Key Takeaways
- Calculated Approach: Math your way out by calculating compile time and processing requirements to ensure your system performs under peak loads.
- Architectural Redesign: Sometimes, the biggest performance gains come from redesigning the entire architecture, not just tweaking the existing system.
- No-Update Strategy: Eliminating database updates, favoring reads and inserts can dramatically reduce lock contention.
- Creative Constraints: Working within technological limitations can spark ingenious solutions when you think outside the box.
- Junior Courage: Don’t let inexperience stop you from tackling complex problems — sometimes, fresh eyes see solutions that veterans miss.
When the Server Crashes Every Week…
It had become a ritual at the company: Someone would reboot the central logging server every week or so after it inevitably crashed. Queries that should have taken seconds dragged on for minutes. A few too many requests would bring the entire system to its knees.
As a junior self-taught engineer with less than a year of experience, no degree, and no formal IT training, I was fed up watching this cycle repeat. Why was everyone accepting this as normal? Why wasn’t anyone taking action?
When I received a legal compliance notice requesting specific connection logs, I discovered that hundreds of thousands of logs were missing due to the flawed system design. This was the final straw that made me say “Enough is enough” and take matters into my own hands to fix it.
“I’ll fix it,” I announced to the shocked faces of my colleagues and the CTO.
Their response was predictable: “It’s too complex… it’s central to our operations… you might break it even more.”
But sometimes, the person least qualified on paper is the most qualified to question the status quo. The CTO, an exceptional network engineer who recognized the value of fresh perspectives, gave me the green light.
The Math That Mattered
I started by calculating what we needed to achieve. At our highest reported peak, we handled 115 requests per second. With a single CPU and two threads, that translated to 6,900 requests per minute. To keep our CPU load under control, each process needed to complete in under 0.0174 seconds per request.
How could we possibly compile Perl scripts, run them, query the database, and orchestrate network operations in such a short time?
To make matters worse, I wasn’t allowed to switch to compiled languages. Management insisted on using scripting languages so engineers could log into the server and modify scripts on the fly. I had to work within these constraints.
Even as a junior, I knew pre-compilation could solve major performance bottlenecks. My timing tests in that machine showed that compilation alone sometimes took upwards to 0.2 seconds per request — a significant bottleneck when multiplied by hundreds of requests.
The Lock Problem
The fundamental issue was clear: our database was experiencing high read and write volumes with excessive locks. With 115 processes per second, each requiring 2-3 locks, no wonder queries took minutes and the server crashed regularly.
This was a PPPoE (Point-to-Point Protocol over Ethernet) authentication server responsible for managing user logins, dynamic IP allocations, and VLAN assignments. We needed to store these connections for legal compliance and be able to query them efficiently.
Remember, this was MySQL almost two decades ago. The solution wasn’t adding more power — it was rethinking the architecture entirely. Today, we have many powerful database solutions, but back then, we had to work smarter with the limited tools available.
The Solution: No More Updates
The secret sauce of my approach was simple but radical: “No updates”. I divided our tables into reads and writes, keeping active tables small and blazing fast.
- One table stored all available IP addresses.
- Another table stored active connections.
- One table for the archive that was optimized for read, meaning it only appended data, so reads were blazing fast.
When a disconnection happened, I used a CTE (Common Table Expression) to “move” data from the active connections table to the archive table. Everything became either a read, an insert, or a delete, allowing me to bypass most locks that were causing performance issues.
Creating Tables Is Sometimes Faster Than Querying Them
No matter how much I tried to optimize the indexes to work with ranges of dates, it constantly caused a full table scan. For single dates, the indexes worked well, but ranges were problematic. That’s when I had a crazy idea: “What if I create a table with those dates and join? This will hit the index well.”
To my surprise, it worked incredibly well. I discovered something counterintuitive: it was exponentially faster to create a temporary table, pre-fill it with just the specific dates in the needed range, query via joins on the main table, and then drop the table — rather than letting the query builder do a full table lookup.
This approach prevented inefficient full-table scans and delivered extraordinary performance gains. Sometimes, breaking conventional database rules is exactly what’s needed.
Beware of Hidden “Features”
During my work, I discovered the dangers of hidden “features” — default behaviors that create mysterious problems. One such issue almost derailed my entire project: primary key auto-trimming.
I had meticulously designed combined primary keys to ensure uniqueness, and everything worked perfectly in isolation. But in production, mysterious duplicate key errors appeared.
After a quick debugging session, I discovered that when keys exceeded MySQL’s length limit, the system silently trimmed them, resulting in duplicate keys. What should have been impossible became a reality due to this hidden behavior. It took me just a few minutes to fix by splitting the primary key into two — the last time the server ever encountered errors.
This is why we must always question what’s happening behind the scenes in our systems.
The Results Speak for Themselves
I confidently told the CTO that our new system would handle a quarter billion logged connections without performance degradation. My colleagues were skeptical. Years later, after I had moved on to new challenges, I heard that the system had never crashed once, had surpassed 70 million logged connections, and query times remained constant at around 0.2 seconds.
The original system:
- Created a new table every month.
- Slowed dramatically when nearing 2.5 million records.
- Required frequent server reboots, leading to data loss.
Let’s do the math on that improvement:
- Original system: 2.5 million records till crashing → ~180 seconds (3 minutes)
- Per record: 0.000072 seconds
- At 70 million records, the original system would take 84 min
- New system: 70 million records → 0.2 seconds
- Performance improvement: 84 minutes ÷ 0.2 seconds = 25,200x faster
And this only covers the logged connection queries. Active connection management became blazing fast too, with zero data loss and no reboots ever required.
Why It Matters: Beyond Just Performance
This wasn’t just about making a database faster — it was about:
- Legal Compliance: ISPs must retain accurate connection logs. My solution ensured complete data retention.
- Security: Authentication servers are prime targets; frequent crashes create vulnerabilities.
- User Experience: Slow authentication frustrates users and increases support tickets.
- Operational Efficiency: No more weekly reboots — engineers could focus on real innovation.
- Scalability: The new architecture handled 28x more data without slowdown, supporting business growth.
Be Courageous
When I embarked on this task, I was just a junior engineer, teaching myself everything on the fly. I had no qualifications suggesting I could solve a problem that seasoned engineers avoided.
But sometimes, being underqualified is your greatest asset. You haven’t yet learned what’s “impossible” or what “shouldn’t be done.” You see solutions where others see insurmountable challenges.
In a world obsessed with narrow specialization, the most valuable professionals will be those who can adapt instantly to new technologies, think holistically rather than in silos, and constantly ask, “What if?”
Note: I recalled this story after recently finding an old notebook with performance targets scribbled in the margins: “0.0087 per thread, max CPU time per process 17ms.” While the core achievements and approach are accurate, some finer technical details of the final solution may have faded over the past two decades. The principles, however, remain as relevant as ever.