Changes I would make to Go

  • By Jesse Hallett
  •  • 
  • 21st Feb 2017
  •  • 
  • 45 min read
  •  • 
  • Tags: 
  • Go
  • Rust

I have been programming primarily in Go for about six months. I find it frustrating. There are two reasons for this:

First, functional programming is particularly difficult in Go. In fact the language discourages functional programming. This frustrates me because the imperative code that I write requires a lot of boilerplate, and I think it is more error-prone than it could be if I could use functional abstractions.

Second, I see Go as a missed opportunity. There is exciting innovation in programming languages - especially in areas of type checking and type inference to make code safer, faster, and more ergonomic. I wish Google had chosen to put their weight behind some of those ideas.

I am not the first person to look at Go this way. Here are other posts that echo my feelings:

I will add to those with some of my own opinions. To highlight exactly how I think Go could be better, I want to make comparisons to Rust.

Work on Go and Rust began at close to the same time: Go was first announced to the world in 2009, Rust in 2010. There is a lot of overlap in their philosophies; both languages:

  • compile to fast, native binaries
  • eschew inheritance in favor of composition
  • support imperative programming
  • omit exception-catching in favor of explicitly passing error results
  • emphasize concurrency
  • come with static type-checking
  • come with a modern packaging system that promotes modularity

Both languages may have been intended to replace C++: Go’s designers have said that a primary motivation for Go was their dislike of the complexity of C++. One of Mozilla’s prominent applications for Rust is Servo, a potential replacement for the Gecko HTML rendering engine, which is written in C++.

As I see it the key differences are that Rust aims for soundness,[1] powerful abstractions, and high performance; Go aims to be accessible, consistently simple, and fast to compile.

That said, Rust is not necessarily a replacement for Go, and I do not mean to say that everyone using Go should switch to Rust. Rust supports real-time operation, can operate with stack-only allocation if necessary, and has a sophisticated type system that can, for example, detect problems due to concurrent access to shared data at compile time. Those requirements add to the complexity of Rust programs. The borrow-checker in particular has a reputation for its learning curve. My purpose for making comparisons to Rust is to provide context for specific ways in which I think Go could be better. Rust borrows a lot of good ideas from other languages, and puts them together in one nice package. Most of those ideas are not even tied to borrow-checking. I think that Go could be a better language if it adopted some of the same ideas that Rust has adopted.

You can grab the code examples from this post. The examples come with executable tests to encourage experimentation.

So, here is my view of how Go compares:

The Good

I think that Go’s use of interfaces to promote composition is great.

I like the separation of behavior and data: structs store data, methods manipulate data in structs - it is clean separation of state (structs), and behavior (methods). I think that distinction can become unfortunately blurry in languages that use inheritance.

Go is easy to learn. Go repurposes object-oriented concepts to make something new in a way that makes it approachable to programmers who are familiar with other object-oriented languages.

There is often a pretty clear “Go way” to solve a problem. This is also an oft-touted virtue of, for example, Python. Encouraging consistent idioms via the languages makes it likely that any Go programmer will be able to understand code written by any other Go programmer. This is part of a philosophy of simplicity that is described in the keynote Simplicity and collaboration.

There are lots of features in the Go standard libraries that have clearly had a lot of thought put into them. This is one of my favorites:

fiveSeconds := 5 * time.Seconds

Goroutines are cheap, so programs can be structured in the way that makes most sense algorithmically, even if that involves spawning large numbers of goroutines. (But this is not unique to Go. Erlang and Scala also implement lightweight actors. Rust and other languages have their own solutions for lightweight concurrent and parallel programming.)

Since I am using Rust as a point-of-reference I will point out that Rust has a separation of behavior and data that is a lot like Go’s, and Rust also goes for composition-over inheritance. Instead of structs and interfaces, Rust uses structs, enums, and traits. Rust traits serve the same purpose as interfaces; but they are different enough that they might seem a little weird to programmers with an object-oriented background. Rust and Go differ in that Rust prioritizes expressiveness over simplicity, and type safety over fast compile times. (Fast compile times are a high priority for the Rust team - just not the top priority.)

I could go on - there are plenty of nice features in Go. But there are also:

The Bad

These are features of Go that I find especially frustrating.


I am disappointed by the decision to include null pointers in a new language when safer solutions have been in use for decades. Or to be more precise: I think it is a bad idea for a language to use nil as a bottom-ish type, where nil is type-compatible with every pass-by-reference type.

I understand that nil is not technically a null pointer - but its behavior is close enough that criticisms that apply to null pointers also apply to nil. I have read Understanding Nil, and I understand that it is possible to implement methods where the receiver is nil, and that nil can be useful. Go does some nice things to make nil less bad than it could be. But the fact remains: nil is type-compatible with every pass-by-reference type, whether or not methods on that type have sensible behavior for nil receivers, and that leads to lots of opportunities for runtime errors. To me that seems like an opportunity to make language changes so that it is easier for the type checker to catch problems at compile time.

