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Clean Go Code
Preface: Why Write Clean Code?
This document is a reference for the Go community that aims to help developers write cleaner code. Whether you're working on a personal project or as part of a larger team, writing clean code is an important skill to have. Establishing good paradigms and consistent, accessible standards for writing clean code can help prevent developers from wasting many meaningless hours on trying to understand their own (or others') work.
We don’t read code, we decode it – Peter Seibel
As developers, we're sometimes tempted to write code in a way that's convenient for the time being without regard for best practices; this makes code reviews and testing more difficult. In a sense, we're encoding—and, in doing so, making it more difficult for others to decode our work. But we want our code to be usable, readable, and maintainable. And that requires coding the right way, not the easy way.
This document begins with a simple and short introduction to the fundamentals of writing clean code. Later, we'll discuss concrete refactoring examples specific to Go.
A short word on gofmt
I'd like to take a few sentences to clarify my stance on gofmt because there are plenty of things I disagree with when it comes to this tool. I prefer snake case over camel case, and I quite like my constant variables to be uppercase. And, naturally, I also have many opinions on bracket placement. That being said, gofmt does allow us to have a common standard for writing Go code, and that's a great thing. As a developer myself, I can certainly appreciate that Go programmers may feel somewhat restricted by gofmt, especially if they disagree with some of its rules. But in my opinion, homogeneous code is more important than having complete expressive freedom.
Table of Contents
Introduction to Clean Code
Clean code is the pragmatic concept of promoting readable and maintainable software. Clean code establishes trust in the codebase and helps minimize the chances of careless bugs being introduced. It also helps developers maintain their agility, which typically plummets as the codebase expands due to the increased risk of introducing bugs.
Test-Driven Development
Test-driven development is the practice of testing your code frequently throughout short development cycles or sprints. It ultimately contributes to code cleanliness by inviting developers to question the functionality and purpose of their code. To make testing easier, developers are encouraged to write short functions that only do one thing. For example, it's arguably much easier to test (and understand) a function that's only 4 lines long than one that's 40.
Test-driven development consists of the following cycle:
- Write (or execute) a test
- If the test fails, make it pass
- Refactor your code accordingly
- Repeat
Testing and refactoring are intertwined in this process. As you refactor your code to make it more understandable or maintainable, you need to test your changes thoroughly to ensure that you haven't altered the behavior of your functions. This can be incredibly useful as the codebase grows.
Naming Conventions
Comments
First things first: I want to address the topic of comments. Unnecessary comments are the biggest indicator of code smell. Comments are usually added to a codebase because something is so unclear that it's necessary to explain it so that the reader can understand what's going on. But this isn't always the case, and comments tend to be misused.
In Go, according to gofmt, all public variables and functions should be annotated. I think this is absolutely fine, as it gives us consistent rules for documenting our code. However, I always want to distinguish between comments that enable auto-generated documentation and all other comments. Annotation comments, for documentation, should be written like documentation—they should be at a high level of abstraction and concern the logical implementation of the code as little as possible.
I say this because there are other ways to explain code and ensure that it's being written comprehensibly and expressively. If the code is neither of those, some people find it acceptable to introduce a comment explaining the convoluted logic. Most people simply won't read comments, as they tend to be very intrusive to the experience of reviewing code.
Let's take a step back and look at some concrete examples. Here's how you shouldn't comment your code:
// iterate over the range 0 to 9
// and invoke the doSomething function
// for each iteration
for i := 0; i < 10; i++ {
doSomething(i)
}
This is what I like to call a tutorial comment; it's fairly common in tutorials, which often explain the low-level functionality of a language (or programming in general). While these comments may be helpful for beginners, they're absolutely useless in production code. Hopefully, we aren't collaborating with programmers who don't understand something as simple as a looping construct by the time they've begun working on a development team. As programmers, we shouldn't have to read the comment to understand what's going on—we know that we're iterating over the range 0 to 9 because we can simply read the code. Hence the proverb:
Document why, not how. – Venkat Subramaniam
Following this logic, we can now change our comment to explain why we are iterating from the range 0 to 9:
// instatiate 10 threads to handle upcoming work load
for i := 0; i < 10; i++ {
doSomething(i)
}
Now we understand why we have a loop and can tell what we're doing by simply reading the code... Sort of.
This still isn't what I'd consider clean code. The comment is worrying because it probably should not be necessary to express such an explanation in prose, assuming the code is well written (which it isn't). Technically, we're still saying what we're doing, not why we're doing it. We can easily express this "what" directly in our code by using more meaningful names:
for workerID := 0; workerID < 10; workerID++ {
instantiateThread(workerID)
}
With just a few changes to our variable and function names, we've managed to explain what we're doing directly in our code. This is much clearer for the reader because they won't have to read the comment and then map the prose to the code. Instead, they can simply read the code to understand what it's doing.
Of course, this was a relatively trivial example. Writing clear and expressive code is unfortunately not always so easy; it can become increasingly difficult as the codebase itself grows in complexity. The more you practice writing comments in this mindset and avoid explaining what you're doing, the cleaner your code will become.
Function Naming
Let's now move on to function naming conventions. The general rule here is really simple: The more specific the function, the more general its name. In other words, we want to start with a very broad and short function name, such as Run or Parse, that describes the general functionality. Let's imagine that we are creating a configuration parser. Following this naming convention, our top level of abstraction might look something like the following:
func main() {
configpath := flag.String("config-path", "", "configuration file path")
flag.Parse()
config, err := configuration.Parse(*configpath)
...
}
We'll focus on the naming of the Parse function. Despite this function's very short and general name, it's actually quite clear what it attempts to achieve.
When we go one layer deeper, our function naming will become slightly more specific:
func Parse(filepath string) (Config, error) {
switch fileExtension(filepath) {
case "json":
return parseJSON(filepath)
case "yaml":
return parseYAML(filepath)
case "toml":
return parseTOML(filepath)
default:
return Config{}, ErrUnknownFileExtension
}
}
Here, we've clearly distinguished the nested function calls from their parent without being overly specific. This allows each nested function call to make sense on its own as well as within the context of the parent. On the other hand, if we had named the parseJSON function json instead, it couldn't possibly stand on its own. The functionality would become lost in the name, and we would no longer be able to tell whether this function is parsing, creating, or marshalling JSON.
Notice that fileExtension is actually a little more specific. However, this is because its functionality is in fact quite specific in nature:
func fileExtension(filepath string) string {
segments := strings.Split(filepath, ".")
return segments[len(segments)-1]
}
This kind of logical progression in our function names—from a high level of abstraction to a lower, more specific one&mdashmakes the code easier to follow and and read. Consider the alternative: If our highest level of abstraction is too specific, then we'll end up with a name that attempts to cover all bases, like DetermineFileExtensionAndParseConfigurationFile. This is horrendously difficult to read; we are trying to be too specific too soon and end up confusing the reader, despite trying to be clear!
Variable Naming
Rather interestingly, the opposite is true for variables. Unlike functions, our variables should be named from more to less specific the deeper we go into nested scopes.
