Overview
Go occupies a specific niche in the landscape of backend programming languages — a compiled, statically typed language with built-in concurrency primitives, a minimal but expressive standard library, and compilation that produces single self-contained binaries without runtime dependencies. These characteristics make Go particularly well-suited for network services, API backends, command-line tools, and the infrastructure components that need to handle high concurrency with predictable resource usage.
Go is not our primary backend language — Rust handles the performance-critical systems programming layer and C# handles the complex enterprise integration and business logic layer in most of our projects. Go enters the picture when a project's existing infrastructure is Go-based, when a client's team is primarily Go-proficient, or when the specific characteristics of Go — its concurrency model, its standard library's network stack, its straightforward deployment as a single binary — are the best fit for the component being built.
The concurrency model is Go's most distinctive characteristic. Goroutines are lightweight concurrent execution units — far lighter than operating system threads — and channels provide the typed communication mechanism between goroutines that Go's concurrency patterns are built on. A Go service can handle thousands of concurrent network connections with modest resource consumption because goroutines are cheap to create and the Go scheduler efficiently multiplexes many goroutines onto a smaller number of OS threads. For services where the primary work is waiting — waiting for network responses, waiting for database queries, waiting for external API calls — Go's concurrency model makes efficient use of available resources without the complexity that equivalent Rust async code or C# Task-based async code can involve.
The single binary deployment model is practically significant. A Go application compiles to a single executable that contains the application code and the Go runtime — no separate runtime installation, no dependency management at deployment time, no version conflicts. The binary runs on any compatible OS and architecture without any additional setup. For CLI tools, agent processes, and services deployed to many hosts, this eliminates a class of deployment complexity that languages with runtime dependencies introduce.
We work in Go for projects where the existing codebase is Go, where the team's expertise is Go-centric, or where Go's specific characteristics make it the best fit for the component at hand.
Where Go Fits in Our Stack
API backends and microservices. Go's standard library net/http package provides a capable HTTP server that handles high concurrency efficiently. The ecosystem of Go HTTP frameworks and routers — Gin, Echo, Chi, Fiber — adds routing, middleware, and request handling conveniences on top of the standard library. Go API backends that serve as integration points between frontend applications and backend data stores, as internal microservices within a larger service architecture, or as lightweight API proxies.
Network services and agents. Services that maintain persistent network connections — WebSocket servers, TCP servers, services that poll multiple external APIs concurrently, agent processes that report metrics to a central system. Go's goroutine model makes these patterns natural: one goroutine per connection, fan-out to multiple concurrent HTTP requests, worker pools that process incoming messages concurrently. The resource efficiency that comes from goroutines being far cheaper than OS threads.
Command-line tools and operational utilities. Go's compilation to single executables makes it an excellent choice for CLI tools — the binary is distributed without requiring the target system to have any specific runtime installed. DevOps and operational tooling, data processing scripts that need to handle large inputs efficiently, integration utilities that run as scheduled tasks.
Data pipeline components. Pipeline stages that need to process data concurrently — ingesting from one source while writing to another, processing records in parallel workers while maintaining ordering where required, fanning out to multiple downstream consumers. Go's channel-based concurrency model maps naturally to pipeline architectures.
Infrastructure and platform tooling. Go is the dominant language in cloud-native infrastructure tooling — Kubernetes, Docker, Terraform, and most CNCF projects are written in Go. For tooling that integrates tightly with this infrastructure ecosystem, Go's native compatibility with Kubernetes client libraries and the Go ecosystem's infrastructure tooling makes it a natural fit.
What Go Development Covers
HTTP API development. REST API backends built with Go's standard library or with framework additions — Gin for a batteries-included framework experience, Chi for a lightweight, composable router that stays close to the standard library, Echo for middleware-heavy applications.
Middleware patterns: the request lifecycle middleware that handles logging, authentication, rate limiting, request ID propagation, and panic recovery — implemented as composable middleware functions that wrap handler chains. Context propagation: using Go's context.Context for request-scoped values, cancellation signals, and deadline propagation through the call chain — the Go pattern that allows clean cancellation of in-flight operations when a client disconnects or a deadline expires.
Request validation and serialisation: struct-based request binding from JSON, query parameters, and path parameters, with validation libraries (go-playground/validator) that enforce constraints declaratively. Response serialisation with encoding/json and the custom marshalling that specific serialisation requirements need.
Error handling patterns: Go's explicit error return convention and the error wrapping that fmt.Errorf with %w provides. Structured error types that carry additional context alongside the error message. API error response formatting that distinguishes between validation errors, not-found errors, and internal server errors.
Concurrency patterns. The Go concurrency patterns that structure concurrent application logic correctly.
Goroutine lifecycle management: ensuring that goroutines started by the application are properly terminated when the application shuts down — the sync.WaitGroup pattern for waiting for goroutine completion, the context cancellation pattern for signalling goroutines to stop. Avoiding goroutine leaks that accumulate over time in long-running services.
