
(
table
,
columns
,
allow_missing
=
True
)
# Ensure join keys are included, creating null values if missing from table
for
join_key
,
value_type
in
join_keys
.
items
():
if
join_key
not
in
proto_values_by_column
:
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 proto values directly (no need to build a PyArrow array)
proto_values_by_column
[
join_key
]
=
python_values_to_proto_values
(
[
None
]
*
table
.
num_rows
,
value_type
)
# Cache column names set to avoid recreating list in loop
column_names
=
{
c
[
0
]
for
c
in
columns
}
# Adding On Demand Features that are missing from proto_values
for
feature
in
feature_view
.
features
:
if
feature
.
name
in
column_names
and
feature
.
name
not
in
proto_values_by_column
:
# Create null proto values directly (more efficient than building PyArrow array)
proto_values_by_column
[
feature
.
name
]
=
python_values_to_proto_values
(
[
None
]
*
table
.
num_rows
,
feature
.
dtype
.
to_value_type
()
)
entity_keys
=
_build_entity_keys
(
table
.
num_rows
,
join_keys
,
proto_values_by_column
)
# Serialize the features per row
feature_dict
=
{
feature
.
name
:
proto_values_by_column
[
feature
.
name
]
for
feature
in
feature_view
.
features
if
feature
.
name
in
proto_values_by_column
}
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
now
=
_utc_now
()
event_timestamps
=
[
_coerce_datetime
(
pd
.
Timestamp
(
now
))
for
_
in
range
(
table
.
num_rows
)
]
# 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
=
[
_parse_feature_ref
(
ref
)[
2
]
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
:
fv_name
,
version_num
,
feat_name
=
_parse_feature_ref
(
ref
)
# Build the key that matches projection.name_to_use()
if
version_num
is
not
None
:
view_name
=
f"
{
fv_name
}
@v
{
version_num
}
"
else
:
view_name
=
fv_name
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
_apply_aggregations_to_response
(
response_data
:
Union
[
pyarrow
.
Table
,
Dict
[
str
,
List
[
Any
]]],
aggregations
,
group_keys
:
Optional
[
List
[
str
]],
mode
:
str
,
)
->
Union
[
pyarrow
.
Table
,
Dict
[
str
,
List
[
Any
]]]:
"""
Apply aggregations using PandasBackend.
Args:
response_data: Either a pyarrow.Table or dict of lists containing the data
aggregations: List of Aggregation objects to apply
group_keys: List of column names to group by (optional)
mode: Transformation mode ("python", "pandas", or "substrait")
Returns:
Aggregated data in the same format as input
TODO: Consider refactoring to support backends other than pandas in the future.
"""
if
not
aggregations
:
return
response_data
backend
=
PandasBackend
()
# Convert to pandas DataFrame
if
isinstance
(
response_data
,
dict
):
df
=
pd
.
DataFrame
(
response_data
)
else
:
# pyarrow.Table
df
=
backend
.
from_arrow
(
response_data
)
if
df
.
empty
:
return
response_data
# Convert aggregations to agg_ops format
agg_ops
=
aggregation_specs_to_agg_ops
(
aggregations
,
time_window_unsupported_error_message
=
(
"Time window aggregation is not supported in online serving."
),
)
# Apply aggregations using PandasBackend
if
group_keys
:
result_df
=
backend
.
groupby_agg
(
df
,
group_keys
,
agg_ops
)
else
:
# No grouping - aggregate over entire dataset
result_df
=
backend
.
groupby_agg
(
df
, [],
agg_ops
)
# Convert back to original format
if
mode
==
"python"
:
return
{
col
:
result_df
[
col
].
tolist
()
for
col
in
result_df
.
columns
}
else
:
# pandas or substrait
return
backend
.
to_arrow
(
result_df
)
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
,
feature_types
:
Optional
[
Dict
[
str
,
"ValueType"
]]
=
None
,
):
"""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
=
_parse_feature_ref
(
feature_ref
)
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
,
feature_types
=
feature_types
)
initial_response_arrow
:
Optional
[
pyarrow
.
Table
]
=
None
initial_response_dict
:
Optional
[
Dict
[
str
,
List
[
Any
]]]
=
None
def
_is_metrics_active
():
try
:
from
feast
.
metrics
import
_config
return
_config
.
online_features
except
Exception
:
return
False
_metrics_active
=
_is_metrics_active
()
# 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
:
_should_track
=
_metrics_active
and
getattr
(
odfv
,
"track_metrics"
,
False
)
if
_should_track
:
import
time
as
_time
_transform_start
=
_time
.
monotonic
()
# Apply aggregations if configured.
if
odfv
.
aggregations
:
if
odfv
.
mode
==
"python"
:
if
initial_response_dict
is
None
:
initial_response_dict
=
initial_response
.
to_dict
()
initial_response_dict
=
_apply_aggregations_to_response
(
initial_response_dict
,
odfv
.
aggregations
,
odfv
.
entities
,
odfv
.
mode
,
)
elif
odfv
.
mode
in
{
"pandas"
,
"substrait"
}:
if
initial_response_arrow
is
None
:
initial_response_arrow
=
initial_response
.
to_arrow
()
initial_response_arrow
=
_apply_aggregations_to_response
(
initial_response_arrow
,
odfv
.
aggregations
,
odfv
.
entities
,
odfv
.
mode
,
)
continue
# Apply transformation. Note: aggregations and transformation configs are mutually exclusive
# TODO: Fix to make it work for having both aggregation and transformation
# ticket: https://github.com/feast-dev/feast/issues/5689
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'."
)
if
_should_track
:
from
feast
.
metrics
import
track_transformation
track_transformation
(
odfv_name
,
odfv
.
mode
,
_time
.
monotonic
()
-
_transform_start
)
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_by_name
:
Dict
[
str
,
"Entity"
]
=
{
entity
.
name
:
entity
for
entity
in
entities
}
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
=
entity_by_name
.
get
(
entity_name
)
if
entity
is
None
:
from
feast
.
errors
import
EntityNotFoundException
raise
EntityNotFoundException
(
entity_name
,
project
=
project
)
# 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
:
dtype
=
entity_column
.
dtype
.
to_value_type
()
entity_join_key_column_name
=
feature_view
.
projection
.
join_key_map
.
get
(
entity_column
.
name
,
entity_column
.
name
)
entity_type_map
[
entity_join_key_column_name
]
=
dtype
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.
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