You shouldn’t name your variables after their types for the same reason you wouldn’t name your pets 'dog' or 'cat'. – Dave Cheney
Why should our variable names become less specific as we travel deeper into a function's scope? Simply put, as a variable's scope becomes smaller, it becomes increasingly clear for the reader what that variable represents, thereby eliminating the need for specific naming. In the example of the previous function fileExtension, we could even shorten the name of the variable segments to s if we wanted to. The context of the variable is so clear that it's unnecessary to explain it any further with longer variable names. Another good example of this is in nested for loops:
func PrintBrandsInList(brands []BeerBrand) {
for _, b := range brands {
fmt.Println(b)
}
}
In the above example, the scope of the variable b is so small that we don't need to spend any additional brain power on remembering what exactly it represents. However, because the scope of brands is slightly larger, it helps for it to be more specific. When expanding the variable scope in the function below, this distinction becomes even more apparent:
func BeerBrandListToBeerList(beerBrands []BeerBrand) []Beer {
var beerList []Beer
for _, brand := range beerBrands {
for _, beer := range brand {
beerList = append(beerList, beer)
}
}
return beerList
}
Great! This function is easy to read. Now, let's apply the opposite (i.e., wrong) logic when naming our variables:
func BeerBrandListToBeerList(b []BeerBrand) []Beer {
var bl []Beer
for _, beerBrand := range b {
for _, beerBrandBeerName := range beerBrand {
bl = append(bl, beerBrandBeerName)
}
}
return bl
}
Even though it's possible to figure out what this function is doing, the excessive brevity of the variable names makes it difficult to follow the logic as we travel deeper. This could very well spiral into full-blown confusion because we're mixing short and long variable names inconsistently.
Cleaning Functions
Now that we know some best practices for naming our variables and functions, as well as clarifying our code with comments, let's dive into some specifics of how we can refactor functions to make them cleaner.
Function Length
How small should a function be? Smaller than that! – Robert C. Martin
When writing clean code, our primary goal is to make our code easily digestible. The most effective way to do this is to make our functions as short as possible. It's important to understand that we don't necessarily do this to avoid code duplication. The more important reason is to improve code comprehension.
It can help to look at a function's description at a very high level to understand this better:
fn GetItem:
- parse json input for order id
- get user from context
- check user has appropriate role
- get order from database
By writing short functions (which are typically 5–8 lines in Go), we can create code that reads almost as naturally as our description above:
var (
NullItem = Item{}
ErrInsufficientPrivileges = errors.New("user does not have sufficient privileges")
)
func GetItem(ctx context.Context, json []bytes) (Item, error) {
order, err := NewItemFromJSON(json)
if err != nil {
return NullItem, err
}
if !GetUserFromContext(ctx).IsAdmin() {
return NullItem, ErrInsufficientPrivliges
}
return db.GetItem(order.ItemID)
}
Using smaller functions also eliminates another horrible habit of writing code: indentation hell. Indentation hell typically occurs when a chain of if statements are carelessly nested in a function. This makes it very difficult for human beings to parse the code and should be eliminated whenever spotted. Indentation hell is particularly common when working with interface{} and using type casting:
func GetItem(extension string) (Item, error) {
if refIface, ok := db.ReferenceCache.Get(extension); ok {
if ref, ok := refIface.(string); ok {
if itemIface, ok := db.ItemCache.Get(ref); ok {
if item, ok := itemIface.(Item); ok {
if item.Active {
return Item, nil
} else {
return EmptyItem, errors.New("no active item found in cache")
}
} else {
return EmptyItem, errors.New("could not cast cache interface to Item")
}
} else {
return EmptyItem, errors.New("extension was not found in cache reference")
}
} else {
return EmptyItem, errors.New("could not cast cache reference interface to Item")
}
}
return EmptyItem, errors.New("reference not found in cache")
}
First, indentation hell makes it difficult for other developers to understand the flow of your code. Second, if the logic in our if statements expands, it'll become exponentially more difficult to figure out which statement returns what (and to ensure that all paths return some value). Yet another problem is that this deep nesting of conditional statements forces the reader to frequently scroll and keep track of many logical states in their head. It also makes it more difficult to test the code and catch bugs because there are so many different nested possibilities that you have to account for.
Indentation hell can result in reader fatigue if a developer has to constantly parse unwieldy code like the sample above. Naturally, this is something we want to avoid at all costs.
So, how do we clean this function? Fortunately, it's actually quite simple. On our first iteration, we will try to ensure that we are returning an error as soon as possible. Instead of nested the if and else statements, we want to "push our code to the left," so to speak. Take a look:
func GetItem(extension string) (Item, error) {
refIface, ok := db.ReferenceCache.Get(extension)
if !ok {
return EmptyItem, errors.New("reference not found in cache")
}
if ref, ok := refIface.(string); ok {
// return cast error on reference
}
if itemIface, ok := db.ItemCache.Get(ref); ok {
// return no item found in cache by reference
}
if item, ok := itemIface.(Item); ok {
// return cast error on item interface
}
if !item.Active {
// return no item active
}
return Item, nil
}
Once we're done with our first attempt at refactoring the function, we can proceed to split up the function into smaller functions. Here's a good rule of thumb: If the value, err := pattern is repeated more than once in a function, this is an indication that we can split the logic of our code into smaller pieces:
func GetItem(extension string) (Item, error) {
if ref, ok := getReference(extension) {
return EmptyItem, ErrReferenceNotFound
}
return getItemByReference(ref)
}
func getReference(extension string) (string, bool) {
refIface, ok := db.ReferenceCache.Get(extension)
if !ok {
return EmptyItem, false
}
return refIface.(string)
}
func getItemByReference(reference string) (Item, error) {
item, ok := getItemFromCache(reference)
if !item.Active || !ok {
return EmptyItem, ErrItemNotFound
}
return Item, nil
}
func getItemFromCache(reference string) (Item, bool) {
if itemIface, ok := db.ItemCache.Get(ref); ok {
return EmptyItem, false
}
return itemIface.(Item), true
}
As mentioned previously, indentation hell can make it difficult to test our code. On the other hand, when we split up our functions like we did above, it becomes much easier to get 100% code coverage because we're dealing with functions that are maybe only 4 lines each (when written by a sane person), as opposed to 400. That's just common sense.
Note: For production code, one should elaborate on the code even further by returning errors instead of
boolvalues. This makes it much easier to understand where the error is originating from. However, as these are just example functions, returningboolvalues will suffice for now. Examples of returning errors more explicitly will be explained in more detail later.
You'll notice that the clean version of our function has resulted in more lines of code. However, the code itself is far easier to read. It's layered in an onion-style fashion, where we can ignore "layers" that we aren't interested in and simply peel back the ones that we do want to examine. This makes it easier to understand low-level functionality because we only have to read maybe 3–5 lines at a time.
