20 nəticə — yol xəritələri, maaş bələdçiləri, sertifikatlar və iş bazarı təhlili.
The worst outage I ever caused was a database migration. A simple ALTER TABLE to add a column to a 40-million-row table. I expected it to take a few seconds. It took 47 minutes. During those 47 minutes, the table was locked, every query that touched it timed out, and the API returned 500 errors to every user. The post-mortem was brutal.
The slowest query in our application took 4.2 seconds. It joined six tables, aggregated a month of analytics data, and ran every time a user loaded their dashboard. We spent two weeks trying to optimize the SQL. We reduced it to 1.8 seconds. Still too slow. Then we added a single line of Redis caching with a 5-minute TTL. Response time: 3 milliseconds. The 4.2-second query ran once every 5 minutes instead of hundreds of times.
Early in my career, I deployed an application to a single server and called it a day. Traffic grew. The server got slow. I vertically scaled it (bigger machine). Traffic grew more. The server got slow again. Eventually, I hit the ceiling: the biggest machine wasn't big enough. That's when I learned about load balancers, and everything changed.
When I built BirJob's scraping pipeline, I faced a classic data engineering decision. We scrape 80+ job sites daily, producing roughly 15,000 job listings per run. Each listing needs deduplication, normalization, categorization, and enrichment before it's visible to users. The naive approach — scrape everything, process synchronously, write to the database — took 4 hours and crashed regularly when a single source timed out. After redesigning around a proper pipeline architecture, the same workload processes in 35 minutes with zero crashes. The architecture choice was the difference between a fragile script and a reliable system.
A couple of years ago, I was debugging a checkout flow that worked perfectly in development but crumbled under real traffic. Orders were duplicated. Inventory went negative. The payment gateway timed out, but the order still went through. I sat in front of my screen at 2 AM, staring at logs from three different services, and realized: I was building a distributed system, and I had no idea what I was doing.
Three years ago, I joined a startup as the third engineer. The codebase was 18 months old, built under extreme time pressure, and it showed. A single feature — adding a filter to the job search page — took 11 days. Not because the feature was complex, but because the search module had accumulated so much technical debt that every change required understanding 14 files across 3 services, running a 40-minute test suite that failed intermittently, and manually testing 6 edge cases that had no automated coverage. The team knew the debt existed. What they didn't have was a framework for deciding what to fix, when to fix it, and how to justify the investment to stakeholders.
When I launched BirJob.com — a job aggregator for Azerbaijan that pulls listings from roughly 80+ sources across the local market — I set up Google Analytics like everyone does. GA4, the tracking snippet, the whole thing. And for a while I convinced myself that sessions, bounce rates, and page views were telling me something useful.
Last year, we shipped a Next.js app to production with zero monitoring. No error tracking, no performance metrics, no log aggregation. It was a job aggregator processing 50,000+ scraping operations daily, serving 200,000+ page views monthly. When things broke — and they broke often — we found out from users. Sometimes days later. The fix-it-when-it-breaks approach cost us an estimated 15% of daily active users before we instrumented everything.