styleguide

Go Style Best Practices

https://google.github.io/styleguide/go/best-practices

Overview | Guide | Decisions | Best practices

Note: This is part of a series of documents that outline Go Style at Google. This document is neither normative nor canonical, and is an auxiliary document to the core style guide. See the overview for more information.

About

This file documents guidance about how to best apply the Go Style Guide. This guidance is intended for common situations that arise frequently, but may not apply in every circumstance. Where possible, multiple alternative approaches are discussed along with the considerations that go into the decision about when and when not to apply them.

See the overview for the full set of Style Guide documents.

Naming

Function and method names

Avoid repetition

When choosing the name for a function or method, consider the context in which the name will be read. Consider the following recommendations to avoid excess repetition at the call site:

• The following can generally be omitted from function and method names:

  • The types of the inputs and outputs (when there is no collision)
  • The type of a method’s receiver
  • Whether an input or output is a pointer

• For functions, do not repeat the name of the package.

  // Bad:
  package yamlconfig
  
  func ParseYAMLConfig(input string) (*Config, error)
  

  // Good:
  package yamlconfig
  
  func Parse(input string) (*Config, error)
  

• For methods, do not repeat the name of the method receiver.

  // Bad:
  func (c *Config) WriteConfigTo(w io.Writer) (int64, error)
  

  // Good:
  func (c *Config) WriteTo(w io.Writer) (int64, error)
  

• Do not repeat the names of variables passed as parameters.

  // Bad:
  func OverrideFirstWithSecond(dest, source *Config) error
  

  // Good:
  func Override(dest, source *Config) error
  

• Do not repeat the names and types of the return values.

  // Bad:
  func TransformYAMLToJSON(input *Config) *jsonconfig.Config
  

  // Good:
  func Transform(input *Config) *jsonconfig.Config
  

When it is necessary to disambiguate functions of a similar name, it is acceptable to include extra information.

// Good:
func (c *Config) WriteTextTo(w io.Writer) (int64, error)
func (c *Config) WriteBinaryTo(w io.Writer) (int64, error)

Naming conventions

There are some other common conventions when choosing names for functions and methods:

• Functions that return something are given noun-like names.

  // Good:
  func (c *Config) JobName(key string) (value string, ok bool)
  

  A corollary of this is that function and method names should avoid the prefix Get.

  // Bad:
  func (c *Config) GetJobName(key string) (value string, ok bool)
  

• Functions that do something are given verb-like names.

  // Good:
  func (c *Config) WriteDetail(w io.Writer) (int64, error)
  

• Identical functions that differ only by the types involved include the name of the type at the end of the name.

  // Good:
  func ParseInt(input string) (int, error)
  func ParseInt64(input string) (int64, error)
  func AppendInt(buf []byte, value int) []byte
  func AppendInt64(buf []byte, value int64) []byte
  

  If there is a clear “primary” version, the type can be omitted from the name for that version:

  // Good:
  func (c *Config) Marshal() ([]byte, error)
  func (c *Config) MarshalText() (string, error)
  

Test double packages and types

There are several disciplines you can apply to naming packages and types that provide test helpers and especially test doubles. A test double could be a stub, fake, mock, or spy.

These examples mostly use stubs. Update your names accordingly if your code uses fakes or another kind of test double.

Suppose you have a well-focused package providing production code similar to this:

package creditcard

import (
    "errors"

    "path/to/money"
)

// ErrDeclined indicates that the issuer declines the charge.
var ErrDeclined = errors.New("creditcard: declined")

// Card contains information about a credit card, such as its issuer,
// expiration, and limit.
type Card struct {
    // omitted
}

// Service allows you to perform operations with credit cards against external
// payment processor vendors like charge, authorize, reimburse, and subscribe.
type Service struct {
    // omitted
}

func (s *Service) Charge(c *Card, amount money.Money) error { /* omitted */ }

Creating test helper packages

Suppose you want to create a package that contains test doubles for another. We’ll use package creditcard (from above) for this example:

One approach is to introduce a new Go package based on the production one for testing. A safe choice is to append the word test to the original package name (“creditcard” + “test”):

// Good:
package creditcardtest

Unless stated explicitly otherwise, all examples in the sections below are in package creditcardtest.

Simple case

You want to add a set of test doubles for Service. Because Card is effectively a dumb data type, similar to a Protocol Buffer message, it needs no special treatment in tests, so no double is required. If you anticipate only test doubles for one type (like Service), you can take a concise approach to naming the doubles:

// Good:
import (
    "path/to/creditcard"
    "path/to/money"
)

// Stub stubs creditcard.Service and provides no behavior of its own.
type Stub struct{}

func (Stub) Charge(*creditcard.Card, money.Money) error { return nil }

This is strictly preferable to a naming choice like StubService or the very poor StubCreditCardService, because the base package name and its domain types imply what creditcardtest.Stub is.

Finally, if the package is built with Bazel, make sure the new go_library rule for the package is marked as testonly:

# Good:
go_library(
    name = "creditcardtest",
    srcs = ["creditcardtest.go"],
    deps = [
        ":creditcard",
        ":money",
    ],
    testonly = True,
)

The approach above is conventional and will be reasonably well understood by other engineers.

See also:

• Go Tip #42: Authoring a Stub for Testing

Multiple test double behaviors

When one kind of stub is not enough (for example, you also need one that always fails), we recommend naming the stubs according to the behavior they emulate. Here we rename Stub to AlwaysCharges and introduce a new stub called AlwaysDeclines:

// Good:
// AlwaysCharges stubs creditcard.Service and simulates success.
type AlwaysCharges struct{}

func (AlwaysCharges) Charge(*creditcard.Card, money.Money) error { return nil }

// AlwaysDeclines stubs creditcard.Service and simulates declined charges.
type AlwaysDeclines struct{}

func (AlwaysDeclines) Charge(*creditcard.Card, money.Money) error {
    return creditcard.ErrDeclined
}

Multiple doubles for multiple types

But now suppose that package creditcard contains multiple types worth creating doubles for, as seen below with Service and StoredValue:

package creditcard

type Service struct {
    // omitted
}

type Card struct {
    // omitted
}

// StoredValue manages customer credit balances.  This applies when returned
// merchandise is credited to a customer's local account instead of processed
// by the credit issuer.  For this reason, it is implemented as a separate
// service.
type StoredValue struct {
    // omitted
}

func (s *StoredValue) Credit(c *Card, amount money.Money) error { /* omitted */ }

In this case, more explicit test double naming is sensible:

// Good:
type StubService struct{}

func (StubService) Charge(*creditcard.Card, money.Money) error { return nil }

type StubStoredValue struct{}

func (StubStoredValue) Credit(*creditcard.Card, money.Money) error { return nil }

Local variables in tests

When variables in your tests refer to doubles, choose a name that most clearly differentiates the double from other production types based on context. Consider some production code you want to test:

package payment

import (
    "path/to/creditcard"
    "path/to/money"
)

type CreditCard interface {
    Charge(*creditcard.Card, money.Money) error
}

type Processor struct {
    CC CreditCard
}

var ErrBadInstrument = errors.New("payment: instrument is invalid or expired")

func (p *Processor) Process(c *creditcard.Card, amount money.Money) error {
    if c.Expired() {
        return ErrBadInstrument
    }
    return p.CC.Charge(c, amount)
}

In the tests, a test double called a “spy” for CreditCard is juxtaposed against production types, so prefixing the name may improve clarity.

// Good:
package payment

import "path/to/creditcardtest"

func TestProcessor(t *testing.T) {
    var spyCC creditcardtest.Spy

    proc := &Processor{CC: spyCC}

    // declarations omitted: card and amount
    if err := proc.Process(card, amount); err != nil {
        t.Errorf("proc.Process(card, amount) = %v, want %v", got, want)
    }

    charges := []creditcardtest.Charge{
        {Card: card, Amount: amount},
    }

    if got, want := spyCC.Charges, charges; !cmp.Equal(got, want) {
        t.Errorf("spyCC.Charges = %v, want %v", got, want)
    }
}

This is clearer than when the name is not prefixed.

// Bad:
package payment

import "path/to/creditcardtest"

func TestProcessor(t *testing.T) {
    var cc creditcardtest.Spy

    proc := &Processor{CC: cc}

    // declarations omitted: card and amount
    if err := proc.Process(card, amount); err != nil {
        t.Errorf("proc.Process(card, amount) = %v, want %v", got, want)
    }

    charges := []creditcardtest.Charge{
        {Card: card, Amount: amount},
    }

    if got, want := cc.Charges, charges; !cmp.Equal(got, want) {
        t.Errorf("cc.Charges = %v, want %v", got, want)
    }
}

Shadowing

Note: This explanation uses two informal terms, stomping and shadowing. They are not official concepts in the Go language spec.

Like many programming languages, Go has mutable variables: assigning to a variable changes its value.

// Good:
func abs(i int) int {
    if i < 0 {
        i *= -1
    }
    return i
}

When using short variable declarations with the := operator, in some cases a new variable is not created. We can call this stomping. It’s OK to do this when the original value is no longer needed.

// Good:
// innerHandler is a helper for some request handler, which itself issues
// requests to other backends.
func (s *Server) innerHandler(ctx context.Context, req *pb.MyRequest) *pb.MyResponse {
    // Unconditionally cap the deadline for this part of request handling.
    ctx, cancel := context.WithTimeout(ctx, 3*time.Second)
    defer cancel()
    ctxlog.Info(ctx, "Capped deadline in inner request")

    // Code here no longer has access to the original context.
    // This is good style if when first writing this, you anticipate
    // that even as the code grows, no operation legitimately should
    // use the (possibly unbounded) original context that the caller provided.

    // ...
}

Be careful using short variable declarations in a new scope, though: that introduces a new variable. We can call this shadowing the original variable. Code after the end of the block refers to the original. Here is a buggy attempt to shorten the deadline conditionally:

// Bad:
func (s *Server) innerHandler(ctx context.Context, req *pb.MyRequest) *pb.MyResponse {
    // Attempt to conditionally cap the deadline.
    if *shortenDeadlines {
        ctx, cancel := context.WithTimeout(ctx, 3*time.Second)
        defer cancel()
        ctxlog.Info(ctx, "Capped deadline in inner request")
    }

    // BUG: "ctx" here again means the context that the caller provided.
    // The above buggy code compiled because both ctx and cancel
    // were used inside the if statement.

