Golang

Go Wiki: Home - The Go Programming Language

The Go programming language, also known as Golang, has gained popularity among software practitioners since its release in 2009. This language offers various features and advantages that make it a preferred choice for many developers. Golang is an awesome language, but it has sanctioned our country. It is similar to C programming language.

We can use it for writing code as terminal applications or servers. I had experience in using it for writing ReST API and GraphQL servers.

Multiple Errors

Go 1.20 expands support for error wrapping to permit an error to wrap multiple other errors:

An error e can wrap more than one error by providing an Unwrap method that returns a []error.
The errors.Is and errors.As functions have been updated to inspect multiply wrapped errors.
The fmt.Errorf function now supports multiple occurrences of the %w format verb, which will cause it to return an error that wraps all of those error operands.
The new function errors.Join returns an error wrapping a list of errors.

`func As(err error, target any) bool`

As finds the first error in err’s tree that matches target, and if one is found, sets target to that error value and returns true. Otherwise, it returns false.

The tree consists of err itself, followed by the errors obtained by repeatedly calling its Unwrap() error or Unwrap() []error method. When err wraps multiple errors, As examines err followed by a depth-first traversal of its children.

Enum

type Weekday int {
    Sunday Weekday = iota
    Monday
    Tuesday
    Wednesday
    Thursday
    Friday
    Saturday
}

In this example, we’re defining a new type named Weekday which is based on the int type. We then define constants for each day of the week. The iota keyword is a special constant that is used in conjunction with the const keyword. It starts at zero and increments by one for each constant defined, so Sunday will be 0, Monday will be 1, and so on.

You can then use these values like so:

var today Weekday = Monday
fmt.Println(today)  // prints "1"

This approach provides some of the benefits of enums, such as type safety and readable code, but it’s not a perfect replacement. For example, Go doesn’t prevent you from assigning any int value to a Weekday variable, whereas true enums in other languages often restrict you to a specific set of values.

go-enum

go-enum will take a commented type declaration like this:

// ENUM(jpeg, jpg, png, tiff, gif)
type ImageType int

and generate a file with the iota definition along various optional niceties that you may need:

const (
 // ImageTypeJpeg is a ImageType of type Jpeg.
 ImageTypeJpeg ImageType = iota
 // ImageTypeJpg is a ImageType of type Jpg.
 ImageTypeJpg
 // ImageTypePng is a ImageType of type Png.
 ImageTypePng
 // ImageTypeTiff is a ImageType of type Tiff.
 ImageTypeTiff
 // ImageTypeGif is a ImageType of type Gif.
 ImageTypeGif
)

// String implements the Stringer interface.
func (x ImageType) String() string

// ParseImageType attempts to convert a string to a ImageType.
func ParseImageType(name string) (ImageType, error)

// MarshalText implements the text marshaller method.
func (x ImageType) MarshalText() ([]byte, error)

// UnmarshalText implements the text unmarshaller method.
func (x *ImageType) UnmarshalText(text []byte) error

Nil Away

NilAway is a static analysis tool that seeks to help developers avoid nil panics in production by catching them at compile time rather than runtime. NilAway is similar to the standard nilness analyzer, however, it employs much more sophisticated and powerful static analysis techniques to track nil flows within a package as well across packages, and report errors providing users with the nilness flows for easier debugging.

https://github.com/uber-go/nilaway

`comparable`

type comparable interface{}

comparable is an interface that is implemented by all comparable types (booleans, numbers, strings, pointers, channels, arrays of comparable types, structs whose fields are all comparable types). The comparable interface may only be used as a type parameter constraint, not as the type of variable.

`cmp` package

`cmp.Or`

Or returns the first of its arguments that is not equal to the zero value. If no argument is non-zero, it returns the zero value.

func Or[T comparable](vals ...T) T

// Suppose we have some user input
// that may or may not be an empty string
userInput1 := ""
userInput2 := "some text"

fmt.Println(cmp.Or(userInput1, "default"))
fmt.Println(cmp.Or(userInput2, "default"))
fmt.Println(cmp.Or(userInput1, userInput2, "default"))

`cmp.Ordered`

Ordered is a constraint that permits any ordered type: any type that supports the operators < <= >= >. If future releases of Go add new ordered types, this constraint will be modified to include them.

