How to Write Bulletproof Code in Go: A Workflow for Building Reliable Servers
As a backend or full-stack developer, the reliability of your servers and services is of utmost importance. Server downtime or bugs can lead to lost revenue, frustrated users, and reputational damage for your company. Even for non-critical systems, the cost of maintenance and debugging production issues rises quickly when you have an unstable codebase.
That‘s why it‘s crucial to implement a rigorous development workflow that minimizes the chances of shipping faulty code. In this article, we‘ll dive into a set of practices that have served me well for writing bulletproof server code in Go. While the examples are Go-specific, many of the principles apply to other languages as well.
Why Go is great for writing robust server code
Let‘s start by examining some of the characteristics that make Go a good choice for reliable backend development:
Static typing
Go is statically typed, meaning variables‘ types are checked at compile time. This allows the compiler to catch type mismatches and many other bugs before you even run your code. Languages without static typing, like JavaScript, require you to write your own validation logic to check that functions are being called with the correct types. Offloading this to the compiler saves you development effort and is less error-prone.
Simplicity and clarity
Go was designed to be simple to learn and read. It eschews many complex language features in favor of a small set of orthogonal primitives. There is usually one clear way to tackle a given problem.
This simplicity makes it easier to understand code that you or someone else wrote. There are fewer dark corners where bugs can hide. Straightforward code is also easier to cover with tests.
Explicit error handling
Go is explicit and verbose when it comes to dealing with errors. Functions indicate that they can fail by returning an error type. The developer is then forced to handle the error at the call site, either by checking the error, calling another function, or letting the error bubble up the stack:
func doSomething() error {
err := doSomethingElse()
if err != nil {
// handle the error
return err
}
return nil
}
There‘s no way to accidentally ignore an error, as can happen with unchecked exceptions in languages like Java. Being explicit about what can go wrong makes you think deeply about how to recover when things do fail.
Rich standard library and ecosystem
Go ships with a robust standard library that covers the fundamental needs of server development, from HTTP handling to encryption to JSON parsing. This means you can write a lot of functionality without importing third-party libraries.
Where you do need external dependencies, Go has a thriving ecosystem through its decentralized package management. Well-regarded libraries exist for databases, messaging queues, and other common needs.
Of course, even the best language is no substitute for good development practices. Let‘s look at a workflow for making the most of Go‘s strengths.
Architecting your codebase for testability
Automated testing is the bedrock of confidence in your code‘s correctness. But to achieve a high level of test coverage, you need to design your application with testability in mind from the start.
Break your application into services
A monolithic architecture, where all functionality lives in a single codebase, is hard to fully test. There are too many complex interactions and edge cases to consider.
Instead, split your application into small, focused services that communicate over well-defined APIs. Each service should have a single responsibility. This will allow you to test them independently and more exhaustively.
For example, consider a financial app that needs to process transactions between user accounts and serve information about balances. We might split this into three services:
-
Account Management Service: Responsible for creating and administering user accounts. Provides an API for retrieving account details.
-
Ledger Service: Stores and updates account balances. Validates that transactions do not cause negative balances.
-
Transaction Service: Handles requests to transfer money between accounts. Orchestrates the process by calling the other two services.
Separating concerns this way allows us to set clear boundaries and avoids tangling unrelated functionality. We can test each piece with a manageable number of permutations.
Define service interfaces in a language-agnostic way
To keep service implementations decoupled, define the interfaces (API surface) between them using a language like protocol buffers. This makes it explicit what kinds of requests and responses are supported. Here‘s what the definition for the Ledger API might look like:
package ledger;
service LedgerService {
rpc GetBalance(GetBalanceRequest) returns (Balances) {}
rpc UpdateBalance(UpdateBalanceRequest) returns (Balances) {}
}
message GetBalanceRequest {
string userId = 1;
}
message UpdateBalanceRequest {
string userId = 1;
double amount = 2;
}
message Balances {
double available = 1;
double pending = 2;
}
Using a data interchange format (vs plain Go interfaces) has several benefits:
- The interface is clearly versioned and can be evolved without breaking consumers
- You can generate client and server stubs in multiple languages from the same definition
- The data structures and service endpoints are self-documenting
With this interface, the Ledger service can evolve its internal implementation without breaking the Transaction service that depends on it, as long as the public API stays constant.
