| :mod:`airflow.operators.druid_check_operator` |
| ============================================= |
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| .. py:module:: airflow.operators.druid_check_operator |
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| Module Contents |
| --------------- |
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| .. py:class:: DruidCheckOperator(sql, druid_broker_conn_id='druid_broker_default', *args, **kwargs) |
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| Bases: :class:`airflow.operators.check_operator.CheckOperator` |
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| Performs checks against Druid. The ``DruidCheckOperator`` 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. |
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| Note that Python bool casting evals the following as ``False``: |
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| * ``False`` |
| * ``0`` |
| * Empty string (``""``) |
| * Empty list (``[]``) |
| * Empty dictionary or set (``{}``) |
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| 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. |
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| :param sql: the sql to be executed |
| :type sql: str |
| :param druid_broker_conn_id: reference to the druid broker |
| :type druid_broker_conn_id: str |
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| .. method:: get_db_hook(self) |
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| Return the druid db api hook. |
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| .. method:: get_first(self, sql) |
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| Executes the druid sql to druid broker and returns the first resulting row. |
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| :param sql: the sql statement to be executed (str) |
| :type sql: str |
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| .. method:: execute(self, context=None) |
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