`slices` package

Package slices defines various functions useful with slices of any type.

`slices.Contains`

Contains reports whether v is present in s.

func Contains[S ~[]E, E comparable](s S, v E) bool

`maps` package

Package maps defines various functions useful with maps of any type.

`maps.Equal`

Equal reports whether two maps contain the same key/value pairs. Values are compared using ==.

func Equal[M1, M2 ~map[K]V, K, V comparable](m1 M1, m2 M2) bool

`log/slog` package

Package slog provides structured logging, in which log records include a message, a severity level, and various other attributes expressed as key-value pairs.

It defines a type, Logger, which provides several methods (such as Logger.Info and Logger.Error) for reporting events of interest.

`intrange`

From Go 1.22 (expected release February 2024), you will be able to write:

for i := range 10 {
    fmt.Println(i+1)
}

(ranging over an integer in Go iterates from 0 to one less than that integer).

For versions of Go before 1.22, the idiomatic approach is to write a for loop like this.

for i := 1; i <= 10; i++ {
    fmt.Println(i)
}

Modules

In Go 1.16, module-aware commands report an error after discovering a problem in go.mod or go.sum instead of attempting to fix the problem automatically. In most cases, the error message recommends a command to fix the problem.

`gotip`

One useful tool that comes with Golang is gotip, which allows you to test upcoming changes and experimental features.

go install golang.org/dl/gotip@latest
gotip download

After installation, use the gotip command instead of your normal go command to have latest experimental features. To update, run gotip download again. This will always download the main branch. To download an alternative branch, run gotip download BRANCH and to download a specific CL, run gotip download NUMBER.

`libc`

You can control to use cgo with CGO_ENABLED flag in go build. We have different implementation of C standard library.

Testing with `testify`

To test with go, @stretchr/testify is an awesome library. It has suite, require and assert.

// Define the suite, and absorb the built-in basic suite
// functionality from testify - including a T() method which
// returns the current testing context
type ExampleTestSuite struct {
    suite.Suite
    VariableThatShouldStartAtFive int
}

// Make sure that VariableThatShouldStartAtFive is set to five
// before each test
func (suite *ExampleTestSuite) SetupTest() {
    suite.VariableThatShouldStartAtFive = 5
}

// All methods that begin with "Test" are run as tests within a
// suite.
func (suite *ExampleTestSuite) TestExample() {
    assert.Equal(suite.T(), 5, suite.VariableThatShouldStartAtFive)
    suite.Equal(5, suite.VariableThatShouldStartAtFive)
}

// In order for 'go test' to run this suite, we need to create
// a normal test function and pass our suite to suite.Run
func TestExampleTestSuite(t *testing.T) {
    suite.Run(t, new(ExampleTestSuite))
}

// SetupAllSuite has a SetupSuite method, which will run before the
// tests in the suite are run.
type SetupAllSuite interface {
 SetupSuite()
}

// SetupTestSuite has a SetupTest method, which will run before each
// test in the suite.
type SetupTestSuite interface {
 SetupTest()
}

// TearDownAllSuite has a TearDownSuite method, which will run after
// all the tests in the suite have been run.
type TearDownAllSuite interface {
 TearDownSuite()
}

// TearDownTestSuite has a TearDownTest method, which will run after
// each test in the suite.
type TearDownTestSuite interface {
 TearDownTest()
}

// BeforeTest has a function to be executed right before the test
// starts and receives the suite and test names as input
type BeforeTest interface {
 BeforeTest(suiteName, testName string)
}

// AfterTest has a function to be executed right after the test
// finishes and receives the suite and test names as input
type AfterTest interface {
 AfterTest(suiteName, testName string)
}

// WithStats implements HandleStats, a function that will be executed
// when a test suite is finished. The stats contain information about
// the execution of that suite and its tests.
type WithStats interface {
 HandleStats(suiteName string, stats *SuiteInformation)
}

Writing tests with testify is awesome, so use them. Also, I write tests with mock for the higher modules and tests the low level one with the real dependencies. For application that has really great mocks like redis, I have used them instead of real one.

