tfx.components.example_gen.component.QueryBasedExampleGen

A TFX component to ingest examples from query based systems.

Inherits From: BaseComponent

tfx.components.example_gen.component.QueryBasedExampleGen(
    input_config: Union[example_gen_pb2.Input, Dict[Text, Any]],
    output_config: Optional[Union[example_gen_pb2.Output, Dict[Text, Any]]] = None,
    custom_config: Optional[Union[example_gen_pb2.CustomConfig, Dict[Text, Any]]] = None,
    example_artifacts: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None
)

The QueryBasedExampleGen component can be extended to ingest examples from query based systems such as Presto or Bigquery. The component will also convert the input data into tf.record](https://www.tensorflow.org/tutorials/load_data/tf_records) and generate train and eval example splits for downsteam components.

Example

_query = "SELECT * FROM `bigquery-public-data.chicago_taxi_trips.taxi_trips`"
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = BigQueryExampleGen(query=_query)

Args
input_config	An example_gen_pb2.Input instance, providing input configuration. 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. required
output_config	An example_gen_pb2.Output instance, providing output configuration. If unset, the default splits will be labeled as 'train' and 'eval' with a distribution ratio of 2:1. If any field is provided as a RuntimeParameter, output_config should be constructed as a dict with the same field names as Output proto message.
custom_config	An example_gen_pb2.CustomConfig instance, providing custom configuration for ExampleGen. If any field is provided as a RuntimeParameter, output_config should be constructed as a dict.
example_artifacts	Channel of standard_artifacts.Examples for output train and eval examples.
instance_name	Optional unique instance name. Required only if multiple ExampleGen components are declared in the same pipeline.

Attributes
component_id	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: component_id is deprecated, use id instead
component_type	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: component_type is deprecated, use type instead
downstream_nodes
exec_properties
id	Node id, unique across all TFX nodes in a pipeline. If instance name is available, node_id will be: . otherwise, node_id will be:
inputs
outputs
type
upstream_nodes

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

View source

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

View source

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

View source

@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_id

View source

@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

View source

to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

Class Variables
EXECUTOR_SPEC	tfx.components.base.executor_spec.ExecutorClassSpec