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Go Error Handling Patterns: Building Robust Applications That Fail Gracefully

Learn Go error handling best practices with patterns for checking, wrapping, custom types, retry logic & structured logging. Build robust applications that fail gracefully. Master Go errors today.

Go Error Handling Patterns: Building Robust Applications That Fail Gracefully

Let’s talk about mistakes. In software, things go wrong. Files disappear, networks fail, users enter gibberish. A program’s strength isn’t just in what it does when everything works, but in how it responds when things break. In Go, we don’t have exceptions. We have values. An error is just a value that a function can return, and we are required to look at it. This simple design forces us to think about failure from the very beginning. It can feel tedious at first, all those if err != nil checks, but this explicit handling is the foundation of writing reliable systems. I’ve found that adopting clear patterns turns this from a chore into a powerful tool for building robust applications.

Here are some ways I structure error handling to make my Go code clearer, more maintainable, and easier to debug.

The most fundamental pattern is checking errors immediately. You see it everywhere in Go. When a function returns an error, you handle it right there. Don’t ignore it. Don’t just log it and continue unless you are absolutely certain that’s the correct thing to do. This immediate check stops problems from propagating silently through your system.

file, err := os.Open("data.txt")
if err != nil {
    // Handle it NOW. Do not proceed.
    return fmt.Errorf("could not open data file: %v", err)
}
// Only use 'file' here because we know err is nil.
defer file.Close()

Often, when you get an error, you need to add your own context before passing it back up the chain. A raw “file not found” error is less helpful than knowing which file you were trying to open and why. This is where error wrapping comes in. Using fmt.Errorf with the %w verb lets you create a chain of errors, like a stack trace made of messages.

func loadUserConfig(userID string) (*Config, error) {
    path := fmt.Sprintf("/configs/%s.json", userID)
    data, err := os.ReadFile(path)
    if err != nil {
        // Wrap the OS error with our context.
        return nil, fmt.Errorf("load config for user %s: %w", userID, err)
    }
    // ... parse data ...
}

Later, you or a caller can inspect this chain. You can check if a specific error is somewhere in the chain using errors.Is. This is perfect for sentinel errors—pre-defined error values that signal a specific, expected failure condition.

var ErrConfigNotFound = errors.New("config not found")

func getConfig() error {
    // Simulate an error deep down.
    return fmt.Errorf("file system: %w", ErrConfigNotFound)
}

func main() {
    err := getConfig()
    if errors.Is(err, ErrConfigNotFound) {
        // We can identify this specific condition.
        fmt.Println("Creating a default config.")
        return
    }
    if err != nil {
        // It was some other error.
        log.Fatal(err)
    }
}

Sometimes you need more than a simple string or a sentinel value. You might need to attach structured data to an error: a status code, a timestamp, a suggested retry delay. For this, you define a custom type that implements the error interface. This is just a struct with an Error() string method.

type RateLimitError struct {
    Limit      int
    ResetTime  time.Time
    Requested  string
}

func (e RateLimitError) Error() string {
    return fmt.Sprintf("rate limit of %d exceeded for %s, resets at %v",
        e.Limit, e.Requested, e.ResetTime)
}

func makeAPIRequest() error {
    // ... request logic ...
    return RateLimitError{
        Limit:      100,
        ResetTime:  time.Now().Add(1 * time.Minute),
        Requested:  "GET /api/data",
    }
}

To get that structured data back, you use errors.As. It tries to find your custom error type in the chain and, if successful, copies it into your variable. This lets you make decisions based on the error’s fields.

err := makeAPIRequest()
var rle RateLimitError
if errors.As(err, &rle) {
    // We have access to the structured data.
    waitTime := time.Until(rle.ResetTime)
    fmt.Printf("Hit limit. Please wait %v seconds.\n", waitTime.Seconds())
} else if err != nil {
    // It was a different kind of error.
    log.Println("Failed:", err)
}

What about when you’re doing several independent things and some fail while others succeed? You don’t want to stop at the first error, and you also don’t want to lose track of the failures. Error aggregation collects multiple errors into a single container. A simple way is to use a slice, but libraries like golang.org/x/sync/errgroup or github.com/hashicorp/go-multierror offer more polished solutions.