Logging

As I said there are mainly two types of applications developed by Golang. In the terminal applications, it is better to use pterm to show the logs and other information instead of simply printing them. In case of the server applications, zap is a better choice because it has structure logging, and you can also write the logs on console and syslog at the same time.

Structure logging increase the search efficiency for when you want to search among your logs on your log aggregation system.

Observability

Observability lets us understand a system from the outside, by letting us ask questions about that system without knowing its inner workings.

Telemetry refers to data emitted from a system, about its behavior. The data can come in the form of traces, metrics, and logs.

Instrumentation is the act of adding observability code to an app yourself.

SLI, or Service Level Indicator, represents a measurement of a service’s behavior. SLO, or Service Level Objective, is the means by which reliability is communicated to an organization/other teams.

I want to use a single library for all the telemetries, but until that day we need to use different libraries for each of them.

For tracing and metrics:

@open-telemetry/opentelemetry-go

For logging:

@uber-go/zap

For metrics:

@prometheus/client_golang

Span

A span represents a unit of work or operation. It tracks specific operation that a request makes, painting a picture of what happen during the time in which that operation was executed.

An event is a human-readable message on a span that represents “something happening” during its lifetime.

A status can be set on a span, typically used to specify that a span has not completed successfully. If you have an operation that failed, and you wish to capture the error it produced, you can record that error.

Distributed Traces

A distributed trace, more commonly known as a trace, records the paths taken by requests (made by application or end-user) as they propagate through multiservice architectures, like microservice and serverless applications.

Traces can extend beyond a single process. This requires context propagation, a mechanism where identifiers for a trace are sent to remote process.

OpenTelemetry

OpenTelemetry is an Observability framework and toolkit designed to create and manage telemetry data such as traces, metrics and logs. A major goal of OpenTelemetry is that you can easily instrument your applications or systems, no matter their language, infrastructure, or runtime environment.

The current status of the major functional components for OpenTelemetry Go is as follows:

Tracing	Metrics	Logging
Stable	Stable	In development

OpenTelemetry Go API and SDK

Exporter

The SDK requires an exporter to be created. Exporters are packages that allow telemetry data to be emitted somewhere - either to the console (which is what we’re doing here), or to a remote system or collector for further analysis and/or enrichment. OpenTelemetry supports a variety of exporters through its ecosystem including popular open source tools like Jaeger, Zipkin, and Prometheus.

import stdout "go.opentelemetry.io/otel/exporters/stdout/stdouttrace"

func main() {
 exporter, err := stdout.New(
  stdout.WithPrettyPrint(),
 )
 if err != nil {
  log.Fatalf("failed to initialize stdout export pipeline: %v", err)
 }
}

Exporter Package	Metrics	Traces
go.opentelemetry.io/otel/exporters/otlp/otlpmetric/otlpmetricgrpc	✓
go.opentelemetry.io/otel/exporters/otlp/otlpmetric/otlpmetrichttp	✓
go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc		✓
go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracehttp		✓
go.opentelemetry.io/otel/exporters/prometheus	✓
go.opentelemetry.io/otel/exporters/stdout/stdoutmetric	✓
go.opentelemetry.io/otel/exporters/stdout/stdouttrace		✓
go.opentelemetry.io/otel/exporters/zipkin		✓

Tracer Provider

This block of code will create a new batch span processor, a type of span processor that batches up multiple spans over a period of time, that writes to the exporter we created in the previous step.

ctx := context.Background()

bsp := sdktrace.NewBatchSpanProcessor(exporter)
s := sdktrace.AlwaysSampler()
tp := sdktrace.NewTracerProvider(sdktrace.WithSpanProcessor(bsp), sdktrace.WithSampler(s))

// Handle this error in a sensible manner where possible
defer func() { _ = tp.Shutdown(ctx) }()

OpenTelemetry requires a trace provider to be initialized in order to generate traces. A trace provider can have multiple span processors, which are components that allow for span data to be modified or exported after it’s created.

