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        "author": "Robinhood Markets, Inc.",
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            "License :: OSI Approved :: BSD License",
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            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
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        "description": "# Airflow Prometheus Exporter\n\n[![Build Status](https://travis-ci.org/robinhood/airflow-prometheus-exporter.svg?branch=master)](https://travis-ci.org/robinhood/airflow-prometheus-exporter)\n\nThe Airflow Prometheus Exporter exposes various metrics about the Scheduler, DAGs and Tasks which helps improve the observability of an Airflow cluster.\n\nThe exporter is based on this [prometheus exporter for Airflow](https://github.com/epoch8/airflow-exporter).\n\n## Requirements\n\nThe plugin has been tested with:\n\n- Airflow >= 1.10.4\n- Python 3.6+\n\nThe scheduler metrics assume that there is a DAG named `canary_dag`. In our setup, the `canary_dag` is a DAG which has a tasks which perform very simple actions such as establishing database connections. This DAG is used to test the uptime of the Airflow scheduler itself.\n\n## Installation\n\nThe exporter can be installed as an Airflow Plugin using:\n\n```pip install airflow-prometheus-exporter```\n\nThis should ideally be installed in your Airflow virtualenv.\n\n## Metrics\n\nMetrics will be available at\n\n`http://<your_airflow_host_and_port>/admin/metrics/`\n\n### Task Specific Metrics\n\n#### `airflow_task_status`\n\nNumber of tasks with a specific status.\n\nAll the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L46).\n\n#### `airflow_task_duration`\n\nDuration of successful tasks in seconds.\n\n#### `airflow_task_fail_count`\n\nNumber of times a particular task has failed.\n\n### Dag Specific Metrics\n\n#### `airflow_dag_status`\n\nNumber of DAGs with a specific status.\n\nAll the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L59)\n\n#### `airflow_dag_run_duration`\nDuration of successful DagRun in seconds.\n\n### Scheduler Metrics\n\n#### `airflow_dag_scheduler_delay`\n\nScheduling delay for a DAG Run in seconds. This metric assumes there is a `canary_dag`.\n\nThe scheduling delay is measured as the delay between when a DAG is marked as `SCHEDULED` and when it actually starts `RUNNING`.\n\n#### `airflow_task_scheduler_delay`\n\nScheduling delay for a Task in seconds. This metric assumes there is a `canary_dag`.\n\n#### `airflow_num_queued_tasks`\n\nNumber of tasks in the `QUEUED` state at any given instance.\n\n\n",
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