17 nəticə — yol xəritələri, maaş bələdçiləri, sertifikatlar və iş bazarı təhlili.
Last year, a new engineer joined our team and asked a simple question: "Why does this service use MongoDB instead of PostgreSQL?" Nobody on the team knew. The engineer who made that decision had left two years ago. There was no documentation, no Slack thread, no commit message that explained the reasoning. We spent three days researching the trade-offs before concluding that the original decision was actually wrong for our current use case — but we had no way to know that without re-discovering all the constraints the original author considered.
The best technical decision I ever made wasn't a technology choice. It was writing a three-page RFC (Request for Comments) before starting a project that would have taken two months. The RFC process surfaced a fundamental flaw in the architecture within a week. We pivoted to a better design before writing a single line of code. Two months of work, saved by three pages of structured thinking.
I once watched a partner integration take down our entire API. Not a DDoS attack. Not a bug. Just one enthusiastic client running a data sync without any delay between requests. They sent 12,000 API calls in 45 seconds. Our database connection pool was exhausted in under a minute. Every other customer got 503 errors for the next ten minutes while we scrambled to recover.
I shipped a catastrophic bug to 100% of our users at 4 PM on a Friday. A simple UI change that broke the checkout flow. It took 45 minutes to push a fix through our CI/CD pipeline. During those 45 minutes, we lost roughly $12,000 in revenue.
Three years ago, my team spent six weeks setting up Istio. We read the docs, watched the conference talks, followed the tutorials. When we finally got it working, our cluster's resource usage had doubled, our deployment time had tripled, and nobody on the team could explain what the Envoy sidecar was actually doing. We ripped it out two months later.
In 2022, our team migrated a microservices system from REST to a hybrid gRPC + GraphQL architecture. The REST API had 147 endpoints across 12 services. Response times averaged 340ms for composite operations that required data from multiple services. After the migration: internal service-to-service calls dropped to 12ms average via gRPC, and client-facing queries via GraphQL reduced payload sizes by 62%. But the migration took eight months, introduced new categories of bugs we'd never seen, and required retraining the entire frontend team. Was it worth it? Absolutely — but only because we chose the right protocol for each use case.
At 2:47 AM on a Tuesday, our job aggregator's database connection pool exhausted. Not because of traffic — because a single scraper hung for 8 minutes holding a connection, triggering a cascade that starved every other service of database access. The site returned 500 errors for 23 minutes. We had retry logic, we had connection timeouts, we had circuit breakers — but none of them had ever been tested under this specific failure mode. After the post-mortem, I decided to start breaking things deliberately.
When I built the notification system for BirJob, I needed real-time job alerts: when a new job matching a user's criteria gets scraped, they should see it within seconds. I started with polling (check the API every 30 seconds). It worked, but it hammered our database with 200,000 unnecessary queries per day and created a 30-second delay users noticed. I switched to Server-Sent Events, and the database load dropped 94% while notifications became instant. The entire migration took one afternoon.
I once inherited a 140,000-line TypeScript codebase that had zero design patterns. Every feature request took three days of archaeology before the first line of code. Services were entangled like Christmas lights in January. State leaked across boundaries like water through a sieve. After six months of systematic refactoring — introducing patterns one at a time, measuring before and after — our average feature delivery time dropped from 11 days to 3. That experience taught me that design patterns aren't academic exercises; they're survival tools.
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.