Metric Provider

To start producing metrics, you’ll need to have an initialized MetricProvider that lets you create a Meter. Meters let you create instruments that you can use to create different kinds of metrics. OpenTelemetry Go currently supports the following instruments:

Counter, a synchronous instrument that supports non-negative increments.
Asynchronous Counter, an asynchronous instrument which supports non-negative increments
Histogram, a synchronous instrument that supports arbitrary values that are statistically meaningful, such as histograms summaries, or percentile
Asynchronous Gauge, an asynchronous instrument that supports increments and decrements, such as the number of active requests
UpDownCounter, a synchronous instrument that supports increments and decrements, such as the number of active requests
Asynchronous UpDown Counter, an asynchronous instrument that supports increments and decrements

Sampler

Sampling is a process that restricts the amount of traces that are generated by a system.

Resource

Resource holds information about the entities for which telemetry is exported.

res, err := resource.Merge(
    resource.Default(),
    resource.NewSchemaless(
        semconv.ServiceNamespaceKey.String(cfg.Namespace),
        semconv.ServiceNameKey.String(cfg.ServiceName),
    ),
)
if err != nil {
    panic(err)
}

Quick Start

First, we’re asking the global trace provider for an instance of a tracer, which is the object that manages spans for our service.

tracer := otel.Tracer("ex.com/basic")
ctx = baggage.ContextWithValues(ctx,
    fooKey.String("foo1"),
    barKey.String("bar1"),
)

func(ctx context.Context) {
    var span trace.Span
    ctx, span = tracer.Start(ctx, "operation")
    defer span.End()

    span.AddEvent("Nice operation!", trace.WithAttributes(attribute.Int("bogons", 100)))
    span.SetAttributes(anotherKey.String("yes"))

    meter.RecordBatch(
        // Note: call-site variables added as context Entries:
        baggage.ContextWithValues(ctx, anotherKey.String("xyz")),
        commonAttributes,

        valueRecorder.Measurement(2.0),
    )

    func(ctx context.Context) {
        var span trace.Span
        ctx, span = tracer.Start(ctx, "Sub operation...")
        defer span.End()

        span.SetAttributes(lemonsKey.String("five"))
        span.AddEvent("Sub span event")
        boundRecorder.Record(ctx, 1.3)
    }(ctx)
}(ctx)

RabbitMQ

There is an official library for AMQP in Golang:

An AMQP 0-9-1 Go client maintained by the RabbitMQ team

Also, there is another official library for Streams in Golang:

A client library for RabbitMQ streams

Context

Context is one of the foundational building blocks in Go. Anyone with even a cursory experience with the language is likely to have encountered it, as it’s the first argument passed to functions that accept contexts. I see the purpose of context as twofold:

Provide a control-flow mechanism across API boundaries with signals.
Carrying request-scoped data across API boundaries.

A couple of rules of thumb to start

Only entry-point functions (the one at the top of a call chain) should create an empty context (i.e., context.Background()). For example, main(), TestXxx(). The HTTP library creates a custom context for each request, which you should access and pass. Of course, mid-chain functions can create child contexts to pass along if they need to share data or have flow control over the functions they call.
```
// Create a new context.
parent, cancelParent := context.WithCancel(context.Background())
// Derive child contexts from parent.
childA, _ := context.WithTimeout(parent, 5 * time.Secound)
childB, _ := context.WithDeadline(parent, time.Now().Add(1 * time.Minute)
```
Contexts are (only) passed down the call chain. If you’re not in an entry-point function, and you need to call a function that takes a context, your function should accept a context and pass that along. But what if, for some reason, you can’t currently get access to the context at the top of the chain? In that case, use context.TODO(). This signals that the context is not yet available, and further work is required. Perhaps maintainers of another library you depend on will need to extend their functions to accept a context so that you, in turn, can pass it on. Of course, a function should never be returning a context.

