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# Copyright 2020 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
enum
import
warnings
from
abc
import
ABC
,
abstractmethod
from
datetime
import
timedelta
from
typing
import
Any
,
Callable
,
Dict
,
Iterable
,
List
,
Optional
,
Tuple
from
google
.
protobuf
.
duration_pb2
import
Duration
from
google
.
protobuf
.
json_format
import
MessageToJson
from
typeguard
import
typechecked
from
feast
import
type_map
from
feast
.
data_format
import
StreamFormat
from
feast
.
field
import
Field
from
feast
.
protos
.
feast
.
core
.
DataSource_pb2
import
DataSource
as
DataSourceProto
from
feast
.
repo_config
import
RepoConfig
,
get_data_source_class_from_type
from
feast
.
types
import
from_value_type
from
feast
.
value_type
import
ValueType
class
KafkaOptions
:
"""
DataSource Kafka options used to source features from Kafka messages
"""
def
__init__
(
self
,
kafka_bootstrap_servers
:
str
,
message_format
:
StreamFormat
,
topic
:
str
,
watermark_delay_threshold
:
Optional
[
timedelta
]
=
None
,
):
self
.
kafka_bootstrap_servers
=
kafka_bootstrap_servers
self
.
message_format
=
message_format
self
.
topic
=
topic
self
.
watermark_delay_threshold
=
watermark_delay_threshold
or
None
@
classmethod
def
from_proto
(
cls
,
kafka_options_proto
:
DataSourceProto
.
KafkaOptions
):
"""
Creates a KafkaOptions from a protobuf representation of a kafka option
Args:
kafka_options_proto: A protobuf representation of a DataSource
Returns:
Returns a KafkaOptions object based on the kafka_options protobuf
"""
watermark_delay_threshold
=
None
if
kafka_options_proto
.
HasField
(
"watermark_delay_threshold"
):
watermark_delay_threshold
=
(
timedelta
(
days
=
0
)
if
kafka_options_proto
.
watermark_delay_threshold
.
ToNanoseconds
()
==
0
else
kafka_options_proto
.
watermark_delay_threshold
.
ToTimedelta
()
)
kafka_options
=
cls
(
kafka_bootstrap_servers
=
kafka_options_proto
.
kafka_bootstrap_servers
,
message_format
=
StreamFormat
.
from_proto
(
kafka_options_proto
.
message_format
),
topic
=
kafka_options_proto
.
topic
,
watermark_delay_threshold
=
watermark_delay_threshold
,
)
return
kafka_options
def
to_proto
(
self
)
->
DataSourceProto
.
KafkaOptions
:
"""
Converts an KafkaOptionsProto object to its protobuf representation.
Returns:
KafkaOptionsProto protobuf
"""
watermark_delay_threshold
=
None
if
self
.
watermark_delay_threshold
is
not
None
:
watermark_delay_threshold
=
Duration
()
watermark_delay_threshold
.
FromTimedelta
(
self
.
watermark_delay_threshold
)
kafka_options_proto
=
DataSourceProto
.
KafkaOptions
(
kafka_bootstrap_servers
=
self
.
kafka_bootstrap_servers
,
message_format
=
self
.
message_format
.
to_proto
(),
topic
=
self
.
topic
,
watermark_delay_threshold
=
watermark_delay_threshold
,
)
return
kafka_options_proto
class
KinesisOptions
:
"""
DataSource Kinesis options used to source features from Kinesis records
"""
def
__init__
(
self
,
record_format
:
StreamFormat
,
region
:
str
,
stream_name
:
str
,
):
self
.
record_format
=
record_format
self
.
region
=
region
self
.
stream_name
=
stream_name
@
classmethod
def
from_proto
(
cls
,
kinesis_options_proto
:
DataSourceProto
.