Some newer languages have a null, but treat it as a distinct type that is generally not compatible with other types. (For example, Fantom and Flow do this.) In those languages values are non-nullable by default. Here is how one might declare and use a nullable variable in Flow when writing React code:

function LoginForm(props) {
  // The `?` in front of `HTMLInputElement` indicates that `emailInput` might be
  // `null`.
  let emailInput: ?HTMLInputElement

  // JSX syntax permits HTML-like tags in code
  return <form onSubmit={event => props.onLogin(event, emailInput)}>
    <input type="email" ref={thisElement => emailInput = thisElement} />
    <input type="submit" />

function onLoginTake1(event: Event, emailInput: ?HTMLInputElement) {

  // Type error! Cannot read property `value` on possibly `null` or `undefined`
  // value.

function onLoginTake2(event: Event, emailInput: ?HTMLInputElement) {

  if (emailInput) {
    // This is ok because Flow infers that `emailInput` cannot be `null` or
    // `undefined` in this block.

Without a concept of nullability, uses of nil are contradictions of what is stated in your type signatures. This Go signature claims that the argument is a pointer to a User struct - but if you take that claim at face value you are likely to get a nil pointer dereference error:

func validate(user *User) bool {
	return user.Name != ""

In Go every variable with a pass-by-reference type comes with the implied ambiguity, “…or it might be nil”. Support for non-nullable types makes a language expressive enough to avoid that ambiguity.

The problem with nil in Go is exacerbated by the fact that nil checks sometimes fail. If an interface value has a concrete type but its value is nil, a nil check will not return true. The purist explanation for this is that the value is not really nil: it is an interface value that happens to have nil in its value slot. I don’t find that explanation to be satisfactory. When a method is dispatched on that not-really-nil value, the receiver value will really be nil in the method body.

But what about zero values? What would the zero value be for a function type or an interface type without nil? Well, I think that zero values are also a bad idea.

One of the design decisions in Go is that every type must have a well-defined default value, which is called the zero value. This can be convenient because it means you do not have to write constructors by hand when all you need is a default value. But I suspect that the real reason why zero values are a part of Go is that they provide well-defined behavior for what happens when you use uninitialized variables. C and C++ are notorious for undefined behaviors, which lead to traps for programmers, and to problems making code portable between compiler implementations. Use of uninitialized variables is one notable example of undefined behavior in both languages.[2] I imagine that the designers of Go learned from C and C++, and set out to clearly define as much of Go’s behavior as possible. I think that is a great attitude for language designers to adopt! But there is another option that I think leads to better code safety: Rust, Flow, and other languages use data-flow analysis to detect uses of uninitialized variables, and fail type-checking if any such uses are found.

The zero-value requirement introduces a constraint that nil must exist and must be assignable to a variety of types. A lot of types have no sensible default value, so nil is the only choice. So that is one problem. Another problem is that the language does not have enough information to produce sensible default values for domain-specific types. The fact that it tries anyway undermines soundness in code. The zero values for functions and interface values (i.e., interface values with no runtime type tag) are not going to be useful under any circumstances. Pointer types can implement methods with nil receivers - but that is not useful for types that do not have sensible behavior for uninitialized values. Default struct values can be useful in some cases - but in other cases the default value violates invariants that would have been enforced by a hand-written constructor.

The author of Three Months of Go called out problems with zero values in work at Pusher:

Zero values caused so many problems over the summer, because everything would appear to be fine, then it suddenly breaks because the zero value wasn’t sensible for its context of use. Perhaps it’s an unrelated change that causes things to break (like a struct getting an extra field).

How Rust does it differently

Rust goes a step beyond nullable types: it does not have a null or nil value. Rust uses enums, which are types whose values come in multiple variants, where each variant is effectively a distinct struct. If you want to represent an absence of a value you use an enum variant that holds no data. The generalized form of this pattern is known as the “Option Pattern”. The definition of the Option type from Rust’s standard library looks approximately like this:

pub enum Option<T> {
    None,    // Holds no data
    Some(T), // Holds a value of the given type

None and Some are constructors: essentially they are each a function that returns a value of type Option<T>. Some takes one argument, None takes zero arguments. Given an Option<T> value, you can use pattern matching to determine which constructor was used to create the value; and pattern matching is also how you read back any constructor arguments. (In the case of a value created by calling Some(x), pattern matching lets you get access to that x value.)

Here is the Option pattern in action (source):

fn checked_division(dividend: i32, divisor: i32) -> Option<i32> {
    if divisor == 0 {
        // Failure is represented as the `None` variant
    } else {
        // Result is wrapped in a `Some` variant
        Some(dividend / divisor)

fn divides_a_number() {
    let result = checked_division(12, 4);
    match result {
        Some(num) => assert_eq!(3, num), // Pattern on the left binds the variable `num`
        None      => assert!(false, "Expected `Some` but got `None`")

An advantage of the option pattern over nullable types is that you can distinguish between values like None versus Some(None). If you are looking up values in a cache (for example), a None result might indicate that there is no entry in the cache for a given key; and a Some(None) result might indicate that there is an entry, and the value of the entry is None.

I once advocated for use of the Option pattern at a Java company - but at least one of my coworkers was put off by the idea of allocating an extra object on the heap just to distinguish between a value and an absence of a value. Rust is built with the Option pattern in mind, and prioritizes zero-cost abstractions: If the type parameter for Option<T> is a reference type, then the None case can be safely represented at runtime as a null pointer. So the Some or None wrapper often disappears at compile time. In those cases the code is just as efficient as it would have been if the language allowed uses of unsafe null values in source code.