This example illustrates that we cannot measure the cleanliness of our code by the number of lines it uses. The first version of the code was certainly much shorter. However, it was artificially short and very difficult to read. In most cases, cleaning code will initially expand the existing codebase in terms of the number of lines. But this is highly preferable to the alternative of having messy, convoluted logic. If you're ever in doubt about this, just consider how you feel about the following function, which does exactly the same thing as our code but only uses two lines:
func GetItemIfActive(extension string) (Item, error) {
if refIface,ok := db.ReferenceCache.Get(extension); ok {if ref,ok := refIface.(string); ok { if itemIface,ok := db.ItemCache.Get(ref); ok { if item,ok := itemIface.(Item); ok { if item.Active { return Item,nil }}}}} return EmptyItem, errors.New("reference not found in cache")
}
Function Signatures
Creating a good function naming structure makes it easier to read and understand the intent of the code. As we saw above, making our functions shorter helps us understand the function's logic. The last part of cleaning our functions involves understanding the context of the function input. With this comes another easy-to-follow rule: Function signatures should only contain one or two input parameters. In certain exceptional cases, three can be acceptable, but this is where we should start considering a refactor. Much like the rule that our functions should only be 5–8 lines long, this can seem quite extreme at first. However, I feel that this rule is much easier to justify.
Take the following function from RabbitMQ's introduction tutorial to its Go library:
q, err := ch.QueueDeclare(
"hello", // name
false, // durable
false, // delete when unused
false, // exclusive
false, // no-wait
nil, // arguments
)
The function QueueDeclare takes six input parameters, which is quite a lot. With some effort, it's possible to understand what this code does thanks to the comments. However, the comments are actually part of the problem—as mentioned earlier, they should be substituted with descriptive code whenever possible. After all, there's nothing preventing us from invoking the QueueDeclare function without comments:
q, err := ch.QueueDeclare("hello", false, false, false, false, nil)
Now, without looking at the commented version, try to remember what the fourth and fifth false arguments represent. It's impossible, right? You will inevitably forget at some point. This can lead to costly mistakes and bugs that are difficult to correct. The mistakes might even occur through incorrect comments—imagine labeling the wrong input parameter. Correcting this mistake will be unbearably difficult to correct, especially when familiarity with the code has deteriorated over time or was low to begin with. Therefore, it is recommended to replace these input parameters with an 'Options' struct instead:
type QueueOptions struct {
Name string
Durable bool
DeleteOnExit bool
Exclusive bool
NoWait bool
Arguments []interface{}
}
q, err := ch.QueueDeclare(QueueOptions{
Name: "hello",
Durable: false,
DeleteOnExit: false,
Exclusive: false,
NoWait: false,
Arguments: nil,
})
This solves two problems: misusing comments, and accidentally labeling the variables incorrectly. Of course, we can still confuse properties with the wrong value, but in these cases, it will be much easier to determine where our mistake lies within the code. The ordering of the properties also doesn't matter anymore, so incorrectly ordering the input values is no longer a concern. The last added bonus of this technique is that we can use our Option struct to infer the default values of our function's input parameters. When structures in Go are declared, all properties are initialised to their default value. This means that our QueueDeclare option can actually be invoked in the following way:
q, err := ch.QueueDeclare(QueueOptions{
Name: "hello",
})
The rest of the values are initialised to their default value of false (except for Arguments, which as an interface has a default value of nil). Not only are we much safer with this approach, but we are also much clearer with our intentions. In this case, we could actually write less code. This is an all-around win for everyone on the project.
One final note on this: It's not always possible to change a function's signature. In this case, for example, we don't actually have control over our QueueDeclare function signature because it's from the RabbitMQ library. It's not our code, so we can't change it. However, we can wrap these functions to suit our purposes:
type RMQChannel struct {
channel *amqp.Channel
}
func (rmqch *RMQChannel) QueueDeclare(opts QueueOptions) (Queue, error) {
return rmqch.channel.QueueDeclare(
opts.Name,
opts.Durable,
opts.DeleteOnExit,
opts.Exclusive,
opts.NoWait,
opts.Arguments,
)
}
Basically, we create a new structure named RMQChannel that contains the amqp.Channel type, which has the QueueDeclare method. We then create our own version of this method, which essentially just calls the old version of the RabbitMQ library function. Our new method has all the advantages described before, and we achieved this without actually having to change any of the code in the RabbitMQ library.
We'll use this idea of wrapping functions to introduce more clean and safe code later when discussing interface{}.
Variable Scope
Now, let's take a step back and revisit the idea of writing smaller functions. This has another nice side effect that we didn't cover in the previous chapter: Writing smaller function can typically eliminate reliance on mutable variables that leak into the global scope. Writing code with global variables is a practice of the past—it doesn't belong in clean code. But why is that?
The problem with using global variables is that we make it very difficult for programmers to understand the current state of a variable. If a variable is global and mutable, then by definition, its value can be changed by any part of the codebase. At no point can you guarantee that this variable is going to be a specific value... And that's a headache for everyone. This is yet another example of a trivial problem that's exacerbated when the codebase expands.
Let's look at a short example of how non-global variables with a large scope can cause problems. These variables also introduce the issue of variable shadowing, as demonstrated in the code taken from an article titled Golang scope issue:
func doComplex() (string, error) {
return "Success", nil
}
func main() {
var val string
num := 32
switch num {
case 16:
// do nothing
case 32:
val, err := doComplex()
if err != nil {
panic(err)
}
if val == "" {
// do something else
}
case 64:
// do nothing
}
fmt.Println(val)
}
What's the problem with this code? From a quick skim, it seems the var val string value should be printed out as Success by the end of the main function. Unfortunately, this is not the case. The reason for this lies in the following line:
val, err := doComplex()
This declares a new variable val in the switch's case 32 scope and has nothing to do with the variable declared in the first line of main. Of course, it can be argued that Go syntax is a little tricky, which I don't necessarily disagree with, but there is a much worse issue at hand. The declaration of var val string as a mutable, largely scoped variable is completely unnecessary. If we do a very simple refactor, we will no longer have this issue:
func getStringResult(num int) (string, error) {
switch num {
case 16:
// do nothing
case 32:
return doComplex()
case 64:
// do nothing
}
return ""
}
func main() {
val, err := getStringResult(32)
if err != nil {
panic(err)
}
if val == "" {
// do something else
}
fmt.Println(val)
}
After our refactor, val is no longer modified, and the scope has been reduced. Again, keep in mind that these functions are very simple. Once this kind of code style becomes a part of larger, more complex systems, it can be impossible to figure out why errors are occurring. We don't want this to happen—not only because we generally dislike software errors but also because it's disrespectful to our colleagues, and ourselves; we are potentially wasting each others' time having to debug this type of code. Developers need to take responsibility for their own code rather than blaming these issues on the variable declaration syntax of a particular language like Go.
On a side not, if the // do something else part is another attempt to mutate the val variable, we should extract that logic out as its own self-contained function, as well as the previous part of it. This way, instead of expanding the mutable scope of our variables, we can just return a new value:
func getVal(num int) (string, error) {
val, err := getStringResult(32)
if err != nil {
return "", err
}
if val == "" {
return NewValue() // pretend function
}
}
func main() {
val, err := getVal(32)
if err != nil {
panic(err)
}
fmt.Println(val)
}
Variable Declaration
Other than avoiding issues with variable scope and mutability, we can also improve readability by declaring variables as close to their usage as possible. In C programming, it's common to see the following approach to declaring variables:
func main() {
var err error
var items []Item
var sender, receiver chan Item
items = store.GetItems()
sender = make(chan Item)
receiver = make(chan Item)
for _, item := range items {
...
