?? Million Logs, ?? No Space Left: My AWS RDS Optimization Journey ??

A few weeks ago, I had one of those “oh no” moments—the kind that makes your heart race and your mind scramble for solutions.

It all started with a Sentry alert in our production environment. At first glance, everything seemed fine. No downtime, no broken APIs. But then I checked our Amazon RDS database metrics and saw the nightmare—we were running out of storage! ??

We immediately added buffer space to avoid downtime. But the real question was: What was eating up all that space?

Step 1: The Unexpected Culprit

I started digging through our database, expecting to find an oversized table or bloated indexes. Instead, the culprit was… logs.

Yep. Millions of logs. ??

We log every API request for debugging, and over time, those logs had quietly grown to nearly 10 million records. The database wasn’t overloaded with actual business data—it was drowning in logs!

Step 2: The Painful Deletion Process ???

Alright, simple fix, right? Just delete old logs.

I started deleting in batches:

• 1 lakh records → Took 5 minutes. Not bad.
• 5 lakh records → Took a few hours, but still okay.
• 7.9 million records → That’s when things got ugly.

I tried deleting 10,000 records at a time—each query took 30 minutes. Tried deleting 1 lakh records—query ran for over two hours and never finished.

At this rate, I’d still be deleting logs next year. ??

Step 3: The Indexing Breakthrough ?

Frustrated, I turned to my favorite debugging method: asking Google and my senior teammates.

That’s when I had my Aha! moment—INDEXING.

I had heard of indexing before, but this was my first time seeing its true power in action.

• Added an index on the timestamp column.
• Waited 30 minutes for it to complete.
• Ran the same delete query again…

?? It took just a few seconds!

What was previously taking hours now took less than 3 minutes per month’s data. Game-changer.

Step 4: The Plot Twist: Ghost Data ??

I excitedly refreshed the AWS RDS storage metrics, expecting to see a ton of free space.

Nothing. No space was freed up. ??

Turns out, PostgreSQL doesn’t immediately free up space when you delete records. Instead, it leaves behind dead tuples—ghost records that still occupy storage.

To truly free up space, I needed to run… VACUUM.

Step 5: Midnight Cleanup ??

Running VACUUM on a live database can lock tables, so I waited until midnight when traffic was low.

I ran the command… held my breath…

And finally, AWS storage metrics showed the freed-up space! ??

Lessons Learned from This Battle:

1?? Log storage adds up FAST. Keep an eye on your log tables before they quietly take over.

2?? Indexing is magic. It can turn a painfully slow query into a near-instant operation.

3?? Deleting records ≠ freeing space. PostgreSQL keeps ghost data until you run VACUUM.

4?? Always monitor storage trends. A few proactive checks can save you from late-night fire drills.

This was one of those challenges that felt frustrating in the moment but turned into a powerful learning experience. Now, I’m way more confident in query optimization, indexing, and database storage management.

?? Have you ever faced a database issue like this? Would love to hear how you solved it! Let’s talk in the comments.??

#AWS #CloudComputing #DatabaseOptimization #PostgreSQL #RDSTuning #DevOps