In the example above since neither Option<i32> nor the i32 parameter are reference types, the compiler allocates contiguous space on the stack for the numeric result, and for a tag to distinguish between Some and None. There is no extra heap allocation, and no added pointer indirection.

The Rust Book has a lot more detail on error handling.

It is possible to implement the Option pattern in Go with similar efficiency; and one could even get a compile-time check that errors are handled by implementing a match method that uses the visitor pattern (example). But without generics there would be no type safety for values wrapped in an Option type.

Error-handling boilerplate & lack of compile-time checks unhandled errors

Error handling in Go has two related problems: there is a lot of boilerplate required; and if the programmer neglects to check for an error, or makes a small mistake such as checking the wrong error variable, the compiler will not detect the problem.

func doStuff() error {
	_, err := doThing1()
	if err != nil {
		return err

	_, errr := doThing2()  // Error not propagated due to a bouncy key
	if errr != nil {
		return err

	return nil

Rust has a type, Result<T,E>, that is quite similar to Option<T>. The difference is that the failure variant of the Result<T,E> enum is not empty - it contains an error value (of type E). A returned value of type Result<T,E> may be either Ok(value) (on success) or Err(err) (on error).

pub enum Result<T, E> {

A common complaint about the Option and Result patterns is that unwrapping success values is a pain. But a language with support for pattern matching makes unwrapping painless, and first-class result values permit combinators that can handle a series of potential failures more cleanly and safely than explicit error checks.

Consider this Go function:

func fetchAllBySameAuthor(postID string) ([]Post, error) {
	post, err := fetchPost(postID)
	if err != nil {
		return nil, err

	author, err := fetchUser(post.AuthorID)
	if err != nil {
		return nil, err

	return fetchPosts(author.Posts)

There are a few ways the fetchAllBySameAuthor function this could be implemented in Rust. The pattern matching approach is probably the most accessible to people who do not have prior experience with the Option or Result Pattern:

fn fetch_all_by_same_author(post_id: &str) -> Result<Vec<Post>, io::Error> {
    let post = match fetch_post(post_id) {
        Ok(p)    => p,
        Err(err) => return Err(err),

    let author = match fetch_user(&post.author_id) {
        Ok(a)    => a,
        Err(err) => return Err(err),


The match keyword introduces a pattern-match block. The block includes a pattern for each possible variant of the type of the expression in the head of the match paired with an expression to evaluate if that pattern matches. This is somewhat like the type switch feature in Go, where the code that runs depends on the type of the variable in the head of the switch block. But the Rust version comes with a compile-time check that a pattern is given for every possible variant of the given type, which avoids potential runtime errors. This is especially helpful when a custom type is updated to add new variants: the compiler will immediately point out any uses of the type that need to be updated.

Anyway, that Rust code is no less verbose than the Go version - but it demonstrates that unwrapping Result<T,E> or Option<T> values does not have to be any more onerous than nil checks; and if we had left out a failure check in the Rust version Rust would have emitted an error at compile time.

Rust has a macro, try!, that abstracts the pattern matches and early returns that we see above. So this is an equivalent function:

fn fetch_all_by_same_author(post_id: &str) -> Result<Vec<Post>, io::Error> {
    let post   = try!(fetch_post(post_id));
    let author = try!(fetch_user(&post.author_id));

try! rewrites an expression at compile time. For example, try!(fetch_post(post_id)) is expanded to put the fetch_post call inside a match, and inserts the boilerplate pattern matches for the Ok and Err match cases.

The try! macro was used so often that Rust’s designers decided to extend the language a bit to better support the pattern: putting the ? postfix operator at the end of an expression has the same effect. For example, the line let post = try!(fetch_post(post_id)); can be equivalently written as let post = fetch_post(post_id)?; And type-checking will fail appropriately if you forget the ?.

But Go does not support macros. Thankfully, the Result pattern does not require macros to be concise. For the more functionally-inclined, here is another equivalent implementation that uses combinator methods:

fn fetch_all_by_same_author(post_id: &str) -> Result<Vec<Post>, io::Error> {
    let post   = fetch_post(post_id);
    let author = post.and_then(|p| fetch_user(&p.author_id));
    author.and_then(|a| fetch_posts(&a.posts))

and_then is a method on Result<T,E> values. If the value is a successful result, it runs the given callback, which should return a new Result<U,E> value. Or if the value is an error result, and_then short-circuits, and passes the error result through. and_then is a lot like the then method on Javascript promises.

But wait - what if you want to wrap error results to add context? There is a combinator for that to: map_err permits arbitrary transformations to error results.

let post = fetch_post(post_id)
    .map_err(|e| io::Error::new(io::ErrorKind::NotFound, e));

The idea is that failure checks are almost always the same: check for an error, return the error if it exists, otherwise continue. The DRY thing to do is to abstract the common pattern into a helper method or a macro. And again, a common theme in all of these Rust implementations is that there is a compile-time guarantee that every error is handled. That could be with some recovery code, or by passing the error up the call stack.

