https://github.com/arunvelsriram/dag-schedule-graph

Airflow plugin for visualising DAG schedules within 24 hour window of a day.
https://github.com/arunvelsriram/dag-schedule-graph

airflow airflow-dags airflow-plugin graph python

Last synced: 21 days ago
JSON representation

Airflow plugin for visualising DAG schedules within 24 hour window of a day.

• Host: GitHub
• URL: https://github.com/arunvelsriram/dag-schedule-graph
• Owner: arunvelsriram
• License: mit
• Created: 2020-12-06T14:01:10.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2020-12-12T10:52:22.000Z (almost 4 years ago)
• Last Synced: 2024-09-30T09:19:03.430Z (about 1 month ago)
• Topics: airflow, airflow-dags, airflow-plugin, graph, python
• Language: Python
• Homepage: https://github.com/arunvelsriram/dag-schedule-graph
• Size: 1.02 MB
• Stars: 1
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
# DAG Schedule Graph

Airflow plugin for visualising DAG schedules within 24 hour window of a day.

![Airflow dag-schedule-graph plugin screenshot](./images/screenshot-1.png)

Each bubble indicates the number of DAGs that will run at that instant. Bubble radius is relative to the DAG count.

## Install

```shell

pip install dag-schedule-graph

```

## Trying it out using Docker

```shell

# Start the services

docker-compose up

# Access the webserver

open http://localhost:8082/dag-schedule-graph/

# Cleanup containers, networks and volumes

docker-compose down -v

```

## Development

```shell

# Create virtual environment using conda  

conda create -n dag-schedule-graph python=3.7.9

# Activate the environment

conda activate dag-schedule-graph

# Load environemnt variables

source .env

# Create Postgres database and user

createuser airflow_rbac

createdb -O airflow_rbac airflow_rbac

# Install plugin and all dependencies

pip install -e '.[dev]'

# Running tests

pytest tests

# Initialize Airflow

airflow initdb

# Create Airflow user 

airflow create_user -u admin -e [email protected] -p admin -f admin -l admin -r Admin

# Build static assets

npm run build

# Start Airflow Webserver

airflow webserver

# Access webserver

open http://localhost:8080/dag-schedule-graph/

```