    // ...
}

A correct version of the code might be:

// Good:
func (s *Server) innerHandler(ctx context.Context, req *pb.MyRequest) *pb.MyResponse {
    if *shortenDeadlines {
        var cancel func()
        // Note the use of simple assignment, = and not :=.
        ctx, cancel = context.WithTimeout(ctx, 3*time.Second)
        defer cancel()
        ctxlog.Info(ctx, "Capped deadline in inner request")
    }
    // ...
}

In the case we called stomping, because there’s no new variable, the type being assigned must match that of the original variable. With shadowing, an entirely new entity is introduced so it can have a different type. Intentional shadowing can be a useful practice, but you can always use a new name if it improves clarity.

It is not a good idea to use variables with the same name as standard packages other than very small scopes, because that renders free functions and values from that package inaccessible. Conversely, when picking a name for your package, avoid names that are likely to require import renaming or cause shadowing of otherwise good variable names at the client side.

// Bad:
func LongFunction() {
    url := "https://example.com/"
    // Oops, now we can't use net/url in code below.
}

Util packages

Go packages have a name specified on the package declaration, separate from the import path. The package name matters more for readability than the path.

Go package names should be related to what the package provides. Naming a package just util, helper, common or similar is usually a poor choice (it can be used as part of the name though). Uninformative names make the code harder to read, and if used too broadly they are liable to cause needless import conflicts.

Instead, consider what the callsite will look like.

// Good:
db := spannertest.NewDatabaseFromFile(...)

_, err := f.Seek(0, io.SeekStart)

b := elliptic.Marshal(curve, x, y)

You can tell roughly what each of these do even without knowing the imports list (cloud.google.com/go/spanner/spannertest, io, and crypto/elliptic). With less focused names, these might read:

// Bad:
db := test.NewDatabaseFromFile(...)

_, err := f.Seek(0, common.SeekStart)

b := helper.Marshal(curve, x, y)

Package size

If you’re asking yourself how big your Go packages should be and whether to place related types in the same package or split them into different ones, a good place to start is the Go blog post about package names. Despite the post title, it’s not solely about naming. It contains some helpful hints and cites several useful articles and talks.

Here are some other considerations and notes.

Users see godoc for the package in one page, and any methods exported by types supplied by the package are grouped by their type. Godoc also group constructors along with the types they return. If client code is likely to need two values of different type to interact with each other, it may be convenient for the user to have them in the same package.

Code within a package can access unexported identifiers in the package. If you have a few related types whose implementation is tightly coupled, placing them in the same package lets you achieve this coupling without polluting the public API with these details. A good test for this coupling is to imagine a hypothetical user of two packages, where the packages cover closely related topics: if the user must import both packages in order to use either in any meaningful way, combining them together is usually the right thing to do. The standard library generally demonstrates this kind of scoping and layering well.

All of that being said, putting your entire project in a single package would likely make that package too large. When something is conceptually distinct, giving it its own small package can make it easier to use. The short name of the package as known to clients together with the exported type name work together to make a meaningful identifier: e.g. bytes.Buffer, ring.New. The blog post has more examples.

Go style is flexible about file size, because maintainers can move code within a package from one file to another without affecting callers. But as a general guideline: it is usually not a good idea to have a single file with many thousands of lines in it, or having many tiny files. There is no “one type, one file” convention as in some other languages. As a rule of thumb, files should be focused enough that a maintainer can tell which file contains something, and the files should be small enough that it will be easy to find once there. The standard library often splits large packages to several source files, grouping related code by file. The source for package bytes is a good example. Packages with long package documentation may choose to dedicate one file called doc.go that has the package documentation, a package declaration, and nothing else, but this is not required.

Within the Google codebase and in projects using Bazel, directory layout for Go code is different than it is in open source Go projects: you can have multiple go_library targets in a single directory. A good reason to give each package its own directory is if you expect to open source your project in the future.

See also:

• Test double packages

Imports

Protos and stubs

Proto library imports are treated differently than standard Go imports due to their cross-language nature. The convention for renamed proto imports are based on the rule that generated the package:

• The pb suffix is generally used for go_proto_library rules.
• The grpc suffix is generally used for go_grpc_library rules.

Generally, a short one- or two-letter prefix is used:

// Good:
import (
    fspb "path/to/package/foo_service_go_proto"
    fsgrpc "path/to/package/foo_service_go_grpc"
)

If there is only a single proto used by a package or the package is tied closely to that proto, the prefix can be omitted:

import ( pb “path/to/package/foo_service_go_proto” grpc “path/to/package/foo_service_go_grpc” )

If the symbols in the proto are generic or are not very self-descriptive, or if shortening the package name with an acronym is unclear, a short word can suffice as the prefix:

// Good:
import (
    mapspb "path/to/package/maps_go_proto"
)

In this case mapspb.Address might be clearer than mpb.Address if the code in question is not already clearly related to maps.

Import ordering

Imports are typically grouped into the following two (or more) blocks, in order:

1. Standard library imports (e.g., "fmt")
2. imports (e.g., “/path/to/somelib”)
3. (optional) Protobuf imports (e.g., fpb "path/to/foo_go_proto")
4. (optional) Side-effect imports (e.g., _ "path/to/package")

If a file does not have a group for one of the optional categories above, the relevant imports are included in the project import group.

Any import grouping that is clear and easy to understand is generally fine. For example, a team may choose to group gRPC imports separately from protobuf imports.

Note: For code maintaining only the two mandatory groups (one group for the standard library and one for all other imports), the goimports tool produces output consistent with this guidance.

However, goimports has no knowledge of groups beyond the mandatory ones; the optional groups are prone to invalidation by the tool. When optional groups are used, attention on the part of both authors and reviewers is required to ensure that groupings remain compliant.

Either approach is fine, but do not leave the imports section in an inconsistent, partially grouped state.

Error handling

In Go, errors are values; they are created by code and consumed by code. Errors can be:

• Converted into diagnostic information for display to humans
• Used by the maintainer
• Interpreted by an end user

Error messages also show up across a variety of different surfaces including log messages, error dumps, and rendered UIs.

Code that processes (produces or consumes) errors should do so deliberately. It can be tempting to ignore or blindly propagate an error return value. However, it is always worth considering whether the current function in the call frame is positioned to handle the error most effectively. This is a large topic and it is hard to give categorical advice. Use your judgment, but keep the following considerations in mind:

• When creating an error value, decide whether to give it any structure.
• When handling an error, consider adding information that you have but that the caller and/or callee might not.
• See also guidance on error logging.

While it is usually not appropriate to ignore an error, a reasonable exception to this is when orchestrating related operations, where often only the first error is useful. Package errgroup provides a convenient abstraction for a group of operations that can all fail or be canceled as a group.

See also:

• Effective Go on errors
• A post by the Go Blog on errors
• Package errors
• Package upspin.io/errors
• GoTip #89: When to Use Canonical Status Codes as Errors
• GoTip #48: Error Sentinel Values
• GoTip #13: Designing Errors for Checking

Error structure

If callers need to interrogate the error (e.g., distinguish different error conditions), give the error value structure so that this can be done programmatically rather than having the caller perform string matching. This advice applies to production code as well as to tests that care about different error conditions.

The simplest structured errors are unparameterized global values.

type Animal string

var (
    // ErrDuplicate occurs if this animal has already been seen.
    ErrDuplicate = errors.New("duplicate")

    // ErrMarsupial occurs because we're allergic to marsupials outside Australia.
    // Sorry.
    ErrMarsupial = errors.New("marsupials are not supported")
)

func process(animal Animal) error {
    switch {
    case seen[animal]:
        return ErrDuplicate
    case marsupial(animal):
        return ErrMarsupial
    }
    seen[animal] = true
    // ...
    return nil
}

The caller can simply compare the returned error value of the function with one of the known error values:

// Good:
func handlePet(...) {
    switch err := process(an); err {
    case ErrDuplicate:
        return fmt.Errorf("feed %q: %v", an, err)
    case ErrMarsupial:
        // Try to recover with a friend instead.
        alternate = an.BackupAnimal()
        return handlePet(..., alternate, ...)
    }
}

The above uses sentinel values, where the error must be equal (in the sense of ==) to the expected value. That is perfectly adequate in many cases. If process returns wrapped errors (discussed below), you can use errors.Is.

// Good:
func handlePet(...) {
    switch err := process(an); {
    case errors.Is(err, ErrDuplicate):
        return fmt.Errorf("feed %q: %v", an, err)
    case errors.Is(err, ErrMarsupial):
        // ...
    }
}

Do not attempt to distinguish errors based on their string form. (See Go Tip #13: Designing Errors for Checking for more.)

// Bad:
func handlePet(...) {
    err := process(an)
    if regexp.MatchString(`duplicate`, err.Error()) {...}
    if regexp.MatchString(`marsupial`, err.Error()) {...}
}

If there is extra information in the error that the caller needs programmatically, it should ideally be presented structurally. For example, the os.PathError type is documented to place the pathname of the failing operation in a struct field which the caller can easily access.

Other error structures can be used as appropriate, for example a project struct containing an error code and detail string. Package status is a common encapsulation; if you choose this approach (which you are not obligated to do), use canonical codes. See Go Tip #89: When to Use Canonical Status Codes as Errors to know if using status codes is the right choice.

Adding information to errors

Any function returning an error should strive to make the error value useful. Often, the function is in the middle of a callchain and is merely propagating an error from some other function that it called (maybe even from another package). Here there is an opportunity to annotate the error with extra information, but the programmer should ensure there’s sufficient information in the error without adding duplicate or irrelevant detail. If you’re unsure, try triggering the error condition during development: that’s a good way to assess what the observers of the error (either humans or code) will end up with.

Convention and good documentation help. For example, the standard package os advertises that its errors contain path information when it is available. This is a useful style, because callers getting back an error don’t need to annotate it with information that they had already provided the failing function.

// Good:
if err := os.Open("settings.txt"); err != nil {
    return err
}

// Output:
//
// open settings.txt: no such file or directory

If there is something interesting to say about the meaning of the error, of course it can be added. Just consider which level of the callchain is best positioned to understand this meaning.

// Good:
if err := os.Open("settings.txt"); err != nil {
    // We convey the significance of this error to us. Note that the current
    // function might perform more than one file operation that can fail, so
    // these annotations can also serve to disambiguate to the caller what went
    // wrong.
    return fmt.Errorf("launch codes unavailable: %v", err)
}

// Output:
//
// launch codes unavailable: open settings.txt: no such file or directory

Contrast with the redundant information here:

// Bad:
if err := os.Open("settings.txt"); err != nil {
    return fmt.Errorf("could not open settings.txt: %w", err)
}

// Output:
//
// could not open settings.txt: open settings.txt: no such file or directory

When adding information to a propagated error, you can either wrap the error or present a fresh error. Wrapping the error with the %w verb in fmt.Errorf allows callers to access data from the original error. This can be very useful at times, but in other cases these details are misleading or uninteresting to the caller. See the blog post on error wrapping for more information. Wrapping errors also expands the API surface of your package in a non-obvious way, and this can cause breakages if you change the implementation details of your package.

