Category Archives: Go

The Zen of Go

This article was derived from my GopherCon Israel 2020 presentation. It’s also quite long. If you’d prefer a shorter version, head over to

A recording of the presentation is available on YouTube.

How should I write good code?

Something that I’ve been thinking about a lot recently, when reflecting on the body of my own work, is a common subtitle, how should I write good code? Given nobody actively seeks to write bad code, this leads to the question; how do you know when you’ve written good Go code?

If there’s a continuum between good and bad, how to do we know what the good parts are? What are its properties, its attributes, its hallmarks, its patterns, and its idioms?

Idiomatic Go

Which brings me to idiomatic Go. To say that something is idiomatic is to say that it follows the style of the time. If something is not idiomatic, it is not following the prevailing style. It is unfashionable.

More importantly, to say to someone that their code is not idiomatic does not explain why it’s not idiomatic. Why is this? Like all truths, the answer is found in the dictionary.

idiom (noun): a group of words established by usage as having a meaning not deducible from those of the individual words.

Idioms are hallmarks of shared values. Idiomatic Go is not something you learn from a book, it’s something that you acquire by being part of a community.

My concern with the mantra of idiomatic Go is, in many ways, it can be exclusionary. It’s saying “you can’t sit with us.” After all, isn’t that what we mean when critique of someone’s work as non-idiomatic? They didn’t do It right. It doesn’t look right. It doesn’t follow the style of time.

I offer that idiomatic Go is not a suitable mechanism for teaching how to write good Go code because it is defined, fundamentally, by telling someone they did it wrong. Wouldn’t it be better if the advice we gave didn’t alienate the author right at the point they were most willing to accept it?


Stepping away problematic idioms, what other cultural artefacts do Gophers have? Perhaps we can turn to Rob Pike’s wonderful Go Proverbs. Are these suitable teaching tools? Will these tell newcomers how to write good Go code?

In general, I don’t think so. This is not to dismiss Pike’s work, it is just that the Go Proverbs, like Segoe Kensaku’s original, are observations, not statements of value. Again, the dictionary comes to the rescue:

proverb (noun): a short, well-known pithy saying, stating a general truth or piece of advice.

The goal of the Go Proverbs are to reveal a deeper truth about the design of the language, but how useful is advice like the empty interface says nothing to a novice from a language that doesn’t have structural typing?

It’s important to recognise that, in a growing community, at any time the people learning Go far outnumber those who claim to have mastered the language. Thus proverbs are perhaps not the best teaching tool in this scenario.

Engineering Values

Dan Luu found an old presentation by Mark Lucovsky about the engineering culture of the windows team around the windows NT-windows 2000 timeframe. The reason I mention it is Lukovsky’s description of a culture as a common way of evaluating designs and making tradeoffs.

There are many ways of discussing culture, but with respect to an engineering culture Lucovsky’s description is apt. The central idea is values guide decisions in an unknown design space. The values of the NT team were; portability, reliability, security, and extensibility. Engineering values are, crudely translated, the way things are done around here.

Go’s values

What are the explicit values of Go? What are the core beliefs or philosophy that define the way a Go programmer interprets the world? How are they promulgated? How are they taught? How are they enforced? How do they change over time?

How will you, as a newly minted Go programmer, inculcate the engineering values of Go? Or, how will you, a seasoned Go professional promulgate your values to a future generations? And just so we’re clear, this process of knowledge transfer is not optional. Without new blood and new ideas, our community become myopic and wither.

The values of other languages

To set the scene for what I’m getting at we can look to other languages we see examples of their engineering values.

For example, C++ (and by extension Rust) believe that a programmer should not have to pay for a feature they do not use. If a program does not use some computationally expensive feature of the language, then it shouldn’t be forced to shoulder the cost of that feature. This value extends from the language, to its standard library, and is used as a yardstick for judging the design of all code written in C++.

In Java, and Ruby, and Smalltalk, the core value that everything is an object drives the design of programs around message passing, information hiding, and polymorphism. Designs that shoehorn a procedural style, or even a functional style, into these languages are considered to be wrong–or as Gophers would say, non idiomatic.

Turning to our own community, what are the engineering values that bind Go programmers? Discourse in our community is often fractious, so deriving a set of values from first principles would be a formidable challenge. Consensus is critical, but exponentially more difficult as the number of contributors to the discussion increases. But what if someone had done the hard work for us.

The Zen of Python Go

Several decades ago Tim Peters sat down and penned PEP-20, the Zen of Python. Peters’ attempted to document the engineering values that he saw Guido van Rossum apply in his role as BDFL for Python.

For the remainder of this article, I’m going to look towards the Zen of Python and ask, is there anything that can inform the engineering values of Go programmers?

A good package starts with a good name

Let’s start with something spicy,

“Namespaces are one honking great idea–let’s do more of those!”

The Zen of Python, Item 19

This is pretty unequivocal, Python programmers should use namespaces. Lots of them.

In Go parlance a namespace is a package. I doubt there is any question that grouping things into packages is good for design and potentially reuse. But there might be some confusion, especially if you’re coming with a decade of experience in another language, about the right way to do this.

In Go each package should have a purpose, and the best way to know a package’s purpose is by its name—a noun. A package’s name describes what it provides. So too reinterpret Peters’ words, every Go package should have a single purpose.

This is not a new idea, I’ve been saying this a while, but why should you do this rather than approach where packages are used for fine grained taxonomy? Why, because change.

“Design is the art of arranging code to work today, and be changeable forever.”

Sandi Metz

Change is the name of the game we’re in. What we do as programmers is manage change. When we do that well we call it design, or architecture. When we do it badly we call it technical debt, or legacy code.

If you are writing a program that works perfectly, one time, for one fixed set of inputs then nobody cares if the code is good or bad because ultimately the output of the program is all the business cares about.

But this is never true. Software has bugs, requirements change, inputs change, and very few programs are written solely to be executed once, thus your program will change over time. Maybe it’s you who’ll be tasked with this, more likely it will be someone else, but someone has to change that code. Someone has to maintain that code.

So, how can we make it easy to for programs to change? Interfaces everywhere? Make everything mockable? Pernicious dependency injection? Well, maybe, for some classes of programs, but not many, those techniques will be useful. However, for the majority of programs, designing something to be flexible up front is over engineering.

What if, instead, we take a position that rather than enhancing components, we replace them. Then the best way to know when something needs to be replaced, is when it doesn’t do what it says on the tin.

A good package starts with choosing a good name. Think of your package’s name as an elevator pitch, using just one word, to describe what it provides. When the name no longer matches the requirement, find a replacement.

Simplicity matters

“Simple is better than complex.”

The Zen of Python, Item 3

PEP-20 says simple is better than complex, I couldn’t agree more. A couple of years ago I made this tweet;

My observation, at least at the time, was that I couldn’t think of a language introduced in my life time that didn’t purport to be simple. Each new language offered as a justification, and an enticement, their inherent simplicity. But as I researched, I found that simplicity was not a core value of the many of the languages considered Go’s contemporaries. 1 Maybe this is just a cheap shot, but could it be that either these languages aren’t simple, or they don’t think of themselves as being simple. They don’t consider simplicity to be a core value.

Call me old fashioned, but when did being simple fall out of style? Why does the commercial software development industry continually, gleefully, forget this fundamental truth?

“There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies. The first method is far more difficult.”

C. A. R. Hoare, The Emperor’s Old Clothes, 1980 Turing Award Lecture

Simple does not mean easy, we know that. Often it is more work to make something simple to use, than easy to build.

“Simplicity is prerequisite for reliability.”

Edsger W Dijkstra, EWD498, 18 June 1975

Why should we strive for simplicity? Why is important that Go programs be simple? Simple doesn’t mean crude, it means readable and maintainable. Simple doesn’t mean unsophisticated, it means reliable, relatable, and understandable.

“Controlling complexity is the essence of computer programming.”