`Rangefunc`

Go Wiki: Rangefunc Experiment - The Go Programming Language

Generics

The Go 1.18 release adds support for generics. Generics are a way of writing code that is independent of the specific types being used. Functions and types may now be written to use any of a set of types.

Type Parameters

Functions and types are now permitted to have type parameters. A type parameter list looks like an ordinary parameter list, except that it uses square brackets instead of parentheses.

To show how this works, let’s start with the basic non-generic Min function for floating point values:

func Min(x, y float64) float64 {
    if x < y {
        return x
    }
    return y
}

We can make this function generic-make it work for different types-by adding a type parameter list. In this example we add a type parameter list with a single type parameter T, and replace the uses of float64 with T.

import "constraints"

func GMin[T constraints.Ordered](x, y T) T {
    if x < y {
        return x
    }
    return y
}

It is now possible to call this function with a type argument by writing a call like

x := GMin[int](2, 3)

Providing the type argument to GMin, in this case int, is called instantiation. Instantiation happens in two steps. First, the compiler substitutes all type arguments for their respective type parameters throughout the generic function or type. Second, the compiler verifies that each type argument satisfies the respective constraint.

After successful instantiation we have a non-generic function that can be called just like any other function. For example, in code like

fmin := GMin[float64]
m := fmin(2.71, 3.14)

Type Sets

In Go, type constraints must be interfaces. That is, an interface type can be used as a value type, and it can also be used as a meta-type. Interfaces define methods, so obviously we can express type constraints that require certain methods to be present. But constraints.Ordered is an interface type too, and the < operator is not a method.

To make this work, we look at interfaces in a new way.

Until recently, the Go spec said that an interface defines a method set, which is roughly the set of methods enumerated in the interface. Any type that implements all those methods implements that interface.

But another way of looking at this is to say that the interface defines a set of types, namely the types that implement those methods. From this perspective, any type that is an element of the interface’s type set implements the interface.

For our purposes, though, the type set view has an advantage over the method set view:

We have extended the syntax for interface types to make this work. For instance, interface{ int|string|bool } defines the type set containing the types int, string, and bool.

Now let’s look at the actual definition of constraints.Ordered:

type Ordered interface {
    Integer|Float|~string
}

This declaration says that the Ordered interface is the set of all integer, floating-point, and string types. The vertical bar expresses a union of types (or sets of types in this case). Integer and Float are interface types that are similarly defined in the constraints package. Note that there are no methods defined by the Ordered interface.

For type constraints we usually don’t care about a specific type, such as string; we are interested in all string types. That is what the ~ token is for. The expression ~string means the set of all types whose underlying type is string. This includes the type string itself as well as all types declared with definitions such as type MyString string.

Configuration 🔧

Personally, I love to have all configuration well-defined and structured, the thing that I couldn’t achieve with viper, so I prefer koanf. For using koanf you need to install its core package besides the required providers and parsers as follows:

go get -u github.com/knadh/koanf/v2

go get -u github.com/knadh/koanf/providers/file
go get -u github.com/knadh/koanf/providers/env
go get -u github.com/knadh/koanf/providers/structs

go get -u github.com/knadh/koanf/parsers/toml

koanf.Provider is a generic interface that provides configuration, for example, from files, environment variables, HTTP sources, or anywhere. The configuration can either be raw bytes that a parser can parse, or it can be a nested map[string]interface{} that can be directly loaded.

koanf.Parser is a generic interface that takes raw bytes, parses, and returns a nested map[string]interface{}. For example, JSON and YAML parsers.