It is best to avoid using %w unless you also document (and have tests that validate) the underlying errors that you expose. If you do not expect your caller to call errors.Unwrap, errors.Is and so on, don’t bother with %w.

The same concept applies to structured errors like *status.Status (see canonical codes). For example, if your server sends malformed requests to a backend and receives an InvalidArgument code, this code should not be propagated to the client, assuming that the client has done nothing wrong. Instead, return an Internal canonical code to the client.

However, annotating errors helps automated logging systems preserve the status payload of an error. For example, annotating the error is appropriate in an internal function:

// Good:
func (s *Server) internalFunction(ctx context.Context) error {
    // ...
    if err != nil {
        return fmt.Errorf("couldn't find remote file: %w", err)
    }
}

Code directly at system boundaries (typically RPC, IPC, storage, and similar) should report errors using the canonical error space. It is the responsibility of code here to handle domain-specific errors and represent them canonically. For example:

// Bad:
func (*FortuneTeller) SuggestFortune(context.Context, *pb.SuggestionRequest) (*pb.SuggestionResponse, error) {
    // ...
    if err != nil {
        return nil, fmt.Errorf("couldn't find remote file: %w", err)
    }
}

// Good:
import (
    "google.golang.org/grpc/codes"
    "google.golang.org/grpc/status"
)
func (*FortuneTeller) SuggestFortune(context.Context, *pb.SuggestionRequest) (*pb.SuggestionResponse, error) {
    // ...
    if err != nil {
        // Or use fmt.Errorf with the %w verb if deliberately wrapping an
        // error which the caller is meant to unwrap.
        return nil, status.Errorf(codes.Internal, "couldn't find fortune database", status.ErrInternal)
    }
}

See also:

• Error Documentation Conventions

Placement of %w in errors

Prefer to place %w at the end of an error string.

Errors can be wrapped with the %w verb, or by placing them in a structured error that implements Unwrap() error (ex: fs.PathError).

Wrapped errors form error chains: each new layer of wrapping adds a new entry to the front of the error chain. The error chain can be traversed with the Unwrap() error method. For example:

err1 := fmt.Errorf("err1")
err2 := fmt.Errorf("err2: %w", err1)
err3 := fmt.Errorf("err3: %w", err2)

This forms an error chain of the form,

flowchart LR
  err3 == err3 wraps err2 ==> err2;
  err2 == err2 wraps err1 ==> err1;

Regardless of where the %w verb is placed, the error returned always represents the front of the error chain, and the %w is the next child. Similarly, Unwrap() error always traverses the error chain from newest to oldest error.

Placement of the %w verb does, however, affect whether the error chain is printed newest to oldest, oldest to newest, or neither:

// Good:
err1 := fmt.Errorf("err1")
err2 := fmt.Errorf("err2: %w", err1)
err3 := fmt.Errorf("err3: %w", err2)
fmt.Println(err3) // err3: err2: err1
// err3 is a newest-to-oldest error chain, that prints newest-to-oldest.

// Bad:
err1 := fmt.Errorf("err1")
err2 := fmt.Errorf("%w: err2", err1)
err3 := fmt.Errorf("%w: err3", err2)
fmt.Println(err3) // err1: err2: err3
// err3 is a newest-to-oldest error chain, that prints oldest-to-newest.

// Bad:
err1 := fmt.Errorf("err1")
err2 := fmt.Errorf("err2-1 %w err2-2", err1)
err3 := fmt.Errorf("err3-1 %w err3-2", err2)
fmt.Println(err3) // err3-1 err2-1 err1 err2-2 err3-2
// err3 is a newest-to-oldest error chain, that neither prints newest-to-oldest
// nor oldest-to-newest.

Therefore, in order for error text to mirror error chain structure, prefer placing the %w verb at the end with the form [...]: %w.

Logging errors

Functions sometimes need to tell an external system about an error without propagating it to their callers. Logging is an obvious choice here; but be conscious of what and how you log errors.

• Like good test failure messages, log messages should clearly express what went wrong and help the maintainer by including relevant information to diagnose the problem.

• Avoid duplication. If you return an error, it’s usually better not to log it yourself but rather let the caller handle it. The caller can choose to log the error, or perhaps rate-limit logging using rate.Sometimes. Other options include attempting recovery or even stopping the program. In any case, giving the caller control helps avoid logspam.

  The downside to this approach, however, is that any logging is written using the caller’s line coordinates.

• Be careful with PII. Many log sinks are not appropriate destinations for sensitive end-user information.

• Use log.Error sparingly. ERROR level logging causes a flush and is more expensive than lower logging levels. This can have serious performance impact on your code. When deciding between error and warning levels, consider the best practice that messages at the error level should be actionable rather than “more serious” than a warning.

• Inside Google, we have monitoring systems that can be set up for more effective alerting than writing to a log file and hoping someone notices it. This is similar but not identical to the standard library package expvar.

Custom verbosity levels

Use verbose logging (log.V) to your advantage. Verbose logging can be useful for development and tracing. Establishing a convention around verbosity levels can be helpful. For example:

• Write a small amount of extra information at V(1)
• Trace more information in V(2)
• Dump large internal states in V(3)

To minimize the cost of verbose logging, you should ensure not to accidentally call expensive functions even when log.V is turned off. log.V offers two APIs. The more convenient one carries the risk of this accidental expense. When in doubt, use the slightly more verbose style.

// Good:
for _, sql := range queries {
  log.V(1).Infof("Handling %v", sql)
  if log.V(2) {
    log.Infof("Handling %v", sql.Explain())
  }
  sql.Run(...)
}

// Bad:
// sql.Explain called even when this log is not printed.
log.V(2).Infof("Handling %v", sql.Explain())

Program initialization

Program initialization errors (such as bad flags and configuration) should be propagated upward to main, which should call log.Exit with an error that explains how to fix the error. In these cases, log.Fatal should not generally be used, because a stack trace that points at the check is not likely to be as useful as a human-generated, actionable message.

Program checks and panics

As stated in the decision against panics, standard error handling should be structured around error return values. Libraries should prefer returning an error to the caller rather than aborting the program, especially for transient errors.

It is occasionally necessary to perform consistency checks on an invariant and terminate the program if it is violated. In general, this is only done when a failure of the invariant check means that the internal state has become unrecoverable. The most reliable way to do this in the Google codebase is to call log.Fatal. Using panic in these cases is not reliable, because it is possible for deferred functions to deadlock or further corrupt internal or external state.

Similarly, resist the temptation to recover panics to avoid crashes, as doing so can result in propagating a corrupted state. The further you are from the panic, the less you know about the state of the program, which could be holding locks or other resources. The program can then develop other unexpected failure modes that can make the problem even more difficult to diagnose. Instead of trying to handle unexpected panics in code, use monitoring tools to surface unexpected failures and fix related bugs with a high priority.

Note: The standard net/http server violates this advice and recovers panics from request handlers. Consensus among experienced Go engineers is that this was a historical mistake. If you sample server logs from application servers in other languages, it is common to find large stacktraces that are left unhandled. Avoid this pitfall in your servers.

When to panic

The standard library panics on API misuse. For example, reflect issues a panic in many cases where a value is accessed in a way that suggests it was misinterpreted. This is analogous to the panics on core language bugs such as accessing an element of a slice that is out of bounds. Code review and tests should discover such bugs, which are not expected to appear in production code. These panics act as invariant checks that do not depend on a library, as the standard library does not have access to the levelled log package that the Google codebase uses.

Another case in which panics can be useful, though uncommon, is as an internal implementation detail of a package which always has a matching recover in the callchain. Parsers and similar deeply nested, tightly coupled internal function groups can benefit from this design, where plumbing error returns adds complexity without value. The key attribute of this design is that these panics are never allowed to escape across package boundaries and do not form part of the package’s API. This is typically accomplished with a top-level deferred recover that translates a propagating panic into a returned error at the public API surfaces.

Panic is also used when the compiler cannot identify unreachable code, for example when using a function like log.Fatal that will not return:

// Good:
func answer(i int) string {
    switch i {
    case 42:
        return "yup"
    case 54:
        return "base 13, huh"
    default:
        log.Fatalf("Sorry, %d is not the answer.", i)
        panic("unreachable")
    }
}

Do not call log functions before flags have been parsed. If you must die in an init func, a panic is acceptable in place of the logging call.

Documentation

Conventions

This section augments the decisions document’s commentary section.

Go code that is documented in familiar style is easier to read and less likely to be misused than something misdocumented or not documented at all. Runnable examples show up in Godoc and Code Search and are an excellent way of explaining how to use your code.

Parameters and configuration

Not every parameter must be enumerated in the documentation. This applies to:

• function and method parameters
• struct fields
• APIs for options

Document the error-prone or non-obvious fields and parameters by saying why they are interesting.

In the following snippet, the highlighted commentary adds little useful information to the reader:

// Bad:
// Sprintf formats according to a format specifier and returns the resulting
// string.
//
// format is the format, and data is the interpolation data.
func Sprintf(format string, data ...any) string

However, this snippet demonstrates a code scenario similar to the previous where the commentary instead states something non-obvious or materially helpful to the reader:

// Good:
// Sprintf formats according to a format specifier and returns the resulting
// string.
//
// The provided data is used to interpolate the format string. If the data does
// not match the expected format verbs or the amount of data does not satisfy
// the format specification, the function will inline warnings about formatting
// errors into the output string as described by the Format errors section
// above.
func Sprintf(format string, data ...any) string

Consider your likely audience in choosing what to document and at what depth. Maintainers, newcomers to the team, external users, and even yourself six months in the future may appreciate slightly different information from what is on your mind when you first come to write your docs.

See also:

• GoTip #41: Identify Function Call Parameters
• GoTip #51: Patterns for Configuration

Contexts

It is implied that the cancellation of a context argument interrupts the function it is provided to. If the function can return an error, conventionally it is ctx.Err().

This fact does not need to be restated:

// Bad:
// Run executes the worker's run loop.
//
// The method will process work until the context is cancelled and accordingly
// returns an error.
func (Worker) Run(ctx context.Context) error

Because that is implied, the following is better:

// Good:
// Run executes the worker's run loop.
func (Worker) Run(ctx context.Context) error

Where context behavior is different or non-obvious, it should be expressly documented if any of the following are true.

