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Official TFX BigQueryExampleGen component.
Inherits From: QueryBasedExampleGen
, BaseComponent
, BaseNode
tfx.extensions.google_cloud_big_query.example_gen.component.BigQueryExampleGen(
query: Optional[Text] = None,
input_config: Optional[example_gen_pb2.Input] = None,
output_config: Optional[example_gen_pb2.Output] = None,
example_artifacts: Optional[tfx.types.Channel
] = None,
instance_name: Optional[Text] = None
)
The BigQuery examplegen component takes a query, and generates train and eval examples for downsteam components.
Args | |
---|---|
query
|
BigQuery sql string, query result will be treated as a single split, can be overwritten by input_config. |
input_config
|
An example_gen_pb2.Input instance with Split.pattern as BigQuery sql string. If set, it overwrites the 'query' arg, and allows different queries per split. If any field is provided as a RuntimeParameter, input_config should be constructed as a dict with the same field names as Input proto message. |
output_config
|
An example_gen_pb2.Output instance, providing output configuration. If unset, default splits will be 'train' and 'eval' with size 2:1. If any field is provided as a RuntimeParameter, input_config should be constructed as a dict with the same field names as Output proto message. |
example_artifacts
|
Optional channel of 'ExamplesPath' for output train and eval examples. |
instance_name
|
Optional unique instance name. Necessary if multiple BigQueryExampleGen components are declared in the same pipeline. |
Raises | |
---|---|
RuntimeError
|
Only one of query and input_config should be set. |
Attributes | |
---|---|
component_id
|
|
component_type
|
|
downstream_nodes
|
|
exec_properties
|
|
id
|
Node id, unique across all TFX nodes in a pipeline.
If |
inputs
|
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outputs
|
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type
|
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upstream_nodes
|
Child Classes
Methods
add_downstream_node
add_downstream_node(
downstream_node
)
Experimental: Add another component that must run after this one.
This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.
Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.
It is symmetric with add_upstream_node
.
Args | |
---|---|
downstream_node
|
a component that must run after this node. |
add_upstream_node
add_upstream_node(
upstream_node
)
Experimental: Add another component that must run before this one.
This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.
Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.
It is symmetric with add_downstream_node
.
Args | |
---|---|
upstream_node
|
a component that must run before this node. |
from_json_dict
@classmethod
from_json_dict( dict_data: Dict[Text, Any] ) -> Any
Convert from dictionary data to an object.
get_class_type
@classmethod
get_class_type() -> Text
get_id
@classmethod
get_id( instance_name: Optional[Text] = None )
Gets the id of a node.
This can be used during pipeline authoring time. For example: from tfx.components import Trainer
resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))
Args | |
---|---|
instance_name
|
(Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id. |
Returns | |
---|---|
an id for the node. |
to_json_dict
to_json_dict() -> Dict[Text, Any]
Convert from an object to a JSON serializable dictionary.
with_id
with_id(
id: Text
) -> "BaseNode"
with_platform_config
with_platform_config(
config: message.Message
) -> "BaseComponent"
Attaches a proto-form platform config to a component.
The config will be a per-node platform-specific config.
Args | |
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config
|
platform config to attach to the component. |
Returns | |
---|---|
the same component itself. |
Class Variables | |
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EXECUTOR_SPEC |
Instance of tfx.dsl.components.base.executor_spec.ExecutorClassSpec
|