// Load JSON config.
if err := k.Load(file.Provider("mock/mock.json"), json.Parser()); err != nil {
 log.Fatalf("error loading config: %v", err)
}

// Load environment variables and merge into the loaded config.
// "MYVAR" is the prefix to filter the env vars by.
// "." is the delimiter used to represent the key hierarchy in env vars.
// The (optional, or can be nil) function can be used to transform
// the env var names, for instance, to lowercase them.
//
// For example, env vars: MYVAR_TYPE and MYVAR_PARENT1__CHILD1__NAME
// will be merged into the "type" and the nested "parent1.child1.name"
// keys in the config file here as we lowercase the key,
// replace `_` with `.` and strip the MYVAR_ prefix so that
// only "parent1.child1.name" remains.
k.Load(env.Provider("MYVAR_", ".", func(s string) string {
 return strings.ReplaceAll(strings.ToLower(
  strings.TrimPrefix(s, "MYVAR_")), "__", ".")
}), nil)

Standard CLI 💾

Having multiple sub-command for things like migrations, insert ground data, etc.

cobra

There is also another options, which uses generics and more advance concepts:

cli

HTTP Frameworks

There are multiple frameworks in Go. I prefer echo for general use cases, but when there are performance criteria, I will choose fiber (seems there is no need for fiber since 1.22). Huma is something new which wraps currently implemented routers and provides general add-ons like OpenAPI specification, CLI, etc.

Advanced Console UIs 💅

pterm is useful when you need colorful texts. But when you need advance TUI features:

Testing 🧨

You can write tests using suites or using the behavior testing.

https://github.com/onsi/ginkgo

https://github.com/stretchr/testify

ORM

ORM means Object Relational Mapper, it helps you to manage your database models and queries easier. In Go, people may prefer to write down their queries like man, but we have the following ORMs in Go:

ent
bun is a SQL-first Golang ORM provided by Uptrace.
gorm is easy and fun, but you also prefer to write down your queries, like man 💪.
sqlboiler

Redis

`go-redis`

The popular and backward-compatible Redis library which has context and an awesome sub-package named extra which has things like tracing, monitoring, etc.

`rueidis`

It is a new library which is fun and only works on new versions of Redis:

MongoDB 🥭

There is no good ORM for MongoDB in Go, so its official database driver is the best choice. Besides that, it has otelmongo telemetry package.

mongo-go-driver

Task Queue 😴

Sometimes you want to queue tasks and process them later. Asynq library can do it for you with Redis, but I think using Redis on scale is a drawback of this library. I prefer to use Jetstream for these kinds of things.

asynq

Dependency Injection 💉

Generating code on Golang is not my interest, but this framework is really nice and easily can generate useful binding. These frameworks can do the dependency injection without code generation, and I kinda like them:

do
wire

Fx

Dependency injection system for Go.

Eliminate globals: Fx helps you remove global state from your application. No more init() or global variables. Use Fx-managed singletons.

Code reuse: Fx lets teams within your organization build loosely-coupled and well-integrated shareable components.

Battle-tested: Fx is the backbone of nearly all Go services at Uber.

We built an empty Fx application by calling fx.New with no arguments. Applications will normally pass arguments to fx.New to set up their components.

We then run this application with the App.Run method. This method blocks until it receives a signal to stop, and it then runs any cleanup operations necessary before exiting.

Fx is primarily intended for long-running server applications; these applications typically receive a signal from the deployment system when it’s time to shut down.

An App is a modular application built around dependency injection. Most users will only need to use the New constructor and the all-in-one Run convenience method. In more unusual cases, users may need to use the Err, Start, Done, and Stop methods by hand instead of relying on Run.

In addition to that built-in functionality, users typically pass a handful of Provide options and one or more Invoke options. The Provide options teach the application how to instantiate a variety of types, and the Invoke options describe how to initialize the application.

When created, the application immediately executes all the functions passed via Invoke options. To supply these functions with the parameters they need, the application looks for constructors that return the appropriate types; if constructors for any required types are missing or any invocations return an error, the application will fail to start (and Err will return a descriptive error message).