• The function returns an error other than ctx.Err() when the context is cancelled:

  // Good:
  // Run executes the worker's run loop.
  //
  // If the context is cancelled, Run returns a nil error.
  func (Worker) Run(ctx context.Context) error
  

• The function has other mechanisms that may interrupt it or affect lifetime:

  // Good:
  // Run executes the worker's run loop.
  //
  // Run processes work until the context is cancelled or Stop is called.
  // Context cancellation is handled asynchronously internally: run may return
  // before all work has stopped. The Stop method is synchronous and waits
  // until all operations from the run loop finish. Use Stop for graceful
  // shutdown.
  func (Worker) Run(ctx context.Context) error
  
  func (Worker) Stop()
  

• The function has special expectations about context lifetime, lineage, or attached values:

  // Good:
  // NewReceiver starts receiving messages sent to the specified queue.
  // The context should not have a deadline.
  func NewReceiver(ctx context.Context) *Receiver
  
  // Principal returns a human-readable name of the party who made the call.
  // The context must have a value attached to it from security.NewContext.
  func Principal(ctx context.Context) (name string, ok bool)
  

  Warning: Avoid designing APIs that make such demands (like contexts not having deadlines) from their callers. The above is only an example of how to document this if it cannot be avoided, not an endorsement of the pattern.

Concurrency

Go users assume that conceptually read-only operations are safe for concurrent use and do not require extra synchronization.

The extra remark about concurrency can safely be removed in this Godoc:

// Len returns the number of bytes of the unread portion of the buffer;
// b.Len() == len(b.Bytes()).
//
// It is safe to be called concurrently by multiple goroutines.
func (*Buffer) Len() int

Mutating operations, however, are not assumed to be safe for concurrent use and require the user to consider synchronization.

Similarly, the extra remark about concurrency can safely be removed here:

// Grow grows the buffer's capacity.
//
// It is not safe to be called concurrently by multiple goroutines.
func (*Buffer) Grow(n int)

Documentation is strongly encouraged if any of the following are true.

• It is unclear whether the operation is read-only or mutating:

  // Good:
  package lrucache
  
  // Lookup returns the data associated with the key from the cache.
  //
  // This operation is not safe for concurrent use.
  func (*Cache) Lookup(key string) (data []byte, ok bool)
  

  Why? A cache hit when looking up the key mutate a LRU cache internally. How this is implemented may not be obvious to all readers.

• Synchronization is provided by the API:

  // Good:
  package fortune_go_proto
  
  // NewFortuneTellerClient returns an *rpc.Client for the FortuneTeller service.
  // It is safe for simultaneous use by multiple goroutines.
  func NewFortuneTellerClient(cc *rpc.ClientConn) *FortuneTellerClient
  

  Why? Stubby provides synchronization.

  Note: If the API is a type and the API provides synchronization in entirety, conventionally only the type definition documents the semantics.

• The API consumes user-implemented types of interfaces, and the interface’s consumer has particular concurrency requirements:

  // Good:
  package health
  
  // A Watcher reports the health of some entity (usually a backend service).
  //
  // Watcher methods are safe for simultaneous use by multiple goroutines.
  type Watcher interface {
      // Watch sends true on the passed-in channel when the Watcher's
      // status has changed.
      Watch(changed chan<- bool) (unwatch func())
  
      // Health returns nil if the entity being watched is healthy, or a
      // non-nil error explaining why the entity is not healthy.
      Health() error
  }
  

  Why? Whether an API is safe for use by multiple goroutines is part of its contract.

Cleanup

Document any explicit cleanup requirements that the API has. Otherwise, callers won’t use the API correctly, leading to resource leaks and other possible bugs.

Call out cleanups that are up to the caller:

// Good:
// NewTicker returns a new Ticker containing a channel that will send the
// current time on the channel after each tick.
//
// Call Stop to release the Ticker's associated resources when done.
func NewTicker(d Duration) *Ticker

func (*Ticker) Stop()

If it is potentially unclear how to clean up the resources, explain how:

// Good:
// Get issues a GET to the specified URL.
//
// When err is nil, resp always contains a non-nil resp.Body.
// Caller should close resp.Body when done reading from it.
//
//    resp, err := http.Get("http://example.com/")
//    if err != nil {
//        // handle error
//    }
//    defer resp.Body.Close()
//    body, err := io.ReadAll(resp.Body)
func (c *Client) Get(url string) (resp *Response, err error)

See also:

• GoTip #110: Don’t Mix Exit With Defer

Errors

Document significant error sentinel values or error types that your functions return to callers so that callers can anticipate what types of conditions they can handle in their code.

// Good:
package os

// Read reads up to len(b) bytes from the File and stores them in b. It returns
// the number of bytes read and any error encountered.
//
// At end of file, Read returns 0, io.EOF.
func (*File) Read(b []byte) (n int, err error) {

When a function returns a specific error type, correctly note whether the error is a pointer receiver or not:

// Good:
package os

type PathError struct {
    Op   string
    Path string
    Err  error
}

// Chdir changes the current working directory to the named directory.
//
// If there is an error, it will be of type *PathError.
func Chdir(dir string) error {

Documenting whether the values returned are pointer receivers enables callers to correctly compare the errors using errors.Is, errors.As, and package cmp. This is because a non-pointer value is not equivalent to a pointer value.

Note: In the Chdir example, the return type is written as error rather than *PathError due to how nil interface values work.

Document overall error conventions in the package’s documentation when the behavior is applicable to most errors found in the package:

// Good:
// Package os provides a platform-independent interface to operating system
// functionality.
//
// Often, more information is available within the error. For example, if a
// call that takes a file name fails, such as Open or Stat, the error will
// include the failing file name when printed and will be of type *PathError,
// which may be unpacked for more information.
package os

Thoughtful application of these approaches can add extra information to errors without much effort and help callers avoid adding redundant annotations.

See also:

• Go Tip #106: Error Naming Conventions
• Go Tip #89: When to Use Canonical Status Codes as Errors

Preview

Go features a documentation server. It is recommended to preview the documentation your code produces both before and during the code review process. This helps to validate that the godoc formatting is rendered correctly.

Godoc formatting

Godoc provides some specific syntax to format documentation.

• A blank line is required to separate paragraphs:

  // Good:
  // LoadConfig reads a configuration out of the named file.
  //
  // See some/shortlink for config file format details.
  

• Test files can contain runnable examples that appear attached to the corresponding documentation in godoc:

  // Good:
  func ExampleConfig_WriteTo() {
    cfg := &Config{
      Name: "example",
    }
    if err := cfg.WriteTo(os.Stdout); err != nil {
      log.Exitf("Failed to write config: %s", err)
    }
    // Output:
    // {
    //   "name": "example"
    // }
  }
  

• Indenting lines by an additional two spaces formats them verbatim:

  // Good:
  // Update runs the function in an atomic transaction.
  //
  // This is typically used with an anonymous TransactionFunc:
  //
  //   if err := db.Update(func(state *State) { state.Foo = bar }); err != nil {
  //     //...
  //   }
  

  Note, however, that it can often be more appropriate to put code in a runnable example instead of including it in a comment.

  This verbatim formatting can be leveraged for formatting that is not native to godoc, such as lists and tables:

  // Good:
  // LoadConfig reads a configuration out of the named file.
  //
  // LoadConfig treats the following keys in special ways:
  //   "import" will make this configuration inherit from the named file.
  //   "env" if present will be populated with the system environment.
  

• A single line that begins with a capital letter, contains no punctuation except parentheses and commas, and is followed by another paragraph, is formatted as a header:

  // Good:
  // The following line is formatted as a heading.
  //
  // Using headings
  //
  // Headings come with autogenerated anchor tags for easy linking.
  

Signal boosting

Sometimes a line of code looks like something common, but actually isn’t. One of the best examples of this is an err == nil check (since err != nil is much more common). The following two conditional checks are hard to distinguish:

// Good:
if err := doSomething(); err != nil {
    // ...
}

// Bad:
if err := doSomething(); err == nil {
    // ...
}

You can instead “boost” the signal of the conditional by adding a comment:

// Good:
if err := doSomething(); err == nil { // if NO error
    // ...
}

The comment draws attention to the difference in the conditional.

Variable declarations

Initialization

For consistency, prefer := over var when initializing a new variable with a non-zero value.

// Good:
i := 42

// Bad:
var i = 42

Non-pointer zero values

The following declarations use the zero value:

// Good:
var (
    coords Point
    magic  [4]byte
    primes []int
)

You should declare values using the zero value when you want to convey an empty value that is ready for later use. Using composite literals with explicit initialization can be clunky:

// Bad:
var (
    coords = Point{X: 0, Y: 0}
    magic  = [4]byte{0, 0, 0, 0}
    primes = []int(nil)
)

A common application of zero value declaration is when using a variable as the output when unmarshalling:

// Good:
var coords Point
if err := json.Unmarshal(data, &coords); err != nil {

If you need a lock or other field that must not be copied in your struct, you can make it a value type to take advantage of zero value initialization. It does mean that the containing type must now be passed via a pointer and not a value. Methods on the type must take pointer receivers.

// Good:
type Counter struct {
    // This field does not have to be "*sync.Mutex". However,
    // users must now pass *Counter objects between themselves, not Counter.
    mu   sync.Mutex
    data map[string]int64
}

// Note this must be a pointer receiver to prevent copying.
func (c *Counter) IncrementBy(name string, n int64)

It’s acceptable to use value types for local variables of composites (such as structs and arrays) even if they contain such uncopyable fields. However, if the composite is returned by the function, or if all accesses to it end up needing to take an address anyway, prefer declaring the variable as a pointer type at the outset. Similarly, protobufs should be declared as pointer types.

// Good:
func NewCounter(name string) *Counter {
    c := new(Counter) // "&Counter{}" is also fine.
    registerCounter(name, c)
    return c
}

var myMsg = new(pb.Bar) // or "&pb.Bar{}".

This is because *pb.Something satisfies proto.Message while pb.Something does not.

// Bad:
func NewCounter(name string) *Counter {
    var c Counter
    registerCounter(name, &c)
    return &c
}

var myMsg = pb.Bar{}

Important: Map types must be explicitly initialized before they can be modified. However, reading from zero-value maps is perfectly fine.

For map and slice types, if the code is particularly performance sensitive and if you know the sizes in advance, see the size hints section.

Composite literals

The following are composite literal declarations:

// Good:
var (
    coords   = Point{X: x, Y: y}
    magic    = [4]byte{'I', 'W', 'A', 'D'}
    primes   = []int{2, 3, 5, 7, 11}
    captains = map[string]string{"Kirk": "James Tiberius", "Picard": "Jean-Luc"}
)

You should declare a value using a composite literal when you know initial elements or members.

In contrast, using composite literals to declare empty or memberless values can be visually noisy compared to zero-value initialization.