Result<T,E> does not get the same disappear-at-compile-time optimization that Option<T> does because both enum variants hold data. But its efficiency compares favorably with Go’s multiple return values. Go allocates enough space for every value in a multiple return. Rust allocates enough space to hold either T or E (i.e., enough space to hold the largest possible value), plus a tag to distinguish between an Ok(value) value and an Err(err) value.

A nice thing about the generality of Rust enums is that if Result<T,E> did not exist, it would be easy to implement it as a library. So what about using the Result pattern in Go? Well, we can’t put methods on Go tuples (a.k.a, multiple return values), because they are not first-class values. It is not possible to define a function that that accepts a tuple and a callback: a Go function that takes a tuple cannot accept additional arguments (because Go tuples are not first-class values). Those constraints make the combinator pattern difficult. We could implement a custom struct type - but without generics it would not be very useful.

List manipulation is not practical

There is a special slap-in-the-face for functional programmers built into Go: there is no good way to write a polymorphic function that can manipulate slices with arbitrary types. In Rust you can write a function with a signature that looks like this:

fn map<A, B, F>(callback: F, xs: &[A]) -> Vec<B>
    where F: Fn(&A) -> B {

That is a function that takes a callback and an input slice, and returns a new array that is computed by accumulating the results of applying the callback to each element in the input array. Even better, there are built-in methods in Rust’s iterator types that do exactly this. The input slice might hold any type of values; type variables allow the type checker to track how the type of the output array relates to the type of the input slice, and also allow the type checker to check that the callback has the appropriate input and output types.

That pattern does not work well in Go. Without type variables the only way to express a type that is polymorphic over all slice types is to use the top-type: []interface{}. For example:

func Map(callback func(x interface{}) interface{}, xs []interface{}) []interface{} {
	ys := make([]interface{}, len(xs))
	for i, x := range xs {
		ys[i] = f(x)
	return ys

But that function is not really polymorphic: a slice type with a more specific type parameter (e.g., []int) is not type-compatible with []interface{}. So you cannot pass a variable with type []int to that Map function. You have to create a new slice of type []interface{} first, and copy int values to the new slice one-by-one in a for loop. Then after getting a result from Map you have to copy result values into yet another slice to get the proper final slice type. That means two custom loops are required around every invocation of Map - plus a runtime type assertion or type switch in the callback implementation.

A slice with an arbitrary type parameter is type-compatible with the top-type, interface{}. If you just use interface{} type for every polymorphic argument you get a signature like this:

func Map(callback interface{}, xs interface{}) interface{}

With that signature you can pass in any slice type and callback type that you want, and assign the result to a variable with the proper type. But to make that work it is necessary to use the reflection API to fix up runtime type tags for the input slice, the input callback, and the output slice. The process is described in Writing type parametric functions in Go. The reflection code is ugly, but can be hidden in implementations of general-purpose functions. The unavoidable downsides are that you lose all compile-time type-safety, and there is an order-of-magnitude performance cost.

The same problem applies to other list manipulation functions: Filter, Take, Reduce, etc. This is bad because list manipulation is the bread-and-butter of functional programming. The fact that Go discourages such a basic building block as Map means that functional programming is not likely to thrive in Go, and the Go community will not be able to benefit from the advantages of functional programming.

You might have noticed a pattern here: Go does not support generics, and that leads to problems. Except the issue is not just lack of generics. Dynamic languages like Javascript, Python, and Ruby do not support generics either - at least not in the sense of compile-time checking. But functional idioms work just fine in those languages. For example, in Javascript you can pass any list to a generic list manipulation function, and it just works. Go occupies an awkward middle ground where it checks types at compile time, but does not provide tools to explain to the compiler how input and output types relate to each other.

Generics - and type variables in particular - are a means to “talk” about types. They let you use function signatures to make statements like, “This function takes a slice of values of some type, and returns a slice of values of the same type.” Working in a programming language with no type variables is as frustrating to me as talking in a spoken language without the word “the”.

So Go code must re-implement list abstractions everywhere. Consider this Go function:

// Take the titles of the first `count` docs that are not archived
func LatestTitles(docs []Document, count int) []string {
	var latest []string
	for _, doc := range docs {
		if len(latest) >= count {
			return latest
		if !doc.IsArchived {
			latest = append(latest, doc.Title)
	return latest

That function iterates over an input collection, skips over some values, does something with the values that are not skipped, and returns a collection with the results. In other words, this is a filter, map, take operation. Here is an equivalent Rust implementation:

fn latest_titles(docs: &[Document], count: usize) -> Vec<&str> {
        .filter(|doc| !doc.is_archived)
        .map(|doc| doc.title.as_str())

The Rust version lets you say what you mean. In other words, the Rust function is “declarative”. And the difference becomes more pronounced if you want to process values concurrently - for example to make network requests in parallel. More on that in a bit.

I once complained to a coworker about a lack of abstractions in Go. My coworker replied, “Well, maybe you shouldn’t be doing that.” So I want to emphasize that functional abstractions do not necessarily make code less efficient. Rust typically does “list” manipulation using lazily-evaluated iterators. (This is also how the standard data structures in Clojure work.) So you can chain filter, map, take, without intermediate collection allocations, and without wasting cycles on computing values beyond those that the caller requests. The function above does not run those filter and map callbacks on every element in the input collection - it stops processing elements as soon as it has enough results to satisfy the take(count) step. On top of that, iter, filter, map, take, and collect are polymorphic methods, but they are dispatched statically thanks to a compile-time monomorphization step. And the compiler will probably inline the filter and map callbacks. There are some notes in the Rust Book on performance of functional abstractions on iterators.