Testing at every level
With the application split into testable services, it‘s time to build a layered testing strategy to verify every aspect of the system. There are several kinds of tests to employ:
Unit tests
Unit tests exercise individual functions in isolation to verify that they produce the expected output for a given input. In Go, unit tests live in a file next to the implementation named xxx_test.go and are run with go test. For example:
ledger.go
func (l *ledger) UpdateBalance(userId string, amount float64) (Balances, error) {
// logic to update balance
}
ledger_test.go
func TestUpdateBalance(t *testing.T) {
l := &ledger{…}
bal, err := l.UpdateBalance("user1", 100)
if err != nil {
t.Fatalf("Expected no error, got %v", err)
}
if bal.Available != 100 {
t.Errorf("Expected available balance of 100, got %f", bal.Available)
}
}
You should write unit tests to cover both the success case and every possible error case for each public function. Aim for 100% test coverage – you can check what percent of statements are covered using go test -cover.
Integration tests
Integration tests verify that multiple units work together correctly. For our purposes, we‘ll have integration tests that exercise a whole service end-to-end by making calls to its public API.
A nice way to structure this is using Go‘s "black box" testing package, which allows tests to live in a separate package from the implementation. The service and its dependencies are instantiated and "wired up", then tests are performed against its public interface. These tests should cover key success and failure scenarios for the entire service.
Here‘s an example for the Transaction service:
transaction/service.go
type TransactionService struct {
ledgerClient LedgerClient
}
func (ts TransactionService) Transfer(ctx context.Context, req TransferRequest) (*TransferResponse, error) {
// logic to check balances and call ledger service
}
transaction/integration_test.go
func TestTransfer(t *testing.T) {
ledgerMock := &ledgerMock{…}
ts := &TransactionService{ledgerClient: ledgerMock}
// test success case
req := &TransferRequest{FromUserId: "user1", ToUserId: "user2", Amount: 100}
res, err := ts.Transfer(context.Background(), req)
if err != nil {
t.Fatalf("Expected no error, got %v", err)
}
if res.FromUserBalance != 900 {
t.Errorf("Expected user 1 balance of 900, got %f", res.FromUserBalance)
}
// test insufficient funds error
ledgerMock.SetBalance("user1", 0)
_, err = ts.Transfer(context.Background(), req)
if err == nil {
t.Errorf("Expected error for insufficient funds, got nil")
}
}
Note the use of a mock LedgerClient that allows us to simulate different responses from the Ledger service. This is crucial to test error handling.
End-to-end tests
Finally, end-to-end tests exercise your whole system as a user would experience it. If you have a REST API, these tests would make HTTP calls to your public endpoints and validate the responses. The goal is to verify that the key user flows work as expected.
There are many approaches to writing good end-to-end tests, such as Behavior-Driven Development. The main thing is to cover the most important paths a user can take through your product.
Ensuring production-readiness
Testing isn‘t the only ingredient for bulletproof code. There are a few other key steps before deployment:
Fuzz testing
Unit and integration tests, while comprehensive, only exercise your code with inputs you thought to include. Fuzz testing or "fuzzing" feeds your functions random, invalid, or unexpected inputs to see if they crash or behave strangely.
Go has a built-in fuzzing engine that makes it easy to get started. You simply define a fuzz target function that takes a random byte array, then go test –fuzz=. will bombard your code with all sorts of inputs. Fuzzing often uncovers edge cases and security holes you never would have considered otherwise.
Load and performance testing
All the testing so far has been about correctness, not performance. But slow or non-responsive servers are often just as bad as incorrect ones. Before going to production, you need to measure how your system performs under load and ensure it meets your target metrics for latency, error rate, and throughput.
There are many tools available for applying synthetic load to an API, like Apache Bench or Vegeta. Configure these to simulate your expected production traffic patterns, then monitor your server‘s resource usage. Identify any bottlenecks or points of contention.
Also consider unlikely scenarios, like if traffic spikes 10x due to a special promotion. You may need to build in rate limiting, caching, or autoscaling to maintain good performance.
Beta testing with real users
As a final "smoke test", release your server to a small group of beta users before rolling it out to everyone. There‘s no substitute for real user behavior to surface bugs.
Monitor your logs and metrics carefully during the beta period. Be prepared to quickly roll back or patch if something goes wrong. Iron out any remaining wrinkles before opening the floodgates to production traffic.
Conclusion
Writing truly bulletproof server code is an ambitious undertaking. It requires diligence and commitment at every phase of development. But by breaking your application into small, testable services, and applying testing and quality control best practices, you can achieve a high level of confidence before releasing to production.
The Go language, with its focus on simplicity, performance, and maintainability, is an excellent choice for building the kind of fault-tolerant systems that power today‘s businesses. Its type system, standard library, and testing tools help you move quickly while avoiding common pitfalls.
No server can be 100% immune to failure. The best you can do is to minimize mistakes, recognize when you do make them, and quickly recover. Hopefully the techniques described here can help you do that more effectively. Now go forth and build those rock-solid services!