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from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, SupportsAbs, Union
from airflow.compat.functools import cached_property
from airflow.exceptions import AirflowException
from airflow.hooks.base import BaseHook
from airflow.hooks.dbapi import DbApiHook
from airflow.models import BaseOperator, SkipMixin
from airflow.utils.context import Context
[docs]def parse_boolean(val: str) -> Union[str, bool]:
"""Try to parse a string into boolean.
Raises ValueError if the input is not a valid true- or false-like string value.
"""
val = val.lower()
if val in ('y', 'yes', 't', 'true', 'on', '1'):
return True
if val in ('n', 'no', 'f', 'false', 'off', '0'):
return False
raise ValueError(f"{val!r} is not a boolean-like string value")
[docs]class BaseSQLOperator(BaseOperator):
"""
This is a base class for generic SQL Operator to get a DB Hook
The provided method is .get_db_hook(). The default behavior will try to
retrieve the DB hook based on connection type.
You can custom the behavior by overriding the .get_db_hook() method.
"""
def __init__(
self,
*,
conn_id: Optional[str] = None,
database: Optional[str] = None,
hook_params: Optional[Dict] = None,
**kwargs,
):
super().__init__(**kwargs)
self.conn_id = conn_id
self.database = database
self.hook_params = {} if hook_params is None else hook_params
@cached_property
def _hook(self):
"""Get DB Hook based on connection type"""
self.log.debug("Get connection for %s", self.conn_id)
conn = BaseHook.get_connection(self.conn_id)
hook = conn.get_hook(hook_params=self.hook_params)
if not isinstance(hook, DbApiHook):
raise AirflowException(
f'The connection type is not supported by {self.__class__.__name__}. '
f'The associated hook should be a subclass of `DbApiHook`. Got {hook.__class__.__name__}'
)
if self.database:
hook.schema = self.database
return hook
[docs] def get_db_hook(self) -> DbApiHook:
"""
Get the database hook for the connection.
:return: the database hook object.
:rtype: DbApiHook
"""
return self._hook
[docs]class SQLCheckOperator(BaseSQLOperator):
"""
Performs checks against a db. The ``SQLCheckOperator`` 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.
:param sql: the sql to be executed. (templated)
:param conn_id: the connection ID used to connect to the database.
:param database: name of database which overwrite the defined one in connection
"""
[docs] template_fields: Sequence[str] = ("sql",)
[docs] template_ext: Sequence[str] = (
".hql",
".sql",
)
[docs] template_fields_renderers = {"sql": "sql"}
def __init__(
self, *, sql: str, conn_id: Optional[str] = None, database: Optional[str] = None, **kwargs
) -> None:
super().__init__(conn_id=conn_id, database=database, **kwargs)
self.sql = sql
[docs] def execute(self, context: Context):
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):
raise AirflowException(f"Test failed.\nQuery:\n{self.sql}\nResults:\n{records!s}")
self.log.info("Success.")
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
"""
try:
ret = float(s)
except (ValueError, TypeError):
ret = s
return ret
[docs]class SQLValueCheckOperator(BaseSQLOperator):
"""
Performs a simple value check using sql code.
:param sql: the sql to be executed. (templated)
:param conn_id: the connection ID used to connect to the database.
:param database: name of database which overwrite the defined one in connection
"""
[docs] __mapper_args__ = {"polymorphic_identity": "SQLValueCheckOperator"}
[docs] template_fields: Sequence[str] = (
"sql",
"pass_value",
)
[docs] template_ext: Sequence[str] = (
".hql",
".sql",
)
[docs] template_fields_renderers = {"sql": "sql"}
def __init__(
self,
*,
sql: str,
pass_value: Any,
tolerance: Any = None,
conn_id: Optional[str] = None,
database: Optional[str] = None,
**kwargs,
):
super().__init__(conn_id=conn_id, database=database, **kwargs)
self.sql = sql
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
[docs] 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 = str(self.tol * 100) + "%" if self.has_tolerance else None
error_msg = (
"Test failed.\nPass value:{pass_value_conv}\n"
"Tolerance:{tolerance_pct_str}\n"
"Query:\n{sql}\nResults:\n{records!s}"
).format(
pass_value_conv=pass_value_conv,
tolerance_pct_str=tolerance_pct_str,
sql=self.sql,
records=records,
)
if not is_numeric_value_check:
tests = self._get_string_matches(records, pass_value_conv)
elif is_numeric_value_check:
try:
numeric_records = self._to_float(records)
except (ValueError, TypeError):
raise AirflowException(f"Converting a result to float failed.\n{error_msg}")
tests = self._get_numeric_matches(numeric_records, pass_value_conv)
else:
tests = []
if not all(tests):
raise AirflowException(error_msg)
def _to_float(self, records):
return [float(record) for record in records]
def _get_string_matches(self, records, pass_value_conv):
return [str(record) == pass_value_conv for record in records]
def _get_numeric_matches(self, numeric_records, numeric_pass_value_conv):
if self.has_tolerance:
return [
numeric_pass_value_conv * (1 - self.tol) <= record <= numeric_pass_value_conv * (1 + self.tol)
for record in numeric_records
]
return [record == numeric_pass_value_conv for record in numeric_records]
[docs]class SQLIntervalCheckOperator(BaseSQLOperator):
"""
Checks that the values of metrics given as SQL expressions are within
a certain tolerance of the ones from days_back before.