}
}
This suffers from the same symptom as described in our discussion of variable scope. Even though these variables might not actually be reassigned at any point, this kind of coding style keeps the readers on their toes, in all the wrong ways. Much like computer memory, our brain's short-term memory has a limited capacity. Having to keep track of which variables are mutable and whether or not a particular fragment of code will mutate them makes it more difficult to understand what the code is doing. Figuring out the eventually returned value can be a nightmare. Therefore, to makes this easier for our readers (and our future selves), it's recommended that you declare variables as close to their usage as possible:
func main() {
var sender chan Item
sender = make(chan Item)
go func() {
for {
select {
case item := <- sender:
// do something
}
}
}()
}
However, we can do even better by invoking the function directly after its declaration. This makes it much clearer that the function logic is associated with the declared variable:
func main() {
sender := func() chan Item {
channel := make(chan Item)
go func() {
for {
select { ... }
}
}()
return channel
}
}
And coming full circle, we can move the anonymous function to make it a named function instead:
func main() {
sender := NewSenderChannel()
}
func NewSenderChannel() chan Item {
channel := make(chan Item)
go func() {
for {
select { ... }
}
}()
return channel
}
It is still clear that we are declaring a variable, and the logic associated with the returned channel is simple, unlike in the first example. This makes it easier to traverse the code and understand the role of each variable.
Of course, this doesn't actually prevent us from mutating our sender variable. There is nothing that we can do about this, as there is no way of declaring a const struct or static variables in Go. This means that we'll have to restrain ourselves from modifying this variable at a later point in the code.
NOTE: The keyword
constdoes exist but is limited in use to primitive types only.
One way of getting around this can at least limit the mutability of a variable to the package level. The trick involves creating a structure with the variable as a private property. This private property is thenceforth only accessible through other methods provided by this wrapping structure. Expanding on our channel example, this would look something like the following:
type Sender struct {
sender chan Item
}
func NewSender() *Sender {
return &Sender{
sender: NewSenderChannel(),
}
}
func (s *Sender) Send(item Item) {
s.sender <- item
}
We have now ensured that the sender property of our Sender struct is never mutated—at least not from outside of the package. As of writing this document, this is the only way of creating publicly immutable non-primitive variables. It's a little verbose, but it's truly worth the effort to ensure that we don't end up with strange bugs resulting from accidental variable modification.
func main() {
sender := NewSender()
sender.Send(&Item{})
}
Looking at the example above, it's clear how this also simplifies the usage of our package. This way of hiding the implementation is beneficial not only for the maintainers of the package but also for the users. Now, when initialising and using the Sender structure, there is no concern over its implementation. This opens up for a much looser architecture. Because our users aren't concerned with the implementation, we are free to change it at any point, since we have reduced the point of contact that users have with the package. If we no longer wish to use a channel implementation in our package, we can easily change this without breaking the usage of the Send method (as long as we adhere to its current function signature).
NOTE: There is a fantastic explanation of how to handle the abstraction in client libraries, taken from the talk AWS re:Invent 2017: Embracing Change without Breaking the World (DEV319).
Clean Go
This section will describe some less generic aspects of writing clean Go code, but rather be discussing aspects that are very go specific. Like the previous section, there will still be a mix of generic and specific concepts being discussed, however, this section marks the start of the document, where the document changes from a generic description of clean code with Go examples, to Go specific descriptions, based on clean code principles.
Return Values
Returning Defined Errors
We will be started out nice an easy, by describing a cleaner way to return errors. Like discussed earlier, our main goals with writing clean code, is to ensure readability, testability and maintainability of the code base. This error returning method will improve all three aspects, with very little effort.
Let's consider the normal way to return a custom error. This is a hypothetical example taken from a thread-safe map implementation, we have named Store:
package smelly
func (store *Store) GetItem(id string) (Item, error) {
store.mtx.Lock()
defer store.mtx.Unlock()
item, ok := store.items[id]
if !ok {
return Item{}, errors.New("item could not be found in the store")
}
return item, nil
}
There is nothing inherently smelly about this function, in its isolation. We look into the items map of our Store struct, to see if we already have an item with this id. If we do, we return the item, if we don't, we return an error. Pretty standard. So, what is the issue with returning custom errors like this? Well, let's look at what happens, when we use this function, from another package:
func GetItemHandler(w http.ReponseWriter, r http.Request) {
item, err := smelly.GetItem("123")
if err != nil {
if err.Error() == "item could not be found in the store" {
http.Error(w, err.Error(), http.StatusNotFound)
return
}
http.Error(w, errr.Error(), http.StatusInternalServerError)
return
}
json.NewEncoder(w).Encode(item)
}
This is actually not too bad. However, there is one glaring problem with this. Errors in Go, are simply just an interface which implements a function (Error()) which returns a string. Therefore, we are now hardcoding the expected error code into our code base. This isn't too great. Mainly, because if the error message value changes, our code breaks (softly). Our code is too closely coupled, meaning that we would have to change our code in, possibly, many different places. Even worse would be, if a client used our package to write this code. Their software would inexplicably break all of a sudden after a package update, should we choose to change the message of the returning error. This is quite obviously something that we want to avoid. Fortunately, the fix is very simple.
package clean
var (
NullItem = Item{}
ErrItemNotFound = errors.New("item could not be found in the store")
)
func (store *Store) GetItem(id string) (Item, error) {
store.mtx.Lock()
defer store.mtx.Unlock()
item, ok := store.items[id]
if !ok {
return NullItem, ErrItemNotFound
}
return item, nil
}
With this simple change of making the error into a variable ErrItemNotFound, we ensure that anyone using this package can check against the variable, rather than the actual string that it returns:
func GetItemHandler(w http.ReponseWriter, r http.Request) {
item, err := clean.GetItem("123")
if err != nil {
if err == clean.ErrItemNotFound {
http.Error(w, err.Error(), http.StatusNotFound)
return
}
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
json.NewEncoder(w).Encode(item)
}
This feels much nicer and is also much safer. Some would even say that it's easier to read as well. In the case of a more verbose error message, it certainly would be preferable for a developer to simply read ErrItemNotFound rather than a novel on why a certain error has been returned.
This approach is not limited to errors and can be used for other returned values. As an example, we are also returning a NullItem instead of Item{} as we did before. There are many different scenarios in which it might be preferable to return a defined object, rather than initialising it on return.
Returning default Null values like the previous examples, can also be more safe, in certain cases. As an example, a user of our package could forget to check for errors and end up initialising a variable, pointing to an empty struct containing a default value of nil as one or more property values. When attempting to access this nil value later in the code, this could cause a panic in their code. However, when we return our custom default value instead, we can ensure that all values, which otherwise would default to nil, are initialised and thereby ensure that we do not cause panics in our users / clients software. This is also beneficial for ourselves, as if we wanted to achieve the same safety, without returning a default value, we would have to change our code, every place in which we return this type of empty value. However, with our default value approach, we now only have to change our code in a single place:
var NullItem = Item{
itemMap: map[string]Item{},
}
NOTE: In many scenarios, invoking the panic will actually be preferable. To indicate that there is an error check missing.
