[GitHub] [airflow] mik-laj commented on a change in pull request #17741: Add Snowflake DQ Operators

[GitBox <gi...@apache.org>]

mik-laj commented on a change in pull request #17741:
URL: https://github.com/apache/airflow/pull/17741#discussion_r695248154



##########
File path: airflow/providers/snowflake/operators/snowflake.py
##########
@@ -125,3 +126,299 @@ def execute(self, context: Any) -> None:
 
         if self.do_xcom_push:
             return execution_info
+
+
+class _SnowflakeDbHookMixin:
+    def get_db_hook(self) -> SnowflakeHook:
+        """
+        Create and return SnowflakeHook.
+        :return: a SnowflakeHook instance.
+        :rtype: SnowflakeHook
+        """
+        return SnowflakeHook(
+            snowflake_conn_id=self.snowflake_conn_id,
+            warehouse=self.warehouse,
+            database=self.database,
+            role=self.role,
+            schema=self.schema,
+            authenticator=self.authenticator,
+            session_parameters=self.session_parameters,
+        )
+
+
+class SnowflakeCheckOperator(_SnowflakeDbHookMixin, SQLCheckOperator):
+    """
+    Performs a check against Snowflake. The ``SnowflakeCheckOperator`` 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 code to be executed. (templated)
+    :type sql: Can receive a str representing a sql statement,
+        a list of str (sql statements), or reference to a template file.
+        Template reference are recognized by str ending in '.sql'
+    :param use_legacy_sql: Whether to use legacy SQL (true)

Review comment:
       Is it used?




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