Worker pools: the pattern that limits the number of concurrent goroutines processing tasks from a queue — preventing the unbounded goroutine creation that can occur when processing high-volume input with one goroutine per item. Buffered channels as work queues, worker goroutines that read from the channel, and the synchronisation that waits for all work to complete.
Fan-out and fan-in: concurrent dispatch of work to multiple goroutines and collection of results — the pattern for making multiple concurrent HTTP requests and assembling the results, for processing items in parallel and collecting the outputs in order.
Select statements and channel coordination: select for non-blocking channel operations, timeouts, and the coordination of multiple channel operations. The heartbeat pattern for detecting unresponsive goroutines. The done channel pattern for signalling graceful shutdown.
Database integration. Go's database/sql package for SQL database access — the interface that works with any SQL database through driver implementations. PostgreSQL with lib/pq or jackc/pgx, MySQL with go-sql-driver/mysql. Connection pool configuration: maximum open connections, maximum idle connections, connection lifetime — the parameters that determine how the connection pool behaves under load.
Query patterns: parameterised queries that prevent SQL injection, row scanning into structs, transaction management with Begin/Commit/Rollback. The sqlx library for reduced boilerplate in common query patterns. GORM for applications where an ORM reduces development friction without introducing unacceptable performance overhead.
Database migration management: Goose or Flyway for SQL migration management in Go applications — the migration files and the tooling that applies them in order and tracks which migrations have been applied.
External API integration. Go HTTP clients for consuming external REST APIs — the http.Client configuration with appropriate timeouts, retry logic with exponential backoff, and response parsing. Rate limiter implementations using Go's time.Ticker and token bucket patterns that respect external API rate limits. Circuit breaker patterns for external dependencies that may become unavailable.
Testing. Go's built-in testing package and the test tooling that the ecosystem provides. Table-driven tests that exercise multiple input/output combinations concisely. Subtests for grouping related test cases and running them independently. The httptest package for testing HTTP handlers without a live server. Interface-based mocking with testify/mock or with hand-written mock implementations. Integration tests that use real database instances via Docker Compose or testcontainers-go.
gRPC services. Protocol Buffer service definitions compiled to Go with protoc and the Go gRPC plugin. gRPC server implementation with unary and streaming RPC methods. gRPC client generation and connectivity with the gRPC-Go client library. gRPC gateway for exposing gRPC services as HTTP/JSON APIs alongside the native gRPC interface.
Go's Limitations and When We Choose Differently
Rust for performance-critical systems. When raw performance, memory control, or the type system guarantees that Rust's ownership model provides are required — for low-latency trading infrastructure, for high-throughput data processing, for safety-critical systems where the compiler enforces correctness — Rust is a better fit than Go. Go's garbage collector introduces latency pauses that are acceptable for most services but not for latency-sensitive trading systems or high-frequency data processing.
C# for enterprise integration. When the integration targets are enterprise systems with mature .NET SDKs — ERP systems, healthcare HL7 FHIR, institutional trading APIs — and when the business logic complexity benefits from C#'s richer type system and object-oriented patterns, C# is a better fit. Go's standard library is excellent but its enterprise ecosystem is not as deep as .NET's.
Python for data science and ML. When the work involves statistical modelling, machine learning, or the data science ecosystem — NumPy, Pandas, scikit-learn — Python is the only practical choice. Go has limited data science library support.
Go as the right choice. When the project is a high-concurrency network service, when deployment as a single binary is operationally significant, when the existing codebase or team is Go-centric, or when the specific component being built maps naturally to Go's concurrency model and standard library.
Technologies Used
- Go 1.22+ — current language version
- Gin / Echo / Chi — HTTP frameworks and routers
- gRPC-Go / protoc — gRPC service development
- database/sql / sqlx / GORM — database access
- lib/pq / pgx — PostgreSQL drivers
- go-sql-driver/mysql — MySQL driver
- go-playground/validator — request validation
- testify — testing assertions and mocks
- Goose / golang-migrate — database migration management
- Docker — containerised deployment
- GitHub Actions — CI/CD pipeline integration
Practical Concurrency Without the Complexity
Go's primary contribution to our toolkit is the concurrency model — goroutines and channels that make concurrent service development significantly more approachable than the equivalent patterns in lower-level languages, without the runtime overhead that higher-level concurrency models introduce. For the right class of problem — network services, concurrent data processing, infrastructure tooling — Go's concurrency model is genuinely productive and the resulting services are efficient and maintainable.
The single binary deployment and the fast compilation are secondary advantages that compound the productivity benefit for services and tools that deploy frequently or to many hosts.
Go Where Go Fits
We use Go when it is the right tool for the component being built — when concurrency is the primary design concern, when single binary deployment matters, or when the project's existing infrastructure is Go-based. We do not use Go where Rust, C#, or Python is a better fit. The goal is the right technology for the problem, not a technology preference imposed on every problem.