NOTE: Every interface property in Go, has a default value of
nil. This means that this is useful, for any struct, which has an interface property. This is also true for structs which contain channels, maps and slices, which could potentially also have anilvalue.
Returning Dynamic Errors
There are certainly some scenarios, where returning an error variable might not actually be viable. In cases where customised errors' information is dynamic, to describe error events more specifically, we cannot define and return our static errors anymore. As an example:
func (store *Store) GetItem(id string) (Item, error) {
store.mtx.Lock()
defer store.mtx.Unlock()
item, ok := store.items[id]
if !ok {
return NullItem, fmt.Errorf("Could not find item with ID: %s", id)
}
return item, nil
}
So, what to do? There is no well defined / standard method for handling and returning these kind of dynamic errors. My personal preference, is to return a new interface, with a bit of added functionality:
type ErrorDetails interface {
Error() string
Type() string
}
type errDetails struct {
errtype error
details string
}
func NewErrorDetails(err error, details ...interface{}) ErrorDetails {
return &errDetails{
errtype: err,
details: details,
}
}
func (err *errDetails) Error() string {
return fmt.Sprintf("%v: %v", err.details)
}
func (err *errDetails) Type() error {
return err.errtype
}
This new data structure still works as our standard error. We can still compare it to nil since it's an interface implementation and we can still call .Error() on it, so it won't break any already existing implementations. However, the advantage is that we can now check our error type as we could previously, despite our error now containing the dynamic details:
func (store *Store) GetItem(id string) (Item, error) {
store.mtx.Lock()
defer store.mtx.Unlock()
item, ok := store.items[id]
if !ok {
return NullItem, fmt.Errorf("Could not find item with ID: %s", id)
}
return item, nil
}
And our http handler function can then be refactored to check for a specific error again:
func GetItemHandler(w http.ReponseWriter, r http.Request) {
item, err := clean.GetItem("123")
if err != nil {
if err.Type() == clean.ErrItemNotFound {
http.Error(w, err.Error(), http.StatusNotFound)
return
}
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
json.NewEncoder(w).Encode(item)
}
Nil Values
A controversial aspect of Go, is the addition of nil. This value corresponds to the value NULL in C and is essentially an uninitialised pointer. Previously, we traversed in explained the troubles nil can cause, but to sum up: Things break, when you try to access methods or properties of a nil value. In the mentioned section, it was recommended to try an minimise usage of returning a nil value. This way, users of our code, would be less prone to accidentally access nil values by a mistake.
There are other scenarios in which it is common to find nil values, which can cause some unnecessary pain. As an example, the incorrect initialisation of a struct can lead to the struct containing nil properties. If accessed, they will cause a panic. An example of this, can be seen below:
type App struct {
Cache *KVCache
}
type KVCache struct {
mtx sync.RWMutex
store map[string]string
}
func (cache *KVCache) Add(key, value string) {
cache.mtx.Lock()
defer cache.mtx.Unlock()
cache.store[key] = value
}
This code is absolutely fine. However, we are exposed by the fact that our App can be initialised incorrectly, without initialising our Cache property within. Should the following code be invoked, our application will panic:
app := App{}
app.Cache.Add("panic", "now")
The Cache property, has never been initialised and is therefore a nil pointer the Add method, is invoked. Running this code will result in a panic, with the following message:
panic: runtime error: invalid memory address or nil pointer dereference
Instead, we can turn our Cache property of our App structure into a private property and create a getter-like method, to access the Cache property of our App. This gives us more control of what we are returning and ensuring that we aren't returning a nil value.
type App struct {
cache *KVCache
}
func (app *App) Cache() *KVCache {
if app.cache == nil {
app.cache = NewKVCache()
}
return app.cache
}
We now ensure that we will never experience returning a nil pointer, when trying to access the Cache property. Our code, which previously panicked, will now be refactored to the following:
app := App{}
app.Cache().Add("panic", "now")
The reason why this is preferable, is that we are ensuring that users of our package aren't worrying about the implementation and whether they are using our package in an unsafe manner. All they need to worry about is writing their own clean code.
NOTE: There are other methods to achieve a similar safe outcome, however, I think that this is the most straightforward method of doing this.
Pointers in Go
Pointers in go are rather a large topic. They are a very big part of working with the language, so much so, that it is essentially impossible to write go, without some knowledge of pointers and their workings in go. I will not go into detail, of the inner workings of Pointers in go in this article. Instead, we will focus on their quirks and how to handle them in go.
Pointers add complexity, however, as mentioned, it's almost impossible to avoid them when writing go. Therefore, it is important to understand how to use pointers, without adding unnecessary complexity and thereby keeping your codebase clean. Without restraining oneself, the incorrect use of pointers can introduce nasty side-effects, introducing bugs that are particularly difficult to debug. Of course, when sticking to the basic principles of writing clean code, introduced in the first part of this article, we limit our exposure of introducing this complexity, but pointers are a particular case, which can still undo all of our previous hard work, of making our code clean.
Pointer Mutability
I have already used the word mutability more than once in this article, as a negative. Mutability is obviously not a clear-cut bad thing and I am by no means an advocate for writing 100% pure functional programs. Mutability is a powerful tool, but we should really only ever use it, when it's necessary. Let's have a look at a code example illustrating why:
func (store *UserStore) Insert(user *User) error {
if store.userExists(user.ID) {
return ErrItemAlreaydExists
}
store.users[user.ID] = user
return nil
}
func (store *UserStore) userExists(id int64) bool {
_, ok := store.users[id]
return ok
}
At first glance, this doesn't seem too bad. In fact, it might even seem like a rather simple insert function for a common list structure. We accept a pointer as input and if no other users with this id exist, then we insert the user pointer into our list. Now, we use this functionality in our public API for creating new users:
func CreateUser(w http.ResponseWriter, r *http.Request) {
user, err := parseUserFromRequest(r)
if err != nil {
http.Error(w, err, http.StatusBadRequest)
return
}
if err := insertUser(w, user); err != nil {
http.Error(w, err, http.StatusInternalServerError)
return
}
}
func insertUser(w http.ResponseWriter, user User) error {
if err := store.Insert(user); err != nil {
return err
}
user.Password = ""
return json.NewEncoder(w).Encode(user)
}
Once again, at first glance everything looks fine. We parse the user from the received request and insert the user struct into our store. Once we have inserted our user into the store successfully, we then set the password to nothing, before returning the user as a JSON object to our client. This is all quite common practice, typically when returning a user object, where the password has been hashed, we don't want to return the hashed password.
However, imagine that we are using an in-memory store, based on a map, this code will produce some unexpected results. If we check in our user store, see that the change we made to the users password in the http handler function, also affected the object in our store. This is because the pointer address returned by parseUserFromRequest, is what we populated our store with, rather than an actual value. Therefore, when making changes to the dereferenced password value, we end up changing the value of the object we are pointing to in our store.
