airflow.operators.check_operator

Module Contents

class airflow.operators.check_operator.CheckOperator(sql, conn_id=None, *args, **kwargs)

Bases: airflow.models.BaseOperator

Performs checks against a db. The CheckOperator expects a sql query that will return a single row. Each value on that first row is evaluated using python bool casting. If any of the values return False the check is failed and errors out.

Note that Python bool casting evals the following as False:

• False

• 0

• Empty string ("")

• Empty list ([])

• Empty dictionary or set ({})

Given a query like SELECT COUNT(*) FROM foo, it will fail only if the count == 0. You can craft much more complex query that could, for instance, check that the table has the same number of rows as the source table upstream, or that the count of today’s partition is greater than yesterday’s partition, or that a set of metrics are less than 3 standard deviation for the 7 day average.

This operator can be used as a data quality check in your pipeline, and depending on where you put it in your DAG, you have the choice to stop the critical path, preventing from publishing dubious data, or on the side and receive email alerts without stopping the progress of the DAG.

Note that this is an abstract class and get_db_hook needs to be defined. Whereas a get_db_hook is hook that gets a single record from an external source.

Parameters

sql (str) – the sql to be executed. (templated)

template_fields :Iterable[str] = ['sql']

template_ext :Iterable[str] = ['.hql', '.sql']

ui_color = #fff7e6

execute(self, context=None)

get_db_hook(self)

airflow.operators.check_operator._convert_to_float_if_possible(s)

A small helper function to convert a string to a numeric value

if appropriate

Parameters

s (str) – the string to be converted

class airflow.operators.check_operator.ValueCheckOperator(sql, pass_value, tolerance=None, conn_id=None, *args, **kwargs)

Bases: airflow.models.BaseOperator

Performs a simple value check using sql code.

Note that this is an abstract class and get_db_hook needs to be defined. Whereas a get_db_hook is hook that gets a single record from an external source.

Parameters

sql (str) – the sql to be executed. (templated)

__mapper_args__

template_fields :Iterable[str] = ['sql', 'pass_value']

template_ext :Iterable[str] = ['.hql', '.sql']

ui_color = #fff7e6

execute(self, context=None)

_to_float(self, records)

_get_string_matches(self, records, pass_value_conv)

_get_numeric_matches(self, numeric_records, numeric_pass_value_conv)

get_db_hook(self)

class airflow.operators.check_operator.IntervalCheckOperator(table, metrics_thresholds, date_filter_column='ds', days_back=-7, ratio_formula='max_over_min', ignore_zero=True, conn_id=None, *args, **kwargs)

Bases: airflow.models.BaseOperator

Checks that the values of metrics given as SQL expressions are within a certain tolerance of the ones from days_back before.

Note that this is an abstract class and get_db_hook needs to be defined. Whereas a get_db_hook is hook that gets a single record from an external source.

Parameters

• table (str) – the table name

• days_back (int) – number of days between ds and the ds we want to check against. Defaults to 7 days

• ratio_formula (str) –

  which formula to use to compute the ratio between the two metrics. Assuming cur is the metric of today and ref is the metric to today - days_back.

  max_over_min: computes max(cur, ref) / min(cur, ref) relative_diff: computes abs(cur-ref) / ref

  Default: ‘max_over_min’

• ignore_zero (bool) – whether we should ignore zero metrics

• metrics_threshold (dict) – a dictionary of ratios indexed by metrics

__mapper_args__

template_fields :Iterable[str] = ['sql1', 'sql2']

template_ext :Iterable[str] = ['.hql', '.sql']

ui_color = #fff7e6

ratio_formulas

execute(self, context=None)

get_db_hook(self)