Skip to main content

An Apache Airflow plugin that visualizes DAG runs in a Gantt chart, predicts future runs, and identifies tasks that won't execute. Enhance your workflow monitoring and planning with intuitive visualizations.

Project description

Airflow Schedule Insides Plugin

The Airflow Schedule Insides Plugin for Apache Airflow allows you to visualize DAG runs in a Gantt chart, predict future runs, and identify DAGs that won't run, providing a seamless and efficient workflow for managing your pipelines. Enhance your workflow monitoring and planning with intuitive visualizations.

Tests Status Code style: black

System Requirements

  • Airflow Versions: 2.4.0 or newer

How to Install

Add airflow-schedule-insights to your requirements.txt and restart the web server.

How to Use

  1. Navigate to Schedule Insides in the Browse tab to access the plugin:

    Menu

  2. View all DAG runs in a Gantt chart:

    Gantt Chart Logs

  3. Toggle the Show Future Runs? option to predict the next runs for your DAGs and generate a list of all the DAGs that won't run.

    Note: All event-driven DAGs (scheduled by datasets and triggers) are predicted only to their next run.

  4. Future DAGs will be highlighted in gray on the Gantt chart:

    Gantt Chart Future Runs

  5. A table of future runs will be displayed, with events ordered by their start date:

    Future Runs Table

  6. Below this, you will find a table listing all the DAGs that won't run:

    Missing Future Runs Table

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

airflow_schedule_insights-0.1.0.tar.gz (24.1 kB view details)

Uploaded Source

Built Distribution

airflow_schedule_insights-0.1.0-py3-none-any.whl (25.7 kB view details)

Uploaded Python 3

File details

Details for the file airflow_schedule_insights-0.1.0.tar.gz.

File metadata

  • Download URL: airflow_schedule_insights-0.1.0.tar.gz
  • Upload date:
  • Size: 24.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.4 CPython/3.10.15 Linux/6.5.0-1025-azure

File hashes

Hashes for airflow_schedule_insights-0.1.0.tar.gz
Algorithm Hash digest
SHA256 65cdcf4edbe769d6bf7013a15a3312d50f57242b010baea1a13254b194259508
MD5 7afec03b6101ec04a6bc6ea84bde930e
BLAKE2b-256 db82c263a428d092c1f54268f13b314b243e9bcddccb953dda470dab27298e68

See more details on using hashes here.

File details

Details for the file airflow_schedule_insights-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for airflow_schedule_insights-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 4802359cdfcc6de6f978a71ce0a762195923977090192210844c2a7ae7492607
MD5 3a8fc5441efe391068e1b7242cbbca3d
BLAKE2b-256 57f3f07810719d4d9007504fb5074ab1a32fa3925b6345ea2408bb0ce7af2184

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page