It is possible that my coworker was more concerned with cognitive burden than with performance. I think that complaints of cognitive burden may be partly a result of looking at unfamiliar idioms. To an experienced functional programmer, a call to, e.g., map states a clear intention: “The input collection will be transformed according to this mapping function.” With some practice declarative code is fast to read and understand. And type checkers are more effective at checking declarative code than at checking custom for loops.

Let’s get into the parallel-fetch problem that I mentioned. Here is a Go function that I wrote recently to fetch a set of documents in parallel:

func (client docClient) FetchDocuments(ids []int64) ([]models.Document, error) {
	docs := make([]models.Document, len(ids))
	var err error

	var wg sync.WaitGroup

	for i, id := range ids {
		go func(i int, id int64) {
			doc, e := client.FetchDocument(id)
			if e != nil {
				err = e
			} else {
				docs[i] = *doc
		}(i, id)


	return docs, err

The use of a WaitGroup, the allocation of a new slice, copying result values, and explicit error checks are all low-level details that I should not have to reimplement every time I want to do things concurrently.

Now that I am looking at this again, I realize there might be a problem here with concurrent access to the docs slice. Maybe it would be a good idea to use a mutex around updates to docs, or to send results from goroutines back to the main thread via a channel. But if I use a channel I will need to implement a custom struct or use two channels, because I want to capture errors, and I cannot send the type (models.Document, error) over a channel, because Go tuples are not first-class values…

Rust gives a compile-time error if a mutable reference to a thread-unsafe data structure is passed to a function that could run in another thread. So I don’t have to worry as much about what is or is not thread-safe when writing Rust code. But that is almost made moot by the fact that Rust can hide hairy concurrency details in library functions.

Compare the Go code to an equivalent Rust function that uses the futures library:

fn fetch_documents(ids: &[i64]) -> Result<Vec<Document>, io::Error> {
    let results = ids.iter()
        .map(|&id| fetch_document_future(id));

// The implementation of `fetch_document_future` is left as an exercise to the reader.

The Rust function works the same way that the Go function does: if all fetches are successful, you get a collection of data from responses. But if any fetches fail, you get the first failure as an error value. The difference is that in the Rust version the concurrency, mapping, and error-checking details are handled by a general-purpose library. (Another difference is that the Rust version returns as soon as any fetch fails, but the Go version always waits for all fetches to finish.)

This example shows that the map abstraction is so powerful that concurrent versus sequential execution is an implementation detail. (I will talk more about more about functional parallel processing in a later section.)

The Rust implementation assumes that fetch_document returns a Future. The function future::join_all also returns a Future. (Futures work very similarly to promises in Javascript: they represent an eventual result or error.) It would be more idiomatic to return that last future directly instead of using wait to block on the result. But blocking gives us a function that is logically equivalent to the Go version, and shows that using futures in Rust does not lock you into using callback-driven code everywhere.

Using futures, and the related Stream type, makes some kinds of network server implementations much simpler than with blocking IO. In particular, streaming requests and responses are easy if code is implemented in terms of Stream values.

Third-party libraries are second-class citizens

Go has a magical function called make that seems to do whatever the standard library authors need for a given type. It takes a type as an argument, which is unlike most other Go functions. Called with one argument it initializes a small slice, map, or channel. make can take one or two integer arguments, depending on the choice of the first argument. For example, when creating a slice you can provide a length and a capacity:

mySlice := make([]int, 16, 32)

When creating a channel, you can specify the channel’s buffer size with a second argument to make. As far as I know, this is the only way to set a channel’s buffer size.

It seems that standard libraries have a special privilege to overload make to delegate to custom constructor code when initializing their types. Third-party libraries do not get to do this.

We see something like this with the range operator also. range is one of the few constructs that changes its behavior depending on the number of arguments that are assigned from its output:

for idx, value := range values { /* ... */ }  // `range` returns indexes and values
for idx := range values { /* ... */ }  // this time it just returns indexes

But more importantly: only standard library types get to be range-able. There is no way to make a third-party data type iterable. Library authors can implement adapters to output a view of their data structure as a slice, or to spit out values over a channel. But that puts extra complexity on code that uses third-party data structures, and requires programmers to use non-standard idioms.

Yet another privilege is that only standard types can be compared using ==, >, etc.

And of course only standard libraries are allowed to define generic types. This is severely limiting to the library ecosystem around Go. It means that, for example, a third-party functional data structures library, or a third-party futures library will never be as usable as the standard library collection types.

Rust supports generics for third-party code; and Rust implements iteration, equality, and comparison via traits that any third-party type can implement. Third-party Rust types are nearly indistinguishable from standard library types, which promotes innovation in Rust’s library ecosystem.