KinesisOptions
):
"""
Creates a KinesisOptions from a protobuf representation of a kinesis option
Args:
kinesis_options_proto: A protobuf representation of a DataSource
Returns:
Returns a KinesisOptions object based on the kinesis_options protobuf
"""
kinesis_options
=
cls
(
record_format
=
StreamFormat
.
from_proto
(
kinesis_options_proto
.
record_format
),
region
=
kinesis_options_proto
.
region
,
stream_name
=
kinesis_options_proto
.
stream_name
,
)
return
kinesis_options
def
to_proto
(
self
)
->
DataSourceProto
.
KinesisOptions
:
"""
Converts an KinesisOptionsProto object to its protobuf representation.
Returns:
KinesisOptionsProto protobuf
"""
kinesis_options_proto
=
DataSourceProto
.
KinesisOptions
(
record_format
=
self
.
record_format
.
to_proto
(),
region
=
self
.
region
,
stream_name
=
self
.
stream_name
,
)
return
kinesis_options_proto
_DATA_SOURCE_OPTIONS
=
{
DataSourceProto
.
SourceType
.
BATCH_FILE
:
"feast.infra.offline_stores.file_source.FileSource"
,
DataSourceProto
.
SourceType
.
BATCH_BIGQUERY
:
"feast.infra.offline_stores.bigquery_source.BigQuerySource"
,
DataSourceProto
.
SourceType
.
BATCH_REDSHIFT
:
"feast.infra.offline_stores.redshift_source.RedshiftSource"
,
DataSourceProto
.
SourceType
.
BATCH_SNOWFLAKE
:
"feast.infra.offline_stores.snowflake_source.SnowflakeSource"
,
DataSourceProto
.
SourceType
.
BATCH_TRINO
:
"feast.infra.offline_stores.contrib.trino_offline_store.trino_source.TrinoSource"
,
DataSourceProto
.
SourceType
.
BATCH_SPARK
:
"feast.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSource"
,
DataSourceProto
.
SourceType
.
BATCH_ATHENA
:
"feast.infra.offline_stores.contrib.athena_offline_store.athena_source.AthenaSource"
,
DataSourceProto
.
SourceType
.
STREAM_KAFKA
:
"feast.data_source.KafkaSource"
,
DataSourceProto
.
SourceType
.
STREAM_KINESIS
:
"feast.data_source.KinesisSource"
,
DataSourceProto
.
SourceType
.
REQUEST_SOURCE
:
"feast.data_source.RequestSource"
,
DataSourceProto
.
SourceType
.
PUSH_SOURCE
:
"feast.data_source.PushSource"
,
}
@
typechecked
class
DataSource
(
ABC
):
"""
DataSource that can be used to source features.
Args:
name: Name of data source, which should be unique within a project
timestamp_field (optional): Event timestamp field used for point-in-time joins of
feature values.
created_timestamp_column (optional): Timestamp column indicating when the row
was created, used for deduplicating rows.
field_mapping (optional): A dictionary mapping of column names in this data
source to feature names in a feature table or view. Only used for feature
columns, not entity or timestamp columns.
description (optional) A human-readable description.
tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
owner (optional): The owner of the data source, typically the email of the primary
maintainer.
date_partition_column (optional): Timestamp column used for partitioning. Not supported by all offline stores.
"""
name
:
str
timestamp_field
:
str
created_timestamp_column
:
str
field_mapping
:
Dict
[
str
,
str
]
description
:
str
tags
:
Dict
[
str
,
str
]
owner
:
str
date_partition_column
:
str
def
__init__
(
self
,
*
,
name
:
str
,
timestamp_field
:
Optional
[
str
]
=
None
,
created_timestamp_column
:
Optional
[
str
]
=
None
,
field_mapping
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
description
:
Optional
[
str
]
=
""
,
tags
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
owner
:
Optional
[
str
]
=
""
,
date_partition_column
:
Optional
[
str
]
=
None
,
):
"""
Creates a DataSource object.
