◆ Apache Airflow

Clean up Airflow 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
See the prompt↓All Apache Airflow tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskWe need to clear out the old task logs and XComs from the database used for workflow…+
We need to clear out the old task logs and XComs from the database used for workflow orchestration. It's getting too big and slowing things down. Just run the cleanup script for anything older than 90 days by Friday. Make sure to back up the database first.
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 we run the database cleanup, let's make sure it's not going to disrupt any critical…+
Before we run the database cleanup, let's make sure it's not going to disrupt any critical reporting. Can you identify any DAGs that might be impacted by removing XComs older than 90 days and flag potential data loss for our analytics team?
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 database cleanup script just failed with a 'disk full' error, even though monitoring shows…+
The database cleanup script failed with a 'disk full' error, but there should have been enough space.
The database cleanup script just failed with a 'disk full' error, even though monitoring shows plenty of space. I'm afraid running it again without understanding why will just make things worse, especially with the end-of-quarter dashboards due. Is this a permissions issue, a hidden temp file problem, or something else entirely? What's the safest next step to diagnose this without taking down the whole instance?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternWe keep having to manually intervene and run cleanup scripts when the database used for…+
We keep having to manually intervene when the Airflow database gets too large.
We keep having to manually intervene and run cleanup scripts when the database used for workflow orchestration gets too large, leading to unexpected downtime and scramble. This always seems to happen right before big data loads. What habit can we change to proactively manage database size and prevent these last-minute crises from impacting our reporting cycles?
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.