Incidentally, make and range are ad-hoc examples of a pattern that has generalized support in Rust: functions that are polymorphic in their return type. Rust traits are more flexible than typical object-oriented interfaces: when an interface method is dispatched the choice of the method implementation to execute is determined solely by the type of the receiver of the method. But a trait method implementation can be selected based on the type of the receiver, the type of any argument position, the combination of types of multiple argument positions (e.g., an equality trait can require that the two polymorphic arguments to an equals method have the same type), or by the expected return type. An implementation of make in Rust might look like this:

use std::sync::mpsc::{Receiver, Sender, channel};

// A `trait` declares some common behavior
trait Make {
    fn make() -> Self;
    fn make_with_capacity(capacity: usize) -> Self;

// An `impl` provides implementations of trait methods for given concrete types.
// Note that we can implement `Make` for the standard `Vec<T>` type right here.
// It is not necessary to change the original implementation of `Vec<T>`.
impl <T> Make for Vec<T> {
    fn make() -> Vec<T> {

    fn make_with_capacity(capacity: usize) -> Vec<T> {

// `Sender` and `Receiver` are Rust's standard channel types.
// This trait implementation constructs a connected sender/receiver pair.
impl <T> Make for (Sender<T>, Receiver<T>) {
    // Rust channels do not have a fixed buffer size - a channel is either
    // non-blocking (with an unlimited buffer), or is blocking. But a non-blocking
    // channel has a different type; so that will require a different trait impl.
    fn make() -> (Sender<T>, Receiver<T>) {

    fn make_with_capacity(_: usize) -> (Sender<T>, Receiver<T>) {

fn makes_a_vec() {
    // We specify a type for the variable that holds the output of `make`.
    // This instructs the compiler which implementation of `make` to call.
    let mut v: Vec<&str> = Make::make();
    v.push("some string");
    assert_eq!("some string", v[0]);

fn makes_a_sized_vec() {
    let v: Vec<isize> = Make::make_with_capacity(4);
    assert_eq!(4, v.capacity());

fn makes_a_channel() {
    // Or we can just let the compiler infer the type that we expect.
    let (sender, receiver) = Make::make();
    let msg    = "hello";
    let _      = sender.send(msg);
    let result = receiver.recv().expect("a successfully received value");
    assert_eq!(msg, result);

Any type can implement any trait. The only rule is that the code for an implementation must be in the same crate as either the type or the trait. (A “crate” is a distributable Rust package.) So if Rust itself or a Rust library implemented make, then any third-party library could define their own custom implementations.

I had to implement make and make_with_capacity as separate methods because Rust does not support method overloading. But in theory neither does Go.

The Ugly

These are some features of Go that I dislike on what I think are more subjective grounds than the “bad parts”, or that could be worked around if some of the bad parts were fixed.

No tagged unions, limited pattern matching

Scala is another language that encourages passing messages over channels. Scala supports tagged-union types in combination with pattern matching. These features make great companions for channels: a tagged union describes a fixed set of message types that a channel can accept or produce.

Rust supports tagged-union types in the form of enums:

use std::sync::mpsc::{Receiver, Sender, channel};
use std::thread;

// This is a description of all of the messages that may be sent to a counter.
// Sending a value that is not produced by one of these enum constructors is a
// compile-time error.
#[derive(Clone, Copy, Debug)]
pub enum CounterInstruction {
    Increment(isize),  // `isize` is an integer type that matches the platform word size

pub type CounterResponse = isize;

use self::CounterInstruction::*;

pub fn new_counter() -> (Sender<CounterInstruction>, Receiver<CounterResponse>) {
    // Creating a channel produces a connected sender / receiver pair
    let (instr_tx, instr_rx) = channel::<CounterInstruction>();
    let (resp_tx,  resp_rx)  = channel::<CounterResponse>();

    // Run the counter in a background thread
    thread::spawn(move || {
        let mut count: isize = 0;

        // If the channel has closed then `recv()` will produce an `Err` value
        // instead of `Ok`, and the loop will terminate.
        while let Ok(instr) = instr_rx.recv() {

            // We match on messages to pull out the possible distinct values and
            // types from channel messages. Because each `enum` type is sealed,
            // it is a compile-time error to omit any valid patterns.
            // This avoids potential run-time failures if we add message types
            // later, but forget to update the match here.
            match instr {
                Increment(amount) => count += amount,
                Reset             => count = 0,
                Terminate         => return

            // `send` returns a `Result` because sending might fail in some
            // circumstances. But for this example we assign the result to `_`
            // to ignore it.
            let _ = resp_tx.send(count);


    // Return the instruction sender, and the response receiver
    (instr_tx, resp_rx)

fn runs_a_counter() {
    let (tx, rx) = new_counter();
    let _ = tx.send(Increment(1));
    let _ = tx.send(Increment(1));
    let _ = tx.send(Terminate);

    let mut latest_count = 0;
    while let Ok(resp) = rx.recv() {
        latest_count = resp

    assert_eq!(2, latest_count);

Rust does not require channels to be closed explicitly. Channel senders and receivers implement a Drop trait; any type can implement Drop to schedule some cleanup code to run when a value goes out of scope. In this example when the background thread terminates resp_tx goes out of scope, and closes automatically.