Args:
name: Name of data source, which should be unique within a project.
timestamp_field (optional): Event timestamp field used for point-in-time joins of
feature values.
created_timestamp_column (optional): Timestamp column indicating when the row
was created, used for deduplicating rows.
field_mapping (optional): A dictionary mapping of column names in this data
source to feature names in a feature table or view. Only used for feature
columns, not entity or timestamp columns.
description (optional): A human-readable description.
tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
owner (optional): The owner of the data source, typically the email of the primary
maintainer.
date_partition_column (optional): Timestamp column used for partitioning. Not supported by all stores
"""
self
.
name
=
name
self
.
timestamp_field
=
timestamp_field
or
""
self
.
created_timestamp_column
=
(
created_timestamp_column
if
created_timestamp_column
else
""
)
self
.
field_mapping
=
field_mapping
if
field_mapping
else
{}
if
(
self
.
timestamp_field
and
self
.
timestamp_field
==
self
.
created_timestamp_column
):
raise
ValueError
(
"Please do not use the same column for 'timestamp_field' and 'created_timestamp_column'."
)
self
.
description
=
description
or
""
self
.
tags
=
tags
or
{}
self
.
owner
=
owner
or
""
self
.
date_partition_column
=
(
date_partition_column
if
date_partition_column
else
""
)
def
__hash__
(
self
):
return
hash
((
self
.
name
,
self
.
timestamp_field
))
def
__str__
(
self
):
return
str
(
MessageToJson
(
self
.
to_proto
()))
def
__eq__
(
self
,
other
):
if
other
is
None
:
return
False
if
not
isinstance
(
other
,
DataSource
):
raise
TypeError
(
"Comparisons should only involve DataSource class objects."
)
if
(
self
.
name
!=
other
.
name
or
self
.
timestamp_field
!=
other
.
timestamp_field
or
self
.
created_timestamp_column
!=
other
.
created_timestamp_column
or
self
.
field_mapping
!=
other
.
field_mapping
or
self
.
date_partition_column
!=
other
.
date_partition_column
or
self
.
description
!=
other
.
description
or
self
.
tags
!=
other
.
tags
or
self
.
owner
!=
other
.
owner
):
return
False
return
True
@
staticmethod
@
abstractmethod
def
from_proto
(
data_source
:
DataSourceProto
)
->
Any
:
"""
Converts data source config in protobuf spec to a DataSource class object.
Args:
data_source: A protobuf representation of a DataSource.
Returns:
A DataSource class object.
Raises:
ValueError: The type of DataSource could not be identified.
"""
data_source_type
=
data_source
.
type
if
not
data_source_type
or
(
data_source_type
not
in
list
(
_DATA_SOURCE_OPTIONS
.
keys
())
+
[
DataSourceProto
.
SourceType
.
CUSTOM_SOURCE
]
):
raise
ValueError
(
"Could not identify the source type being added."
)
if
data_source_type
==
DataSourceProto
.
SourceType
.
CUSTOM_SOURCE
:
cls
=
get_data_source_class_from_type
(
data_source
.
data_source_class_type
)
return
cls
.
from_proto
(
data_source
)
cls
=
get_data_source_class_from_type
(
_DATA_SOURCE_OPTIONS
[
data_source_type
])
return
cls
.
from_proto
(
data_source
)
@
abstractmethod
def
to_proto
(
self
)
->
DataSourceProto
:
"""
Converts a DataSourceProto object to its protobuf representation.
"""
raise
NotImplementedError
def
validate
(
self
,
config
:
RepoConfig
):
"""
Validates the underlying data source.
Args:
config: Configuration object used to configure a feature store.
"""
raise
NotImplementedError
@
staticmethod
@
abstractmethod
def
source_datatype_to_feast_value_type
()
->
Callable
[[
str
],
ValueType
]:
"""
Returns the callable method that returns Feast type given the raw column type.
"""
raise
NotImplementedError
def
get_table_column_names_and_types
(
self
,
config
:
RepoConfig
)
->
Iterable
[
Tuple
[
str
,
str
]]:
"""
Returns the list of column names and raw column types.
Args:
config: Configuration object used to configure a feature store.
"""
raise
NotImplementedError
def
get_table_query_string
(
self
)
->
str
:
"""
Returns a string that can directly be used to reference this table in SQL.
