`great_expectations.datasource.datasource`¶

Module Contents¶

Classes¶

LegacyDatasource(name, data_context=None, data_asset_type=None, batch_kwargs_generators=None, **kwargs)

A Datasource connects to a compute environment and one or more storage environments and produces batches of data

great_expectations.datasource.datasource.logger¶

great_expectations.datasource.datasource.yaml¶

great_expectations.datasource.datasource.default_flow_style = False¶

class great_expectations.datasource.datasource.LegacyDatasource(name, data_context=None, data_asset_type=None, batch_kwargs_generators=None, **kwargs)¶

A Datasource connects to a compute environment and one or more storage environments and produces batches of data that Great Expectations can validate in that compute environment.

Each Datasource provides Batches connected to a specific compute environment, such as a SQL database, a Spark cluster, or a local in-memory Pandas DataFrame.

Datasources use Batch Kwargs to specify instructions for how to access data from relevant sources such as an existing object from a DAG runner, a SQL database, S3 bucket, or local filesystem.

To bridge the gap between those worlds, Datasources interact closely with generators which are aware of a source of data and can produce produce identifying information, called “batch_kwargs” that datasources can use to get individual batches of data. They add flexibility in how to obtain data such as with time-based partitioning, downsampling, or other techniques appropriate for the datasource.

For example, a batch kwargs generator could produce a SQL query that logically represents “rows in the Events table with a timestamp on February 7, 2012,” which a SqlAlchemyDatasource could use to materialize a SqlAlchemyDataset corresponding to that batch of data and ready for validation.

Since opinionated DAG managers such as airflow, dbt, prefect.io, dagster can also act as datasources and/or batch kwargs generators for a more generic datasource.

When adding custom expectations by subclassing an existing DataAsset type, use the data_asset_type parameter to configure the datasource to load and return DataAssets of the custom type.

Feature Maturity

Datasource - S3 - How-to Guide

Support for connecting to Amazon Web Services S3 as an external datasource.

Maturity: Production

Details:

API Stability: medium
Implementation Completeness: Complete
Unit Test Coverage: : Complete
Integration Infrastructure/Test Coverage: None
Documentation Completeness: Minimal/Spotty
Bug Risk: Low

Datasource - Filesystem - How-to Guide

Support for using a mounted filesystem as an external datasource.

Maturity: Production

Details:

API Stability: Medium
Implementation Completeness: Complete
Unit Test Coverage: Complete
Integration Infrastructure/Test Coverage: Partial
Documentation Completeness: Partial
Bug Risk: Low (Moderate for Windows users because of path issues)

Datasource - GCS - How-to Guide

Support for Google Cloud Storage as an external datasource

Maturity: Experimental

Details:

API Stability: Medium (supported via native ‘gs://’ syntax in Pandas and Pyspark; medium because we expect configuration to evolve)
Implementation Completeness: Medium (works via passthrough, not via CLI)
Unit Test Coverage: Minimal
Integration Infrastructure/Test Coverage: Minimal
Documentation Completeness: Minimal
Bug Risk: Moderate

Datasource - Azure Blob Storage - How-to Guide

Support for Microsoft Azure Blob Storage as an external datasource

Maturity: In Roadmap (Sub-Experimental - “Not Impossible”)

Details:

API Stability: N/A (Supported on Databricks Spark via ‘wasb://’ / ‘wasps://’ url; requires local download first for Pandas)
Implementation Completeness: Minimal
Unit Test Coverage: N/A
Integration Infrastructure/Test Coverage: N/A
Documentation Completeness: Minimal
Bug Risk: Unknown

recognized_batch_parameters¶

classmethod from_configuration(cls, **kwargs)¶

Build a new datasource from a configuration dictionary.

Parameters: **kwargs – configuration key-value pairs
Returns: the newly-created datasource
Return type: datasource (Datasource)

classmethod build_configuration(cls, class_name, module_name='great_expectations.datasource', data_asset_type=None, batch_kwargs_generators=None, **kwargs)¶

Build a full configuration object for a datasource, potentially including batch kwargs generators with defaults.

Parameters

class_name – The name of the class for which to build the config
module_name – The name of the module in which the datasource class is located
data_asset_type – A ClassConfig dictionary
batch_kwargs_generators – BatchKwargGenerators configuration dictionary
**kwargs – Additional kwargs to be part of the datasource constructor’s initialization

Returns

A complete datasource configuration.

property name(self)¶: Property for datasource name

property config(self)¶

property data_context(self)¶: Property for attached DataContext

_build_generators(self)¶

Build batch kwargs generator objects from the datasource configuration.

Returns: None

add_batch_kwargs_generator(self, name, class_name, **kwargs)¶

Add a BatchKwargGenerator to the datasource.

Parameters

name (str) – the name of the new BatchKwargGenerator to add
class_name – class of the BatchKwargGenerator to add
kwargs – additional keyword arguments will be passed directly to the new BatchKwargGenerator’s constructor

Returns

BatchKwargGenerator (BatchKwargGenerator)

_build_batch_kwargs_generator(self, **kwargs)¶: Build a BatchKwargGenerator using the provided configuration and return the newly-built generator.

get_batch_kwargs_generator(self, name)¶

Get the (named) BatchKwargGenerator from a datasource

Parameters: name (str) – name of BatchKwargGenerator (default value is ‘default’)
Returns: BatchKwargGenerator (BatchKwargGenerator)

list_batch_kwargs_generators(self)¶

List currently-configured BatchKwargGenerator for this datasource.

Returns: each dictionary includes “name” and “type” keys
Return type: List(dict)

process_batch_parameters(self, limit=None, dataset_options=None)¶

Use datasource-specific configuration to translate any batch parameters into batch kwargs at the datasource level.

Parameters

limit (int) – a parameter all datasources must accept to allow limiting a batch to a smaller number of rows.
dataset_options (dict) – a set of kwargs that will be passed to the constructor of a dataset built using these batch_kwargs

Returns

Result will include both parameters passed via argument and configured parameters.

Return type

batch_kwargs

abstract get_batch(self, batch_kwargs, batch_parameters=None)¶

Get a batch of data from the datasource.

Parameters

batch_kwargs – the BatchKwargs to use to construct the batch
batch_parameters – optional parameters to store as the reference description of the batch. They should reflect parameters that would provide the passed BatchKwargs.

Returns

Batch

get_available_data_asset_names(self, batch_kwargs_generator_names=None)¶

Returns a dictionary of data_asset_names that the specified batch kwarg generator can provide. Note that some batch kwargs generators may not be capable of describing specific named data assets, and some (such as filesystem glob batch kwargs generators) require the user to configure data asset names.

Parameters

batch_kwargs_generator_names – the BatchKwargGenerator for which to get available data asset names.

Returns

{
  generator_name: {
    names: [ (data_asset_1, data_asset_1_type), (data_asset_2, data_asset_2_type) ... ]
  }
  ...
}

Return type

dictionary consisting of sets of generator assets available for the specified generators

build_batch_kwargs(self, batch_kwargs_generator, data_asset_name=None, partition_id=None, **kwargs)¶

great_expectations.datasource.datasource¶

Module Contents¶

Classes¶

`great_expectations.datasource.datasource`¶