:param table: the table name
:param conn_id: the connection ID used to connect to the database.
:param database: name of database which will overwrite the defined one in connection
:param days_back: number of days between ds and the ds we want to check
against. Defaults to 7 days
:param date_filter_column: The column name for the dates to filter on. Defaults to 'ds'
:param ratio_formula: 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'
:param ignore_zero: whether we should ignore zero metrics
:param metrics_thresholds: a dictionary of ratios indexed by metrics
"""
[docs] __mapper_args__ = {"polymorphic_identity": "SQLIntervalCheckOperator"}
[docs] template_fields: Sequence[str] = ("sql1", "sql2")
[docs] template_ext: Sequence[str] = (
".hql",
".sql",
)
[docs] template_fields_renderers = {"sql1": "sql", "sql2": "sql"}
}
def __init__(
self,
*,
table: str,
metrics_thresholds: Dict[str, int],
date_filter_column: Optional[str] = "ds",
days_back: SupportsAbs[int] = -7,
ratio_formula: Optional[str] = "max_over_min",
ignore_zero: bool = True,
conn_id: Optional[str] = None,
database: Optional[str] = None,
**kwargs,
):
super().__init__(conn_id=conn_id, database=database, **kwargs)
if ratio_formula not in self.ratio_formulas:
msg_template = "Invalid diff_method: {diff_method}. Supported diff methods are: {diff_methods}"
raise AirflowException(
msg_template.format(diff_method=ratio_formula, diff_methods=self.ratio_formulas)
)
self.ratio_formula = ratio_formula
self.ignore_zero = ignore_zero
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)
sqlexp = ", ".join(self.metrics_sorted)
sqlt = f"SELECT {sqlexp} FROM {table} WHERE {date_filter_column}="
self.sql1 = sqlt + "'{{ ds }}'"
self.sql2 = sqlt + "'{{ macros.ds_add(ds, " + str(self.days_back) + ") }}'"
[docs] def execute(self, context=None):
hook = self.get_db_hook()
self.log.info("Using ratio formula: %s", self.ratio_formula)
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(f"The query {self.sql2} returned None")
if not row1:
raise AirflowException(f"The query {self.sql1} returned None")
current = dict(zip(self.metrics_sorted, row1))
reference = dict(zip(self.metrics_sorted, row2))
ratios = {}
test_results = {}
for metric in self.metrics_sorted:
cur = current[metric]
ref = reference[metric]
threshold = self.metrics_thresholds[metric]
if cur == 0 or ref == 0:
ratios[metric] = None
test_results[metric] = self.ignore_zero
else:
ratios[metric] = self.ratio_formulas[self.ratio_formula](current[metric], reference[metric])
test_results[metric] = ratios[metric] < threshold
self.log.info(
(
"Current metric for %s: %s\n"
"Past metric for %s: %s\n"
"Ratio for %s: %s\n"
"Threshold: %s\n"
),
metric,
cur,
metric,
ref,
metric,
ratios[metric],
threshold,
)
if not all(test_results.values()):
failed_tests = [it[0] for it in test_results.items() if not it[1]]
self.log.warning(
"The following %s tests out of %s failed:",
len(failed_tests),
len(self.metrics_sorted),
)
for k in failed_tests:
self.log.warning(
"'%s' check failed. %s is above %s",
k,
ratios[k],
self.metrics_thresholds[k],
)
raise AirflowException(f"The following tests have failed:\n {', '.join(sorted(failed_tests))}")
self.log.info("All tests have passed")
[docs]class SQLThresholdCheckOperator(BaseSQLOperator):
"""
Performs a value check using sql code against a minimum threshold
and a maximum threshold. Thresholds can be in the form of a numeric
value OR a sql statement that results a numeric.
:param sql: the sql to be executed. (templated)
:param conn_id: the connection ID used to connect to the database.