"""
raise
NotImplementedError
@
typechecked
class
KafkaSource
(
DataSource
):
def
__init__
(
self
,
*
,
name
:
str
,
timestamp_field
:
str
,
message_format
:
StreamFormat
,
bootstrap_servers
:
Optional
[
str
]
=
None
,
kafka_bootstrap_servers
:
Optional
[
str
]
=
None
,
topic
:
Optional
[
str
]
=
None
,
created_timestamp_column
:
Optional
[
str
]
=
""
,
field_mapping
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
description
:
Optional
[
str
]
=
""
,
tags
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
owner
:
Optional
[
str
]
=
""
,
batch_source
:
Optional
[
DataSource
]
=
None
,
watermark_delay_threshold
:
Optional
[
timedelta
]
=
None
,
):
"""
Creates a KafkaSource object.
Args:
name: Name of data source, which should be unique within a project
timestamp_field: Event timestamp field used for point-in-time joins of feature values.
message_format: StreamFormat of serialized messages.
bootstrap_servers: (Deprecated) The servers of the kafka broker in the form "localhost:9092".
kafka_bootstrap_servers (optional): The servers of the kafka broker in the form "localhost:9092".
topic (optional): The name of the topic to read from in the kafka source.
created_timestamp_column (optional): Timestamp column indicating when the row
was created, used for deduplicating rows.
field_mapping (optional): A dictionary mapping of column names in this data
source to feature names in a feature table or view. Only used for feature
columns, not entity or timestamp columns.
description (optional): A human-readable description.
tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
owner (optional): The owner of the data source, typically the email of the primary
maintainer.
batch_source (optional): The datasource that acts as a batch source.
watermark_delay_threshold (optional): The watermark delay threshold for stream data.
Specifically how late stream data can arrive without being discarded.
"""
if
bootstrap_servers
:
warnings
.
warn
(
(
"The 'bootstrap_servers' parameter has been deprecated in favor of 'kafka_bootstrap_servers'. "
"Feast 0.25 and onwards will not support the 'bootstrap_servers' parameter."
),
DeprecationWarning
,
)
super
().
__init__
(
name
=
name
,
timestamp_field
=
timestamp_field
,
created_timestamp_column
=
created_timestamp_column
,
field_mapping
=
field_mapping
,
description
=
description
,
tags
=
tags
,
owner
=
owner
,
)
self
.
batch_source
=
batch_source
kafka_bootstrap_servers
=
kafka_bootstrap_servers
or
bootstrap_servers
or
""
topic
=
topic
or
""
self
.
kafka_options
=
KafkaOptions
(
kafka_bootstrap_servers
=
kafka_bootstrap_servers
,
message_format
=
message_format
,
topic
=
topic
,
watermark_delay_threshold
=
watermark_delay_threshold
,
)
def
__eq__
(
self
,
other
):
if
not
isinstance
(
other
,
KafkaSource
):
raise
TypeError
(
"Comparisons should only involve KafkaSource class objects."
)
if
not
super
().
__eq__
(
other
):
return
False
if
(
self
.
kafka_options
.
kafka_bootstrap_servers
!=
other
.
kafka_options
.
kafka_bootstrap_servers
or
self
.
kafka_options
.
message_format
!=
other
.
kafka_options
.
message_format
or
self
.
kafka_options
.
topic
!=
other
.
kafka_options
.
topic
or
self
.
kafka_options
.
watermark_delay_threshold
!=
other
.
kafka_options
.
watermark_delay_threshold
):
return
False
return
True
def
__hash__
(
self
):
return
super
().
__hash__
()
@
staticmethod
def
from_proto
(
data_source
:
DataSourceProto
):
watermark_delay_threshold
=
None
if
data_source
.
kafka_options
.
watermark_delay_threshold
:
watermark_delay_threshold
=
(
timedelta
(
days
=
0
)
if
data_source
.
kafka_options
.
watermark_delay_threshold
.
ToNanoseconds
()
==
0
else
data_source
.
kafka_options
.
watermark_delay_threshold
.
