Intro
We started with a monolithic backend that powered the entire product: authentication, payments, user profiles, analytics, and background jobs were all living inside a single codebase and deployed as a single unit.
At early stages this approach was not only acceptable — it was optimal. It allowed fast iteration, minimal DevOps overhead, and extremely simple deployment pipelines. One service, one database, one CI pipeline. Everything felt predictable.
However, as the product grew, the same simplicity started becoming a constraint rather than an advantage.
Bottlenecks
The first real issues were not dramatic outages but subtle degradation patterns:
- API latency gradually increased under load
- database contention started appearing during peak traffic
- background jobs began lagging behind real-time events
- deployments became risky because everything was tightly coupled
Eventually, even small changes in unrelated modules had system-wide consequences. A single slow query could affect authentication flows, payment processing, and analytics pipelines simultaneously.
The core problem was not infrastructure — it was coupling.
Migration strategy
We explicitly avoided a full rewrite. Instead, we chose incremental decomposition:
- extracted high-load domains first (jobs, analytics)
- introduced service boundaries based on business domains, not technical layers
- kept the monolith as a “core system” during transition
- migrated gradually with dual-running systems
This allowed us to keep shipping features while progressively reducing system complexity.
Event system
The biggest shift came with the introduction of event-driven communication using Kafka.
Instead of synchronous request chains:
User → API → DB → service → response
We moved to:
User → API → event → async processing → eventual consistency
This removed cascading failure chains and significantly reduced system fragility under load.
Infrastructure
We modernized infrastructure gradually:
- Kubernetes for orchestration
- horizontal autoscaling instead of vertical scaling
- centralized logging + metrics
- distributed tracing (OpenTelemetry)
- isolated CI/CD pipelines per service
The biggest improvement was not performance — it was observability.
Results
After migration:
- p95 latency dropped by ~50–60% on critical paths
- deployments became safe and reversible
- incidents became isolated instead of systemic
- scaling became horizontal and predictable
Lessons
The main lesson was simple but critical:
Scaling problems are almost never infrastructure problems first — they are coupling problems.
Reducing dependencies between systems had more impact than any hardware upgrade.