Brian W. Kernighan, Software Tools (1976)

Whether Python abides by its mantra of simplicity is a matter for debate, but Go holds simplicity as a core value. I think that we can all agree that when it comes to Go, simple code is preferable to clever code.

Avoid package level state

“Explicit is better than implicit.”

The Zen of Python, Item 2

This is a place where I think Peters’ was more aspirational than factual. Many things in Python are not explicit; decorators, dunder methods, and so on. Without doubt they are powerful, there’s a reason those features exists. Each feature is something someone cared enough about to do the work to implement it, especially the complicated ones. But heavy use of those features makes is harder for the reader to predict the cost of an operation.

The good news is we have a choice, as Go programmers, to choose to make our code explicit. Explicit could mean many things, perhaps you may be thinking explicit is just a nice way of saying bureaucratic and long winded, but that’s a superficial interpretation. It’s a misnomer to focus only on the syntax on the page, to fret about line lengths and DRYing up expressions. The more valuable, in my opinon, place to be explicit are to do with coupling and with state.

Coupling is a measure of the amount one thing depends on another. If two things are tightly coupled, they move together. An action that affects one is directly reflected in another. Imagine a train, each carriage joined–ironically the correct word is coupled–together; where the engine goes, the carriages follow.

Another way to describe coupling is the word cohesion. Cohesion measures how well two things naturally belong together. We talk about a cohesive argument, or a cohesive team; all their parts fit together as if they were designed that way.

Why does coupling matter? Because just like trains, when you need to change a piece of code, all the code that is tightly coupled to it must change. A prime example, someone release a new version of their API and now your code doesn’t compile.

APIs are an unavoidable source of coupling but there are more insidious forms of coupling. Clearly everyone knows that if an API’s signature changes the data passing into and out of that call changes. It’s right there in the signature of the function; I take values of these types and return values of other types. But what if the API passed data another way? What if every time you called this API the result was based on the previous time you called that API even though you didn’t change your parameters.

This is state, and management of state is the problem in computer science.

package counter

var count int

func Increment(n int) int {
        count += n
        return count

Suppose we have this simple counter package. You can call Increment to increment the counter, you can even get the value back if you Increment with a value of zero.

Suppose you had to test this code, how would you reset the counter after each test? Suppose you wanted to run those tests in parallel, could you do it? Now suppose that you wanted to count more than one thing per program, could you do it?

No, of course not. Clearly the answer is to encapsulate the count variable in a type.

package counter

type Counter struct {
        count int

func (c *Counter) Increment(n int) int {
        c.count += n
        return c.count

Now imagine that this problem isn’t restricted to just counters, but your applications main business logic. Can you test it in isolation? Can you test it in parallel? Can you use more than one instance at a time? If the answer those question is no, the reason is package level state.

Avoid package level state. Reduce coupling and spooky action at a distance by providing the dependencies a type needs as fields on that type rather than using package variables.

Plan for failure, not success

“Errors should never pass silently.”

The Zen of Python, Item 10

It’s been said of languages that favour exception handling follow the Samurai principle; return victorious or not at all. In exception based languages functions only return valid results. If they don’t succeed then control flow takes an entirely different path.

Unchecked exceptions are clearly an unsafe model to program in. How can you possibly write code that is robust in the presence of errors when you don’t know which statements could throw an exception? Java tried to make exceptions safer by introducing the notion of a checked exception which, to the best of my knowledge, has not been repeated in another mainstream language. There are plenty of languages which use exceptions but they all, with the singular exception of Java, do so in the unchecked variety.

Obviously Go chose a different path. Go programmers believe that robust programs are composed from pieces that handle the failure cases before they handle the happy path. In the space that Go was designed for; server programs, multi threaded programs, programs that handle input over the network, dealing with unexpected data, timeouts, connection failures and corrupted data must be front and centre of the programmer’s mind if they are to produce robust programs.

“I think that error handling should be explicit, this should be a core value of the language.”

Peter Bourgon, GoTime #91

I want to echo Peter’s assertion, as it was the impetus for this article. I think so much of the success of Go is due to the explicit way errors are handled. Go programmers thinks about the failure case first. We solve the “what if…​” case first. This leads to programs where failures are handled at the point of writing, rather than the point they occur in production.

The verbosity of

if err != nil {
    return err

is outweighed by the value of deliberately handling each failure condition at the point at which they occur. Key to this is the cultural value of handling each and every error explicitly.

Return early rather than nesting deeply

“Flat is better than nested.”

The Zen of Python, Item 5

This is sage advice coming from a language where indentation is the primary form of control flow. How can we interpret this advice in terms of Go? gofmt controls the overall whitespace of a Go program so there’s not thing doing there.

I wrote earlier about package names, and there is probably some advice here about avoiding a complicated package hierarchy. In my experience the more a programmer tries to subdivide and taxonimise their Go codebase the more they risk hitting the dead end that is package import loops.

I think the best application of item 5’s advice is the control flow within a function. Simply put, avoid control flow that requires deep indentation.

“Line of sight is a straight line along which an observer has unobstructed vision.”

May Ryer, Code: Align the happy path to the left edge

Mat Ryer describes this idea as line of sight coding. Light of sight coding means things like:

  • Using guard clauses to return early if a precondition is not met.
  • Placing the successful return statement at the end of the function rather than inside a conditional block.
  • Reducing the overall indentation level of the function by extracting functions and methods.

Key to this advice is the thing that you care about, the thing that the function does, is never in danger of sliding out of sight to the right of your screen. This style has a bonus side effect that you’ll avoid pointless arguments about line lengths on your team.

Every time you indent you add another precondition to the programmers stack, consuming one of their 7 ±2 short term memory slots. Rather than nesting deeply, keep the successful path of the function close to the left hand side of your screen.

If you think it’s slow, prove it with a benchmark

“In the face of ambiguity, refuse the temptation to guess.”

The Zen of Python, Item 12

Programming is based on mathematics and logic, two concepts which rarely involve the element of chance. But there are many things we, as programmers, guess about every day. What does this variable do? What does this parameter do? What happens if I pass nil here? What happens if I call Register twice? There’s actually a lot of guesswork in modern programming, especially when it comes to using libraries you didn’t write.

“APIs should be easy to use and hard to misuse.”

Josh Bloch

One of the best ways I know to help a programmer avoid having to guess is to, when building an API, focus on the default use case. Make it as easy as you can for the caller to do the most common thing. However, I’ve written and talked a lot about API design in the past, so instead my interpretation of item 12 is; don’t guess about performance.

Despite how you may feel about Knuth’s advice, one of the drivers of Go’s success is its efficient execution. You can write efficient programs in Go and thus people will choose Go because of this. There are a lot of misconceptions about performance, so my request is, when you’re looking to performance tune your code or you’re facing some dogmatic advice like defer is slow, CGO is expensive, or always use atomics not mutexes, don’t guess.

Don’t complicate your code because of outdated dogma, and, if you think something is slow, first prove it with a benchmark. Go has excellent benchmarking and profiling tools that come in the distribution for free. Use them to find your bottlenecks.

Before you launch a goroutine, know when it will stop

At this point I think I think I’ve mined the valuable points from PEP-20 and possibly stretched its reinterpretation beyond the point of good taste. I think that’s fine, because although this was a useful rhetorical device, ultimately we are talking about two different languages.

“You type g o, a space, and then a function call. Three keystrokes, you can’t make it much shorter than that. Three keystrokes and you’ve just started a sub process.”

Rob Pike, Simplicity is Complicated, dotGo 2015

The next two suggestions I’ll dedicate to goroutines. Goroutines are the signature feature of the language, our answer for first class concurrency. They are so easy to use, just put the word go in front of the statement and you’ve launched that function asynchronously. It’s so simple, no threads, no stack sizes, no thread pool executors, no ID’s, no tracking completion status.

Goroutines are cheap. Because of the runtime’s ability to multiplex goroutines onto a small pool of threads (which you don’t have to manage), hundreds of thousands, millions of goroutines are easily accommodated. This opens up designs that would be not be practical under competing concurrency models like threads or evented callbacks.