ToTimedelta
()
)
return
KafkaSource
(
name
=
data_source
.
name
,
field_mapping
=
dict
(
data_source
.
field_mapping
),
kafka_bootstrap_servers
=
data_source
.
kafka_options
.
kafka_bootstrap_servers
,
message_format
=
StreamFormat
.
from_proto
(
data_source
.
kafka_options
.
message_format
),
watermark_delay_threshold
=
watermark_delay_threshold
,
topic
=
data_source
.
kafka_options
.
topic
,
created_timestamp_column
=
data_source
.
created_timestamp_column
,
timestamp_field
=
data_source
.
timestamp_field
,
description
=
data_source
.
description
,
tags
=
dict
(
data_source
.
tags
),
owner
=
data_source
.
owner
,
batch_source
=
DataSource
.
from_proto
(
data_source
.
batch_source
)
if
data_source
.
batch_source
else
None
,
)
def
to_proto
(
self
)
->
DataSourceProto
:
data_source_proto
=
DataSourceProto
(
name
=
self
.
name
,
type
=
DataSourceProto
.
STREAM_KAFKA
,
field_mapping
=
self
.
field_mapping
,
kafka_options
=
self
.
kafka_options
.
to_proto
(),
description
=
self
.
description
,
tags
=
self
.
tags
,
owner
=
self
.
owner
,
)
data_source_proto
.
timestamp_field
=
self
.
timestamp_field
data_source_proto
.
created_timestamp_column
=
self
.
created_timestamp_column
if
self
.
batch_source
:
data_source_proto
.
batch_source
.
MergeFrom
(
self
.
batch_source
.
to_proto
())
return
data_source_proto
def
validate
(
self
,
config
:
RepoConfig
):
raise
NotImplementedError
def
get_table_column_names_and_types
(
self
,
config
:
RepoConfig
)
->
Iterable
[
Tuple
[
str
,
str
]]:
raise
NotImplementedError
@
staticmethod
def
source_datatype_to_feast_value_type
()
->
Callable
[[
str
],
ValueType
]:
return
type_map
.
redshift_to_feast_value_type
def
get_table_query_string
(
self
)
->
str
:
raise
NotImplementedError
@
typechecked
class
RequestSource
(
DataSource
):
"""
RequestSource that can be used to provide input features for on demand transforms
Attributes:
name: Name of the request data source
schema: Schema mapping from the input feature name to a ValueType
description: A human-readable description.
tags: A dictionary of key-value pairs to store arbitrary metadata.
owner: The owner of the request data source, typically the email of the primary
maintainer.
"""
name
:
str
schema
:
List
[
Field
]
description
:
str
tags
:
Dict
[
str
,
str
]
owner
:
str
def
__init__
(
self
,
*
,
name
:
str
,
schema
:
List
[
Field
],
description
:
Optional
[
str
]
=
""
,
tags
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
owner
:
Optional
[
str
]
=
""
,
):
"""Creates a RequestSource object."""
super
().
__init__
(
name
=
name
,
description
=
description
,
tags
=
tags
,
owner
=
owner
)
self
.
schema
=
schema
def
validate
(
self
,
config
:
RepoConfig
):
raise
NotImplementedError
def
get_table_column_names_and_types
(
self
,
config
:
RepoConfig
)
->
Iterable
[
Tuple
[
str
,
str
]]:
raise
NotImplementedError
def
__eq__
(
self
,
other
):
if
not
isinstance
(
other
,
RequestSource
):
raise
TypeError
(
"Comparisons should only involve RequestSource class objects."
)
if
not
super
().
__eq__
(
other
):
return
False
if
isinstance
(
self
.
schema
,
List
)
and
isinstance
(
other
.
schema
,
List
):
for
field1
,
field2
in
zip
(
self
.
schema
,
other
.
schema
):
if
field1
!=
field2
:
return
False
return
True
else
:
return
False
def
__hash__
(
self
):
return
super
().