:param database: name of database which overwrite the defined one in connection
:param min_threshold: numerical value or min threshold sql to be executed (templated)
:param max_threshold: numerical value or max threshold sql to be executed (templated)
"""
[docs] template_fields: Sequence[str] = ("sql", "min_threshold", "max_threshold")
[docs] template_ext: Sequence[str] = (
".hql",
".sql",
)
[docs] template_fields_renderers = {"sql": "sql"}
def __init__(
self,
*,
sql: str,
min_threshold: Any,
max_threshold: Any,
conn_id: Optional[str] = None,
database: Optional[str] = None,
**kwargs,
):
super().__init__(conn_id=conn_id, database=database, **kwargs)
self.sql = sql
self.min_threshold = _convert_to_float_if_possible(min_threshold)
self.max_threshold = _convert_to_float_if_possible(max_threshold)
[docs] def execute(self, context=None):
hook = self.get_db_hook()
result = hook.get_first(self.sql)[0]
if isinstance(self.min_threshold, float):
lower_bound = self.min_threshold
else:
lower_bound = hook.get_first(self.min_threshold)[0]
if isinstance(self.max_threshold, float):
upper_bound = self.max_threshold
else:
upper_bound = hook.get_first(self.max_threshold)[0]
meta_data = {
"result": result,
"task_id": self.task_id,
"min_threshold": lower_bound,
"max_threshold": upper_bound,
"within_threshold": lower_bound <= result <= upper_bound,
}
self.push(meta_data)
if not meta_data["within_threshold"]:
error_msg = (
f'Threshold Check: "{meta_data.get("task_id")}" failed.\n'
f'DAG: {self.dag_id}\nTask_id: {meta_data.get("task_id")}\n'
f'Check description: {meta_data.get("description")}\n'
f"SQL: {self.sql}\n"
f'Result: {round(meta_data.get("result"), 2)} is not within thresholds '
f'{meta_data.get("min_threshold")} and {meta_data.get("max_threshold")}'
)
raise AirflowException(error_msg)
self.log.info("Test %s Successful.", self.task_id)
[docs] def push(self, meta_data):
"""
Optional: Send data check info and metadata to an external database.
Default functionality will log metadata.
"""
info = "\n".join(f"""{key}: {item}""" for key, item in meta_data.items())
self.log.info("Log from %s:\n%s", self.dag_id, info)
[docs]class BranchSQLOperator(BaseSQLOperator, SkipMixin):
"""
Allows a DAG to "branch" or follow a specified path based on the results of a SQL query.
:param sql: The SQL code to be executed, should return true or false (templated)
Template reference are recognized by str ending in '.sql'.
Expected SQL query to return Boolean (True/False), integer (0 = False, Otherwise = 1)
or string (true/y/yes/1/on/false/n/no/0/off).
:param follow_task_ids_if_true: task id or task ids to follow if query returns true
:param follow_task_ids_if_false: task id or task ids to follow if query returns false
:param conn_id: the connection ID used to connect to the database.
:param database: name of database which overwrite the defined one in connection
:param parameters: (optional) the parameters to render the SQL query with.
"""
[docs] template_fields: Sequence[str] = ("sql",)
[docs] template_ext: Sequence[str] = (".sql",)
[docs] template_fields_renderers = {"sql": "sql"}
def __init__(
self,
*,
sql: str,
follow_task_ids_if_true: List[str],
follow_task_ids_if_false: List[str],
conn_id: str = "default_conn_id",
database: Optional[str] = None,
parameters: Optional[Union[Mapping, Iterable]] = None,
**kwargs,
) -> None:
super().__init__(conn_id=conn_id, database=database, **kwargs)
self.sql = sql
self.parameters = parameters
self.follow_task_ids_if_true = follow_task_ids_if_true
self.follow_task_ids_if_false = follow_task_ids_if_false
[docs] def execute(self, context: Context):
self.log.info(
"Executing: %s (with parameters %s) with connection: %s",
self.sql,
self.parameters,
self.conn_id,
)
record = self.get_db_hook().get_first(self.sql, self.parameters)
if not record:
raise AirflowException(
"No rows returned from sql query. Operator expected True or False return value."
)
if isinstance(record, list):
if isinstance(record[0], list):
query_result = record[0][0]
else:
query_result = record[0]
elif isinstance(record, tuple):
query_result = record[0]
else:
query_result = record
self.log.info("Query returns %s, type '%s'", query_result, type(query_result))
follow_branch = None
try:
if isinstance(query_result, bool):
if query_result:
follow_branch = self.follow_task_ids_if_true
elif isinstance(query_result, str):
# return result is not Boolean, try to convert from String to Boolean
if parse_boolean(query_result):
follow_branch = self.follow_task_ids_if_true
elif isinstance(query_result, int):
if bool(query_result):
follow_branch = self.follow_task_ids_if_true
else:
raise AirflowException(
f"Unexpected query return result '{query_result}' type '{type(query_result)}'"
)
if follow_branch is None:
follow_branch = self.follow_task_ids_if_false
except ValueError:
raise AirflowException(
f"Unexpected query return result '{query_result}' type '{type(query_result)}'"
)
self.skip_all_except(context["ti"], follow_branch)