But as cheap as goroutines are, they’re not free. At a minimum there’s a few kilobytes for their stack, which, when you’re getting up into the 10^6 goroutines, does start to add up. This is not to say you shouldn’t use millions of goroutines if that is what the design calls for, but when you do, it’s critical that you keep track of them because 10^6 of anything can consume a non trivial amount of resources in aggregate.

Goroutines are the key to resource ownership in Go. To be useful a goroutine has to do something, and that means it almost always holds reference to, or ownership of, a resource; a lock, a network connection, a buffer with data, the sending end of a channel. While that goroutine is alive, the lock is held, the network connection remains open, the buffer retained and the receivers of the channel will continue to wait for more data.

The simplest way to free those resources is to tie them to the lifetime of the goroutine–when the goroutine exits, the resource has been freed. So while it’s near trivial to start a goroutine, before you write those three letters, g o and a space, make sure you have an answer to these questions:

  • Under what condition will a goroutine stop? Go doesn’t have a way to tell a goroutine to exit. There is no stop or kill function, for good reason. If we cannot command a goroutine to stop, we must instead ask it, politely. Almost always this comes down to a channel operation. Range loops over a channel exit when the channel is closed. A channel will become selectable if it is closed. The signal from one goroutine to another is best expressed as a closed channel.
  • What is required for that condition to arise? If channels are both the vehicle to communicate between goroutines and the mechanism for them to signal completion, the next question to the programmer becomes, who will close the channel, when will that happen?
  • What signal will you use to know the goroutine has stopped? When you signal a goroutine to stop, that stopping will happen at some time in the future relative to the goroutine’s frame of reference. It might happen quickly in terms of human perception, but computers execute billions of instructions every second, and from the point of view of each goroutine, their execution of instructions is unsynchronised. The solution is often to use a channel to signal back or a waitgroup where a fan in approach is needed.

Leave concurrency to the caller

It is likely that in any serious Go program you write there will be concurrency involved. This raises the problem, many of the libraries and code that we write fall into this a one goroutine per connection, or worker pattern. How will you manage the lifetime of those goroutines?

net/http is a prime example. Shutting down the server owning the listening socket is relatively straight forward, but what about a goroutines spawned from that accepting socket? net/http does provide a context object inside the request object which can be used to signal–to code that is listening–that the request should be canceled, thereby terminating the goroutine, however it is less clear how to know when all of these things have been done. It’s one thing to call context.Cancel, its another to know that the cancellation has completed.2

The point I want to make about net/http is that its a counter example to good practice. Because each connection is handled by a goroutine spawned inside the net/http.Server type, the program, living outside the net/http package, does not have an ability to control the goroutines spawned for the accepting socket.

This is an area of design that is still evolving, with efforts like go-kit’s run.Group and the Go team’s ErrGroup which provide a framework to execute, cancel and wait on functions run asynchronously.

The bigger design maxim here is for library writers, or anyone writing code that could be run asynchronously, leave the responsibility of starting to goroutine to your caller. Let the caller choose how they want to start, track, and wait on your functions execution.

Write tests to lock in the behaviour of your package’s API

Perhaps you were hoping to read an article from me where I didn’t rant about testing. Sadly, today is not that day.

Your tests are the contract about what your software does and does not do. Unit tests at the package level should lock in the behaviour of the package’s API. They describe, in code, what the package promises to do. If there is a unit test for each input permutation, you have defined the contract for what the code will do in code, not documentation.

This is a contract you can assert as simply as typing go test. At any stage, you can know with a high degree of confidence, that the behaviour people relied on before your change continues to function after your change.

Tests lock in api behaviour. Any change that adds, modifies or removes a public api must include changes to its tests.

Moderation is a virtue

Go is a simple language, only 25 keywords. In some ways this makes the features that are built into the language stand out. Equally these are the features that the language sells itself on, lightweight concurrency, structural typing.

I think all of us have experienced the confusion that comes from trying to use all of Go’s features at once. Who was so excited to use channels that they used them as much as they could, as often as they could? Personally for me I found the result was hard to test, fragile, and ultimately overcomplicated. Am I alone?

I had the same experience with goroutines, attempting to break the work into tiny units I created a hard to manage hurd of Goroutines and ultimately missed the observation that most of my goroutines were always blocked waiting for their predecessor– the code was ultimately sequential and I had added a lot of complexity for little real world benefit. Who has experienced something like this?

I had the same experience with embedding. Initially I mistook it for inheritance. Then later I recreated the fragile base class problem by composing complicated types, which already had several responsibilities, into more complicated mega types.

This is potentially the least actionable piece of advice, but one I think is important enough to mention. The advice is always the same, all things in moderation, and Go’s features are no exception. If you can, don’t reach for a goroutine, or a channel, or embed a struct, anonymous functions, going overboard with packages, interfaces for everything, instead prefer simpler approach rather than the clever approach.

Maintainability counts

I want to close with one final item from PEP-20,

“Readability Counts.”

The Zen of Python, Item 7

So much has been said, about the importance of readability, not just in Go, but all programming languages. People like me who stand on stages advocating for Go use words like simplicity, readability, clarity, productivity, but ultimately they are all synonyms for one word–maintainability.

The real goal is to write maintainable code. Code that can live on after the original author. Code that can exist not just as a point in time investment, but as a foundation for future value. It’s not that readability doesn’t matter, maintainability matters more.

Go is not a language that optimises for clever one liners. Go is not a language which optimises for the least number of lines in a program. We’re not optimising for the size of the source code on disk, nor how long it takes to type the program into an editor. Rather, we want to optimise our code to be clear to the reader. Because its the reader who’s going to have to maintain this code.

If you’re writing a program for yourself, maybe it only has to run once, or you’re the only person who’ll ever see it, then do what ever works for you. But if this is a piece of software that more than one person will contribute to, or that will be used by people over a long enough time that requirements, features, or the environment it runs in may change, then your goal must be for your program to be maintainable. If software cannot be maintained, then it will be rewritten; and that could be the last time your company will invest in Go.

Can the thing you worked hard to build be maintained after you’re gone? What can you do today to make it easier for someone to maintain your code tomorrow?

Dynamically scoped variables in Go

This is a thought experiment in API design. It starts with the classic Go unit testing idiom:

func TestOpenFile(t *testing.T) {
        f, err := os.Open("notfound")
        if err != nil {

        // ...

What’s the problem with this code? The assertion. if err != nil { ... } is repetitive and in the case where multiple conditions need to be checked, somewhat error prone if the author of the test uses t.Error not t.Fatal, eg:

        f, err := os.Open("notfound")
        if err != nil {
        f.Close() // boom!

What’s the solution? DRY it up, of course, by moving the repetitive assertion logic to a helper:

func TestOpenFile(t *testing.T) {
        f, err := os.Open("notfound")
        check(t, err)

        // ...
func check(t *testing.T, err error) {
       if err != nil {

Using the check helper the code is a little cleaner, and clearer, check the error, and hopefully the indecision between t.Error and t.Fatal has been solved. The downside of abstracting the assertion to a helper function is now you need to pass a testing.T into each and every invocation. Worse, you need to pass a *testing.T to everything that needs to call check, transitively, just in case.

This is ok, I guess, but I will make the observation that the t variable is only needed when the assertion fails — and even in a testing scenario, most of the time, most of the tests pass, so that means reading, and writing, all these t‘s is a constant overhead for the relatively rare occasion that a test fails.