When you need a pointer to a zero value, you have two options: empty composite literals and new. Both are fine, but the new keyword can serve to remind the reader that if a non-zero value were needed, a composite literal wouldn’t work:

// Good:
var (
  buf = new(bytes.Buffer) // non-empty Buffers are initialized with constructors.
  msg = new(pb.Message) // non-empty proto messages are initialized with builders or by setting fields one by one.
)

Size hints

The following are declarations that take advantage of size hints in order to preallocate capacity:

// Good:
var (
    // Preferred buffer size for target filesystem: st_blksize.
    buf = make([]byte, 131072)
    // Typically process up to 8-10 elements per run (16 is a safe assumption).
    q = make([]Node, 0, 16)
    // Each shard processes shardSize (typically 32000+) elements.
    seen = make(map[string]bool, shardSize)
)

Size hints and preallocation are important steps when combined with empirical analysis of the code and its integrations, to create performance-sensitive and resource-efficient code.

Most code does not need a size hint or preallocation, and can allow the runtime to grow the slice or map as necessary. It is acceptable to preallocate when the final size is known (e.g. when converting between a map and a slice) but this is not a readability requirement, and may not be worth the clutter in small cases.

Warning: Preallocating more memory than you need can waste memory in the fleet or even harm performance. When in doubt, see GoTip #3: Benchmarking Go Code and default to a zero initialization or a composite literal declaration.

Channel direction

Specify channel direction where possible.

// Good:
// sum computes the sum of all of the values. It reads from the channel until
// the channel is closed.
func sum(values <-chan int) int {
    // ...
}

This prevents casual programming errors that are possible without specification:

// Bad:
func sum(values chan int) (out int) {
    for v := range values {
        out += v
    }
    // values must already be closed for this code to be reachable, which means
    // a second close triggers a panic.
    close(values)
}

When the direction is specified, the compiler catches simple errors like this. It also helps to convey a measure of ownership to the type.

See also Bryan Mills’ talk “Rethinking Classical Concurrency Patterns”: slides video.

Function argument lists

Don’t let the signature of a function get too long. As more parameters are added to a function, the role of individual parameters becomes less clear, and adjacent parameters of the same type become easier to confuse. Functions with large numbers of arguments are less memorable and more difficult to read at the call-site.

When designing an API, consider splitting a highly configurable function whose signature is growing complex into several simpler ones. These can share an (unexported) implementation if necessary.

Where a function requires many inputs, consider introducing an option struct for some of the arguments or employing the more advanced variadic options technique. The primary consideration for which strategy to choose should be how the function call looks across all expected use cases.

The recommendations below primarily apply to exported APIs, which are held to a higher standard than unexported ones. These techniques may be unnecessary for your use case. Use your judgment, and balance the principles of clarity and least mechanism.

See also: Go Tip #24: Use Case-Specific Constructions

Option structure

An option structure is a struct type that collects some or all of the arguments of a function or method, that is then passed as the last argument to the function or method. (The struct should be exported only if it is used in an exported function.)

Using an option structure has a number of benefits:

• The struct literal includes both fields and values for each argument, which makes them self-documenting and harder to swap.
• Irrelevant or “default” fields can be omitted.
• Callers can share the options struct and write helpers to operate on it.
• Structs provide cleaner per-field documentation than function arguments.
• Option structs can grow over time without impacting call-sites.

Here is an example of a function that could be improved:

// Bad:
func EnableReplication(ctx context.Context, config *replicator.Config, primaryRegions, readonlyRegions []string, replicateExisting, overwritePolicies bool, replicationInterval time.Duration, copyWorkers int, healthWatcher health.Watcher) {
    // ...
}

The function above could be rewritten with an option structure as follows:

// Good:
type ReplicationOptions struct {
    Config              *replicator.Config
    PrimaryRegions      []string
    ReadonlyRegions     []string
    ReplicateExisting   bool
    OverwritePolicies   bool
    ReplicationInterval time.Duration
    CopyWorkers         int
    HealthWatcher       health.Watcher
}

func EnableReplication(ctx context.Context, opts ReplicationOptions) {
    // ...
}

The function can then be called in a different package:

// Good:
func foo(ctx context.Context) {
    // Complex call:
    storage.EnableReplication(ctx, storage.ReplicationOptions{
        Config:              config,
        PrimaryRegions:      []string{"us-east1", "us-central2", "us-west3"},
        ReadonlyRegions:     []string{"us-east5", "us-central6"},
        OverwritePolicies:   true,
        ReplicationInterval: 1 * time.Hour,
        CopyWorkers:         100,
        HealthWatcher:       watcher,
    })

    // Simple call:
    storage.EnableReplication(ctx, storage.ReplicationOptions{
        Config:         config,
        PrimaryRegions: []string{"us-east1", "us-central2", "us-west3"},
    })
}

Note: Contexts are never included in option structs.

This option is often preferred when some of the following apply:

• All callers need to specify one or more of the options.
• A large number of callers need to provide many options.
• The options are shared between multiple functions that the user will call.

Variadic options

Using variadic options, exported functions are created which return closures that can be passed to the variadic (...) parameter of a function. The function takes as its parameters the values of the option (if any), and the returned closure accepts a mutable reference (usually a pointer to a struct type) that will be updated based on the inputs.

Using variadic options can provide a number of benefits:

• Options take no space at a call-site when no configuration is needed.
• Options are still values, so callers can share them, write helpers, and accumulate them.
• Options can accept multiple parameters (e.g. cartesian.Translate(dx, dy int) TransformOption).
• The option functions can return a named type to group options together in godoc.
• Packages can allow (or prevent) third-party packages to define (or from defining) their own options.

Note: Using variadic options requires a substantial amount of additional code (see the following example), so it should only be used when the advantages outweigh the overhead.

Here is an example of a function that could be improved:

// Bad:
func EnableReplication(ctx context.Context, config *placer.Config, primaryCells, readonlyCells []string, replicateExisting, overwritePolicies bool, replicationInterval time.Duration, copyWorkers int, healthWatcher health.Watcher) {
  ...
}

The example above could be rewritten with variadic options as follows:

// Good:
type replicationOptions struct {
    readonlyCells       []string
    replicateExisting   bool
    overwritePolicies   bool
    replicationInterval time.Duration
    copyWorkers         int
    healthWatcher       health.Watcher
}

// A ReplicationOption configures EnableReplication.
type ReplicationOption func(*replicationOptions)

// ReadonlyCells adds additional cells that should additionally
// contain read-only replicas of the data.
//
// Passing this option multiple times will add additional
// read-only cells.
//
// Default: none
func ReadonlyCells(cells ...string) ReplicationOption {
    return func(opts *replicationOptions) {
        opts.readonlyCells = append(opts.readonlyCells, cells...)
    }
}

// ReplicateExisting controls whether files that already exist in the
// primary cells will be replicated.  Otherwise, only newly-added
// files will be candidates for replication.
//
// Passing this option again will overwrite earlier values.
//
// Default: false
func ReplicateExisting(enabled bool) ReplicationOption {
    return func(opts *replicationOptions) {
        opts.replicateExisting = enabled
    }
}

// ... other options ...

// DefaultReplicationOptions control the default values before
// applying options passed to EnableReplication.
var DefaultReplicationOptions = []ReplicationOption{
    OverwritePolicies(true),
    ReplicationInterval(12 * time.Hour),
    CopyWorkers(10),
}

func EnableReplication(ctx context.Context, config *placer.Config, primaryCells []string, opts ...ReplicationOption) {
    var options replicationOptions
    for _, opt := range DefaultReplicationOptions {
        opt(&options)
    }
    for _, opt := range opts {
        opt(&options)
    }
}

The function can then be called in a different package:

// Good:
func foo(ctx context.Context) {
    // Complex call:
    storage.EnableReplication(ctx, config, []string{"po", "is", "ea"},
        storage.ReadonlyCells("ix", "gg"),
        storage.OverwritePolicies(true),
        storage.ReplicationInterval(1*time.Hour),
        storage.CopyWorkers(100),
        storage.HealthWatcher(watcher),
    )

    // Simple call:
    storage.EnableReplication(ctx, config, []string{"po", "is", "ea"})
}

Prefer this option when many of the following apply:

• Most callers will not need to specify any options.
• Most options are used infrequently.
• There are a large number of options.
• Options require arguments.
• Options could fail or be set incorrectly (in which case the option function returns an error).
• Options require a lot of documentation that can be hard to fit in a struct.
• Users or other packages can provide custom options.

Options in this style should accept parameters rather than using presence to signal their value; the latter can make dynamic composition of arguments much more difficult. For example, binary settings should accept a boolean (e.g. rpc.FailFast(enable bool) is preferable to rpc.EnableFailFast()). An enumerated option should accept an enumerated constant (e.g. log.Format(log.Capacitor) is preferable to log.CapacitorFormat()). The alternative makes it much more difficult for users who must programmatically choose which options to pass; such users are forced to change the actual composition of the parameters rather than simply changing the arguments to the options. Don’t assume that all users will know the full set of options statically.

In general, options should be processed in order. If there is a conflict or if a non-cumulative option is passed multiple times, the last argument should win.

The parameter to the option function is generally unexported in this pattern, to restrict the options to being defined only within the package itself. This is a good default, though there may be times when it is appropriate to allow other packages to define options.

See Rob Pike’s original blog post and Dave Cheney’s talk for a more in-depth look at how these options can be used.

Complex command-line interfaces

Some programs wish to present users with a rich command-line interface that includes sub-commands. For example, kubectl create, kubectl run, and many other sub-commands are all provided by the program kubectl. There are at least the following libraries in common use for achieving this.

If you don’t have a preference or other considerations are equal, subcommands is recommended, since it is the simplest and is easy to use correctly. However, if you need different features that it doesn’t provide, pick one of the other options.

• cobra

  • Flag convention: getopt
  • Common outside the Google codebase.
  • Many extra features.
  • Pitfalls in usage (see below).

• subcommands

  • Flag convention: Go
  • Simple and easy to use correctly.
  • Recommended if you don’t need extra features.

Warning: cobra command functions should use cmd.Context() to obtain a context rather than creating their own root context with context.Background. Code that uses the subcommands package already receives the correct context as a function parameter.

You are not required to place each subcommand in a separate package, and it is often not necessary to do so. Apply the same considerations about package boundaries as in any Go codebase. If your code can be used both as a library and as a binary, it is usually beneficial to separate the CLI code and the library, making the CLI just one more of its clients. (This is not specific to CLIs that have subcommands, but is mentioned here because it is a common place where it comes up.)

Tests

Leave testing to the Test function

Go distinguishes between “test helpers” and “assertion helpers”:

• Test helpers are functions that do setup or cleanup tasks. All failures that occur in test helpers are expected to be failures of the environment (not from the code under test) — for example when a test database cannot be started because there are no more free ports on this machine. For functions like these, calling t.Helper is often appropriate to mark them as a test helper. See error handling in test helpers for more details.

