Source code for airflow.contrib.hooks.datadog_hook

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import time
from airflow.hooks.base_hook import BaseHook
from airflow.exceptions import AirflowException
from datadog import initialize, api

from airflow.utils.log.logging_mixin import LoggingMixin

[docs]class DatadogHook(BaseHook, LoggingMixin): """ Uses datadog API to send metrics of practically anything measurable, so it's possible to track # of db records inserted/deleted, records read from file and many other useful metrics. Depends on the datadog API, which has to be deployed on the same server where Airflow runs. :param datadog_conn_id: The connection to datadog, containing metadata for api keys. :param datadog_conn_id: string """ def __init__(self, datadog_conn_id='datadog_default'): conn = self.get_connection(datadog_conn_id) self.api_key = conn.extra_dejson.get('api_key', None) self.app_key = conn.extra_dejson.get('app_key', None) self.source_type_name = conn.extra_dejson.get('source_type_name', None) # If the host is populated, it will use that hostname instead. # for all metric submissions. = if self.api_key is None: raise AirflowException("api_key must be specified in the " "Datadog connection details") if self.app_key is None: raise AirflowException("app_key must be specified in the " "Datadog connection details")"Setting up api keys for Datadog") options = { 'api_key': self.api_key, 'app_key': self.app_key } initialize(**options) def validate_response(self, response): if response['status'] != 'ok': self.log.error("Datadog returned: %s", response) raise AirflowException("Error status received from Datadog")
[docs] def send_metric(self, metric_name, datapoint, tags=None): """ Sends a single datapoint metric to DataDog :param metric_name: The name of the metric :type metric_name: string :param datapoint: A single integer or float related to the metric :type datapoint: integer or float :param tags: A list of tags associated with the metric :type tags: list """ response = api.Metric.send( metric=metric_name, points=datapoint,, tags=tags) self.validate_response(response) return response
[docs] def query_metric(self, query, from_seconds_ago, to_seconds_ago): """ Queries datadog for a specific metric, potentially with some function applied to it and returns the results. :param query: The datadog query to execute (see datadog docs) :type query: string :param from_seconds_ago: How many seconds ago to start querying for. :type from_seconds_ago: int :param to_seconds_ago: Up to how many seconds ago to query for. :type to_seconds_ago: int """ now = int(time.time()) response = api.Metric.query( start=now - from_seconds_ago, end=now - to_seconds_ago, query=query) self.validate_response(response) return response
[docs] def post_event(self, title, text, tags=None, alert_type=None, aggregation_key=None): """ Posts an event to datadog (processing finished, potentially alerts, other issues) Think about this as a means to maintain persistence of alerts, rather than alerting itself. :param title: The title of the event :type title: string :param text: The body of the event (more information) :type text: string :param tags: List of string tags to apply to the event :type tags: list :param alert_type: The alert type for the event, one of ["error", "warning", "info", "success"] :type alert_type: string :param aggregation_key: Key that can be used to aggregate this event in a stream :type aggregation_key: string """ response = api.Event.create( title=title, text=text,, tags=tags, alert_type=alert_type, aggregation_key=aggregation_key, source_type_name=self.source_type_name) self.validate_response(response) return response