What about if we did something like this instead?

func TestOpenFile(t *testing.T) {
        f, err := os.Open("notfound")
        // ...
func check(err error) {
        if err != nil {

Yeah, that’ll work, but it has a few problems

% go test
--- FAIL: TestOpenFile (0.00s)
panic: open notfound: no such file or directory [recovered]
        panic: open notfound: no such file or directory

goroutine 22 [running]:
        /Users/dfc/go/src/testing/testing.go:874 +0x3a3
panic(0x111b040, 0xc0000866f0)
        /Users/dfc/go/src/runtime/panic.go:679 +0x1b2
        /Users/dfc/src/ +0xa1
testing.tRunner(0xc0000b4400, 0x115ac90)
        /Users/dfc/go/src/testing/testing.go:909 +0xc9
created by testing.(*T).Run
        /Users/dfc/go/src/testing/testing.go:960 +0x350
exit status 2

Let’s start with the good; we didn’t have to pass a testing.T every place we call check, the test fails immediately, and we get a nice message in the panic — albeit twice. But where the assertion failed is hard to see. It occurred on expect_test.go:11 but you’d be forgiven for not knowing that.

So panic isn’t really a good solution, but there’s something in this stack trace that is — can you see it? Here’s a hint,

TestOpenFile has a t value, it was passed to it by tRunner, so there’s a testing.T in memory at address 0xc0000b4400. What if we could get access to that t inside check? Then we could use it to call t.Helper and t.Fatal. Is that possible?

Dynamic scoping

What we want is to be able to access a variable whose declaration is neither global, or local to the function, but somewhere higher in the call stack. This is called dynamic scoping. Go doesn’t support dynamic scoping, but it turns out, for restricted cases, we can fake it. I’ll skip to the chase:

// getT returns the address of the testing.T passed to testing.tRunner
// which called the function which called getT. If testing.tRunner cannot
// be located in the stack, say if getT is not called from the main test
// goroutine, getT returns nil.
func getT() *testing.T {
        var buf [8192]byte
        n := runtime.Stack(buf[:], false)
        sc := bufio.NewScanner(bytes.NewReader(buf[:n]))
        for sc.Scan() {
                var p uintptr
                n, _ := fmt.Sscanf(sc.Text(), "testing.tRunner(%v", &p)
                if n != 1 {
                return (*testing.T)(unsafe.Pointer(p))
        return nil

We know that each Test is called by the testing package in its own goroutine (see the stack trace above). The testing package launches the test via a function called tRunner which takes a *testing.T and a func(*testing.T) to invoke. Thus we grab a stack trace of the current goroutine, scan through it for the line beginning with testing.tRunner — which can only be the testing package as tRunner is a private function — and parse the address of the first parameter, which is a pointer to a testing.T. With a little unsafe we convert the raw pointer back to a *testing.T and we’re done.

If the search fails then it is likely that getT wasn’t called from a Test. This is actually ok because the reason we needed the *testing.T was to call t.Fatal and the testing package already requires that t.Fatal be called from the main test goroutine.

import ""

func TestOpenFile(t *testing.T) {
        f, err := os.Open("notfound")
        // ...

Putting it all together we’ve eliminated the assertion boilerplate and possibly made the expectation of the test a little clearer to read, after opening the file err is expected to be nil.

Is this fine?

At this point you should be asking, is this fine? And the answer is, no, this is not fine. You should be screaming internally at this point. But it’s probably worth introspecting those feelings of revulsion.

Apart from the inherent fragility of scrobbling around in a goroutine’s call stack, there are some serious design issues:

  1. The expect.Nil‘s behaviour now depends on who called it. Provided with the same arguments it may have different behaviour depending on where it appears in the call stack — this is unexpected.
  2. Taken to the extreme dynamic scoping effective brings into the scope of a single function all the variables passed into any function that preceded it. It is a side channel for passing data in to and out of functions that is not explicitly documented in function declaration.

Ironically these are precisely the critiques I have of context.Context. I’ll leave it to you to decide if they are justified.

A final word

This is a bad idea, no argument there. This is not a pattern you should ever use in production code. But, this isn’t production code, it’s a test, and perhaps there are different rules that apply to test code. After all, we use mocks, and stubs, and monkey patching, and type assertions, and reflection, and helper functions, and build flags, and global variables, all so we can test our code effectively. None of those, uh, hacks will ever show up in the production code path, so is it really the end of the world?

If you’ve read this far perhaps you’ll agree with me that as unconventional as this approach is, not having to pass a *testing.T into every function that could possibly need to assert something transitively, makes for clearer test code.

So maybe, in this case, the ends do justify the means.

If you’re interested, I’ve put together a small assertion library using this pattern. Caveat emptor.

Use internal packages to reduce your public API surface

In the beginning, before the go tool, before Go 1.0, the Go distribution stored the standard library in a subdirectory called pkg/ and the commands which built upon it in cmd/. This wasn’t so much a deliberate taxonomy but a by product of the original make based build system. In September 2014, the Go distribution dropped the pkg/ subdirectory, but then this tribal knowledge had set root in large Go projects and continues to this day.

I tend to view empty directories inside a Go project with suspicion. Often they are a hint that the module’s author may be trying to create a taxonomy of packages rather than ensuring each package’s name, and thus its enclosing directory, uniquely describes its purpose. While the symmetry with cmd/ for package main commands is appealing, a directory that exists only to hold other packages is a potential design smell.

More importantly, the boilerplate of an empty pkg/ directory distracts from the more useful idiom of an internal/ directory. internal/ is a special directory name recognised by the go tool which will prevent one package from being imported by another unless both share a common ancestor. Packages within an internal/ directory are therefore said to be internal packages.

To create an internal package, place it within a directory named internal/. When the go command sees an import of a package with internal/ in the import path, it verifies that the importing package is within the tree rooted at the parent of the internal/ directory.

For example, a package /a/b/c/internal/d/e/f can only be imported by code in the directory tree rooted at /a/b/c. It cannot be imported by code in /a/b/g or in any other repository.

If your project contains multiple packages you may find you have some exported symbols which are intended to be used by other packages in your project, but are not intended to be part of your project’s public API. Although Go has limited visibility modifiers–public, exported, symbols and private, non exported, symbols–internal packages provide a useful mechanism for controlling visibility to parts of your project which would otherwise be considered part of its public versioned API.

You can, of course, promote internal packages later if you want to commit to supporting that API; just move them up a directory level or two. The key is this process is opt-in. As the author, internal packages give you control over which symbols in your project’s public API without being forced to glob concepts together into unwieldy mega packages to avoid exporting them.

Be wary of functions which take several parameters of the same type

APIs should be easy to use and hard to misuse.

— Josh Bloch

A good example of a simple looking, but hard to use correctly, API is one which takes two or more parameters of the same type. Let’s compare two function signatures:

func Max(a, b int) int
func CopyFile(to, from string) error

What’s the difference between these functions? Obviously one returns the maximum of two numbers, the other copies a file, but that’s not the important thing.

Max(8, 10) // 10
Max(10, 8) // 10

Max is commutative; the order of its parameters does not matter. The maximum of eight and ten is ten regardless of if I compare eight and ten or ten and eight.

However, this property does not hold true for CopyFile.

CopyFile("/tmp/backup", "")
CopyFile("", "/tmp/backup")

Which one of these statements made a backup of your presentation and which one overwrite your presentation with last week’s version? You can’t tell without consulting the documentation. A code reviewer cannot know if you’ve got the order correct without consulting the documentation.

The general advice is to try to avoid this situation. Just like long parameter lists, indistinct parameter lists are a design smell.

A challenge

When this situation is unavoidable my solution to this class of problem is to introduce a helper type which will be responsible for calling CopyFile correctly.

type Source string

func (src Source) CopyTo(dest string) error {
	return CopyFile(dest, string(src))

func main() {
	var from Source = ""

In this way CopyFile is always called correctly and, given its poor API can possibly be made private, further reducing the likelihood of misuse.

Can you suggest a better solution?

Don’t force allocations on the callers of your API

This is a post about performance. Most of the time when worrying about the performance of a piece of code the overwhelming advice should be (with apologies to Brendan Gregg) don’t worry about it, yet. However there is one area where I counsel developers to think about the performance implications of a design, and that is API design.

Because of the high cost of retrofitting a change to an API’s signature to address performance concerns, it’s worthwhile considering the performance implications of your API’s design on its caller.