• Assertion helpers are functions that check the correctness of a system and fail the test if an expectation is not met. Assertion helpers are not considered idiomatic in Go.

The purpose of a test is to report pass/fail conditions of the code under test. The ideal place to fail a test is within the Test function itself, as that ensures that failure messages and the test logic are clear.

As your testing code grows, it may become necessary to factor out some functionality to separate functions. Standard software engineering considerations still apply, as test code is still code. If the functionality does not interact with the testing framework, then all of the usual rules apply. When the common code interacts with the framework, however, some care must be taken to avoid common pitfalls that can lead to uninformative failure messages and unmaintainable tests.

If many separate test cases require the same validation logic, arrange the test in one of the following ways instead of using assertion helpers or complex validation functions:

• Inline the logic (both the validation and the failure) in the Test function, even if it is repetitive. This works best in simple cases.
• If inputs are similar, consider unifying them into a table-driven test while keeping the logic inlined in the loop. This helps to avoid repetition while keeping the validation and failure in the Test.
• If there are multiple callers who need the same validation function but table tests are not suitable (typically because the inputs are not simple enough or the validation is required as part of a sequence of operations), arrange the validation function so that it returns a value (typically an error) rather than taking a testing.T parameter and using it to fail the test. Use logic within the Test to decide whether to fail, and to provide useful test failures. You can also create test helpers to factor out common boilerplate setup code.

The design outlined in the last point maintains orthogonality. For example, package cmp is not designed to fail tests, but rather to compare (and to diff) values. It therefore does not need to know about the context in which the comparison was made, since the caller can supply that. If your common testing code provides a cmp.Transformer for your data type, that can often be the simplest design. For other validations, consider returning an error value.

// Good:
// polygonCmp returns a cmp.Option that equates s2 geometry objects up to
// some small floating-point error.
func polygonCmp() cmp.Option {
    return cmp.Options{
        cmp.Transformer("polygon", func(p *s2.Polygon) []*s2.Loop { return p.Loops() }),
        cmp.Transformer("loop", func(l *s2.Loop) []s2.Point { return l.Vertices() }),
        cmpopts.EquateApprox(0.00000001, 0),
        cmpopts.EquateEmpty(),
    }
}

func TestFenceposts(t *testing.T) {
    // This is a test for a fictional function, Fenceposts, which draws a fence
    // around some Place object. The details are not important, except that
    // the result is some object that has s2 geometry (github.com/golang/geo/s2)
    got := Fencepost(tomsDiner, 1*meter)
    if diff := cmp.Diff(want, got, polygonCmp()); diff != "" {
        t.Errorf("Fencepost(tomsDiner, 1m) returned unexpected diff (-want+got):\n%v", diff)
    }
}

func FuzzFencepost(f *testing.F) {
    // Fuzz test (https://go.dev/doc/fuzz) for the same.

    f.Add(tomsDiner, 1*meter)
    f.Add(school, 3*meter)

    f.Fuzz(func(t *testing.T, geo Place, padding Length) {
        got := Fencepost(geo, padding)
        // Simple reference implementation: not used in prod, but easy to
        // reason about and therefore useful to check against in random tests.
        reference := slowFencepost(geo, padding)

        // In the fuzz test, inputs and outputs can be large so don't
        // bother with printing a diff. cmp.Equal is enough.
        if !cmp.Equal(got, reference, polygonCmp()) {
            t.Errorf("Fencepost returned wrong placement")
        }
    })
}

The polygonCmp function is agnostic about how it’s called; it doesn’t take a concrete input type nor does it police what to do in case two objects don’t match. Therefore, more callers can make use of it.

Note: There is an analogy between test helpers and plain library code. Code in libraries should usually not panic except in rare circumstances; code called from a test should not stop the test unless there is no point in proceeding.

Designing extensible validation APIs

Most of the advice about testing in the style guide is about testing your own code. This section is about how to provide facilities for other people to test the code they write to ensure that it conforms to your library’s requirements.

Acceptance testing

Such testing is referred to as acceptance testing. The premise of this kind of testing is that the person using the test does not know every last detail of what goes on in the test; they just hand the inputs over to the testing facility to do the work. This can be thought of as a form of inversion of control.

In a typical Go test, the test function controls the program flow, and the no assert and test functions guidance encourages you to keep it that way. This section explains how to author support for these tests in a way that is consistent with Go style.

Before diving into how, consider an example from io/fs, excerpted below:

type FS interface {
    Open(name string) (File, error)
}

While there exist well-known implementations of fs.FS, a Go developer may be expected to author one. To help validate the user-implemented fs.FS is correct, a generic library has been provided in testing/fstest called fstest.TestFS. This API treats the implementation as a blackbox to make sure it upholds the most basic parts of the io/fs contract.

Writing an acceptance test

Now that we know what an acceptance test is and why you might use one, let’s explore building an acceptance test for package chess, a package used to simulate chess games. Users of chess are expected to implement the chess.Player interface. These implementations are the primary thing we will validate. Our acceptance test concerns itself with whether the player implementation makes legal moves, not whether the moves are smart.

1. Create a new package for the validation behavior, customarily named by appending the word test to the package name (for example, chesstest).

2. Create the function that performs the validation by accepting the implementation under test as an argument and exercises it:

   // ExercisePlayer tests a Player implementation in a single turn on a board.
   // The board itself is spot checked for sensibility and correctness.
   //
   // It returns a nil error if the player makes a correct move in the context
   // of the provided board. Otherwise ExercisePlayer returns one of this
   // package's errors to indicate how and why the player failed the
   // validation.
   func ExercisePlayer(b *chess.Board, p chess.Player) error
   

   The test should note which invariants are broken and how. Your design can choose between two disciplines for failure reporting:

   • Fail fast: return an error as soon as the implementation violates an invariant.

     This is the simplest approach, and it works well if the acceptance test is expected to execute quickly. Simple error sentinels and custom types can be used easily here, which conversely makes testing the acceptance test easy.

     for color, army := range b.Armies {
         // The king should never leave the board, because the game ends at
         // checkmate.
         if army.King == nil {
             return &MissingPieceError{Color: color, Piece: chess.King}
         }
     }
     

   • Aggregate all failures: collect all failures, and report them all.

     This approach resembles the keep going guidance in feel and may be preferable if the acceptance test is expected to execute slowly.

     How you aggregate the failures should be dictated by whether you want to give users the ability or yourself the ability to interrogate individual failures (for example, for you to test your acceptance test). Below demonstrates using a custom error type that aggregates errors:

     var badMoves []error
     
     move := p.Move()
     if putsOwnKingIntoCheck(b, move) {
         badMoves = append(badMoves, PutsSelfIntoCheckError{Move: move})
     }
     
     if len(badMoves) > 0 {
         return SimulationError{BadMoves: badMoves}
     }
     return nil
     

The acceptance test should honor the keep going guidance by not calling t.Fatal unless the test detects a broken invariant in the system being exercised.

For example, t.Fatal should be reserved for exceptional cases such as setup failure as usual:

func ExerciseGame(t *testing.T, cfg *Config, p chess.Player) error {
    t.Helper()

    if cfg.Simulation == Modem {
        conn, err := modempool.Allocate()
        if err != nil {
            t.Fatalf("No modem for the opponent could be provisioned: %v", err)
        }
        t.Cleanup(func() { modempool.Return(conn) })
    }
    // Run acceptance test (a whole game).
}

This technique can help you create concise, canonical validations. But do not attempt to use it to bypass the guidance on assertions.

The final product should be in a form similar to this for end users:

// Good:
package deepblue_test

import (
    "chesstest"
    "deepblue"
)

func TestAcceptance(t *testing.T) {
    player := deepblue.New()
    err := chesstest.ExerciseGame(t, chesstest.SimpleGame, player)
    if err != nil {
        t.Errorf("Deep Blue player failed acceptance test: %v", err)
    }
}

Use real transports

When testing component integrations, especially where HTTP or RPC are used as the underlying transport between the components, prefer using the real underlying transport to connect to the test version of the backend.

For example, suppose the code you want to test (sometimes referred to as “system under test” or SUT) interacts with a backend that implements the long running operations API. To test your SUT, use a real OperationsClient that is connected to a test double (e.g., a mock, stub, or fake) of the OperationsServer.

This is recommended over hand-implementing the client, due to the complexity of imitating client behavior correctly. By using the production client with a test-specific server, you ensure your test is using as much of the real code as possible.

Tip: Where possible, use a testing library provided by the authors of the service under test.

t.Error vs. t.Fatal

As discussed in decisions, tests should generally not abort at the first encountered problem.

However, some situations require that the test not proceed. Calling t.Fatal is appropriate when some piece of test setup fails, especially in test setup helpers, without which you cannot run the rest of the test. In a table-driven test, t.Fatal is appropriate for failures that set up the whole test function before the test loop. Failures that affect a single entry in the test table, which make it impossible to continue with that entry, should be reported as follows:

• If you’re not using t.Run subtests, use t.Error followed by a continue statement to move on to the next table entry.
• If you’re using subtests (and you’re inside a call to t.Run), use t.Fatal, which ends the current subtest and allows your test case to progress to the next subtest.

Warning: It is not always safe to call t.Fatal and similar functions. More details here.

Error handling in test helpers

Note: This section discusses test helpers in the sense Go uses the term: functions that perform test setup and cleanup, not common assertion facilities. See the test functions section for more discussion.

Operations performed by a test helper sometimes fail. For example, setting up a directory with files involves I/O, which can fail. When test helpers fail, their failure often signifies that the test cannot continue, since a setup precondition failed. When this happens, prefer calling one of the Fatal functions in the helper:

// Good:
func mustAddGameAssets(t *testing.T, dir string) {
    t.Helper()
    if err := os.WriteFile(path.Join(dir, "pak0.pak"), pak0, 0644); err != nil {
        t.Fatalf("Setup failed: could not write pak0 asset: %v", err)
    }
    if err := os.WriteFile(path.Join(dir, "pak1.pak"), pak1, 0644); err != nil {
        t.Fatalf("Setup failed: could not write pak1 asset: %v", err)
    }
}

This keeps the calling side cleaner than if the helper were to return the error to the test itself:

// Bad:
func addGameAssets(t *testing.T, dir string) error {
    t.Helper()
    if err := os.WriteFile(path.Join(d, "pak0.pak"), pak0, 0644); err != nil {
        return err
    }
    if err := os.WriteFile(path.Join(d, "pak1.pak"), pak1, 0644); err != nil {
        return err
    }
    return nil
}

Warning: It is not always safe to call t.Fatal and similar functions. More details here.