This block is the pattern matching code from the example above:

match instr {
    Increment(amount) => count += amount,
    Reset             => count = 0,
    Terminate         => return

The block matches against instr, which has the type CounterInstruction. There are three variants for CounterInstruction; each variant is represented by a pattern in the match block with code to run in case the pattern matches.

Go has type switches, which are similar to pattern matching. Comparable Go matching code could look like this:

switch instr := <-instrChan.(type) {
	case Increment:
		count += Increment.Amount
	case Reset:
		count = 0
	case Terminate:
		panic("received an unexpected message!")

The difference is in compile-time type-safety: A tagged union describes a fixed set of possible messages. When sending a value to a channel in Rust, the compiler is able to check that the value has a type that the channel consumer knows how to handle. And what is especially valuable is checking that all pieces of code that send or receive on a channel are consistent in the types of values that the they produce or consume. If you make a change to the set of possible messages in a Rust program, but forget to update some critical code to accommodate the change, the type checker will report an error at compile time.

Because Go does not support tagged unions, messages over polymorphic channels are dynamically typed. You can use an interface as the type parameter for a channel, which does limit the types of messages that are sent over the channel. But a Go interface is not sealed: when a new type is created that implements the interface there is no compile-time check to ensure that all consumers of the channel are updated to handle the new type. Unpacking channel messages with a type switch (as opposed to using exclusively interface methods) can lead to bugs that a different type system would have caught.

Dynamic typing

Go makes heavy use of dynamic type-checking. Any use of interface{}, any type assertion or type switch, is dynamic typing. This is good and bad: without generics, it is often necessary to coerce a value to a different type; dynamic type assertions are a safer way to do this than unchecked type casts. Either way the program will (probably) crash at runtime if a value ends up having an unexpected type. But with an unchecked cast the program might corrupt memory or leak information to attackers before crashing.

The fact that errors are reported at runtime means that problems are likely to go unnoticed without good test coverage. That does not just mean 100% code coverage - there might be different combinations of conditions that lead to a code path and a type error might not manifest under every combination.

A type checker that can detect errors at compile time is like an extra test suite that always tests every combination of conditions. Dynamic type tests are nearly unheard of in Rust because the compiler resolves types at compile time. Pattern matches on enum variants looks a bit like a dynamic type test - but pattern matching is qualitatively different because enums are sealed. There is a compile-time guarantee that pattern matching will not fail.

There are two caveats:

  • unsafe blocks, functions, and traits in Rust do make unchecked type casts - especially when interacting with the foreign function interface. But unsafe implementations are delimited regions where the normal rules do not apply. The best practice is to keep unsafe code to a minimum; and most libraries do not use any unsafe code.
  • Rust supports trait objects, which is are cases where the compiler does not resolve a concrete type for a value at compile time. But the compiler does verify at compile-time that the value implements a given trait. A trait object is a lot like a Go interface value - except that a trait object cannot have a nil value. Despite the lack of a compile-time concrete type, it is safe to dispatch trait methods on a trait object because there is compile-time verification that whatever value is in the object implements the given trait and therefore implements the appropriate methods.

Dynamic dispatch, reinventing the wheel

In general Rust resolves every value to a concrete type at compile time. That means that Rust can use static dispatch when invoking trait methods.

(As I mentioned, the exception is trait objects, which do use dynamic dispatch much like Go interface values. My understanding is that idiomatic Rust code uses trait objects sparingly. Most uses of traits in Rust use the trait as a bound on a type variable, which leads to compile-time resolution to a concrete type. For details see the Rust book sections on traits and trait objects.)

In Go every invocation of an interface method uses dynamic dispatch. Dynamic dispatch, type assertions, and type switches require runtime reflection, which adds some runtime overhead.

The technique of resolving concrete types at compile-time is not new to Rust, and it has nothing to do with borrow-checking. Haskell was doing the same thing at least ten years before work began on Go. And Haskell has just as much polymorphic flexibility as Rust or Go. (Rust traits are an adaptation of Haskell type classes.)

The designers of Go wanted a model for polymorphism that is simpler and more flexible than what other object-oriented languages provide. In particular they wanted composition over inheritance, and an ability to implement interfaces after-the-fact so that new interfaces can be applied to pre-existing types. This is exactly what traits and type classes do. The solution that Go introduced feels to me like a less-capable reinvented wheel.

No first-class tuple

Go supports multiple return values. Other languages (including Rust) support multiple return values via first-class data types called “tuples”. First-class values can have methods, can be stored in data structures, and can be passed over channels. Multiple-values in Go cannot do any of those things.

We already saw tuple return values in the implementation of the Make trait, and in the new_counter example. Here is a smaller example:

// Returns a tuple
// (`isize` is an integer type that matches the platform word size)
fn min_and_max(xs: &[isize]) -> (isize, isize) {
    let init = (xs[0], xs[0]);
        .fold(init, |(min, max), &x| (cmp::min(min, x), cmp::max(max, x)))

fn consume_tuple() {
    let xs = vec![1, 2, 3, 4, 5];
    let (min, max) = min_and_max(&xs); // unpack tuple with destructuring assignment
    assert_eq!(1, min);
    assert_eq!(5, max);

A consequence of the lack of first-classness in Go is that there is no obvious way to communicate potential failures over a channel. It seems like this should work, but it does not:

// Nope
results := make(chan (*models.Document, error))

As far as I know the best options for doing this are to define a custom struct type; to coerce both success and error values to interface{}, and use a type switch to distinguish success and error on the other end of the channel; or to send success and error values over two parallel channels.