Once all the invocations (and any required constructors) have been called, New returns and the application is ready to be started using Run or Start . On startup, it executes any OnStart hooks registered with its Lifecycle.

func Provide(lc fx.Lifecycle, store *urlsvc.URLSvc, logger *zap.Logger, tele telemetry.Telemetery) *echo.Echo {
 app := echo.New()

 handler.URL{
  Store:  store,
  Logger: logger.Named("handler").Named("url"),
  Tracer: tele.TraceProvider.Tracer("handler.url"),
 }.Register(app.Group("/api"))

 handler.Healthz{
  Logger: logger.Named("handler").Named("healthz"),
  Tracer: tele.TraceProvider.Tracer("handler.healthz"),
 }.Register(app.Group(""))

 lc.Append(
  fx.Hook{
   OnStart: func(_ context.Context) error {
    go func() {
     if err := app.Start(":1378"); !errors.Is(err, http.ErrServerClosed) {
      logger.Fatal("echo initiation failed", zap.Error(err))
     }
    }()

    return nil
   },
   OnStop: app.Shutdown,
  },
 )

 return app
}

Application lifecycle

The lifecycle of a Fx application has two high-level phases: initialization and execution. Both of these, in turn are comprised of multiple steps.

During initialization, Fx will,

register all constructors passed to fx.Provide
register all decorators passed to fx.Decorate
run all functions passed to fx.Invoke, calling constructors and decorators as needed

During execution, Fx will,

Run all startup hooks appended to the application by providers, decorators, and invoked functions
Wait for a signal to stop running
Run all shutdown hooks appended to the application

fx-flow

Lifecycle hooks

Lifecycle hooks provide the ability to schedule work to be executed by Fx, when the application starts up or shuts down. Fx provides two kinds of hooks:

Startup hooks, also referred to as OnStart hooks. These run in the order they were appended.
Shutdown hooks, also referred to as OnStop hooks. These run in the reverse of the order they were appended.

Typically, components that provide a startup hook also provide a corresponding shutdown hook to release the resources they acquired at startup.

Fx runs both kinds of hooks with a hard timeout enforcement (by default, 15 seconds). Therefore, hooks are expected to block only as long as they need to schedule work. In other words,

hooks must not block to run long-running tasks synchronously
hooks should schedule long-running tasks in background go routines
shutdown hooks should stop the background work started by startup hooks

Modules

A Fx module is a shareable Go library or package that provides self-contained functionality to a Fx application.

Result Structs

Result structs are the inverse of parameter structs. These structs represent multiple outputs from a single function as fields. Fx treats all structs embedding fx.Out as result structs, so other constructors can rely on the result struct’s fields directly.

Without result structs, we sometimes have function definitions like this:

func SetupGateways(conn *sql.DB) (*UserGateway, *CommentGateway, *PostGateway, error) {
  // ...
}

With result structs, we can make this both more readable and easier to modify in the future:

type Gateways struct {
  fx.Out

  Users    *UserGateway
  Comments *CommentGateway
  Posts    *PostGateway
}

func SetupGateways(conn *sql.DB) (Gateways, error) {
  // ...
}

GraphQL

GraphQL is an awesome way to communicate data with the frontend team. Using GraphQL you can ask the frontend team to write queries for accessing the backend data and because of that there is no need to design different APIs for different requests. You have one API to rule them all.

gqlgen can either automatically generate models based on your GraphQL schema or leverage your existing models. When using your own models, gqlgen will automatically generate field resolvers to populate fields defined in the schema, but not defined in your model.

gqlgen.yml:

models:
  Student:
    model:
      - github.com/1995parham-teaching/students/internal/model.Student
  Course:
    model:
      - github.com/1995parham-teaching/students/internal/model.Course

model.go:

package model

type Student struct {
  Name    string   `json:"name"`
  ID      string   `json:"id"`
  Courses []Course `json:"courses"`
}

type Course struct {
  Name string
  ID   string
}

schema.graphql:

type Student {
  id: String!
  name: String!
  courses: [Course!]

  enterance: Int
}

resolver.go:

// Enterance is the resolver for the enterance field.
func (r *studentResolver) Enterance(ctx context.Context, obj *model.Student) (*int, error) {
  enterance := 1401
  return &enterance, nil
}

// Student returns graph.StudentResolver implementation.
func (r *Resolver) Student() graph.StudentResolver { return &studentResolver{r} }

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GraphQL

`func As(err error, target any) bool`

`comparable`

`cmp` package

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