Airflow has most of this already from 3.0 when Jed added Dag Versioning. And capturing the docker image used would be interesting, but right now that would probably have to be injected in, as Airflow makes no assumptions about _how_ you are running. Want to run on bare metal? Go for it, etc.
Your ask is sensible, but you need to do some more reasearch into the current state of things - see https://www.youtube.com/watch?v=DmhDMJ-d_eI&list=PLGudixcDaxY3A4P-wmH1wBHqLUU6c1DOU&index=10&pp=iAQB from last year’s Airflow Summit for example. -ash > On 24 Sep 2026, at 07:35, Daniyar Nurtailakov <[email protected]> wrote: > > Hi everyone, > I want to start another discussion about a common issue with how Airflow > runs historical data: the lack of code and environment freezing for past > runs. > Right now, Airflow's *workflow logic is separated from its runtime > environment*. When a past workflow fails or we need to run a historical > backfill (for example, re-running a task from 3 months ago), Airflow > executes that past task using today’s live code, current environment > variables, and the latest software dependencies. > This causes a few major problems: > > - Broken Backfills: If we renamed a database column or refactored code > today, running a backfill for last year will either break completely or > silently corrupt old data. > - Hard to Debug: It is very difficult to debug past task failures > because the code and Python packages on the worker have changed since the > error happened. > - Compliance & Audits: In regulated industries, proving the exact code > version that generated a financial or user report 6 months ago is > incredibly painful. It requires manually matching Git history, CI logs, and > Airflow logs. > > Proposed Solution: "Time-Travel Pipeline Runs" > I propose saving a snapshot of the exact code and environment every time a > pipeline starts. Airflow would save two unique identifiers directly to the > pipeline run's metadata: > > 1. The Git commit hash (the exact code logic). > 2. The container image ID (the exact software versions and dependencies). > > This turns pipeline re-runs into true time capsules. If you retry a failed > task from last month, Airflow will instruct the executor to pull and run > the exact container image and code version that was active last month. > Example metadata structure: > > > > > > > *{ "execution_snapshot": { "git_commit": "4f82d1c67a", > "docker_digest": "registry.example.com/airflow-dags@sha256:a3f128c.. > <http://registry.example.com/airflow-dags@sha256:a3f128c..>.", > "pinned_at": "2026-09-23T20:00:00Z" }}* > I would love to get your initial feedback here on the mailing list, and I > look forward to introducing this concept on one of future dev calls! > > > Best regards, Daniyar
