◆ Apache Airflow

Clean up the metadata database

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskDelete all DAG run metadata older than 90 days for non-production DAGs to free up database…+
Delete all DAG run metadata older than 90 days for non-production DAGs to free up database space by Friday.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore the database performance degrades further, make it easy to identify and remove stale or…+
Before the database performance degrades further, make it easy to identify and remove stale or irrelevant metadata. The cleanup should prioritize old task logs and XComs from development DAGs, ensuring we retain critical audit trails for production systems and don't accidentally delete anything needed for compliance.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe metadata database is growing rapidly, and I'm worried about performance impact on the UI…+
The metadata database is growing rapidly, and I'm worried about performance, but I don't know what's safe to delete without breaking historical views.
The metadata database is growing rapidly, and I'm worried about performance impact on the UI and scheduler, but I don't know what's safe to delete without breaking historical run views for the analytics team. I can't tell my manager that I'm afraid to perform maintenance. What's the most effective strategy for cleaning up old metadata without losing critical historical context, and how do I verify what's safe to remove?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternOur metadata database keeps growing, leading to slow UI and scheduler performance, and I'm…+
Our metadata database keeps growing, leading to slow UI and scheduler performance, and I'm always reacting to it.
Our metadata database keeps growing, leading to slow UI and scheduler performance, and I'm always reacting to it when it becomes a problem. This impacts everyone on the team. What habit should I change to proactively manage the metadata database size, ensuring consistent performance and preventing future bottlenecks before they impact our daily work?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Apache Airflow's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.