Another consequence is that there is no good way to define methods on multiple return values, or to define a function that accepts multiple return values with a callback. This makes it impractical to define an analog of Rust’s and_then combinator for Go return values.

Shortage of high-level parallelism and concurrency features

The post Channels Are Not Enough makes a detailed argument about problems with Go’s lack of concurrency abstractions. In the section “List manipulation cannot be abstracted” I pointed out that Go does not provide an easy way to run a set of computations in parallel. @kachayev points out that the problem is more general than that. Really this is another symptom of lack of generics.

In the parallel fetch example I showed a Rust solution that uses futures, which are great for concurrent IO. The futures library relates to a larger async IO library called Tokio, which seems to be popular. But async IO is not intended to provide parallelism. The excellent book Parallel and Concurrent Programming in Haskell has this to say about the distinction between parallel and concurrent code:

A parallel program is one that uses a multiplicity of computational hardware (e.g., several processor cores) to perform a computation more quickly. The aim is to arrive at the answer earlier, by delegating different parts of the computation to different processors that execute at the same time.

By contrast, concurrency is a program-structuring technique in which there are multiple threads of control. Conceptually, the threads of control execute “at the same time”; that is, the user sees their effects interleaved. Whether they actually execute at the same time or not is an implementation detail; a concurrent program can execute on a single processor through interleaved execution or on multiple physical processors.

Go uses goroutines for both parallel and concurrent programming. Rust libraries offer a selection of tools whose strengths make them especially useful for different kinds of problems.

When you want true multi-core parallel processing, the Map-Reduce pattern offers a powerful solution. This is another application of list abstractions. You can apply Map-Reduce in Rust using parallel iterators from the Rayon library:

// This import brings the `par_iter` method into scope.
// `par_iter` is a method on a Rayon trait, and Rayon provides an implementation
// of that trait for standard slice types.
use rayon::prelude::*;

pub fn average_response_time(logs: &[LogEntry]) -> usize {
    let total = logs.par_iter()
        .map(|ref entry| entry.end_time - entry.start_time)
    total / logs.len()

Parallel iterators implement a variation of the map method that divides work among a set of job queues that feed into a worker pool, so that invocations of the map callback run in parallel. As with goroutines, this is lightweight parallelism that scales to a large number of parallel tasks. But parallel iterators are a high-level abstraction that take care of some complicated details for you. For example, Rayon transparently splits work into batches, which provides better performance than queueing up every invocation of the map callback separately. (The default batch size is 5000 items, but that value is tunable.) The sum method (which is the Reduce step in this example) is also part of Rayon - which means that it is optimized for consuming batches of results from worker threads.

It might seem strange to have to use two different libraries for concurrent vs parallel code. But those are separate concerns, with different underlying assumptions. Usually you do not actually want both at the same time.


How do I think Go 2.0 could be better? Generics. Nearly all of my complaints boil down to lack of support for generics. But I think that support for non-nil-able types, and getting rid of zero values would also be useful changes. Even Rust-style traits might be workable. Traits would require support for receiver-less methods; bit it might be possible to make traits work with Go’s implicit implementation feature.

In its current form, I prefer not to use Go. It is not that Go is bad - it is just that there are lots of languages available that I find more enjoyable. When I work with Go I cannot help thinking about how I could be doing things differently in another language.

If you are willing to put in the time to understand lifetimes and borrow checking, Rust makes a fantastic language that does everything that Go does, but has none of the “bad parts” that I called out.

Javascript is (like Go) easy to learn, and has wonderful support for concurrency (if not parallelism). When paired with Flow or Typescript you get more robust type safety than Go provides. The combination of Javascript and Flow in particular has none of the “bad parts” from this post.

Erlang and Scala both support lightweight concurrency in the same style that Go does, and they are both great for functional programming.

Clojure is not type-safe - but it does fantastic things! My favorite functional data structure implementations are the ones in Clojure. And Clojure encourages functional programming.

Haskell has amazing type safety, some of the best concurrency and parallelism features that I have seen in any language, and is well-suited to network server code. Haskell is another language that avoids the “bad parts” from this post.

We have better tools than ever for getting work done. Even Go - whether I like it or not - is clearly useful for building cool things. But if you take anything away from this post, I hope it is an interest in taking a look beyond the imperative / object-oriented world. I encourage you to pick one of the languages above, and take some time to learn about it and to get a good feel for its strengths. I think you will be glad that you did.


2017-03-19 — Correct inaccuracies regarding nil, slice type manipulation, and make; add discussion of zero values; lots of edits
2017-03-20 — Add link to The Language I Wish Go Was
2017-03-20 — Change one instance of “nullable” to “non-nullable”; edit to conclusion section
2017-03-20 — Edits based on reader feedback

  1. Soundness is a property of a type system where any “claims” made by program types are guaranteed to hold at runtime. Runtime type errors will not occur if a language is sound.

  2. This may have changed over time; the C and C++ language specifications are evolving, and I am not very familiar with the details.