The failure message should include a description of what happened. This is important, as you may be providing a testing API to many users, especially as the number of error-producing steps in the helper increases. When the test fails, the user should know where, and why.

Tip: Go 1.14 introduced a t.Cleanup function that can be used to register cleanup functions that run when your test completes. The function also works with test helpers. See GoTip #4: Cleaning Up Your Tests for guidance on simplifying test helpers.

The snippet below in a fictional file called paint_test.go demonstrates how (*testing.T).Helper influences failure reporting in a Go test:

package paint_test

import (
    "fmt"
    "testing"
)

func paint(color string) error {
    return fmt.Errorf("no %q paint today", color)
}

func badSetup(t *testing.T) {
    // This should call t.Helper, but doesn't.
    if err := paint("taupe"); err != nil {
        t.Fatalf("Could not paint the house under test: %v", err) // line 15
    }
}

func mustGoodSetup(t *testing.T) {
    t.Helper()
    if err := paint("lilac"); err != nil {
        t.Fatalf("Could not paint the house under test: %v", err)
    }
}

func TestBad(t *testing.T) {
    badSetup(t)
    // ...
}

func TestGood(t *testing.T) {
    mustGoodSetup(t) // line 32
    // ...
}

Here is an example of this output when run. Note the highlighted text and how it differs:

=== RUN   TestBad
    paint_test.go:15: Could not paint the house under test: no "taupe" paint today
--- FAIL: TestBad (0.00s)
=== RUN   TestGood
    paint_test.go:32: Could not paint the house under test: no "lilac" paint today
--- FAIL: TestGood (0.00s)
FAIL

The error with paint_test.go:15 refers to the line of the setup function that failed in badSetup:

t.Fatalf("Could not paint the house under test: %v", err)

Whereas paint_test.go:32 refers to the line of the test that failed in TestGood:

goodSetup(t)

Correctly using (*testing.T).Helper attributes the location of the failure much better when:

• the helper functions grow
• the helper functions call other helpers
• the amount of helper usage in the test functions grow

Tip: If a helper calls (*testing.T).Error or (*testing.T).Fatal, provide some context in the format string to help determine what went wrong and why.

Tip: If nothing a helper does can cause a test to fail, it doesn’t need to call t.Helper. Simplify its signature by removing t from the function parameter list.

Don’t call t.Fatal from separate goroutines

As documented in package testing, it is incorrect to call t.FailNow, t.Fatal, etc. from any goroutine but the one running the Test function (or the subtest). If your test starts new goroutines, they must not call these functions from inside these goroutines.

Test helpers usually don’t signal failure from new goroutines, and therefore it is all right for them to use t.Fatal. If in doubt, call t.Error and return instead.

// Good:
func TestRevEngine(t *testing.T) {
    engine, err := Start()
    if err != nil {
        t.Fatalf("Engine failed to start: %v", err)
    }

    num := 11
    var wg sync.WaitGroup
    wg.Add(num)
    for i := 0; i < num; i++ {
        go func() {
            defer wg.Done()
            if err := engine.Vroom(); err != nil {
                // This cannot be t.Fatalf.
                t.Errorf("No vroom left on engine: %v", err)
                return
            }
            if rpm := engine.Tachometer(); rpm > 1e6 {
                t.Errorf("Inconceivable engine rate: %d", rpm)
            }
        }()
    }
    wg.Wait()

    if seen := engine.NumVrooms(); seen != num {
        t.Errorf("engine.NumVrooms() = %d, want %d", seen, num)
    }
}

Adding t.Parallel to a test or subtest does not make it unsafe to call t.Fatal.

When all calls to the testing API are in the test function, it is usually easy to spot incorrect usage because the go keyword is plain to see. Passing testing.T arguments around makes tracking such usage harder. Typically, the reason for passing these arguments is to introduce a test helper, and those should not depend on the system under test. Therefore, if a test helper registers a fatal test failure, it can and should do so from the test’s goroutine.

Use field names in struct literals

In table-driven tests, prefer to specify field names when initializing test case struct literals. This is helpful when the test cases cover a large amount of vertical space (e.g. more than 20-30 lines), when there are adjacent fields with the same type, and also when you wish to omit fields which have the zero value. For example:

// Good:
func TestStrJoin(t *testing.T) {
    tests := []struct {
        slice     []string
        separator string
        skipEmpty bool
        want      string
    }{
        {
            slice:     []string{"a", "b", ""},
            separator: ",",
            want:      "a,b,",
        },
        {
            slice:     []string{"a", "b", ""},
            separator: ",",
            skipEmpty: true,
            want:      "a,b",
        },
        // ...
    }
    // ...
}

Keep setup code scoped to specific tests

Where possible, setup of resources and dependencies should be as closely scoped to specific test cases as possible. For example, given a setup function:

// mustLoadDataSet loads a data set for the tests.
//
// This example is very simple and easy to read. Often realistic setup is more
// complex, error-prone, and potentially slow.
func mustLoadDataset(t *testing.T) []byte {
    t.Helper()
    data, err := os.ReadFile("path/to/your/project/testdata/dataset")

    if err != nil {
        t.Fatalf("Could not load dataset: %v", err)
    }
    return data
}

Call mustLoadDataset explicitly in test functions that need it:

// Good:
func TestParseData(t *testing.T) {
    data := mustLoadDataset(t)
    parsed, err := ParseData(data)
    if err != nil {
        t.Fatalf("Unexpected error parsing data: %v", err)
    }
    want := &DataTable{ /* ... */ }
    if got := parsed; !cmp.Equal(got, want) {
        t.Errorf("ParseData(data) = %v, want %v", got, want)
    }
}

func TestListContents(t *testing.T) {
    data := mustLoadDataset(t)
    contents, err := ListContents(data)
    if err != nil {
        t.Fatalf("Unexpected error listing contents: %v", err)
    }
    want := []string{ /* ... */ }
    if got := contents; !cmp.Equal(got, want) {
        t.Errorf("ListContents(data) = %v, want %v", got, want)
    }
}

func TestRegression682831(t *testing.T) {
    if got, want := guessOS("zpc79.example.com"), "grhat"; got != want {
        t.Errorf(`guessOS("zpc79.example.com") = %q, want %q`, got, want)
    }
}

The test function TestRegression682831 does not use the data set and therefore does not call mustLoadDataset, which could be slow and failure-prone:

// Bad:
var dataset []byte

func TestParseData(t *testing.T) {
    // As documented above without calling mustLoadDataset directly.
}

func TestListContents(t *testing.T) {
    // As documented above without calling mustLoadDataset directly.
}

func TestRegression682831(t *testing.T) {
    if got, want := guessOS("zpc79.example.com"), "grhat"; got != want {
        t.Errorf(`guessOS("zpc79.example.com") = %q, want %q`, got, want)
    }
}

func init() {
    dataset = mustLoadDataset()
}

A user may wish to run a function in isolation of the others and should not be penalized by these factors:

# No reason for this to perform the expensive initialization.
$ go test -run TestRegression682831

When to use a custom TestMain entrypoint

If all tests in the package require common setup and the setup requires teardown, you can use a custom testmain entrypoint. This can happen if the resource the test cases require is especially expensive to setup, and the cost should be amortized. Typically you have extracted any unrelated tests from the test suite at that point. It is typically only used for functional tests.

Using a custom TestMain should not be your first choice due the amount of care that should be taken for correct use. Consider first whether the solution in the amortizing common test setup section or an ordinary test helper is sufficient for your needs.

// Good:
var db *sql.DB

func TestInsert(t *testing.T) { /* omitted */ }

func TestSelect(t *testing.T) { /* omitted */ }

func TestUpdate(t *testing.T) { /* omitted */ }

func TestDelete(t *testing.T) { /* omitted */ }

// runMain sets up the test dependencies and eventually executes the tests.
// It is defined as a separate function to enable the setup stages to clearly
// defer their teardown steps.
func runMain(ctx context.Context, m *testing.M) (code int, err error) {
    ctx, cancel := context.WithCancel(ctx)
    defer cancel()

    d, err := setupDatabase(ctx)
    if err != nil {
        return 0, err
    }
    defer d.Close() // Expressly clean up database.
    db = d          // db is defined as a package-level variable.

    // m.Run() executes the regular, user-defined test functions.
    // Any defer statements that have been made will be run after m.Run()
    // completes.
    return m.Run(), nil
}

func TestMain(m *testing.M) {
    code, err := runMain(context.Background(), m)
    if err != nil {
        // Failure messages should be written to STDERR, which log.Fatal uses.
        log.Fatal(err)
    }
    // NOTE: defer statements do not run past here due to os.Exit
    //       terminating the process.
    os.Exit(code)
}

Ideally a test case is hermetic between invocations of itself and between other test cases.

At the very least, ensure that individual test cases reset any global state they have modified if they have done so (for instance, if the tests are working with an external database).

Amortizing common test setup

Using a sync.Once may be appropriate, though not required, if all of the following are true about the common setup:

• It is expensive.
• It only applies to some tests.
• It does not require teardown.

// Good:
var dataset struct {
    once sync.Once
    data []byte
    err  error
}

func mustLoadDataset(t *testing.T) []byte {
    t.Helper()
    dataset.once.Do(func() {
        data, err := os.ReadFile("path/to/your/project/testdata/dataset")
        // dataset is defined as a package-level variable.
        dataset.data = data
        dataset.err = err
    })
    if err := dataset.err; err != nil {
        t.Fatalf("Could not load dataset: %v", err)
    }
    return dataset.data
}

When mustLoadDataset is used in multiple test functions, its cost is amortized:

// Good:
func TestParseData(t *testing.T) {
    data := mustLoadDataset(t)

    // As documented above.
}

func TestListContents(t *testing.T) {
    data := mustLoadDataset(t)

    // As documented above.
}

func TestRegression682831(t *testing.T) {
    if got, want := guessOS("zpc79.example.com"), "grhat"; got != want {
        t.Errorf(`guessOS("zpc79.example.com") = %q, want %q`, got, want)
    }
}

The reason that common teardown is tricky is there is no uniform place to register cleanup routines. If the setup function (in this case loadDataset) relies on a context, sync.Once may be problematic. This is because the second of two racing calls to the setup function would need to wait for the first call to finish before returning. This period of waiting cannot be easily made to respect the context’s cancellation.

String concatenation

There are several ways to concatenate strings in Go. Some examples include:

• The “+” operator
• fmt.Sprintf
• strings.Builder
• text/template
• safehtml/template

Though there is no one-size-fits-all rule for which to choose, the following guidance outlines when each method is preferred.

Prefer “+” for simple cases

Prefer using “+” when concatenating few strings. This method is syntactically the simplest and requires no import.

