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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
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from __future__ import annotations
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Any, Iterable, Mapping, Sequence
from tabulate import tabulate
from airflow.exceptions import AirflowException
from airflow.hooks.base import BaseHook
from airflow.models import BaseOperator
from airflow.providers.common.sql.hooks.sql import DbApiHook
from airflow.providers.slack.hooks.slack import SlackHook
from airflow.providers.slack.hooks.slack_webhook import SlackWebhookHook
from airflow.providers.slack.utils import parse_filename
if TYPE_CHECKING:
import pandas as pd
from airflow.utils.context import Context
[docs]class BaseSqlToSlackOperator(BaseOperator):
"""
Operator implements base sql methods for SQL to Slack Transfer operators.
:param sql: The SQL query to be executed
:param sql_conn_id: reference to a specific DB-API Connection.
:param sql_hook_params: Extra config params to be passed to the underlying hook.
Should match the desired hook constructor params.
:param parameters: The parameters to pass to the SQL query.
"""
def __init__(
self,
*,
sql: str,
sql_conn_id: str,
sql_hook_params: dict | None = None,
parameters: Iterable | Mapping[str, Any] | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.sql_conn_id = sql_conn_id
self.sql_hook_params = sql_hook_params
self.sql = sql
self.parameters = parameters
def _get_hook(self) -> DbApiHook:
self.log.debug("Get connection for %s", self.sql_conn_id)
conn = BaseHook.get_connection(self.sql_conn_id)
hook = conn.get_hook(hook_params=self.sql_hook_params)
if not callable(getattr(hook, "get_pandas_df", None)):
raise AirflowException(
"This hook is not supported. The hook class must have get_pandas_df method."
)
return hook
def _get_query_results(self) -> pd.DataFrame:
sql_hook = self._get_hook()
self.log.info("Running SQL query: %s", self.sql)
df = sql_hook.get_pandas_df(self.sql, parameters=self.parameters)
return df
[docs]class SqlToSlackOperator(BaseSqlToSlackOperator):
"""
Executes an SQL statement in a given SQL connection and sends the results to Slack.
The results of the query are rendered into the 'slack_message' parameter as a Pandas
dataframe using a JINJA variable called '{{ results_df }}'. The 'results_df' variable
name can be changed by specifying a different 'results_df_name' parameter. The Tabulate
library is added to the JINJA environment as a filter to allow the dataframe to be
rendered nicely. For example, set 'slack_message' to {{ results_df |
tabulate(tablefmt="pretty", headers="keys") }} to send the results to Slack as an ascii
rendered table.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:SqlToSlackOperator`
:param sql: The SQL query to be executed (templated)
:param slack_message: The templated Slack message to send with the data returned from the SQL connection.
You can use the default JINJA variable {{ results_df }} to access the pandas dataframe containing the
SQL results
:param sql_conn_id: reference to a specific database.
:param sql_hook_params: Extra config params to be passed to the underlying hook.
Should match the desired hook constructor params.
:param slack_conn_id: The connection id for Slack.
:param slack_channel: The channel to send message. Override default from Slack connection.
:param results_df_name: The name of the JINJA template's dataframe variable, default is 'results_df'
:param parameters: The parameters to pass to the SQL query
"""
[docs] template_fields: Sequence[str] = ("sql", "slack_message")
[docs] template_ext: Sequence[str] = (".sql", ".jinja", ".j2")
[docs] template_fields_renderers = {"sql": "sql", "slack_message": "jinja"}
def __init__(
self,
*,
sql: str,
sql_conn_id: str,
slack_conn_id: str,
sql_hook_params: dict | None = None,
slack_channel: str | None = None,
slack_message: str,
results_df_name: str = "results_df",
parameters: Iterable | Mapping[str, Any] | None = None,
**kwargs,
) -> None:
super().__init__(
sql=sql, sql_conn_id=sql_conn_id, sql_hook_params=sql_hook_params, parameters=parameters, **kwargs
)
self.slack_conn_id = slack_conn_id
self.slack_channel = slack_channel
self.slack_message = slack_message
self.results_df_name = results_df_name
self.kwargs = kwargs
def _render_and_send_slack_message(self, context, df) -> None:
# Put the dataframe into the context and render the JINJA template fields
context[self.results_df_name] = df
self.render_template_fields(context)
slack_hook = self._get_slack_hook()
self.log.info("Sending slack message: %s", self.slack_message)
slack_hook.send(text=self.slack_message, channel=self.slack_channel)
def _get_slack_hook(self) -> SlackWebhookHook:
return SlackWebhookHook(slack_webhook_conn_id=self.slack_conn_id)
[docs] def render_template_fields(self, context, jinja_env=None) -> None:
# If this is the first render of the template fields, exclude slack_message from rendering since
# the SQL results haven't been retrieved yet.
if self.times_rendered == 0:
fields_to_render: Iterable[str] = filter(lambda x: x != "slack_message", self.template_fields)
else:
fields_to_render = self.template_fields
if not jinja_env:
jinja_env = self.get_template_env()
# Add the tabulate library into the JINJA environment
jinja_env.filters["tabulate"] = tabulate
self._do_render_template_fields(self, fields_to_render, context, jinja_env, set())
self.times_rendered += 1
[docs] def execute(self, context: Context) -> None:
if not isinstance(self.sql, str):
raise AirflowException("Expected 'sql' parameter should be a string.")