A tale of two API designs

Consider these two Read methods:

func (r *Reader) Read(buf []byte) (int, error)
func (r *Reader) Read() ([]byte, error)

The first method takes a []byte buffer and returns the number of bytes read into that buffer and possibly an error that occurred while reading. The second takes no arguments and returns some data as a []byte or an error.

This first method should be familiar to any Go programmer, it’s io.Reader.Read. As ubiquitous as io.Reader is, it’s not the most convenient API to use. Consider for a moment that io.Reader is the only Go interface in widespread use that returns both a result and an error. Meditate on this for a moment. The standard Go idiom, checking the error and iff it is nil is it safe to consult the other return values, does not apply to Read. In fact the caller must do the opposite. First they must record the number of bytes read into the buffer, reslice the buffer, process that data, and only then, consult the error. This is an unusual API for such a common operation and one that frequently catches out newcomers.

A trap for young players?

Why is it so? Why is one of the central APIs in Go’s standard library written like this? A superficial answer might be io.Reader‘s signature is a reflection of the underlying read(2) syscall, which is indeed true, but misses the point of this post.

If we compare the API of io.Reader to our alternative, func Read() ([]byte, error), this API seems easier to use. Each call to Read() will return the data that was read, no need to reslice buffers, no need to remember the special case to do this before checking the error. Yet this is not the signature of io.Reader.Read. Why would one of Go’s most pervasive interfaces choose such an awkward API? The answer, I believe, lies in the performance implications of the APIs signature on the caller.

Consider again our alternative Read function, func Read() ([]byte, error). On each call Read will read some data into a buffer1 and return the buffer to the caller. Where does this buffer come from? Who allocates it? The answer is the buffer is allocated inside Read. Therefore each call to Read is guaranteed to allocate a buffer which would escape to the heap. The more the program reads, the faster it reads data, the more streams of data it reads concurrently, the more pressure it places on the garbage collector.

The standard libraries’ io.Reader.Read forces the caller to supply a buffer because if the caller is concerned with the number of allocations their program is making this is precisely the kind of thing they want to control. Passing a buffer into Read puts the control of the allocations into the caller’s hands. If they aren’t concerned about allocations they can use higher level helpers like ioutil.ReadAll to read the contents into a []byte, or bufio.Scanner to stream the contents instead.

The opposite, starting with a method like our alternative func Read() ([]byte, error) API, prevents callers from pooling or reusing allocations–no amount of helper methods can fix this. As an API author, if the API cannot be changed you’ll be forced to add a second form to your API taking a supplied buffer and reimplementing your original API in terms of the newer form. Consider, for example, io.CopyBuffer. Other examples of retrofitting APIs for performance reasons are the fmt package and the net/http package which drove the introduction of the sync.Pool type precisely because the Go 1 guarantee prevented the APIs of those packages from changing.

If you want to commit to an API for the long run, consider how its design will impact the size and frequency of allocations the caller will have to make to use it.

Go compiler intrinsics

Go allows authors to write functions in assembly if required. This is called a stub or forward declaration.

package asm

// Add returns the sum of a and b.
func Add(a int64, b int64) int64

Here we’re declaring Add, a function which takes two int64‘s and returns their sum.Add is a normal Go function declaration, except it is missing the function body.

If we were to try to compile this package the compiler, justifiably, complains;

% go build
./decl.go:4:6: missing function body

To satisfy the compiler we must supply a body for Add via assembly, which we do by adding a .s file in the same package.

TEXT ·Add(SB),$0-24
        MOVQ a+0(FP), AX
        ADDQ b+8(FP), AX
        MOVQ AX, ret+16(FP)

Now we can build, test, and use our Add function just like normal Go code. But, there’s a problem, assembly functions cannot be inlined.

This has long been a complaint by Go developers who want to use assembly either for performance or to access operations which are not exposed in the language. Some examples would be vector instructions, atomic instructions, and so on. Without the ability to inline assembly functions writing these functions in Go can have a relatively large overhead.

var Result int64

func BenchmarkAddNative(b *testing.B) {
        var r int64
        for i := 0; i < b.N; i++ {
                r = int64(i) + int64(i)
        Result = r 

func BenchmarkAddAsm(b *testing.B) {
        var r int64
        for i := 0; i < b.N; i++ {
                r = Add(int64(i), int64(i))
        Result = r
BenchmarkAddNative-8    1000000000               0.300 ns/op
BenchmarkAddAsm-8       606165915                1.93 ns/op

Over the years there have been various proposals for an inline assembly syntax similar to gcc’s asm(...) directive. None have been accepted by the Go team. Instead, Go has added intrinsic functions1.

An intrinsic function is Go code written in regular Go. These functions are known the the Go compiler which contains replacements which it can substitute during compilation. As of Go 1.13 the packages which the compiler knows about are:

  • math/bits
  • sync/atomic

The functions in these packages have baroque signatures but this lets the compiler, if your architecture supports a more efficient way of performing the operation, transparently replace the function call with comparable native instructions.

For the remainder of this post we’ll study two different ways the Go compiler produces more efficient code using intrinsics.

Ones count

Population count, the number of 1 bits in a word, is an important cryptographic and compression primitive. Because this is an important operation most modern CPUs provide a native hardware implementation.

The math/bits package exposes support for this operation via the OnesCount series of functions. The various OnesCount functions are recognised by the compiler and, depending on the CPU architecture and the version of Go, will be replaced with the native hardware instruction.

To see how effective this can be lets compare three different ones count implementations. The first is Kernighan’s Algorithm2.

func kernighan(x uint64) int {
        var count int
        for ; x > 0; x &= (x - 1) {
        return count                

This algorithm has a maximum loop count of the number of bits set; the more bits set, the more loops it will take.

The second algorithm is taken from Hacker’s Delight via issue 14813.

func hackersdelight(x uint64) int {
        const m1 = 0x5555555555555555
        const m2 = 0x3333333333333333
        const m4 = 0x0f0f0f0f0f0f0f0f
        const h01 = 0x0101010101010101

        x -= (x >> 1) & m1
        x = (x & m2) + ((x >> 2) & m2)
        x = (x + (x >> 4)) & m4
        return int((x * h01) >> 56)

Lots of clever bit twiddling allows this version to run in constant time and optimises very well if the input is a constant (the whole thing optimises away if the compiler can figure out the answer at compiler time).

Let’s benchmark these implementations against math/bits.OnesCount64.

var Result int

func BenchmarkKernighan(b *testing.B) {
        var r int
        for i := 0; i < b.N; i++ {
                r = kernighan(uint64(i))
        Result = r

func BenchmarkPopcnt(b *testing.B) {
        var r int
        for i := 0; i < b.N; i++ {
                r = hackersdelight(uint64(i))
        Result = r

func BenchmarkMathBitsOnesCount64(b *testing.B) {
        var r int
        for i := 0; i < b.N; i++ {
                r = bits.OnesCount64(uint64(i))
        Result = r

To keep it fair, we’re feeding each function under test the same input; a sequence of integers from zero to b.N. This is fairer to Kernighan’s method as its runtime increases with the number of one bits in the input argument.3

BenchmarkKernighan-8                    100000000               11.2 ns/op
BenchmarkPopcnt-8                       618312062                2.02 ns/op
BenchmarkMathBitsOnesCount64-8          1000000000               0.565 ns/op 

The winner by nearly 4x is math/bits.OnesCount64, but is this really using a hardware instruction, or is the compiler just doing a better job at optimising this code? Let’s check the assembly