// Good:
key := "projectid: " + p

Prefer fmt.Sprintf when formatting

Prefer using fmt.Sprintf when building a complex string with formatting. Using many “+” operators may obscure the end result.

// Good:
str := fmt.Sprintf("%s [%s:%d]-> %s", src, qos, mtu, dst)

// Bad:
bad := src.String() + " [" + qos.String() + ":" + strconv.Itoa(mtu) + "]-> " + dst.String()

Best Practice: When the output of the string-building operation is an io.Writer, don’t construct a temporary string with fmt.Sprintf just to send it to the Writer. Instead, use fmt.Fprintf to emit to the Writer directly.

When the formatting is even more complex, prefer text/template or safehtml/template as appropriate.

Prefer strings.Builder for constructing a string piecemeal

Prefer using strings.Builder when building a string bit-by-bit. strings.Builder takes amortized linear time, whereas “+” and fmt.Sprintf take quadratic time when called sequentially to form a larger string.

// Good:
b := new(strings.Builder)
for i, d := range digitsOfPi {
    fmt.Fprintf(b, "the %d digit of pi is: %d\n", i, d)
}
str := b.String()

Note: For more discussion, see GoTip #29: Building Strings Efficiently.

Constant strings

Prefer to use backticks (`) when constructing constant, multi-line string literals.

// Good:
usage := `Usage:

custom_tool [args]`

// Bad:
usage := "" +
  "Usage:\n" +
  "\n" +
  "custom_tool [args]"

Global state

Libraries should not force their clients to use APIs that rely on global state. They are advised not to expose APIs or export package level variables that control behavior for all clients as parts of their API. The rest of the section uses “global” and “package level state” synonymously.

Instead, if your functionality maintains state, allow your clients to create and use instance values.

Important: While this guidance is applicable to all developers, it is most critical for infrastructure providers who offer libraries, integrations, and services to other teams.

// Good:
// Package sidecar manages subprocesses that provide features for applications.
package sidecar

type Registry struct { plugins map[string]*Plugin }

func New() *Registry { return &Registry{plugins: make(map[string]*Plugin)} }

func (r *Registry) Register(name string, p *Plugin) error { ... }

Your users will instantiate the data they need (a *sidecar.Registry) and then pass it as an explicit dependency:

// Good:
package main

func main() {
  sidecars := sidecar.New()
  if err := sidecars.Register("Cloud Logger", cloudlogger.New()); err != nil {
    log.Exitf("could not setup cloud logger: %v", err)
  }
  cfg := &myapp.Config{Sidecars: sidecars}
  myapp.Run(context.Background(), cfg)
}

There are different approaches to migrating existing code to support dependency passing. The main one you will use is passing dependencies as parameters to constructors, functions, methods, or struct fields on the call chain.

See also:

• Go Tip #5: Slimming Your Client Libraries
• Go Tip #24: Use Case-Specific Constructions
• Go Tip #40: Improving Time Testability with Function Parameters
• Go Tip #41: Identify Function Call Parameters
• Go Tip #44: Improving Time Testability with Struct Fields
• Go Tip #80: Dependency Injection Principles

APIs that do not support explicit dependency passing become fragile as the number of clients increases:

// Bad:
package sidecar

var registry = make(map[string]*Plugin)

func Register(name string, p *Plugin) error { /* registers plugin in registry */ }

Consider what happens in the case of tests exercising code that transitively relies on a sidecar for cloud logging.

// Bad:
package app

import (
  "cloudlogger"
  "sidecar"
  "testing"
)

func TestEndToEnd(t *testing.T) {
  // The system under test (SUT) relies on a sidecar for a production cloud
  // logger already being registered.
  ... // Exercise SUT and check invariants.
}

func TestRegression_NetworkUnavailability(t *testing.T) {
  // We had an outage because of a network partition that rendered the cloud
  // logger inoperative, so we added a regression test to exercise the SUT with
  // a test double that simulates network unavailability with the logger.
  sidecar.Register("cloudlogger", cloudloggertest.UnavailableLogger)
  ... // Exercise SUT and check invariants.
}

func TestRegression_InvalidUser(t *testing.T) {
  // The system under test (SUT) relies on a sidecar for a production cloud
  // logger already being registered.
  //
  // Oops. cloudloggertest.UnavailableLogger is still registered from the
  // previous test.
  ... // Exercise SUT and check invariants.
}

Go tests are executed sequentially by default, so the tests above run as:

1. TestEndToEnd
2. TestRegression_NetworkUnavailability, which overrides the default value of cloudlogger
3. TestRegression_InvalidUser, which requires the default value of cloudlogger registered in package sidecar

This creates an order-dependent test case, which breaks running with test filters, and prevents tests from running in parallel or being sharded.

Using global state poses problems that lack easy answers for you and the API’s clients:

• What happens if a client needs to use different and separately operating sets of Plugins (for example, to support multiple servers) in the same process space?

• What happens if a client wants to replace a registered Plugin with an alternative implementation in a test, like a test double?

  What happens if a client’s tests require hermeticity between instances of a Plugin, or between all of the plugins registered?

• What happens if multiple clients Register a Plugin under the same name? Which one wins, if any?

  How should errors be handled? If the code panics or calls log.Fatal, will that always be appropriate for all places in which API would be called? Can a client verify it doesn’t do something bad before doing so?

• Are there certain stages in a program’s startup phases or lifetime during which Register can be called and when it can’t?

  What happens if Register is called at the wrong time? A client could call Register in func init, before flags are parsed, or after main. The stage at which a function is called affects error handling. If the author of an API assumes the API is only called during program initialization without the requirement that it is, the assumption may nudge the author to design error handling to abort the program by modeling the API as a Must-like function. Aborting is not appropriate for general-purpose library functions that can be used at any stage.

• What if the client’s and the designer’s concurrency needs are mismatched?

See also:

• Go Tip #36: Enclosing Package-Level State
• Go Tip #71: Reducing Parallel Test Flakiness
• Go Tip #80: Dependency Injection Principles
• Error Handling: Look Before You Leap versus Easier to Ask for Forgiveness than Permission
• Unit Testing Practices on Public APIs

Global state has cascading effects on the health of the Google codebase. Global state should be approached with extreme scrutiny.

Global state comes in several forms, and you can use a few litmus tests to identify when it is safe.

Major forms of package state APIs

Several of the most common problematic API forms are enumerated below:

• Top-level variables irrespective of whether they are exported.

  // Bad:
  package logger
  
  // Sinks manages the default output sources for this package's logging API.  This
  // variable should be set at package initialization time and never thereafter.
  var Sinks []Sink
  

  See the litmus tests to know when these are safe.

• The service locator pattern. See the first example. The service locator pattern itself is not problematic, rather the locator being defined as global.

• Registries for callbacks and similar behaviors.

  // Bad:
  package health
  
  var unhealthyFuncs []func
  
  func OnUnhealthy(f func()) {
    unhealthyFuncs = append(unhealthyFuncs, f)
  }
  

• Thick-Client singletons for things like backends, storage, data access layers, and other system resources. These often pose additional problems with service reliability.

  // Bad:
  package useradmin
  
  var client pb.UserAdminServiceClientInterface
  
  func Client() *pb.UserAdminServiceClient {
      if client == nil {
          client = ...  // Set up client.
      }
      return client
  }
  

Note: Many legacy APIs in the Google codebase do not follow this guidance; in fact, some Go standard libraries allow for configuration via global values. Nevertheless, the legacy API’s contravention of this guidance should not be used as precedent for continuing the pattern.

It is better to invest in proper API design today than pay for redesigning later.

Litmus tests

APIs using the patterns above are unsafe when:

• Multiple functions interact via global state when executed in the same program, despite being otherwise independent (for example, authored by different authors in vastly different directories).
• Independent test cases interact with each other through global state.
• Users of the API are tempted to swap or replace global state for testing purposes, particularly to replace any part of the state with a test double, like a stub, fake, spy, or mock.
• Users have to consider special ordering requirements when interacting with global state: func init, whether flags are parsed yet, etc.

Provided the conditions above are avoided, there are a few limited circumstances under which these APIs are safe, namely when any of the following is true:

• The global state is logically constant (example).
• The package’s observable behavior is stateless. For example, a public function may use a private global variable as a cache, but so long as the caller can’t distinguish cache hits from misses, the function is stateless.
• The global state does not bleed into things that are external to the program, like sidecar processes or files on a shared filesystem.
• There is no expectation of predictable behavior (example).

Note: Sidecar processes may not strictly be process-local. They can and often are shared with more than one application process. Moreover, these sidecars often interact with external distributed systems.

Further, the same stateless, idempotent, and local rules in addition to the base considerations above would apply to the code of the sidecar process itself!

An example of one of these safe situations is package image with its image.RegisterFormat function. Consider the litmus tests from above applied to a typical decoder, like the one for handling the PNG format:

• Multiple calls to package image’s APIs that use the registered decoders (for example, image.Decode) cannot interfere with one another, similarly for tests. The only exception is image.RegisterFormat, but that is mitigated by the points below.
• It is extremely unlikely that a user would want to replace a decoder with a test double, as the PNG decoder exemplifies a case in which our codebase’s preference for real objects applies. However, a user would be more likely to replace a decoder with a test double if the decoder statefully interacted with operating system resources (for example, the network).
• Collisions in registration are conceivable, though they are probably rare in practice.
• The decoders are stateless, idempotent, and pure.

Providing a default instance

While not recommended, it is acceptable to provide a simplified API that uses package level state if you need to maximize convenience for the user.

Follow the litmus tests with these guidelines in such cases:

1. The package must offer clients the ability to create isolated instances of package types as described above.
2. The public APIs that use global state must be a thin proxy to the previous API. A good example of this is http.Handle internally calling (*http.ServeMux).Handle on the package variable http.DefaultServeMux.

3. This package-level API must only be used by binary build targets, not libraries, unless the libraries are undertaking a refactoring to support dependency passing. Infrastructure libraries that can be imported by other packages must not rely on package-level state of the packages they import.

   For example, an infrastructure provider implementing a sidecar that is to be shared with other teams using the API from the top should offer an API to accommodate this:

   // Good:
   package cloudlogger
   
   func New() *Logger { ... }
   
   func Register(r *sidecar.Registry, l *Logger) {
     r.Register("Cloud Logging", l)
   }
   

4. This package-level API must document and enforce its invariants (for example, at which stage in the program’s life it can be called, whether it can be used concurrently). Further, it must provide an API to reset global state to a known-good default (for example, to facilitate testing).

See also:

• Go Tip #36: Enclosing Package-Level State
• Go Tip #80: Dependency Injection Principles

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