This is a great example of why both mutability and variable scope, can cause some serious issues and bugs, when used incorrectly. When passing pointers as an input parameter of a function, we are expanding the scope of our variable. Even more worrying, we are expanding the scope to an undefined level. We are almost expanding the scope of the variable to being a globally available variable. Depending on the variable scope of our store. As demonstrated by the above example, this can lead to disastrous bugs, which are particularly difficult to find and eradicate.
Fortunately, the fix for this bug, is rather simple:
func (store *UserStore) Insert(user User) error {
if store.userExists(user.ID) {
return ErrItemAlreaydExists
}
store.users[user.ID] = &user
return nil
}
Instead of passing a pointer to a User struct, we are now passing in a copy of a User. We are still storing a pointer to our store, however, instead of storing the pointer from outside of the function, we are storing the pointer to the copied value, which scope is inside the function. This fixes the immediate problem, but might still cause issues further down the line, if we aren't careful.
func (store *UserStore) Get(id int64) (*User, error) {
user, ok := store.users[id]
if !ok {
return EmptyUser, ErrUserNotFound
}
return store.users[id], nil
}
Again, a very standard very simple implementation of a getter function for our store. However, this is still bad. We are once again expanding the scope of our pointer, which may end up causing unexpected side-effects. When returning the actual pointer value, which we are storing in our user store, we are essentially giving other parts of our application the ability to change our store values. This is bad, because it's bound to ensure confusion. Our store should be the only entity enabled to make changes to the values stored there. The easiest fix available for this, is to either return a value of User rather than returning a pointer.
NOTE: Should our application use multiple threads, which is often the case. Passing pointers to the same memory location, can also potentially result in a race condition. In other words, we aren't only potentially corrupting our data, we could also cause a panic from a data race.
Please keep in mind, that there is intrinsically nothing wrong with returning pointers, however, the expanded scope and number of owners of the variables is the important aspect. This is what categorises our previous example a smelly operation. This is also why, that common Go constructors are also absolutely fine:
func AddName(user *User, name string) {
user.Name = name
}
The reason why this is ok, is that the variable scope, which is defined by whomever invokes the functions, remains the same after the function returns. This combined with the face that the ownership of the variable remains unchanged (it stays solely with the function invoker), means that the pointer cannot be manipulated in an unexpected manner.
Closures are Function Pointers
So, before we go to the next topic of using interfaces in Go. I would like to introduce the commonly overseen alternative, which is what C programmers know as 'function pointers' and most other programmers refer to as 'closures'. Closure are quite simple. They are an input parameter for a function, which act like any other parameter, except for the fact that they are a function. In Javascript, it is very common to use closures as callbacks, which is typically used in scenarios where upon we want to invoke a function after an asynchronous operation has finished. In Go, we don't really have this issue, or at the very least, we have other, much nicer, ways of solving this issue. Instead, in Go, we can use closures to solve a different hurdle: The lack of generics.
Now, don't get too excited. We aren't going to substitute the lack of generics. We are simply going to solve a subset of the lack of generics with the use of closures. Consider the following function signature:
func something(closure func(float64) float64) float64 { ... }
This function takes another function as input and will return a float64. The input function, will take a float64 as input, and will also return a float64. This pattern can be particularly useful, for creating a loosely coupled architecture, making it easier to to add functionality, without affecting other parts of the code. An example use case of this, could be for a struct containing data, which we want to manipulate in some form. Through this structures Do() method, we can perform operations on this data. If we know the operation ahead of time, we can approach problem this by placing the logic for handling the different operations, directly in our Do() method:
func (datastore *Datastore) Do(operation Operation, data []byte) error {
switch(operation) {
case COMPARE:
return datastore.compare(data)
case CONCAT:
return datastore.add(data)
default:
return ErrUnknownOperation
}
}
As we can imagine, this function will perform a predetermined operation on the data contained in the Datastore struct. However, we can also imagine, that at some point we would want to add more operations. Over a longer period of time, this might end up being quite a lot of different operations, making our Do method bloated and possibly even hard to maintain. It might also be an issue for people wanting to use our Datastore object, who don't have access to edit our package code. Keeping in mind, that there is no way of extending structure methods as there is in most OOP languages. This could also become an issue for developers wanting to use our package.
So instead, let's try a different approach, using closures instead:
func (datastore *Datastore) Do(operation func(data []byte, data []byte) ([]byte, error), data []byte) error {
result, err := operation(datastore.data, data)
if err != nil {
return err
}
datastore.data = result
return nil
}
func concat(a []byte, b []byte) ([]byte, error) {
...
}
func main() {
...
datastore.Do(concat, data)
...
}
However, other than this being a very messy function signature, we also have another issue with this. This function isn't particularly generic. What happens, if we find out that we actually want the concat function needs to be able to take multiple byte arrays as input? Or if want to add some completely new functionality, that may also need more or less input values than (data []byte, data []byte) ?
One way to solve this issue, is to change our concat function. In the example below, I have changed it to only take a single byte array as input argument, but it could just as well have been the opposite case.
func concat(data []byte) func(data []byte) ([]byte, error) {
return func(concatting []byte) ([]byte, error) {
return append(data, concatting), nil
}
}
func (datastore *Datastore) Do(operation func(data []byte) ([]byte, error)) error {
result, err := operation(datastore.data)
if err != nil {
return err
}
datastore.data = result
return nil
}
func main() {
...
datastore.Do(compare(data))
...
}
Notice how we have added some of the clutter from the Do method signature. The way that we have accomplished this, is by having our concat function return a function. Within the returned function, we are storing the input values originally passed in to our concat function. The returned function can therefore now take a single input parameter, and within our function logic, we will append it, with our original input value. As a newly introduced concept, this is quite strange, however, getting used to having this as an option can indeed help loosen up program coupling and help get rid of bloated functions.
In the next section, we will talk about interfaces, but let's take a short moment to talk about the difference between interfaces and closures. The problems that interfaces solve, definitely overlap with the problems solved by closures. The implementation of interfaces in Go makes the distinction of when to use one or the other, somewhat difficult at times. Usually, whether an interface or a closure is used, is not really of importance and whichever solves the problem in the simplest manner, is the right choice. Typically, closures will be simpler to implement, if the operation is simple by nature. However, as soon as the logic contained within a closure becomes complex, one should strongly consider using an interface instead.
Dave Cheney has an excellent write up on this topic, and a talk on the same topic:
- https://dave.cheney.net/2016/11/13/do-not-fear-first-class-functions
- https://www.youtube.com/watch?v=5buaPyJ0XeQ&t=9s
Jon Bodner also has a talk about this topic
Interfaces in Go
In general, the go method for handling interface's is quite different from other languages. Interfaces aren't explicitly implemented, like they would be in Java or C#, but are implicitly implemented if they fulfill the contract of the interface. As an example, this means that any struct which has an Error() method, implements / fulfills the Error interface and can be returned as an error. This has it's advantages, as it makes Go feel more fast-paced and dynamic, as interface implementation is extremely easy. There are obviously also disadvantages with this approach to implementing interfaces. As the interface implementation is no longer explicit, it can be difficult to see which interfaces are implemented by a struct. Therefore, the most common way of defining interfaces, is by writing interfaces with as few methods a possible. This way, it will be easier to understand whether or not a struct fulfills the contract of an interface.
