Validation Operators And Actions Introduction

The validate method evaluates one batch of data against one expectation suite and returns a dictionary of validation results. This is sufficient when you explore your data and get to know Great Expectations. When deploying Great Expectations in a real data pipeline, you will typically discover additional needs:

  • validating a group of batches that are logically related

  • validating a batch against several expectation suites

  • doing something with the validation results (e.g., saving them for a later review, sending notifications in case of failures, etc.).

Validation Operators provide a convenient abstraction for both bundling the validation of multiple expectation suites and the actions that should be taken after the validation.

Finding a Validation Operator that is right for you

Each Validation Operator encodes a particular set of business rules around validation. Currently, two Validation Operator implementations are included in the Great Expectations distribution.

The classes that implement them are in great_expectations.validation_operators module.

ActionListValidationOperator

ActionListValidationOperator validates each batch in the list that is passed as assets_to_validate argument to its run method against the expectation suite included within that batch and then invokes a list of configured actions on every validation result.

Actions are Python classes with a run method that takes a result of validating a batch against an expectation suite and does something with it (e.g., save validation results to the disk or send a Slack notification). Classes that implement this API can be configured to be added to the list of actions for this operator (and other operators that use actions). Read more about actions here: Actions.

An instance of this operator is included in the default configuration file great_expectations.yml that great_expectations init command creates.

A user can choose the actions to perform in their instance of the operator.

Read more about ActionListValidationOperator here: ActionListValidationOperator.

WarningAndFailureExpectationSuitesValidationOperator

WarningAndFailureExpectationSuitesValidationOperator implements a business logic pattern that many data practitioners consider useful.

It validates each batch of data against two different expectation suites: “failure” and “warning”. Group the expectations that you create about a data asset into two expectation suites. The critical expectations that you are confident that the data should meet go into the expectation suite that this operator calls “failure” (meaning, not meeting these expectations should be considered a failure in the pipeline). The rest of expectations go into the “warning” expectation suite.

The failure Expectation Suite contains expectations that are considered important enough to justify stopping the pipeline when they are violated.

WarningAndFailureExpectationSuitesValidationOperator retrieves two expectation suites for every data asset in the assets_to_validate argument of its run method.

After completing all the validations, it sends a Slack notification with the success status. Note that it doesn’t use an Action to send its Slack notification, because the notification has also been customized to summarize information from both suites.

Read more about WarningAndFailureExpectationSuitesValidationOperator here: WarningAndFailureExpectationSuitesValidationOperator

Implementing Custom Validation Operators

If you wish to implement some validation handling logic that is not supported by the operators included in Great Expectations, follow these steps:

Once these steps are complete, your new Validation Operator can be configured and used.

If you think that the business logic of your operator can be useful to other data practitioners, please consider contributing it to Great Expectations.

Configuring and Running a Validation Operator

If you are using a Validation Operator that came with GE or was contributed by another developer, you can get to a rich set of useful behaviors with very little coding. This is done by editing the operator’s configuration in the GE configuration file and by extending the operator in case you want to add new behavior.

To use a Validation Operator (one that is included in Great Expectations or one that you implemented in your project’s plugins directory), you need to configure an instance of the operator in your great_expectations.yml file and then invoke this instance from your Python code.

Configuring an operator

All Validation Operators configuration blocks should appear under validation_operators key in great_expectations.yml

To configure an instance of an operator in your Great Expectations context, create a key under validation_operators. This is the name of you will use to invoke this operator.

my_operator:
    class_name: TheClassThatImplementsMyOperator
    module_name: thefilethatyoutheclassisin
    foo: bar

In the example of an operator config block above:

  • the class_name value is the name of the class that implements this operator.

  • the key module_name must be specified if the class is not in the default module.

  • the foo key specifies the value of the foo argument of this class’ constructor. Since every operator class might define its own constructor, the keys will vary.

Invoking an operator

This is an example of invoking an instance of a Validation Operator from Python:

results = context.run_validation_operator(
    assets_to_validate=[batch0, batch1, ...],
    run_id="some_string_that_uniquely_identifies_this_run",
    validation_operator_name="perform_action_list_operator",
)
  • assets_to_validate - an iterable that specifies the data assets that the operator will validate. The members of the list can be either batches or triples that will allow the operator to fetch the batch: (data_asset_name, expectation_suite_name, batch_kwargs) using this method: get_batch()

  • run_id - pipeline run id, a timestamp or any other string that is meaningful to you and will help you refer to the result of this operation later

  • validation_operator_name you can instances of a class that implements a Validation Operator

Each operator class is free to define its own object that the run method returns. Consult the reference of the specific Validation Operator.

Actions

The Validation Operator implementations above invoke actions.

An action is a way to take an arbitrary method and make it configurable and runnable within a data context.

The only requirement from an action is for it to have a take_action method.

GE comes with a list of actions that we consider useful and you can reuse in your pipelines. Most of them take in validation results and do something with them.

last updated: Feb 18, 2020