Test it like It’s a Machine

When I started building pg-collector (another project that uses heavy stress test using pg-test), I ran into a problem very quickly:

I didn’t have a reliable way to break PostgreSQL on demand. I looked and there are lots of generating synthetic data but no comprehensive stress test tools.

And that’s when the idea for pg-stress was born.

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Built from “eat your own dog food”

pg-stress didn’t start as a product idea.

It started as a necessity.

While building pg-collector, I needed:

• real workload patterns

• repeatable failure scenarios

• controlled environments to observe behavior

• run these test in random pattern continously 7/24 for 2 weeks !

So I built a tool to stress PostgreSQL intentionally — not just benchmark it.

That tool became pg-stress.

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Think of it like automobile testing

When a new car is built, it’s not just driven on smooth roads.

It’s tested in:

• freezing cold

• extreme heat

• rough terrain

• high-speed stress conditions

Why?

Before releasing a new query in the **WILD - inject that query in pg-stress. Stress test with 100s of connections, test the joints, insert 10M rows(kidding ! Not really .. I inserted 30M records …)Output the findings to Claude Chat box for advisory!

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Databases are no different.

Production issues show up under:

• traffic spikes

• bad queries

• ORM inefficiencies

• long-running transactions

• unpredictable write patterns

pg-stress is built to simulate exactly that.

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What pg-stress actually tests

This is not just “run SELECT 1 in a loop.”

We push real-world chaos into PostgreSQL:

ORM-like behavior

• bursty inserts / updates / deletes

• inefficient query patterns

• transactional noise

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Jitter + randomness

• non-uniform traffic

• unpredictable workloads

• concurrency spikes

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Bloat & connection pressure

• table/index bloat scenarios

• connection exhaustion

• lock contention

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Query stress

• slow queries under load

• joins at scale

• degradation over time

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From stress → context → intelligence

Most stress tools stop here:

“Here are your TPS and latency numbers.”

That’s not enough.

pg-stress produces structured output designed for context:

• query behavior

• system response

• degradation patterns

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Built for AI-assisted diagnosis

The output of pg-stress is optimized for modern workflows:

Feed it directly into models like Claude

And you can get:

• query tuning recommendations

• index suggestions

• capacity predictions

• failure explanations

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Part of a bigger system

• pg-stress → generates pressure

• pg-collector → observes + learns

• (future) → AI layer predicts and advises

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Final thought

If you can’t break your database in a controlled environment…

It will break in production — on its own terms.

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Repo:

DataEngineering

AIInfrastructure

MachineLearning

PostgreSQL

OpenSource