Source code for airflow.operators.check_operator

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from builtins import zip
from builtins import str

from airflow.exceptions import AirflowException
from airflow.hooks.base_hook import BaseHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults


[docs]class CheckOperator(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. :param sql: the sql to be executed. (templated) :type sql: string """ template_fields = ('sql',) template_ext = ('.hql', '.sql',) ui_color = '#fff7e6' @apply_defaults def __init__( self, sql, conn_id=None, *args, **kwargs): super(CheckOperator, self).__init__(*args, **kwargs) self.conn_id = conn_id self.sql = sql def execute(self, context=None): self.log.info('Executing SQL check: %s', self.sql) records = self.get_db_hook().get_first(self.sql) self.log.info('Record: %s', records) if not records: raise AirflowException("The query returned None") elif not all([bool(r) for r in records]): exceptstr = "Test failed.\nQuery:\n{q}\nResults:\n{r!s}" raise AirflowException(exceptstr.format(q=self.sql, r=records)) self.log.info("Success.") def get_db_hook(self): return BaseHook.get_hook(conn_id=self.conn_id)
def _convert_to_float_if_possible(s): """ A small helper function to convert a string to a numeric value if appropriate :param s: the string to be converted :type s: str """ try: ret = float(s) except (ValueError, TypeError): ret = s return ret
[docs]class ValueCheckOperator(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. :param sql: the sql to be executed. (templated) :type sql: string """ __mapper_args__ = { 'polymorphic_identity': 'ValueCheckOperator' } template_fields = ('sql', 'pass_value',) template_ext = ('.hql', '.sql',) ui_color = '#fff7e6' @apply_defaults def __init__( self, sql, pass_value, tolerance=None, conn_id=None, *args, **kwargs): super(ValueCheckOperator, self).__init__(*args, **kwargs) self.sql = sql self.conn_id = conn_id self.pass_value = str(pass_value) tol = _convert_to_float_if_possible(tolerance) self.tol = tol if isinstance(tol, float) else None self.has_tolerance = self.tol is not None def execute(self, context=None): self.log.info('Executing SQL check: %s', self.sql) records = self.get_db_hook().get_first(self.sql) if not records: raise AirflowException("The query returned None") pass_value_conv = _convert_to_float_if_possible(self.pass_value) is_numeric_value_check = isinstance(pass_value_conv, float) tolerance_pct_str = None if (self.tol is not None): tolerance_pct_str = str(self.tol * 100) + '%' except_temp = ("Test failed.\nPass value:{pass_value_conv}\n" "Tolerance:{tolerance_pct_str}\n" "Query:\n{self.sql}\nResults:\n{records!s}") if not is_numeric_value_check: tests = [str(r) == pass_value_conv for r in records] elif is_numeric_value_check: try: num_rec = [float(r) for r in records] except (ValueError, TypeError) as e: cvestr = "Converting a result to float failed.\n" raise AirflowException(cvestr + except_temp.format(**locals())) if self.has_tolerance: tests = [ pass_value_conv * (1 - self.tol) <= r <= pass_value_conv * (1 + self.tol) for r in num_rec] else: tests = [r == pass_value_conv for r in num_rec] if not all(tests): raise AirflowException(except_temp.format(**locals())) def get_db_hook(self): return BaseHook.get_hook(conn_id=self.conn_id)
[docs]class IntervalCheckOperator(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. :param table: the table name :type table: str :param days_back: number of days between ds and the ds we want to check against. Defaults to 7 days :type days_back: int :param metrics_threshold: a dictionary of ratios indexed by metrics :type metrics_threshold: dict """ __mapper_args__ = { 'polymorphic_identity': 'IntervalCheckOperator' } template_fields = ('sql1', 'sql2') template_ext = ('.hql', '.sql',) ui_color = '#fff7e6' @apply_defaults def __init__( self, table, metrics_thresholds, date_filter_column='ds', days_back=-7, conn_id=None, *args, **kwargs): super(IntervalCheckOperator, self).__init__(*args, **kwargs) self.table = table self.metrics_thresholds = metrics_thresholds self.metrics_sorted = sorted(metrics_thresholds.keys()) self.date_filter_column = date_filter_column self.days_back = -abs(days_back) self.conn_id = conn_id sqlexp = ', '.join(self.metrics_sorted) sqlt = ("SELECT {sqlexp} FROM {table}" " WHERE {date_filter_column}=").format(**locals()) self.sql1 = sqlt + "'{{ ds }}'" self.sql2 = sqlt + "'{{ macros.ds_add(ds, " + str(self.days_back) + ") }}'" def execute(self, context=None): hook = self.get_db_hook() self.log.info('Executing SQL check: %s', self.sql2) row2 = hook.get_first(self.sql2) self.log.info('Executing SQL check: %s', self.sql1) row1 = hook.get_first(self.sql1) if not row2: raise AirflowException("The query {q} returned None".format(q=self.sql2)) if not row1: raise AirflowException("The query {q} returned None".format(q=self.sql1)) current = dict(zip(self.metrics_sorted, row1)) reference = dict(zip(self.metrics_sorted, row2)) ratios = {} test_results = {} rlog = "Ratio for {0}: {1} \n Ratio threshold : {2}" fstr = "'{k}' check failed. {r} is above {tr}" estr = "The following tests have failed:\n {0}" countstr = "The following {j} tests out of {n} failed:" for m in self.metrics_sorted: if current[m] == 0 or reference[m] == 0: ratio = None else: ratio = float(max(current[m], reference[m])) / \ min(current[m], reference[m]) self.log.info(rlog.format(m, ratio, self.metrics_thresholds[m])) ratios[m] = ratio test_results[m] = ratio < self.metrics_thresholds[m] if not all(test_results.values()): failed_tests = [it[0] for it in test_results.items() if not it[1]] j = len(failed_tests) n = len(self.metrics_sorted) self.log.warning(countstr.format(**locals())) for k in failed_tests: self.log.warning( fstr.format(k=k, r=ratios[k], tr=self.metrics_thresholds[k]) ) raise AirflowException(estr.format(", ".join(failed_tests))) self.log.info("All tests have passed") def get_db_hook(self): return BaseHook.get_hook(conn_id=self.conn_id)