path: root/python-airflow-prometheus-exporter.spec

%global _empty_manifest_terminate_build 0
Name:		python-airflow-prometheus-exporter
Version:	1.0.8
Release:	1
Summary:	Prometheus Exporter for Airflow Metrics
License:	BSD 3-Clause
URL:		https://github.com/robinhood/airflow_prometheus_exporter
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/2b/6a/ba5031cd8b10f9ed8cdc6915c2ec2366770a74268f7f8af367e412bb9040/airflow_prometheus_exporter-1.0.8.tar.gz
BuildArch:	noarch

Requires:	python3-apache-airflow
Requires:	python3-prometheus-client
Requires:	python3-bumpversion
Requires:	python3-tox
Requires:	python3-twine

%description
# Airflow Prometheus Exporter

[![Build Status](https://travis-ci.org/robinhood/airflow-prometheus-exporter.svg?branch=master)](https://travis-ci.org/robinhood/airflow-prometheus-exporter)

The Airflow Prometheus Exporter exposes various metrics about the Scheduler, DAGs and Tasks which helps improve the observability of an Airflow cluster.

The exporter is based on this [prometheus exporter for Airflow](https://github.com/epoch8/airflow-exporter).

## Requirements

The plugin has been tested with:

- Airflow >= 1.10.4
- Python 3.6+

The 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.

## Installation

The exporter can be installed as an Airflow Plugin using:

```pip install airflow-prometheus-exporter```

This should ideally be installed in your Airflow virtualenv.

## Metrics

Metrics will be available at

`http://<your_airflow_host_and_port>/admin/metrics/`

### Task Specific Metrics

#### `airflow_task_status`

Number of tasks with a specific status.

All the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L46).

#### `airflow_task_duration`

Duration of successful tasks in seconds.

#### `airflow_task_fail_count`

Number of times a particular task has failed.

#### `airflow_xcom_param`

value of configurable parameter in xcom table

xcom fields is deserialized as a dictionary and if key is found for a paticular task-id, the value is reported as a guage

Add task / key combinations in config.yaml:

```bash
xcom_params:
  -
    task_id: abc
    key: count
  -
    task_id: def
    key: errors

```


a task_id of 'all' will match against all airflow tasks:

```
xcom_params:
 -
    task_id: all
    key: count
```



### Dag Specific Metrics

#### `airflow_dag_status`

Number of DAGs with a specific status.

All the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L59)

#### `airflow_dag_run_duration`
Duration of successful DagRun in seconds.

### Scheduler Metrics

#### `airflow_dag_scheduler_delay`

Scheduling delay for a DAG Run in seconds. This metric assumes there is a `canary_dag`.

The scheduling delay is measured as the delay between when a DAG is marked as `SCHEDULED` and when it actually starts `RUNNING`.

#### `airflow_task_scheduler_delay`

Scheduling delay for a Task in seconds. This metric assumes there is a `canary_dag`.

#### `airflow_num_queued_tasks`

Number of tasks in the `QUEUED` state at any given instance.




%package -n python3-airflow-prometheus-exporter
Summary:	Prometheus Exporter for Airflow Metrics
Provides:	python-airflow-prometheus-exporter
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-airflow-prometheus-exporter
# Airflow Prometheus Exporter

[![Build Status](https://travis-ci.org/robinhood/airflow-prometheus-exporter.svg?branch=master)](https://travis-ci.org/robinhood/airflow-prometheus-exporter)

The Airflow Prometheus Exporter exposes various metrics about the Scheduler, DAGs and Tasks which helps improve the observability of an Airflow cluster.

The exporter is based on this [prometheus exporter for Airflow](https://github.com/epoch8/airflow-exporter).

## Requirements

The plugin has been tested with:

- Airflow >= 1.10.4
- Python 3.6+

The 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.

## Installation

The exporter can be installed as an Airflow Plugin using:

```pip install airflow-prometheus-exporter```

This should ideally be installed in your Airflow virtualenv.

## Metrics

Metrics will be available at

`http://<your_airflow_host_and_port>/admin/metrics/`

### Task Specific Metrics

#### `airflow_task_status`

Number of tasks with a specific status.

