The Monolith Era
When I started my career in web development, Ruby on Rails was the framework of choice for building web applications quickly. The convention-over-configuration philosophy and the rich ecosystem of gems made it possible to ship features at an incredible pace.
Our monolithic Rails application served us well for years. Everything was in one place: models, controllers, views, background jobs, and all the business logic. Deployment was straightforward, and debugging was relatively simple because you could trace a request through a single codebase.
The Breaking Point
As the application grew, we started hitting limitations:
- Build times that stretched from minutes to over 30 minutes
- Deploy times that required maintenance windows
- Team coordination became a bottleneck as multiple teams worked on the same codebase
- Scaling required duplicating the entire application, even if only one component was under load
- Technology lock-in meant we couldn’t experiment with new languages or frameworks
The monolith that had served us so well was now our biggest constraint.
The Microservices Migration
The decision to migrate to microservices wasn’t taken lightly. We knew the challenges:
- Distributed systems complexity
- Network latency and failure modes
- Data consistency across services
- Monitoring and observability at scale
- Service discovery and load balancing
Why Golang?
We chose Golang for our microservices for several reasons:
- Performance: Compiled binaries with excellent runtime performance
- Concurrency: Goroutines made it easy to handle concurrent requests
- Small footprint: Minimal resource usage compared to the Rails monolith
- Fast builds: Go’s compilation speed was a game-changer
- Static typing: Caught errors at compile time instead of production
- Standard library: Rich standard library reduced external dependencies
What We Learned
The Good
Independent deployment: Teams could ship features without coordinating across the entire engineering org.
Technology flexibility: We could choose the best tool for each service. Most were Golang, but we used Python for ML services and Rust for performance-critical components.
Scalability: We could scale services independently based on their specific needs.
Resilience: Failures in one service didn’t bring down the entire system.
The Challenging
Operational complexity: We went from managing one application to orchestrating hundreds of services.
Distributed debugging: Tracing requests across multiple services required sophisticated tooling.
Data management: Maintaining data consistency across services was harder than we anticipated.
Network is unreliable: We had to build retry logic, circuit breakers, and graceful degradation into everything.
The Tools That Made It Possible
- Kubernetes: Container orchestration at scale
- FluxCD: GitOps for declarative deployments
- Bazel: Fast, reproducible builds across all services
- Prometheus & Grafana: Monitoring and alerting
- Jaeger: Distributed tracing
- gRPC: Service-to-service communication
Would I Do It Again?
Yes, but with more deliberation. Microservices aren’t a silver bullet. They trade one set of problems (monolith constraints) for another (distributed systems complexity).
The key lesson: Don’t start with microservices. Start with a well-structured monolith, identify clear service boundaries, and only extract services when you have a compelling reason.
Advice for Teams Considering Microservices
- Start with a modular monolith - Establish clear boundaries within your monolith first
- Invest in infrastructure - You need solid CI/CD, monitoring, and observability
- Build expertise - Understand distributed systems patterns before you need them
- Extract gradually - Don’t rewrite everything at once
- Question every extraction - Each new service adds operational overhead
Microservices enabled us to scale our engineering organization and our infrastructure independently. But they’re not free - they require significant investment in tooling, processes, and expertise.
Choose wisely.