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Engineering Blog

Deep dives, case studies, and hard-won lessons from real engineering teams. No fluff — just practical insights from people who've shipped at scale.

How We Migrated a 10-Year-Old Monolith to Microservices Without Downtime

After 10 years, our monolith had 800,000 lines of code, 3-hour deployment windows, and any change to the payment module required 2 weeks of regression testing. We needed to break it up. But we had 2M active users and couldn't afford downtime. Here's the exact strategy we used — the Strangler Fig pattern, event sourcing for data sync, and how we ran both systems in parallel for 8 months.

The key insight: you don't migrate a monolith, you grow a new system around it. Start with the least-coupled service (for us, notifications), prove the pattern, then systematically extract domains. We made 37 mistakes along the way — here are the 10 most important ones.
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From 2-Hour Deploys to 8 Minutes: Our CI/CD Transformation

Our deployment pipeline took 2 hours. Tests ran sequentially. Docker builds weren't cached. Every deploy was a company-wide event. After 6 months of incremental improvements — parallelizing test suites, layer caching, shifting left on linting — we're at 8 minutes. Here's the breakdown of what we changed and the impact on developer happiness.
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We Deleted 40% of Our Tests. Here's Why That Made Our Suite Better.

We had 4,200 tests and they took 45 minutes to run. Engineers stopped running them locally. CI became a rubber stamp. The problem: we had an ice cream cone — too many brittle E2E tests and too few unit tests. We audited every test for value. The result: 2,400 tests, 12-minute runs, and 3× more bugs caught.
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The $50k Query: Finding and Fixing Our Most Expensive Database Operation

A single N+1 query was costing us $50k/month in database compute. It ran every time a user opened their dashboard — 2 million times per day. We didn't notice until our Postgres CPU hit 90%. How we found it (pg_stat_statements), how we fixed it (one SQL JOIN), and how we set up query monitoring to prevent it happening again.
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Technical Debt: How We Reduced Our Backlog by 60% in One Quarter

Technical debt had slowed us down 40% — every new feature required modifying 3 unrelated modules. We tried ignoring it (didn't work), scheduling "debt sprints" (didn't work), and finally found what did work: the Boy Scout Rule, coupling metrics with SonarQube, and making tech debt visible to product management by translating it into feature velocity impact.
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Event-Driven Architecture: What We Wish We'd Known Before Starting

Event-driven architectures promise loose coupling and independent scaling. They deliver — but they introduce distributed systems complexity that will humble even senior engineers. Duplicate events, out-of-order processing, schema evolution, consumer group management. We learned these lessons the hard way over 18 months. Here's our annotated map of the landmines.
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Staff Engineer vs Engineering Manager: A Framework for the Decision

At senior level, most engineers face this fork: go deep technically (Staff/Principal engineer) or go broad with people (Engineering Manager). The decision isn't permanent, but it shapes the next 5 years. We interviewed 20 engineers who made both choices. Here's the framework that emerged — and the question that cuts through all the noise.
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Our Incident Post-Mortem Process: Turning Outages Into Improvements

We had a culture of blame. When things broke, people got defensive. Incidents went unresolved at root cause because everyone was protecting themselves. We adopted blameless postmortems 18 months ago. Our MTTR dropped 60%. Incident frequency dropped 40%. Team morale improved. This is our exact postmortem template and facilitation guide.
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PostgreSQL Indexing: A Practical Guide to 10× Query Performance

Most developers know about indexes but create them wrong. Wrong column order, missing composite indexes, indexes that never get used, indexes on low-cardinality columns that hurt more than help. We profiled 50 production queries and found $30k/month in preventable compute costs. Here's the complete guide to PostgreSQL indexing strategy.
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