def _suite_edit(suite, datasource, directory, jupyter, batch_kwargs, usage_event):
batch_kwargs_json = batch_kwargs
batch_kwargs = None
context = load_data_context_with_error_handling(directory)
try:
suite = load_expectation_suite(context, suite)
citations = suite.get_citations(require_batch_kwargs=True)
if batch_kwargs_json:
try:
batch_kwargs = json.loads(batch_kwargs_json)
if datasource:
batch_kwargs["datasource"] = datasource
_batch = toolkit.load_batch(context, suite, batch_kwargs)
except json_parse_exception as je:
cli_message(
"<red>Please check that your batch_kwargs are valid JSON.\n{}</red>".format(
je
)
)
send_usage_message(
data_context=context, event=usage_event, success=False
)
sys.exit(1)
except ge_exceptions.DataContextError:
cli_message(
"<red>Please check that your batch_kwargs are able to load a batch.</red>"
)
send_usage_message(
data_context=context, event=usage_event, success=False
)
sys.exit(1)
except ValueError as ve:
cli_message(
"<red>Please check that your batch_kwargs are able to load a batch.\n{}</red>".format(
ve
)
)
send_usage_message(
data_context=context, event=usage_event, success=False
)
sys.exit(1)
elif citations:
citation = citations[-1]
batch_kwargs = citation.get("batch_kwargs")
if not batch_kwargs:
cli_message(
"""
A batch of data is required to edit the suite - let's help you to specify it."""
)
additional_batch_kwargs = None
try:
data_source = select_datasource(context, datasource_name=datasource)
except ValueError as ve:
cli_message("<red>{}</red>".format(ve))
send_usage_message(
data_context=context, event=usage_event, success=False
)
sys.exit(1)
if not data_source:
cli_message("<red>No datasources found in the context.</red>")
send_usage_message(
data_context=context, event=usage_event, success=False
)
sys.exit(1)
if batch_kwargs is None:
(
datasource_name,
batch_kwargs_generator,
data_asset,
batch_kwargs,
) = get_batch_kwargs(context, datasource_name=data_source.name,
additional_batch_kwargs=additional_batch_kwargs)
notebook_name = "edit_{}.ipynb".format(suite.expectation_suite_name)
notebook_path = _get_notebook_path(context, notebook_name)
SuiteEditNotebookRenderer().render_to_disk(suite, notebook_path, batch_kwargs)
if not jupyter:
cli_message(
f"To continue editing this suite, run <green>jupyter notebook {notebook_path}</green>"
)
payload = edit_expectation_suite_usage_statistics(
data_context=context, expectation_suite_name=suite.expectation_suite_name
)
send_usage_message(
data_context=context, event=usage_event, event_payload=payload, success=True
)
if jupyter:
toolkit.launch_jupyter_notebook(notebook_path)
except Exception as e:
send_usage_message(data_context=context, event=usage_event, success=False)
raise e
def _get_datasource(context, datasource):
datasource = select_datasource(context, datasource_name=datasource)
if not datasource:
cli_message("<red>No datasources found in the context.</red>")
sys.exit(1)
return datasource
def create_expectation_suite(
context,
datasource_name=None,
batch_kwargs_generator_name=None,
generator_asset=None,
batch_kwargs=None,
expectation_suite_name=None,
additional_batch_kwargs=None,
empty_suite=False,
show_intro_message=False,
open_docs=False,
profiler_configuration="demo",
):
"""
Create a new expectation suite.
:return: a tuple: (success, suite name)
"""
if show_intro_message and not empty_suite:
cli_message(
"\n<cyan>========== Create sample Expectations ==========</cyan>\n\n"
)
data_source = select_datasource(context, datasource_name=datasource_name)
if data_source is None:
# select_datasource takes care of displaying an error message, so all is left here is to exit.
sys.exit(1)
datasource_name = data_source.name
if expectation_suite_name in context.list_expectation_suite_names():
tell_user_suite_exists(expectation_suite_name)
sys.exit(1)
if (batch_kwargs_generator_name is None or generator_asset is None
or batch_kwargs is None):
(
datasource_name,
batch_kwargs_generator_name,
generator_asset,
batch_kwargs,
) = get_batch_kwargs(
context,
datasource_name=datasource_name,
batch_kwargs_generator_name=batch_kwargs_generator_name,
generator_asset=generator_asset,
additional_batch_kwargs=additional_batch_kwargs,
)
# In this case, we have "consumed" the additional_batch_kwargs
additional_batch_kwargs = {}
if expectation_suite_name is None:
default_expectation_suite_name = _get_default_expectation_suite_name(
batch_kwargs, generator_asset)
while True:
expectation_suite_name = click.prompt(
"\nName the new expectation suite",
default=default_expectation_suite_name)
if expectation_suite_name in context.list_expectation_suite_names(
):
tell_user_suite_exists(expectation_suite_name)
else:
break
if empty_suite:
create_empty_suite(context, expectation_suite_name, batch_kwargs)
return True, expectation_suite_name
profiling_results = _profile_to_create_a_suite(
additional_batch_kwargs,
batch_kwargs,
batch_kwargs_generator_name,
context,
datasource_name,
expectation_suite_name,
generator_asset,
profiler_configuration,
)
build_docs(context, view=False)
if open_docs:
_attempt_to_open_validation_results_in_data_docs(
context, profiling_results)
return True, expectation_suite_name
def suite_edit(suite, datasource, directory, jupyter, batch_kwargs):
"""
Generate a Jupyter notebook for editing an existing expectation suite.