All the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L46).

#### `airflow_task_duration`

Duration of successful tasks in seconds.

#### `airflow_task_fail_count`

Number of times a particular task has failed.

#### `airflow_xcom_param`

value of configurable parameter in xcom table

xcom fields is deserialized as a dictionary and if key is found for a paticular task-id, the value is reported as a guage

Add task / key combinations in config.yaml:

```bash
xcom_params:
  -
    task_id: abc
    key: count
  -
    task_id: def
    key: errors

```


a task_id of 'all' will match against all airflow tasks:

```
xcom_params:
 -
    task_id: all
    key: count
```



### Dag Specific Metrics

#### `airflow_dag_status`

Number of DAGs with a specific status.

All the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L59)

#### `airflow_dag_run_duration`
Duration of successful DagRun in seconds.

### Scheduler Metrics

#### `airflow_dag_scheduler_delay`

Scheduling delay for a DAG Run in seconds. This metric assumes there is a `canary_dag`.

The scheduling delay is measured as the delay between when a DAG is marked as `SCHEDULED` and when it actually starts `RUNNING`.

#### `airflow_task_scheduler_delay`

Scheduling delay for a Task in seconds. This metric assumes there is a `canary_dag`.

#### `airflow_num_queued_tasks`

Number of tasks in the `QUEUED` state at any given instance.




%package help
Summary:	Development documents and examples for airflow-prometheus-exporter
Provides:	python3-airflow-prometheus-exporter-doc
%description help
# Airflow Prometheus Exporter

[![Build Status](https://travis-ci.org/robinhood/airflow-prometheus-exporter.svg?branch=master)](https://travis-ci.org/robinhood/airflow-prometheus-exporter)

The Airflow Prometheus Exporter exposes various metrics about the Scheduler, DAGs and Tasks which helps improve the observability of an Airflow cluster.

The exporter is based on this [prometheus exporter for Airflow](https://github.com/epoch8/airflow-exporter).

## Requirements

The plugin has been tested with:

- Airflow >= 1.10.4
- Python 3.6+

The 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.

## Installation

The exporter can be installed as an Airflow Plugin using:

```pip install airflow-prometheus-exporter```

This should ideally be installed in your Airflow virtualenv.

## Metrics

Metrics will be available at

`http://<your_airflow_host_and_port>/admin/metrics/`

### Task Specific Metrics

#### `airflow_task_status`

Number of tasks with a specific status.

All the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L46).

#### `airflow_task_duration`

Duration of successful tasks in seconds.

#### `airflow_task_fail_count`

Number of times a particular task has failed.

#### `airflow_xcom_param`

value of configurable parameter in xcom table

xcom fields is deserialized as a dictionary and if key is found for a paticular task-id, the value is reported as a guage

Add task / key combinations in config.yaml:

```bash
xcom_params:
  -
    task_id: abc
    key: count
  -
    task_id: def
    key: errors

```


a task_id of 'all' will match against all airflow tasks:

```
xcom_params:
 -
    task_id: all
    key: count
```



### Dag Specific Metrics

#### `airflow_dag_status`

Number of DAGs with a specific status.

All the possible states are listed [here](https://github.com/apache/airflow/blob/master/airflow/utils/state.py#L59)

#### `airflow_dag_run_duration`
Duration of successful DagRun in seconds.

### Scheduler Metrics

#### `airflow_dag_scheduler_delay`

Scheduling delay for a DAG Run in seconds. This metric assumes there is a `canary_dag`.

The scheduling delay is measured as the delay between when a DAG is marked as `SCHEDULED` and when it actually starts `RUNNING`.

#### `airflow_task_scheduler_delay`

Scheduling delay for a Task in seconds. This metric assumes there is a `canary_dag`.

#### `airflow_num_queued_tasks`

Number of tasks in the `QUEUED` state at any given instance.




%prep
%autosetup -n airflow-prometheus-exporter-1.0.8

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-airflow-prometheus-exporter -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.8-1
- Package Spec generated