tecton.BatchSource

Summary

A Tecton BatchSource, used to read batch data into Tecton for use in a BatchFeatureView.

Attributes

Name	Data Type	Description
data_delay	Optional[datetime.timedelta]	Returns the duration that materialization jobs wait after the batch_schedule before starting, typically to ensure that all data has landed.
description	Optional[str]	Returns the description of the Tecton object.
id	str	Returns the unique id of the Tecton object.
info
is_streaming		Deprecated.
name	str	Returns the name of the Tecton object.
owner	Optional[str]	Returns the owner of the Tecton object.
tags	Dict[str,str]	Returns the tags of the Tecton object.
workspace	Optional[str]	Returns the workspace that this Tecton object belongs to.

Methods

Name	Description
__init__(...)	Creates a new BatchSource.
get_columns()	Returns the column names of the Data Source’s schema.
get_dataframe(...)	Returns the data in this Data Source as a Tecton DataFrame.
summary()	Displays a human readable summary of this Data Source.
validate()	Validate this Tecton object and its dependencies (if any).

__init__(...)

Creates a new BatchSource.

Parameters

• name (str) – A unique name of the DataSource.

• description (Optional[str]) – A human-readable description. (Default: None)

• tags (Optional[Dict[str, str]]) – Tags associated with this Tecton Data Source (key-value pairs of arbitrary metadata). (Default: None)

• owner (Optional[str]) – Owner name (typically the email of the primary maintainer). (Default: None)

• prevent_destroy (bool) – If True, this Tecton object will be blocked from being deleted or re-created (i.e. a destructive update) during tecton plan/apply. To remove or update this object, prevent_destroy must be first set to False via a separate tecton apply. prevent_destroy can be used to prevent accidental changes such as inadvertantly deleting a Feature Service used in production or recreating a Feature View that triggers expensive rematerialization jobs. prevent_destroy also blocks changes to dependent Tecton objects that would trigger a recreate of the tagged object, e.g. if prevent_destroy is set on a Feature Service, that will also prevent deletions or re-creates of Feature Views used in that service. prevent_destroy is only enforced in live (i.e. non-dev) workspaces. (Default: False)

• batch_config (Union[FileConfig, HiveConfig, RedshiftConfig, SnowflakeConfig, SparkBatchConfig]) – BatchConfig object containing the configuration of the Batch Data Source to be included in this Data Source.

Example

# Declare a BatchSource with a HiveConfig instance as its batch_config parameter.
# Refer to the "Configs Classes and Helpers" section for other batch_config types.
from tecton import HiveConfig, BatchSource

credit_scores_batch = BatchSource(
    name="credit_scores_batch",
    batch_config=HiveConfig(database="demo_fraud", table="credit_scores", timestamp_field="timestamp"),
    owner="matt@tecton.ai",
    tags={"release": "production"},
)

get_columns()

Returns the column names of the Data Source’s schema.

get_dataframe(...)

Returns the data in this Data Source as a Tecton DataFrame.

Parameters

• start_time (Optional[datetime]) – The interval start time from when we want to retrieve source data. If no timezone is specified, will default to using UTC. Can only be defined if apply_translator is True. (Default: None)

• end_time (Optional[datetime]) – The interval end time until when we want to retrieve source data. If no timezone is specified, will default to using UTC. Can only be defined if apply_translator is True. (Default: None)

• apply_translator (bool) – If True, the transformation specified by post_processor will be applied to the dataframe for the data source. apply_translator is not applicable to batch sources configured with spark_batch_config because it does not have a post_processor. (Default: True)

Returns

A Tecton DataFrame containing the data source’s raw or translated source data.

Raises

• TectonValidationError – If apply_translator is False, but start_time or end_time filters are passed in.

summary()

Displays a human readable summary of this Data Source.

validate()

Validate this Tecton object and its dependencies (if any).

Validation performs most of the same checks and operations as tecton plan.

1. Check for invalid object configurations, e.g. setting conflicting fields.

2. For Data Sources and Feature Views, test query code and derive schemas. e.g. test that a Data Source’s specified s3 path exists or that a Feature View’s SQL code executes and produces supported feature data types.

Objects already applied to Tecton do not need to be re-validated on retrieval (e.g. my_workspace.get_feature_view('my_fv')) since they have already been validated during tecton plan.

Locally defined objects (e.g. my_ds = BatchSource(name="my_ds", ...)) may need to be validated before some of their methods can be called (e.g. my_feature_view.get_historical_features()).