There are other ways of keeping track of whether your structs are fulfilling the interface contract. One method, is to create constructors, which return an interface, rather than the concrete type:
type Writer interface {
Write(p []byte) (n int, err error)
}
type NullWriter struct {}
func (writer *NullWriter) Write(data []byte) (n int, err error) {
// do nothing
return len(data), nil
}
func NewNullWriter() io.Writer {
return &NullWriter{}
}
The above function ensures, that the NullWriter struct implements the Writer interface. If we were to delete the Write method for the NullWriter we would get a compilation error, were we to try and build the solution. This is a good way of ensuring our code behaves in the way that we expect and that we can use the compiler as a safety net to ensure that we aren't producing invalid code.
There is another way of trying to be more explicit about which interfaces a given struct implements. However, this method achieves the opposite of what we wish to achieve. The method being, using embedded interfaces, as a struct property.
Wait what? – Presumably most people
So, let's rewind a little, before we dive deep into the forbidden forest of smelly Go. In Go, we can use embedded structs, as a type of inheritance in our struct definitions. This is really nice as we can decouple our code, by defining reusable structs.
type Metadata struct {
CreatedBy types.User
}
type Document struct {
*Metadata
Title string
Body string
}
type AudioFile struct {
*Metadata
Title string
Body string
}
Above, we are defining a Metadata object, which will provide us with property fields that we are likely to use on many different struct types. The neat thing about using the embedded struct, rather than explicitly defining the properties directly in our struct, is that it has decoupled the Metadata fields. Should be choose to update our Metadata object, we can change it in a single place. As mentioned earlier, we want to ensure that a change one place in our code, doesn't break other parts of our code. Keeping these properties centralised, will keep it clear to users that a structures with embedded Metadata have the same properties. Much like, structures that fulfill interfaces, have the same methods.
Now, let's look at an example of how we can use a constructor, to further prevent breaking our code, when making changes to our Metadata struct:
func NewMetadata(user types.User) Metadata {
return &Metadata{
CreatedBy: user,
}
}
func NewDocument(title string, body string) Document {
return Document{
Metadata: NewMetadata(),
Title: title,
Body: body,
}
}
At a later point in time, we find out, that we would also like a CreatedAt field on our Metadata object. This is now easily achievable, by simply updating our NewMetadata constructor:
func NewMetadata(user types.User) Metadata {
return &Metadata{
CreatedBy: user,
CreatedAt: time.Now(),
}
}
Now, both our Document and AudioFile structures are updated, to also populate these fields on construction. This is the core principle behind decoupling and an excellent example of ensuring maintainability of code. We can also add new methods, without breaking our code:
type Metadata struct {
CreatedBy types.User
CreatedAt time.Time
UpdatedBy types.User
UpdatedAt time.Time
}
func (metadata *Metadata) AddUpdateInfo(user types.User) {
metadata.UpdatedBy = user
metadata.UpdatedAt = time.Now()
}
Again, without breaking the rest of our code base, we are implementing new functionality to our already existing structures. This kind of programming, makes implementing new features very quick and very painless, which is exactly what we are trying to achieve by making our code clean.
Now, I am sorry to break this streak of happiness, because now we return to the smelly forbidden forest of Go. Let's get back to our interfaces and how to show explicitly which interfaces are being implemented by a structure. Instead of embedding a struct, we can embed an interface:
type NullWriter struct {
Writer
}
func NewNullWriter() io.Writer {
return &NullWriter{}
}
The above code compiles. The first time I saw this, I couldn't believe that this was actually compiling. Technically, we are implementing the interface of Writer, because we are embedding the interface and "inheriting" the functions which are associated with this interface. Some see this as a clear way of showing that our NullWriter is implementing the Writer interface. However, we have to be careful using this technique, as we can no longer rely on the compiler to save us:
func main() {
w := NewNullWriter()
w.Write([]byte{1, 2, 3})
}
As mentioned before, the above code will compile. The NewNullWriter returns a Writer and everything is honky-dori, according to the compiler, because NullWriter fulfills the contract of io.Writer, via. the embedded interface. However, running the code above will result in the following:
panic: runtime error: invalid memory address or nil pointer dereference
The explanation being, that an interface method in Go, is essentially a function pointer. In this case, since we are pointing the function of an interface, rather than an actual method implementation, we are trying to invoke a function, which is in actuality a nil pointer. Oops! Personally, I think that this is a massive oversight in the Go compiler. This code should not compile... but while this is being fixed (if it ever will be), let's just promise each other, never to implement code in this way. In an attempt to be more clear with our implementation, we have ended up shooting ourselves in the foot and bypassing compiler checks.
Some people argue that using embedded interfaces, is a good way of creating a mock structure, for testing a subset of interface methods. Essentially, by using an embedded interface, you won't have to implement all of the methods of an interface, but instead only implement the few methods that you wish to be tested. Within testing / mocking, I can see the argument, but I am still not a fan of this approach.
Let's quickly get back to clean code and quickly get back to using interfaces the proper way in Go. Let's talk about using interfaces as function parameters and return values. The most common proverb for interface usage with functions in Go is:
Be conservative in what you do; be liberal in what you accept from others – Jon Postel
FUN FACT: This proverb originally has nothing to do with Go, but is actually taken from an early specification of the TCP networking protocol.
In other words, you should write functions that accept an interface and return a concrete type. This is generally good practice, and becomes super beneficial when doing tests with mocking. As an example, we can create a function which takes a writer interface as input and invokes the Write method of that interface.
type Pipe struct {
writer io.Writer
buffer bytes.Buffer
}
func NewPipe(w io.Writer) *Pipe {
return &Pipe{
writer: w,
}
}
func (pipe *Pipe) Save() error {
if _, err := pipe.writer.Write(pipe.FlushBuffer()); err != nil {
return err
}
return nil
}
Let's assume that we are writing to a file when our application is running, but we don't want to write to a new file for all tests which invokes this function. Therefore, we can implement a new mock type, which will basically do nothing. Essentially, this is just basic dependency injection and mocking, but the point is that it is extremely easy to use in go:
type NullWriter struct {}
func (w *NullWriter) Write(data []byte) (int, error) {
return len(data), nil
}
func TestFn(t *testing.T) {
...
pipe := NewPipe(NullWriter{})
...
}
NOTE: there is actually already a null writer implementation built into the ioutil package named
Discard
When constructing our Pipe struct with the NullWriter (rather than a different writer), when invoking our Save function, nothing will happen. The only thing we had to do, was add 4 lines of code. This is why in idiomatic go, it is encouraged to make interface types as small as possible, to make implement a pattern like this as easy as possible. However, this implementation of interfaces, also comes with a huge downside.
The empty interface{}
Unlike other languages, go does not have an implementation for generics. There have been many implementation proposals, but all have been deemed dissatisfactory by the Go language team. Unfortunately, without generics, developers are trying to find creative ways around this issue, very often using the empty interface{}. The next section, will describe why these, often too creative, implementations should be considered bad practice and unclean code. There will also be good examples of usage of the empty interface{} and how to avoid some pitfalls of writing code with the empty interface{}.
