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apache
airflow
Python2016–2026
apache / airflow

airflow

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

25Distinct people
32Mentions
0Explicit recommenders
47.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:58:02 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year32 dated mentions

Observed mentions: 2016-07-21 12:54:52 UTC to 2026-04-20 13:07:56 UTC.

02468102016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 9 mentions; 0 submissions, 9 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC312
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC202
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC909
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC312
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC211
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC303
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC514
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

3 sources
1) An official Helm chart is coming very soon. We (Astronomer) have a commercial platform that aims to solve this completely including observability, configuration and deployment. Happy to team up to improve Airflow's open-source k8s story if you have some ideas. 2) Yes the UI is outdated, and not responsive. We're going to kick off a process to build a new modern UI in Q3 (a full-featured Swagger API is being built now, which the new UI will rely upon.) 3) I personally think catchup true is a fine default, but whatever. Generally when I launch a new DAG I want to generate some historical data using the DAG. 4) Airflow has tags now since 1.10.8 https://airflow.readthedocs.io/en/latest/howto/add-dag-tags.... - we decided not to do folders. 5) That's true, but it also provides a low bar to entry. There are some guides written on unit testin
Read the rest
g DAGs, but I agree we should be a test-first community. On my roadmap. 6) Celery w/ KEDA is pretty nice - check out https://www.astronomer.io/blog/the-keda-autoscaler/ 7) I personally love the PythonOperator for simple DAGs. Agree that DockerOperator (KubernetesPodOperator if you're running Airflow in K8s) 8) Yes, logging UI will be improved in the UI rewrite. What's your favorite UI for this? 9) That feature is in the queue https://github.com/apache/airflow/issues/7911
In Introduction to Apache Airflow

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