The SUITE argument is required. This is the name you gave to the suite
when you created it.
A batch of data is required to edit the suite, which is used as a sample.
The edit command will help you specify a batch interactively. Or you can
specify them manually by providing --batch-kwargs in valid JSON format.
Read more about specifying batches of data in the documentation: https://docs.greatexpectations.io/
"""
try:
context = DataContext(directory)
except ge_exceptions.ConfigNotFoundError as err:
cli_message("<red>{}</red>".format(err.message))
return
except ge_exceptions.ZeroDotSevenConfigVersionError as err:
_offer_to_install_new_template(err, context.root_directory)
return
suite = _load_suite(context, suite)
if batch_kwargs:
try:
batch_kwargs = json.loads(batch_kwargs)
if datasource:
batch_kwargs["datasource"] = datasource
_batch = context.get_batch(batch_kwargs, suite.expectation_suite_name)
assert isinstance(_batch, DataAsset)
except json_parse_exception as je:
cli_message("<red>Please check that your batch_kwargs are valid JSON.\n{}</red>".format(je))
sys.exit(1)
except ge_exceptions.DataContextError:
cli_message("<red>Please check that your batch_kwargs are able to load a batch.</red>")
sys.exit(1)
except ValueError as ve:
cli_message("<red>Please check that your batch_kwargs are able to load a batch.\n{}</red>".format(ve))
sys.exit(1)
else:
cli_message("""
A batch of data is required to edit the suite - let's help you to specify it."""
)
additional_batch_kwargs = None
try:
data_source = select_datasource(context, datasource_name=datasource)
except ValueError as ve:
cli_message("<red>{}</red>".format(ve))
sys.exit(1)
if not data_source:
cli_message("<red>No datasources found in the context.</red>")
sys.exit(1)
if batch_kwargs is None:
datasource_name, batch_kwarg_generator, data_asset, batch_kwargs = get_batch_kwargs(
context,
datasource_name=data_source.name,
generator_name=None,
generator_asset=None,
additional_batch_kwargs=additional_batch_kwargs
)
notebook_name = "{}.ipynb".format(suite.expectation_suite_name)
notebook_path = os.path.join(context.root_directory, context.GE_EDIT_NOTEBOOK_DIR, notebook_name)
NotebookRenderer().render_to_disk(suite, batch_kwargs, notebook_path)
cli_message(
"To continue editing this suite, run <green>jupyter notebook {}</green>".format(
notebook_path
)
)
if jupyter:
subprocess.call(["jupyter", "notebook", notebook_path])
def _suite_edit(suite, datasource, directory, jupyter, batch_kwargs):
batch_kwargs_json = batch_kwargs
batch_kwargs = None
try:
context = DataContext(directory)
except ge_exceptions.ConfigNotFoundError as err:
cli_message("<red>{}</red>".format(err.message))
return
suite = _load_suite(context, suite)
citations = suite.get_citations(sort=True, require_batch_kwargs=True)
if batch_kwargs_json:
try:
batch_kwargs = json.loads(batch_kwargs_json)
if datasource:
batch_kwargs["datasource"] = datasource
_batch = context.get_batch(batch_kwargs,
suite.expectation_suite_name)
assert isinstance(_batch, DataAsset)
except json_parse_exception as je:
cli_message(
"<red>Please check that your batch_kwargs are valid JSON.\n{}</red>"
.format(je))
sys.exit(1)
except ge_exceptions.DataContextError:
cli_message(
"<red>Please check that your batch_kwargs are able to load a batch.</red>"
)
sys.exit(1)
except ValueError as ve:
cli_message(
"<red>Please check that your batch_kwargs are able to load a batch.\n{}</red>"
.format(ve))
sys.exit(1)
elif citations:
citation = citations[-1]
batch_kwargs = citation.get("batch_kwargs")
if not batch_kwargs:
cli_message("""
A batch of data is required to edit the suite - let's help you to specify it."""