% go test -c
% go tool objdump -s MathBitsOnesCount popcnt-intrinsic.test
TEXT examples/popcnt-intrinsic.BenchmarkMathBitsOnesCount64(SB) /examples/popcnt-intrinsic/popcnt_test.go
   popcnt_test.go:45     0x10f8610               65488b0c2530000000      MOVQ GS:0x30, CX
   popcnt_test.go:45     0x10f8619               483b6110                CMPQ 0x10(CX), SP
   popcnt_test.go:45     0x10f861d               7668                    JBE 0x10f8687
   popcnt_test.go:45     0x10f861f               4883ec20                SUBQ $0x20, SP
   popcnt_test.go:45     0x10f8623               48896c2418              MOVQ BP, 0x18(SP)
   popcnt_test.go:45     0x10f8628               488d6c2418              LEAQ 0x18(SP), BP
   popcnt_test.go:47     0x10f862d               488b442428              MOVQ 0x28(SP), AX
   popcnt_test.go:47     0x10f8632               31c9                    XORL CX, CX
   popcnt_test.go:47     0x10f8634               31d2                    XORL DX, DX
   popcnt_test.go:47     0x10f8636               eb03                    JMP 0x10f863b
   popcnt_test.go:47     0x10f8638               48ffc1                  INCQ CX
   popcnt_test.go:47     0x10f863b               48398808010000          CMPQ CX, 0x108(AX)
   popcnt_test.go:47     0x10f8642               7e32                    JLE 0x10f8676
   popcnt_test.go:48     0x10f8644               803d29d5150000          CMPB $0x0, runtime.x86HasPOPCNT(SB)
   popcnt_test.go:48     0x10f864b               740a                    JE 0x10f8657
   popcnt_test.go:48     0x10f864d               4831d2                  XORQ DX, DX
   popcnt_test.go:48     0x10f8650               f3480fb8d1              POPCNT CX, DX // math/bits.OnesCount64
   popcnt_test.go:48     0x10f8655               ebe1                    JMP 0x10f8638
   popcnt_test.go:47     0x10f8657               48894c2410              MOVQ CX, 0x10(SP)
   popcnt_test.go:48     0x10f865c               48890c24                MOVQ CX, 0(SP)
   popcnt_test.go:48     0x10f8660               e87b28f8ff              CALL math/bits.OnesCount64(SB)
   popcnt_test.go:48     0x10f8665               488b542408              MOVQ 0x8(SP), DX
   popcnt_test.go:47     0x10f866a               488b442428              MOVQ 0x28(SP), AX
   popcnt_test.go:47     0x10f866f               488b4c2410              MOVQ 0x10(SP), CX
   popcnt_test.go:48     0x10f8674               ebc2                    JMP 0x10f8638
   popcnt_test.go:50     0x10f8676               48891563d51500          MOVQ DX, examples/popcnt-intrinsic.Result(SB)
   popcnt_test.go:51     0x10f867d               488b6c2418              MOVQ 0x18(SP), BP
   popcnt_test.go:51     0x10f8682               4883c420                ADDQ $0x20, SP
   popcnt_test.go:51     0x10f8686               c3                      RET
   popcnt_test.go:45     0x10f8687               e884eef5ff              CALL runtime.morestack_noctxt(SB)
   popcnt_test.go:45     0x10f868c               eb82                    JMP examples/popcnt-intrinsic.BenchmarkMathBitsOnesCount64(SB)
   :-1                   0x10f868e               cc                      INT $0x3
   :-1                   0x10f868f               cc                      INT $0x3 

There’s quite a bit going on here, but the key take away is on line 48 (taken from the source code of the _test.go file) the program is using the x86 POPCNT instruction as we hoped. This turns out to be faster than bit twiddling.

Of interest is the comparison two instructions prior to the POPCNT,

CMPB $0x0, runtime.x86HasPOPCNT(SB)

As not all intel CPUs support POPCNT the Go runtime records at startup if the CPU has the necessary support and stores the result in runtime.x86HasPOPCNT. Each time through the benchmark loop the program is checking does the CPU have POPCNT support before it issues the POPCNT request.

The value of runtime.x86HasPOPCNT isn’t expected to change during the life of the program’s execution so the result of the check should be highly predictable making the check relatively cheap.

Atomic counter

As well as generating more efficient code, intrinsic functions are just regular Go code, the rules of inlining (including mid stack inlining) apply equally to them.

Here’s an example of an atomic counter type. It’s got methods on types, method calls several layers deep, multiple packages, etc.

import (

type counter uint64

func (c counter) get() uint64 {
         return atomic.LoadUint64((uint64)(c))

func (c counter) inc() uint64 {
        return atomic.AddUint64((uint64)(c), 1)

func (c counter) reset() uint64 {
        return atomic.SwapUint64((uint64)(c), 0)

var c counter

func f() uint64 {
        return c.reset()

You’d be forgiven for thinking this would have a lot of overhead. However, because of the interaction between inlining and compiler intrinsics, this code collapses down to efficient native code on most platforms.

TEXT main.f(SB) examples/counter/counter.go
   counter.go:23         0x10512e0               90                      NOPL
   counter.go:29         0x10512e1               b801000000              MOVL $0x1, AX
   counter.go:13         0x10512e6               488d0d0bca0800          LEAQ main.c(SB), CX
   counter.go:13         0x10512ed               f0480fc101              LOCK XADDQ AX, 0(CX) //
   counter.go:24         0x10512f2               90                      NOPL
   counter.go:10         0x10512f3               488b05fec90800          MOVQ main.c(SB), AX // c.get
   counter.go:25         0x10512fa               90                      NOPL
   counter.go:16         0x10512fb               31c0                    XORL AX, AX
   counter.go:16         0x10512fd               488701                  XCHGQ AX, 0(CX) // c.reset
   counter.go:16         0x1051300               c3                      RET 

By way of explanation. The first operation, counter.go:13 is a LOCKed XADDQ, which on x86 is an atomic increment. The second, counter.go:10 is c.get which on x86, due to its strong memory consistency model, is a regular load from memory. The final operation, counter.go:16, c.reset is an atomic exchange of the address in CX with AX which was zeroed on the previous line. This puts the value in AX, zero, into the address stored in CX. The value previously stored at (CX) is discarded.


Intrinsics are a neat solution that give Go programmers access to low level architectural operations without having to extend the specification of the language. If an architecture doesn’t have a specific sync/atomic primitive (like some ARM variants), or a math/bits operation, then the compiler transparently falls back to the operation written in pure Go.

Clear is better than clever

This article is based on my GopherCon Singapore 2019 presentation. In the presentation I referenced material from my post on declaring variables and my GolangUK 2017 presentation on SOLID design. For brevity those parts of the talk have been elided from this article. If you prefer, you can watch the recording of the talk.

Readability is often cited as one of Go’s core tenets, I disagree. In this article I’ll discuss the differences between clarity and readability, show you what I mean by clarity and how it applies to Go code, and argue that Go programmers should strive for clarity–not just readability–in their programs.

Why would I read your code?

Before I pick apart the difference between clarity and readability, perhaps the question to ask is, “why would I read your code?” To be clear, when I say I, I don’t mean me, I mean you. And when I say your code I also mean you, but in the third person. So really what I’m asking is, “why would you read another person’s code?”

I think Russ Cox, paraphrasing Titus Winters, put it best:

Software engineering is what happens to programming when you add time and other programmers.

Russ Cox, GopherCon Singapore 2018

The answer to the question, “why would I read your code” is, because we have to work together. Maybe we don’t work in the same office, or live in the same city, maybe we don’t even work at the same company, but we do collaborate on a piece of software, or more likely consume it as a dependency.

This is the essence of Russ and Titus’ observation; software engineering is the collaboration of software engineers over time. I have to read your code, and you read mine, so that I can understand it, so that you can maintain it, and in short, so that any programmer can change it.

Russ is making the distinction between software programming and software engineering. The former is a program you write for yourself, the latter is a program, ​a project, a service, a product, ​that many people will contribute to over time. Engineers will come and go, teams will grow and shrink, requirements will change, features will be added and bugs fixed. This is the nature of software engineering.

We don’t read code, we decode it

It was sometime after that presentation that I finally realized the obvious: Code is not literature. We don’t read code, we decode it.

Peter Seibel

The author Peter Seibel suggests that programs are not read, but are instead decoded. In hindsight this is obvious, after all we call it source code, not source literature. The source code of a program is an intermediary form, somewhere between our concept–what’s inside our heads–and the computer’s executable notation.