__hash__
()
@
staticmethod
def
from_proto
(
data_source
:
DataSourceProto
):
schema_pb
=
data_source
.
request_data_options
.
schema
list_schema
=
[]
for
field_proto
in
schema_pb
:
list_schema
.
append
(
Field
.
from_proto
(
field_proto
))
return
RequestSource
(
name
=
data_source
.
name
,
schema
=
list_schema
,
description
=
data_source
.
description
,
tags
=
dict
(
data_source
.
tags
),
owner
=
data_source
.
owner
,
)
def
to_proto
(
self
)
->
DataSourceProto
:
schema_pb
=
[]
if
isinstance
(
self
.
schema
,
Dict
):
for
key
,
value
in
self
.
schema
.
items
():
schema_pb
.
append
(
Field
(
name
=
key
,
dtype
=
from_value_type
(
value
.
value
)).
to_proto
()
)
else
:
for
field
in
self
.
schema
:
schema_pb
.
append
(
field
.
to_proto
())
data_source_proto
=
DataSourceProto
(
name
=
self
.
name
,
type
=
DataSourceProto
.
REQUEST_SOURCE
,
description
=
self
.
description
,
tags
=
self
.
tags
,
owner
=
self
.
owner
,
)
data_source_proto
.
request_data_options
.
schema
.
extend
(
schema_pb
)
return
data_source_proto
def
get_table_query_string
(
self
)
->
str
:
raise
NotImplementedError
@
staticmethod
def
source_datatype_to_feast_value_type
()
->
Callable
[[
str
],
ValueType
]:
raise
NotImplementedError
@
typechecked
class
KinesisSource
(
DataSource
):
def
validate
(
self
,
config
:
RepoConfig
):
raise
NotImplementedError
def
get_table_column_names_and_types
(
self
,
config
:
RepoConfig
)
->
Iterable
[
Tuple
[
str
,
str
]]:
raise
NotImplementedError
@
staticmethod
def
from_proto
(
data_source
:
DataSourceProto
):
return
KinesisSource
(
name
=
data_source
.
name
,
timestamp_field
=
data_source
.
timestamp_field
,
field_mapping
=
dict
(
data_source
.
field_mapping
),
record_format
=
StreamFormat
.
from_proto
(
data_source
.
kinesis_options
.
record_format
),
region
=
data_source
.
kinesis_options
.
region
,
stream_name
=
data_source
.
kinesis_options
.
stream_name
,
created_timestamp_column
=
data_source
.
created_timestamp_column
,
description
=
data_source
.
description
,
tags
=
dict
(
data_source
.
tags
),
owner
=
data_source
.
owner
,
batch_source
=
DataSource
.
from_proto
(
data_source
.
batch_source
)
if
data_source
.
batch_source
else
None
,
)
@
staticmethod
def
source_datatype_to_feast_value_type
()
->
Callable
[[
str
],
ValueType
]:
raise
NotImplementedError
def
get_table_query_string
(
self
)
->
str
:
raise
NotImplementedError
def
__init__
(
self
,
*
,
name
:
str
,
record_format
:
StreamFormat
,
region
:
str
,
stream_name
:
str
,
timestamp_field
:
Optional
[
str
]
=
""
,
created_timestamp_column
:
Optional
[
str
]
=
""
,
field_mapping
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
description
:
Optional
[
str
]
=
""
,
tags
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
owner
:
Optional
[
str
]
=
""
,
batch_source
:
Optional
[
DataSource
]
=
None
,
):
if
record_format
is
None
:
raise
ValueError
(
"Record format must be specified for kinesis source"
)
super
().
__init__
(
name
=
name
,
timestamp_field
=
timestamp_field
,
created_timestamp_column
=
created_timestamp_column
,
field_mapping
=
field_mapping
,
description
=
description
,
tags
=
tags
,
owner
=
owner
,
)
self
.
batch_source
=
batch_source
self
.
kinesis_options
=
KinesisOptions
(
record_format
=
record_format
,
region
=
region
,
stream_name
=
stream_name
)
def
__eq__
(
self
,
other
):
if
not
isinstance
(
other
,
KinesisSource
):
raise
TypeError
(
"Comparisons should only involve KinesisSource class objects."
)
if
not
super
().