if self.sql is None or self.sql.strip() == "":
raise AirflowException("Expected 'sql' parameter is missing.")
if self.slack_message is None or self.slack_message.strip() == "":
raise AirflowException("Expected 'slack_message' parameter is missing.")
df = self._get_query_results()
self._render_and_send_slack_message(context, df)
self.log.debug("Finished sending SQL data to Slack")
[docs]class SqlToSlackApiFileOperator(BaseSqlToSlackOperator):
"""
Executes an SQL statement in a given SQL connection and sends the results to Slack API as file.
:param sql: The SQL query to be executed
:param sql_conn_id: reference to a specific DB-API Connection.
:param slack_conn_id: :ref:`Slack API Connection <howto/connection:slack>`.
:param slack_filename: Filename for display in slack.
Should contain supported extension which referenced to ``SUPPORTED_FILE_FORMATS``.
It is also possible to set compression in extension:
``filename.csv.gzip``, ``filename.json.zip``, etc.
:param sql_hook_params: Extra config params to be passed to the underlying hook.
Should match the desired hook constructor params.
:param parameters: The parameters to pass to the SQL query.
:param slack_channels: Comma-separated list of channel names or IDs where the file will be shared.
If omitting this parameter, then file will send to workspace.
:param slack_initial_comment: The message text introducing the file in specified ``slack_channels``.
:param slack_title: Title of file.
:param df_kwargs: Keyword arguments forwarded to ``pandas.DataFrame.to_{format}()`` method.
Example:
.. code-block:: python
SqlToSlackApiFileOperator(
task_id="sql_to_slack",
sql="SELECT 1 a, 2 b, 3 c",
sql_conn_id="sql-connection",
slack_conn_id="slack-api-connection",
slack_filename="awesome.json.gz",
slack_channels="#random,#general",
slack_initial_comment="Awesome load to compressed multiline JSON.",
df_kwargs={
"orient": "records",
"lines": True,
},
)
"""
[docs] template_fields: Sequence[str] = (
"sql",
"slack_channels",
"slack_filename",
"slack_initial_comment",
"slack_title",
)
[docs] template_ext: Sequence[str] = (".sql", ".jinja", ".j2")
[docs] template_fields_renderers = {"sql": "sql", "slack_message": "jinja"}
def __init__(
self,
*,
sql: str,
sql_conn_id: str,
sql_hook_params: dict | None = None,
parameters: Iterable | Mapping[str, Any] | None = None,
slack_conn_id: str,
slack_filename: str,
slack_channels: str | Sequence[str] | None = None,
slack_initial_comment: str | None = None,
slack_title: str | None = None,
df_kwargs: dict | None = None,
**kwargs,
):
super().__init__(
sql=sql, sql_conn_id=sql_conn_id, sql_hook_params=sql_hook_params, parameters=parameters, **kwargs
)
self.slack_conn_id = slack_conn_id
self.slack_filename = slack_filename
self.slack_channels = slack_channels
self.slack_initial_comment = slack_initial_comment
self.slack_title = slack_title
self.df_kwargs = df_kwargs or {}
[docs] def execute(self, context: Context) -> None:
# Parse file format from filename
output_file_format, _ = parse_filename(
filename=self.slack_filename,
supported_file_formats=self.SUPPORTED_FILE_FORMATS,
)
slack_hook = SlackHook(slack_conn_id=self.slack_conn_id)
with NamedTemporaryFile(mode="w+", suffix=f"_{self.slack_filename}") as fp:
# tempfile.NamedTemporaryFile used only for create and remove temporary file,
# pandas will open file in correct mode itself depend on file type.
# So we close file descriptor here for avoid incidentally write anything.
fp.close()
output_file_name = fp.name
output_file_format = output_file_format.upper()
df_result = self._get_query_results()
if output_file_format == "CSV":
df_result.to_csv(output_file_name, **self.df_kwargs)
elif output_file_format == "JSON":
df_result.to_json(output_file_name, **self.df_kwargs)
elif output_file_format == "HTML":
df_result.to_html(output_file_name, **self.df_kwargs)
else:
# Not expected that this error happen. This only possible
# if SUPPORTED_FILE_FORMATS extended and no actual implementation for specific format.
raise AirflowException(f"Unexpected output file format: {output_file_format}")
slack_hook.send_file(
channels=self.slack_channels,
file=output_file_name,
filename=self.slack_filename,
initial_comment=self.slack_initial_comment,
title=self.slack_title,
)