)
additional_batch_kwargs = None
try:
data_source = select_datasource(context,
datasource_name=datasource)
except ValueError as ve:
cli_message("<red>{}</red>".format(ve))
sys.exit(1)
if not data_source:
cli_message("<red>No datasources found in the context.</red>")
sys.exit(1)
if batch_kwargs is None:
(
datasource_name,
batch_kwarg_generator,
data_asset,
batch_kwargs,
) = get_batch_kwargs(
context,
datasource_name=data_source.name,
generator_name=None,
generator_asset=None,
additional_batch_kwargs=additional_batch_kwargs,
)
notebook_name = "{}.ipynb".format(suite.expectation_suite_name)
notebook_path = os.path.join(context.root_directory,
context.GE_EDIT_NOTEBOOK_DIR, notebook_name)
NotebookRenderer().render_to_disk(suite, notebook_path, batch_kwargs)
if not jupyter:
cli_message("To continue editing this suite, run <green>jupyter "
f"notebook {notebook_path}</green>")
if jupyter:
subprocess.call(["jupyter", "notebook", notebook_path])
def validation_operator_run(name, run_id, validation_config_file, suite, directory):
# Note though the long lines here aren't pythonic, they look best if Click does the line wraps.
"""
Run a validation operator against some data.
There are two modes to run this command:
1. Interactive (good for development):
Specify the name of the validation operator using the --name argument
and the name of the expectation suite using the --suite argument.
The cli will help you specify the batch of data that you want to
validate interactively.
2. Non-interactive (good for production):
Use the `--validation_config_file` argument to specify the path of the validation configuration JSON file. This file can be used to instruct a validation operator to validate multiple batches of data and use multiple expectation suites to validate each batch.
Learn how to create a validation config file here:
https://great-expectations.readthedocs.io/en/latest/command_line.html#great-expectations-validation-operator-run-validation-config-file-validation-config-file-path
This command exits with 0 if the validation operator ran and the "success" attribute in its return object is True. Otherwise, the command exits with 1.
To learn more about validation operators, go here:
https://great-expectations.readthedocs.io/en/latest/features/validation.html#validation-operators
"""
try:
context = DataContext(directory)
except ge_exceptions.ConfigNotFoundError as err:
cli_message("Failed to process <red>{}</red>".format(err.message))
sys.exit(1)
if validation_config_file is not None:
try:
with open(validation_config_file) as f:
validation_config = json.load(f)
except (
IOError,
json_parse_exception
) as e:
cli_message(f"Failed to process the --validation_config_file argument: <red>{e}</red>")
sys.exit(1)
validation_config_error_message = _validate_valdiation_config(validation_config)
if validation_config_error_message is not None:
cli_message("<red>The validation config in {0:s} is misconfigured: {1:s}</red>".format(validation_config_file, validation_config_error_message))
sys.exit(1)
else:
if suite is None:
cli_message(
"""
Please use --suite argument to specify the name of the expectation suite.
Call `great_expectation suite list` command to list the expectation suites in your project.
"""
)
sys.exit(0)
suite = load_expectation_suite(context, suite)
if name is None:
cli_message(
"""
Please use --name argument to specify the name of the validation operator.
Call `great_expectation validation-operator list` command to list the operators in your project.
"""
)
sys.exit(1)
else:
if name not in context.list_validation_operator_names():
cli_message(
f"""
Could not find a validation operator {name}.
Call `great_expectation validation-operator list` command to list the operators in your project.
"""
)
sys.exit(1)
batch_kwargs = None
cli_message(
"""
Let's help you specify the batch of data your want the validation operator to validate."""
)
try:
data_source = select_datasource(context)
except ValueError as ve:
cli_message("<red>{}</red>".format(ve))
sys.exit(1)
if not data_source:
cli_message("<red>No datasources found in the context.</red>")
sys.exit(1)
if batch_kwargs is None:
(
datasource_name,
batch_kwarg_generator,
data_asset,
batch_kwargs,
) = get_batch_kwargs(
context,
datasource_name=data_source.name,
generator_name=None,
generator_asset=None,
additional_batch_kwargs=None,
)
validation_config = {
"validation_operator_name": name,
"batches": [
{
"batch_kwargs": batch_kwargs,
"expectation_suite_names": [suite.expectation_suite_name]
}
]
}
try:
validation_operator_name = validation_config["validation_operator_name"]
batches_to_validate = []
for entry in validation_config["batches"]:
for expectation_suite_name in entry["expectation_suite_names"]:
batch = context.get_batch(entry["batch_kwargs"], expectation_suite_name)
batches_to_validate.append(batch)
if run_id is None:
run_id = datetime.utcnow().strftime("%Y%m%dT%H%M%S.%fZ")
results = context.run_validation_operator(
validation_operator_name,
assets_to_validate=[batch],
run_id=run_id)
except (
ge_exceptions.DataContextError,
IOError,
SQLAlchemyError,
) as e:
cli_message("<red>{}</red>".format(e))
sys.exit(1)
if not results["success"]:
cli_message("Validation Failed!")
sys.exit(1)
else:
cli_message("Validation Succeeded!")
sys.exit(0)