In my experience, the most common complaint when faced with a foreign codebase written by someone, or some team, is the code is unreadable. Perhaps you agree with me?

But readability as a concept is subjective. Readability is nit picking about line length and variable names. Readability is holy wars about brace position. Readability is the hand to hand combat of style guides and code review guidelines that regulate the use of whitespace.

Clarity ≠ Readability

Clarity, on the other hand, is the property of the code on the page. Clear code is independent of the low level details of function names and indentation because clear code is concerned with what the code is doing, not just how it is written down.

When you or I say that a foreign codebase is unreadable, what I think what we really mean is, I don’t understand it. For the remainder of this article I want to try to explore the difference between clear code and code that is simply readable, because the goal is not how quickly you can read a piece of code, but how quickly you can grasp its meaning.

Keep to the left

Go programs are traditionally written in a style that favours guard clauses and preconditions. This encourages the successful path to proceed down the page rather than indented inside a conditional block. Mat Ryer calls this line of sight coding, because, the active part of your function is not at risk of sliding out of sight beyond the right hand margin of your screen.

By keeping conditional blocks short, and for the exceptional condition, we avoid nested blocks and potentially complex value shadowing. The successful flow of control continues down the page. At every point in the sequence of statements, if you’ve arrived at that point, you are confident that a growing set of preconditions holds true.

func ReadConfig(path string) (*Config, error) {
        f, err := os.Open(path)
        if err != nil {
                return nil, err
        defer f.Close()
        // ...

The canonical example of this is the classic Go error check idiom; if err != nil then return it to the caller, else continue with the function. We can generalise this pattern a little and in pseudocode we have:

if some condition {
        // true: cleanup
 // false: continue 

If some condition is true, then return to the caller, else continue onwards towards the end of the function. 

This form holds true for all preconditions, error checks, map lookups, length checks, and so forth. The exact form of the precondition’s check changes, but the pattern is always the same; the cleanup code is inside the block, terminating with a return, the success condition lies outside the block, and is only reachable if the precondition is false.

Even if you are unsure what the preceding and succeeding code does, how the precondition is formed, and how the cleanup code works, it is clear to the reader that this is a guard clause.

Structured programming

Here we have a comp function that takes two ints and returns an int;

func comp(a, b int) int {
        if a < b {
                return -1
        if a > b {
                return 1
        return 0

The comp function is written in a similar form to guard clauses from earlier. If a is less than b, the return -1 path is taken. If a is greater than b, the return 1 path is taken. Else, a and b are by induction equal, so the final return 0 path is taken.

func comp(a, b int) int {
        if condition A {
                body A
        if condition B {
                 body B
        return 0

The problem with comp as written is, unlike the guard clause, someone maintaining this function has to read all of it. To understand when 0 is returned, the reader has to consult the conditions and the body of each clause. This is reasonable when you’re dealing with functions which fit on a slide, but in the real world complicated functions–​the ones we’re paid for our expertise to maintain–are rarely slide sized, and their conditions and bodies are rarely simple.

Let’s address the problem of making it clear under which condition 0 will be returned:

func comp(a, b int) int {
        if a < b {
                return -1
        } else if a > b {
                return 1
        } else {
                return 0

Now, although this code is not what anyone would argue is readable–​long chains of if else if statements are broadly discouraged in Go–​it is clearer to the reader that zero is only returned if none of the conditions are met.

How do we know this? The Go spec declares that each function that returns a value must end in a terminating statement. This means that the body of all conditions must return a value. Thus, this does not compile:

func comp(a, b int) int {
        if a > b {
                a = b // does not compile
        } else if a < b {
                return 1
        } else {
                return 0

Further, it is now clear to the reader that this code isn’t actually a series of conditions. This is an example of selection. Only one path can be taken regardless of the operation of the condition blocks. Based on the inputs one of -1, 0, or 1 will always be returned. 

func comp(a, b int) int {
        if a < b {
                return -1
        } else if a > b {
                return 1
        } else {
                return 0

However this code is hard to read as each of the conditions is written differently, the first is a simple if a < b, the second is the unusual else if a > b, and the last conditional is actually unconditional.

But it turns out there is a statement which we can use to make our intention much clearer to the reader; switch.

func comp(a, b int) int {
        switch {
        case a < b:
                return -1
        case a > b:
                return 1
                return 0

Now it is clear to the reader that this is a selection. Each of the selection conditions are documented in their own case statement, rather than varying else or else if clauses.

By moving the default condition inside the switch, the reader only has to consider the cases that match their condition, as none of the cases can fall out of the switch block because of the default clause.1

Structured programming submerges structure and emphasises behaviour

–Richard Bircher, The limits of software

I found this quote recently and I think it is apt. My arguments for clarity are in truth arguments intended to emphasise the behaviour of the code, rather than be side tracked by minutiae of the structure itself. Said another way, what is the code trying to do, not how is it is trying to do it.

Guiding principles

I opened this article with a discussion of readability vs clarity and hinted that there were other principles of well written Go code. It seems fitting to close on a discussion of those other principles.

Last year Bryan Cantrill gave a wonderful presentation on operating system principles, wherein he highlighted that different operating systems focus on different principles. It is not that they ignore the principles that differ between their competitors, just that when the chips are down, they prioritise a core set. So what is that core set of principles for Go?


If you were going to say readability, hopefully I’ve provided you with an alternative.

Programs must be written for people to read, and only incidentally for machines to execute.

–Hal Abelson and Gerald Sussman. Structure and Interpretation of Computer Programs

Code is read many more times than it is written. A single piece of code will, over its lifetime, be read hundreds, maybe thousands of times. It will be read hundreds or thousands of times because it must be understood. Clarity is important because all software, not just Go programs, is written by people to be read by other people. The fact that software is also consumed by machines is secondary.

The most important skill for a programmer is the ability to effectively communicate ideas.

–Gastón Jorquera

Legal documents are double spaced to aide the reader, but to the layperson that does nothing to help them comprehend what they just read. Readability is a property of how easy it was to read the words on the screen. Clarity, on the other hand, is the answer to the question “did you understand what you just read?”.

If you’re writing a program for yourself, maybe it only has to run once, or you’re the only person who’ll ever see it, then do what ever works for you. But if this is a piece of software that more than one person will contribute to, or that will be used by people over a long enough time that requirements, features, or the environment it runs in may change, then your goal must be for your program to be maintainable.

The first step towards writing maintainable code is making sure intent of the code is clear.


The next principle is obviously simplicity. Some might argue the most important principle for any programming language, perhaps the most important principle full stop.

Why should we strive for simplicity? Why is important that Go programs be simple?

The ability to simplify means to eliminate the unnecessary so that the necessary may speak

–Hans Hofmann

We’ve all been in a situation where we say “I can’t understand this code”. We’ve all worked on programs we were scared to make a change because we worried that it’ll break another part of the program; a part you don’t understand and don’t know how to fix. 

This is complexity. Complexity turns reliable software in unreliable software. Complexity is what leads to unmaintainable software. Complexity is what kills software projects. Clarity and simplicity are interlocking forces that lead to maintainable software.


The last Go principle I want to highlight is productivity. Developer productivity boils down to this; how much time do you spend doing useful work verses waiting for your tools or hopelessly lost in a foreign code-base? Go programmers should feel that they can get a lot done with Go.

“I started another compilation, turned my chair around to face Robert, and started asking pointed questions. Before the compilation was done, we’d roped Ken in and had decided to do something.”

–Rob Pike, Less is Exponentially more

The joke goes that Go was designed while waiting for a C++ program to compile. Fast compilation is a key feature of Go and a key recruiting tool to attract new developers. While compilation speed remains a constant battleground, it is fair to say that compilations which take minutes in other languages, take seconds in Go. This helps Go developers feel as productive as their counterparts working in dynamic languages without the maintenance issues inherent in those languages.

Design is the art of arranging code to work today, and be changeable  forever.

