◆ Elasticsearch

Manage Elasticsearch indices

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 Elasticsearch tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskCreate a new index template named 'app_logs_template' for indices starting with 'app-logs-',…+
Create a new index template named 'app_logs_template' for indices starting with 'app-logs-', with 3 primary shards and 1 replica. Apply it before the new application goes live on Monday.
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 I create the index template for the new 'app-logs-' indices, help me define the optimal…+
Before I create the index template for the new 'app-logs-' indices, help me define the optimal shard and replica count. I want to balance query performance for the analytics team and storage efficiency, as these logs will grow rapidly, and I need to avoid over-provisioning.
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 'metrics-2023.10.26' index is causing high disk usage and slow query times for the…+
The 'metrics-2023.10.26' index is showing high disk usage and slow queries, despite recent scaling efforts.
The 'metrics-2023.10.26' index is causing high disk usage and slow query times for the reporting team, even after adding more data nodes. I'm afraid we've hit a hot shard problem or the index is too large for its current configuration. We need faster reports for the executive meeting next week. What's the likely diagnosis and the best next step to improve performance without losing historical data?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI keep finding old, unmanaged indices consuming valuable disk space, or critical data getting…+
I'm manually managing index lifecycle policies, which leads to forgotten indices and wasted storage.
I keep finding old, unmanaged indices consuming valuable disk space, or critical data getting accidentally deleted because I missed a manual cleanup. It's a constant drain on my time and a risk for data integrity. What habit should I change to systematically manage index lifecycles, so I'm not always cleaning up messes or risking data loss?
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 Elasticsearch'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.