__eq__
(
other
):
return
False
if
(
self
.
kinesis_options
.
record_format
!=
other
.
kinesis_options
.
record_format
or
self
.
kinesis_options
.
region
!=
other
.
kinesis_options
.
region
or
self
.
kinesis_options
.
stream_name
!=
other
.
kinesis_options
.
stream_name
):
return
False
return
True
def
__hash__
(
self
):
return
super
().
__hash__
()
def
to_proto
(
self
)
->
DataSourceProto
:
data_source_proto
=
DataSourceProto
(
name
=
self
.
name
,
type
=
DataSourceProto
.
STREAM_KINESIS
,
field_mapping
=
self
.
field_mapping
,
kinesis_options
=
self
.
kinesis_options
.
to_proto
(),
description
=
self
.
description
,
tags
=
self
.
tags
,
owner
=
self
.
owner
,
)
data_source_proto
.
timestamp_field
=
self
.
timestamp_field
data_source_proto
.
created_timestamp_column
=
self
.
created_timestamp_column
if
self
.
batch_source
:
data_source_proto
.
batch_source
.
MergeFrom
(
self
.
batch_source
.
to_proto
())
return
data_source_proto
class
PushMode
(
enum
.
Enum
):
ONLINE
=
1
OFFLINE
=
2
ONLINE_AND_OFFLINE
=
3
@
typechecked
class
PushSource
(
DataSource
):
"""
A source that can be used to ingest features on request
"""
# TODO(adchia): consider adding schema here in case where Feast manages pushing events to the offline store
# TODO(adchia): consider a "mode" to support pushing raw vs transformed events
batch_source
:
DataSource
def
__init__
(
self
,
*
,
name
:
str
,
batch_source
:
DataSource
,
description
:
Optional
[
str
]
=
""
,
tags
:
Optional
[
Dict
[
str
,
str
]]
=
None
,
owner
:
Optional
[
str
]
=
""
,
):
"""
Creates a PushSource object.
Args:
name: Name of the push source
batch_source: The batch source that backs this push source. It's used when materializing from the offline
store to the online store, and when retrieving historical features.
description (optional): A human-readable description.
tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
owner (optional): The owner of the data source, typically the email of the primary
maintainer.
"""
super
().
__init__
(
name
=
name
,
description
=
description
,
tags
=
tags
,
owner
=
owner
)
self
.
batch_source
=
batch_source
def
__eq__
(
self
,
other
):
if
not
isinstance
(
other
,
PushSource
):
return
False
if
not
super
().
__eq__
(
other
):
return
False
if
self
.
batch_source
!=
other
.
batch_source
:
return
False
return
True
def
__hash__
(
self
):
return
super
().
__hash__
()
def
validate
(
self
,
config
:
RepoConfig
):
raise
NotImplementedError
def
get_table_column_names_and_types
(
self
,
config
:
RepoConfig
)
->
Iterable
[
Tuple
[
str
,
str
]]:
raise
NotImplementedError
@
staticmethod
def
from_proto
(
data_source
:
DataSourceProto
):
assert
data_source
.
HasField
(
"batch_source"
)
batch_source
=
DataSource
.
from_proto
(
data_source
.
batch_source
)
return
PushSource
(
name
=
data_source
.
name
,
batch_source
=
batch_source
,
description
=
data_source
.
description
,
tags
=
dict
(
data_source
.
tags
),
owner
=
data_source
.
owner
,
)
def
to_proto
(
self
)
->
DataSourceProto
:
batch_source_proto
=
None
if
self
.
batch_source
:
batch_source_proto
=
self
.
batch_source
.
to_proto
()
data_source_proto
=
DataSourceProto
(
name
=
self
.
name
,
type
=
DataSourceProto
.
PUSH_SOURCE
,
description
=
self
.
description
,
tags
=
self
.
tags
,
owner
=
self
.
owner
,
batch_source
=
batch_source_proto
,
)
return
data_source_proto
def
get_table_query_string
(
self
)
->
str
:
raise
NotImplementedError
@
staticmethod
def
source_datatype_to_feast_value_type
()
->
Callable
[[
str
],
ValueType
]:
raise
NotImplementedError
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