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How to run a Checkpoint in Python

This guide will help you run a Checkpoint in Python. This is useful if your pipeline environment or orchestration engine does not have shell access.

Prerequisites: This how-to guide assumes you have:


  1. First, generate the Python script with the command:
great_expectations --v3-api checkpoint script my_checkpoint
  1. Next, you will see a message about where the Python script was created like:
A Python script was created that runs the checkpoint named: `my_checkpoint`  - The script is located in `great_expectations/uncommitted/`  - The script can be run with `python great_expectations/uncommitted/`
  1. Next, open the script which should look like this:
"""This is a basic generated Great Expectations script that runs a Checkpoint.
Checkpoints are the primary method for validating batches of data in production and triggering any followup actions.
A Checkpoint facilitates running a validation as well as configurable Actions such as updating Data Docs, sending anotification to team members about Validation Results, or storing a result in a shared cloud storage.
See also [How to configure a new Checkpoint using test_yaml_config](./how_to_configure_a_new_checkpoint_using_test_yaml_config) for more information about the Checkpoints and how to configure them in your Great Expectations environment.
Checkpoints can be run directly without this script using the `great_expectations checkpoint run` command.  This scriptis provided for those who wish to run Checkpoints in Python.
Usage:- Run this file: `python great_expectations/uncommitted/`.- This can be run manually or via a scheduler such, as cron.- If your pipeline runner supports Python snippets, then you can paste this into your pipeline."""import sys
from great_expectations.checkpoint.types.checkpoint_result import CheckpointResultfrom great_expectations.data_context import DataContext
data_context: DataContext = DataContext(    context_root_dir="/Users/talgluck/Documents/ge_main/quagga/UAT/DataContexts/cli_testing/ge_suite/v3_many_suites_pandas_filesystem_v3_config/great_expectations")
result: CheckpointResult = data_context.run_checkpoint(    checkpoint_name="chk",    batch_request=None,    run_name=None,)
if not result["success"]:    print("Validation failed!")    sys.exit(1)
print("Validation succeeded!")sys.exit(0)
  1. This Python script can then be invoked directly using Python python great_expectations/uncommitted/ or the Python code can be embedded in your pipeline.

    Other arguments to the DataContext.run_checkpoint() method may be required, depending on the amount and specifics of the Checkpoint configuration previously saved in the configuration file of the Checkpoint with the corresponding name. The dynamically specified Checkpoint configuration, provided to the runtime as arguments to DataContext.run_checkpoint() must complement the settings in the Checkpoint configuration file so as to comprise a properly and sufficiently configured Checkpoint with the given name.

Please see How to configure a new Checkpoint using test_yaml_config for additional Checkpoint configuration and DataContext.run_checkpoint() examples.