import "github.com/hashicorp/go-multierror"

func processBatch(items []string) error {
    var result error
    for _, item := range items {
        if err := doWork(item); err != nil {
            // Append each error to a combined error.
            result = multierror.Append(result, err)
        }
    }
    return result // This might be nil, a single error, or a list.
}

Go has panics, which are for truly exceptional, unrecoverable problems (like out-of-bounds slice access). You shouldn’t use them for normal error flow. However, they can happen, especially in code you don’t control. A critical pattern is to contain panics at the boundaries of your goroutines using recover. This prevents one panicking goroutine from bringing down your whole application.

func safeGo(fn func()) {
    go func() {
        defer func() {
            if r := recover(); r != nil {
                // Log the panic details for debugging.
                log.Printf("Recovered from panic in goroutine: %v\n", r)
                // Optionally, report to an error tracking service here.
            }
        }()
        fn()
    }()
}

// Usage
safeGo(func() {
    fmt.Println("This goroutine is protected.")
    // A panic here will be caught and logged, not crash the program.
})

For operations that can fail temporarily, like network calls, automatic retries are essential. The key is a retry loop with a delay that increases (exponential backoff) and a way to stop. You also need to know which errors are worth retrying (like a network timeout) and which are permanent (like “invalid user ID”).

func retryOperation(ctx context.Context, op func() error, maxAttempts int) error {
    var err error
    for i := 0; i < maxAttempts; i++ {
        err = op()
        if err == nil {
            return nil // Success!
        }
        // Check if the error is permanent. If so, stop retrying.
        if isPermanentError(err) {
            return err
        }
        // Calculate backoff: e.g., 1s, 2s, 4s, 8s...
        backoff := time.Duration(1<<uint(i)) * time.Second
        fmt.Printf("Attempt %d failed: %v. Retrying in %v...\n", i+1, err, backoff)
        // Wait, but respect context cancellation.
        select {
        case <-time.After(backoff):
            continue
        case <-ctx.Done():
            return fmt.Errorf("operation cancelled: %w", ctx.Err())
        }
    }
    return fmt.Errorf("failed after %d attempts: %w", maxAttempts, err)
}

Getting errors in production is one thing; understanding them is another. A strong error reporting pattern sends errors to a centralized system with rich context. Attach everything useful: a request ID, user ID, timestamp, and relevant variables. The standard library’s log/slog package is excellent for structured logging.

import "log/slog"

func handleRequest(req *http.Request) error {
    // ... some operation that fails ...
    opErr := fmt.Errorf("database connection failed")
    // Report with context
    slog.Error("request handler failed",
        "error", opErr,
        "request_id", req.Header.Get("X-Request-ID"),
        "path", req.URL.Path,
        "method", req.Method,
    )
    // Also send to a specific error service if you use one
    // sentry.CaptureException(opErr)
    return opErr
}

Finally, there’s a pattern of defining your own error kinds for your application’s domain. Instead of just errors.New("not found"), you might have ErrUserNotFound, ErrInsufficientFunds, or ErrInvalidTransaction. These become part of your API’s contract. They let callers handle specific cases cleanly using errors.Is. This moves error handling from parsing strings to checking types, which is more reliable and expressive.

package myapp

// Define domain errors as sentinels.
var (
    ErrUserNotFound      = errors.New("user not found")
    ErrInvalidPermission = errors.New("invalid permission")
)

func FindUser(email string) (*User, error) {
    // ... lookup logic ...
    if !userExists(email) {
        return nil, fmt.Errorf("query for '%s': %w", email, ErrUserNotFound)
    }
    // ...
}

A caller can then handle this neatly.

user, err := myapp.FindUser("[email protected]")
if errors.Is(err, myapp.ErrUserNotFound) {
    // Show a friendly "sign up" page.
    return renderSignUpPage()
} else if err != nil {
    // Show a generic error page.
    return renderErrorPage()
}
// Proceed with the user...

These patterns are tools. You won’t use every one in every project, but knowing them gives you options. The goal is always the same: to write software that fails in predictable, documented, and manageable ways. It’s about being honest about what can go wrong and having a plan for it. In my experience, time spent on thoughtful error handling pays back many times over when debugging at midnight or trying to understand a problem in a system that’s been running for months. Good error handling isn’t just defensive coding; it’s a clear way of communicating how your code works, and how it can fail, to everyone who reads it, including your future self.

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