But first and foremost. What drives developers to use the empty interface{}? Well, as I said in the previously, the way that Go determines whether a concrete type implements an interface, is by checking whether it implements the methods of a specific interface. So what happens, if our interface implement no methods at all?
type EmptyInterface interface {}
The above being equivalent to the built-in type interface{}. The result of this interface type is that any type is accepted. Meaning, that we can write functions in which any type is accepted. This is super useful for certain kind of functions, such as when creating a printer function. This is how it's possible to give any type to the Println function from the fmt package:
func Println(v ...interface{}) {
...
}
In this case, we aren't only accepting a single interface{} but rather, a slice of types. These types can be of any type and can even be of different types, as long as they implement the empty interface{}, which we are certain that any type will. This is a super common pattern when handling string conversation (both from and to string). The reason being, this is the only way in Go to implement generic methods. Good examples of this, come from the json standard library package:
func InsertItemHandler(w http.ResponseWriter, r *http.Request) {
var item Item
if err := json.NewDecoder(r.Body).Decode(&item); err != nil {
http.Error(w, err.Error(), http.StatusBadRequest)
return
}
if err := db.InsertItem(item); err != nil {
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
w.WriteHeader(http.StatsOK)
}
All the less elegant code, is contained within the Decode function. Developers using this functionality, therefore, won't have to worry about reflection or casting of types. We just have to worry about providing a pointer to a concrete type. This is good, because the Decode() function is, technically, returning a concrete type. We are passing in our Item value, which will be populated from body of the http request and we won't have to deal with the potential risks of handling the interface{} value.
However, even when using the empty interface{} with good practices, we still have some issues. If we pass a JSON string that has nothing to do with our Item type, but is still valid son, we still won't receive an error. Our item variable will just be left with the default values. So, while we don't have to worry about reflection and casting errors, we will still have to make sure that the message sent from our client is a valid Item type. However, as of writing this document, there is no simple / good way to implement these type of generic decoders, without using the empty interface{} type.
The problem with this, is that we are leaning towards using Go (a statically typed language) as a dynamically typed language. This becomes even clearer, when looking at poor implementations of the interface{} type. The most common example of this, comes from developers trying to implement a generic store / list of some sort. Let's look at an example, trying to implement a generic HashMap package, which can store any type, using the interface{}.
type HashMap struct {
store map[string]interface{}
}
func (hashmap *HashMap) Insert(key string, value interface{}) {
hashmap.store[key] = value
}
func (hashmap *HashMap) Get(id string) (interface{}, error) {
value, ok := hashmap.store[key]
if !ok {
return nil, ErrKeyNotFoundInHashMap
}
return value
}
NOTE: I have omitted thread-safety from the example for simplicity
Please keep in mind that the implementation pattern used above, is used in quite a lot of Go packages. It is even used in the standard library sync package, for the sync.Map type. So, what is the big problem with this implementation? Well, let's have a look at an example of using this package.
func SomeFunction(id string) (Item, error) {
itemIface, err := hashmap.Get(id)
if err != nil {
return EmptyItem, err
}
item, ok := itemIface.(Item)
if !ok {
return EmptyItem, ErrCastingItem
}
return item, nil
}
On first glance, this looks fine. However, like mentioned previously. However, we will start getting into trouble, should we add different types in our store, which as of now, is not prevented. There is nothing limiting us from adding something other than the Item type. So what happens when someone starts adding other types into our HashMap? Our function now might return an error. This might even be a small change like someone else in the code base wanting to store a pointer *Item instead of an Item. Worst of all, this might not even be caught by our tests. Depending on the complexity of the system, this might introduce some bugs particularly difficult to debug.
This type of code, should never reach production. The matter of the fact is, that Go does not support generics as of now and as Go programmers, we should accept this. If we want to use generics, then we should use a different language which does support generics, rather than trying hack our way out of this.
So, how do we prevent this code from reaching production? The simples solution for our problem, is basically to just write the functions with concrete types, instead of using interface{} values. Of course, this is not always the best approach, as there might be some functionality within the package which is not trivial to implement ourselves. Therefore, it might be a better approach to create wrappers, which expose the functionality we need, but still ensure type safety:
type ItemCache struct {
kv tinykv.KV
}
func (cache *ItemCache) Get(id string) (Item, error) {
value, ok := cache.kv.Get(id)
if !ok {
return EmptyItem, ErrItemNotFound
}
return interfaceToItem(value)
}
func interfaceToItem(v interface{}) (Item, error) {
item, ok := v.(Item)
if !ok {
return EmptyItem, ErrCouldNotCastItem
}
return item, nil
}
func (cache *ItemCache) Put(id string, item Item) error {
return cache.kv.Put(id, item)
}
NOTE: Implementations of other functionalities of the tinykv.KV cache has been left out for the purpose of brevity.
Creating the wrapper above, will now ensure that we are using the actual types and that we are no longer passing in interface{} types. It is therefore no longer possible to accidentally populate our store with a wrong value type and we have contained our casting of types, as much as possible. This is a very straight forward way of solving our issue, though somewhat manual.
Summary
First of all, thank you for making it all the way through the article. I hope that it has given some insight into what clean code is, as well as how it will help ensure maintainability, readability and stability in your code base. To sum up all the topics covered:
Functions - Naming of functions should become more specific, the smaller the scope of the function. Ensure that all functions are single purpose. A good measure, being to limit your function length to 5-8 lines and only takes 2-3 input arguments.
Variables - Naming of variables should become less specific the smaller the scope, and keep the scope of your variables to a minimum. Also, keep the mutability of your variables to a minimum and be more and more aware of this as their scope grows.
Return Values - Concrete types should be returned whenever they can. Make it as hard as possible for users of your package to create mistakes and as easy for them to understand the values returned by your functions
Pointers - Use pointers with caution and limit scope and mutability to an absolute minimum. Garbage collection only assists with memory management, it does not assist with all the other complexities associated with pointers.
Interfaces - Use interfaces as much as possible to loosen the coupling of your code. Contain any code using the empty interface{} as much as possible and prevent it being exposed.
Of course, what is considered clean code is particularly subjective and I don't think that will ever change. However, much like my statement concerning gofmt, I think it's more important to find a common standard, rather than a standard that everyone agrees with 100%. It's also important to understand that fanaticism is never the goal. A codebase will most likely never be 100% 'clean', in the same way as your office desk isn't either. There is room for stepping outside the rules and boundaries established in this article. However, remember that the most important aspect of writing clean code, is helping one another. We help our support engineers, by ensuring stability in software and easy debugging. We help our fellow developers by ensuring our code is readable and easily digestible. We help everyone involved in the project by establishing a flexible code base, in which we can quickly introduce new features without breaking our current platform. We move quickly by going slowly and thenceforth, everyone is satisfied.
I therefore hope, that you will join the discussion to help what we, the Go community, define as clean code. Let's establish a common ground, so that we improve software. Not only for ourselves, but the sake of everyone.