Picture this: a mission-critical SQL query is crawling along. Not for an hour. Not for two. Fifteen hours. A full workday of the system slowly grinding through data while the business bleeds money and users teeter on the edge of a nervous breakdown. And then — cue the dramatic music — in walks the performance engineer.
After a few hours of intense analysis and a couple of pinpoint code tweaks, the same query that took 15 hours now completes in just… two minutes. Sounds like magic? Nope. This is the thrilling (and very real) world of performance engineering.
Turning pain into performance
This isn’t a fairytale — it’s a real success story from Vadim Laktushin, a performance engineer at Postgres Professional. Moments like this, where you shave hours off system runtimes, are what make performance engineers feel like they truly earn their paycheck.
So… who are these digital speed whisperers?
In short, performance engineers (PEs) are the people who make IT systems run faster, stronger, better. But behind that neat summary lies a world of complexity and detective-level investigation.
Sometimes it's not just tweaking settings (though there's plenty of that, too). More often, developers can’t predict all the ways a system might be stressed in production. That's when a PE becomes a detective — tracking down performance bottlenecks hiding in the application code, the DBMS (like PostgreSQL), Linux configs, network layers, or even hardware quirks.
Once the culprit is found, a PE must fully understand the why, then prescribe the fix — be it a config tweak, a code rewrite, or even a full-on architecture overhaul. This is the intersection of dev, sysadmin, load testing, and deep systems analysis — all in one role.
From accidental start to performance pro
Vadim’s journey into performance engineering started unexpectedly — as a developer, he was once asked to help decide between ClickHouse and Postgres for a project. That single decision dragged him into a rabbit hole of benchmarks, hardware tests, and system tuning.
One question turned into ten. And each answer led to new depths of complexity. “These questions open up such deep problems that I still haven’t climbed out,” he jokes.
He eventually found himself among the experts at Postgres Professional and transitioned almost unnoticed from a mid-level developer to one of their top performance minds.
The art of finding the not-so-obvious
Think performance problems have textbook solutions? Think again.
“If only we could write a manual: ‘Here’s the issue, here’s the fix.’ But in real life, it’s almost never that easy,” Vadim says.
Every case is a new mystery. Like the one where a client migrated from Oracle to Postgres and hit a wall during data loading — specifically, a huge delay writing to the write-ahead log (WAL).
WAL is critical in Postgres — every operation must be logged before it’s committed. But in this case, it had become a bottleneck. A patch was suggested to speed things up. It didn’t help. The client’s in-house experts couldn’t solve it either.
Time to call the performance engineers.
They didn’t blindly trust the patch. Instead, they dove deep, ran experiments, and dug into Postgres internals. “Our work is closer to that of a doctor diagnosing a strange illness than a typical engineer,” Vadim says.
Eventually, they discovered a long-forgotten config tweak: increasing the WAL write window. By writing larger chunks less frequently, performance skyrocketed. One little checkbox — and the problem disappeared.
“Sometimes these ‘hidden switches’ are just sitting there, waiting to be rediscovered,” Vadim adds.
What is WAL and why does it become a bottleneck
WAL (Write-Ahead Log) is a cornerstone of data reliability in Postgres. The idea behind it is simple: before the database changes any actual data in tables, it must first log a detailed description of that change in a special journal — the WAL. These records are what allow the system to recover after a failure (like a sudden power outage), finishing any incomplete operations and ensuring that no confirmed transactions are lost.
But here’s the twist: during heavy write operations — like bulk data loading — WAL can become overwhelmed. The system starts generating a massive stream of log entries, and all of them need to be written to disk. Since disk speed is a physical limitation, and Postgres has to wait for these logs to be safely written, the process can’t keep up with the incoming flood of changes.
The result? The whole system starts to lag, stalling as it waits for WAL writes to finish. That’s when WAL turns from a safety mechanism into a performance chokepoint — a classic bottleneck slowing everything down.
The unwritten rules of the performance game
There’s a code performance engineers live by — unwritten, but ironclad.
Proactivity beats firefighting. A great PE doesn’t wait for systems to collapse. They analyze new architectures in advance, predict scaling issues, and preempt bottlenecks. “The best PE is the one who solves problems before they show up,” says Vadim.
No gut feelings. Only data. Rookies often fall into the “seems faster” trap. “I tweaked something… feels better by 3–5 seconds.” That’s not enough. In PE, only numbers matter — TPS, latency, CPU/memory usage, queue lengths. Without hard before-and-after metrics, nothing is proven.
Beware of tempting ‘quick fixes’. Case in point: bumping
MaxConnectionsin Postgres to handle load. Looks like an easy win — until it causes internal contention, lock storms, and system freeze. A good PE knows: today’s quick fix could be tomorrow’s disaster.
So, how do you become a performance engineer?
Forget special degrees or secret bootcamps. “No one graduates as a ready-made PE,” Vadim explains.
Like DevOps or sysadmins with hi-qualifications, PEs grow from adjacent fields — usually development or administration. Here’s what you’ll need:
Programming & Linux chops. You must think like a dev and understand their pain points. At the same time, you need deep Linux knowledge: from kernel mechanics to user space, process management, and OS metrics. “Linux is mandatory,” Vadim emphasizes.
Algorithms & math. Knowing algorithm complexity is key, but here’s the PE secret weapon: statistical thinking. When gains are subtle (like a 1-2% improvement), how do you prove it’s real? Quantiles, percentiles, distributions — this is where stats save the day.
Communication & attention to detail. A PE has to explain findings clearly, patiently — and diplomatically. Telling a developer “your code’s the problem” can be like dancing through a minefield. Add to that the need to catch subtle system behaviors or weird spikes in logs… and you see why this job isn’t for the faint of heart.
The future of the profession — and the shadow of AI
As systems get more complex, no one can be an expert in everything. Vadim sees a future where PEs specialize — app-level, OS-level, hardware-level (like choosing the right CPU or tuning clock speeds for performance).
What about AI? So far, it’s more assistant than threat. “I’m hoping AI can at least automate the boring stuff,” Vadim says. But for now, it can’t replace human expertise in deep optimization work.
The hunt continues
Performance engineering is part logic puzzle, part adrenaline rush. It's about spotting patterns in chaos, uncovering elegant solutions where others see only roadblocks. And when you shave hours of wait time down to mere minutes? That’s the kind of thrill that keeps PEs coming back.
The hunt for lost milliseconds and idle CPU cycles never ends — and that’s exactly what makes this career so addictive.