–Sandi Metz

More fundamental to the question of developer productivity, Go programmers realise that code is written to be read and so place the act of reading code above the act of writing it. Go goes so far as to enforce, via tooling and custom, that all code be formatted in a specific style. This removes the friction of learning a project specific dialect and helps spot mistakes because they just look incorrect.

Go programmers don’t spend days debugging inscrutable compile errors. They don’t waste days with complicated build scripts or deploying code to production. And most importantly they don’t spend their time trying to understand what their coworker wrote.

Complexity is anything that makes software hard to understand or to modify.

–John Ousterhout, A Philosophy of Software Design

Something I know about each of you reading this post is you will eventually leave your current employer. Maybe you’ll be moving on to a new role, or perhaps a promotion, perhaps you’ll move cities, or follow your partner overseas. Whatever the reason, we must all consider the succession of the maintainership of the programs we create.

If we strive to write programs that are clear, programs that are simple, and to focus on the productivity of those working with us that will set all Go programmers in good stead.

Because if we don’t, as we move from job to job, we’ll leave behind programs which cannot be maintained. Programs which cannot be changed. Programs which are too hard to onboard new developers, and programs which feel like career digression for those that work on them.

If software cannot be maintained, then it will be rewritten; and that could be the last time your company invests in Go.

Sydney High Performance Go workshop

On the 17th of July I’ll be giving a version of my High Performance Go workshop updated for the upcoming changes in Go 1.13. The event is free, as in puppy, however numbers are limited due to the venue size. The event will be held in the Sydney CBD, the address will be provided to registered attendees closer to the date.

You can find a link to the workshop description and syllabus here.

You can find a link to the registration page here.

No show policy

If you register for the event then don’t show up you are be depriving someone else of the opportunity to participate. You can cancel you registration at any time up to 23:59 on the 16th. If you are a no show you will be expected to make a meaningful donation to a charity of my choosing.

Constant Time

This essay is a derived from my dotGo 2019 presentation about my favourite feature in Go.

Many years ago Rob Pike remarked,

“Numbers are just numbers, you’ll never see 0x80ULL in a .go source file”.

—Rob Pike, The Go Programming Language

Beyond this pithy observation lies the fascinating world of Go’s constants. Something that is perhaps taken for granted because, as Rob noted, is Go numbers–constants–just work.
In this post I intend to show you a few things that perhaps you didn’t know about Go’s const keyword.

What’s so great about constants?

To kick things off, why are constants good? Three things spring to mind:

  • Immutability. Constants are one of the few ways we have in Go to express immutability to the compiler.
  • Clarity. Constants give us a way to extract magic numbers from our code, giving them names and semantic meaning.
  • Performance. The ability to express to the compiler that something will not change is key as it unlocks optimisations such as constant folding, constant propagation, branch and dead code elimination.

But these are generic use cases for constants, they apply to any language. Let’s talk about some of the properties of Go’s constants.

A Challenge

To introduce the power of Go’s constants let’s try a little challenge: declare a constant whose value is the number of bits in the natural machine word.

We can’t use unsafe.Sizeof as it is not a constant expression1. We could use a build tag and laboriously record the natural word size of each Go platform, or we could do something like this:

const uintSize = 32 << (^uint(0) >> 32 & 1)

There are many versions of this expression in Go codebases. They all work roughly the same way. If we’re on a 64 bit platform then the exclusive or of the number zero–all zero bits–is a number with all bits set, sixty four of them to be exact.


If we shift that value thirty two bits to the right, we get another value with thirty two ones in it.


Anding that with a number with one bit in the final position give us, the same thing, 1,

0000000000000000000000000000000011111111111111111111111111111111 & 1 = 1

Finally we shift the number thirty two one place to the right, giving us 642.

32 << 1 = 64

This expression is an example of a constant expression. All of these operations happen at compile time and the result of the expression is itself a constant. If you look in the in runtime package, in particular the garbage collector, you’ll see how constant expressions are used to set up complex invariants based on the word size of the machine the code is compiled on.

So, this is a neat party trick, but most compilers will do this kind of constant folding at compile time for you. Let’s step it up a notch.

Constants are values

In Go, constants are values and each value has a type. In Go, user defined types can declare their own methods. Thus, a constant value can have a method set. If you’re surprised by this, let me show you an example that you probably use every day.

const timeout = 500 * time.Millisecond
fmt.Println("The timeout is", timeout) // 500ms

In the example the untyped literal constant 500 is multiplied by time.Millisecond, itself a constant of type time.Duration. The rule for assignments in Go are, unless otherwise declared, the type on the left hand side of the assignment operator is inferred from the type on the right.500 is an untyped constant so it is converted to a time.Duration then multiplied with the constant time.Millisecond.

Thus timeout is a constant of type time.Duration which holds the value 500000000.
Why then does fmt.Println print 500ms, not 500000000?

The answer is time.Duration has a String method. Thus any time.Duration value, even a constant, knows how to pretty print itself.

Now we know that constant values are typed, and because types can declare methods, we can derive that constant values can fulfil interfaces. In fact we just saw an example of this. fmt.Println doesn’t assert that a value has a String method, it asserts the value implements the Stringer interface.

Let’s talk a little about how we can use this property to make our Go code better, and to do that I’m going to take a brief digression into the Singleton pattern.


I’m generally not a fan of the singleton pattern, in Go or any language. Singletons complicate testing and create unnecessary coupling between packages. I feel the singleton pattern is often used not to create a singular instance of a thing, but instead to create a place to coordinate registration. net/http.DefaultServeMux is a good example of this pattern.

package http

// DefaultServeMux is the default ServeMux used by Serve.
var DefaultServeMux = &defaultServeMux

var defaultServeMux ServeMux

There is nothing singular about http.defaultServerMux, nothing prevents you from creating another ServeMux. In fact the http package provides a helper that will create as many ServeMux‘s as you want.

// NewServeMux allocates and returns a new ServeMux.
func NewServeMux() *ServeMux { return new(ServeMux) }

http.DefaultServeMux is not a singleton. Never the less there is a case for things which are truely singletons because they can only represent a single thing. A good example of this are the file descriptors of a process; 0, 1, and 2 which represent stdin, stdout, and stderr respectively.

It doesn’t matter what names you give them, 1 is always stdout, and there can only ever be one file descriptor 1. Thus these two operations are identical:

fmt.Fprintf(os.Stdout, "Hello dotGo\n")
syscall.Write(1, []byte("Hello dotGo\n"))

So let’s look at how the os package defines Stdin, Stdout, and Stderr:

package os

var (
        Stdin  = NewFile(uintptr(syscall.Stdin), "/dev/stdin")
        Stdout = NewFile(uintptr(syscall.Stdout), "/dev/stdout")
        Stderr = NewFile(uintptr(syscall.Stderr), "/dev/stderr")

There are a few problems with this declaration. Firstly their type is *os.File not the respective io.Reader or io.Writer interfaces. People have long complained that this makes replacing them with alternatives problematic. However the notion of replacing these variables is precisely the point of this digression. Can you safely change the value of os.Stdout once your program is running without causing a data race?

I argue that, in the general case, you cannot. In general, if something is unsafe to do, as programmers we shouldn’t let our users think that it is safe, lest they begin to depend on that behaviour.

Could we change the definition of os.Stdout and friends so that they retain the observable behaviour of reading and writing, but remain immutable? It turns out, we can do this easily with constants.

type readfd int

func (r readfd) Read(buf []byte) (int, error) {
       return syscall.Read(int(r), buf)

type writefd int

func (w writefd) Write(buf []byte) (int, error) {
        return syscall.Write(int(w), buf)

const (
        Stdin  = readfd(0)
        Stdout = writefd(1)
        Stderr = writefd(2)

func main() {
        fmt.Fprintf(Stdout, "Hello world")

In fact this change causes only one compilation failure in the standard library.3

Sentinel error values

Another case of things which look like constants but really aren’t, are sentinel error values. io.EOF, sql.ErrNoRows, crypto/x509.ErrUnsupportedAlgorithm, and so on are all examples of sentinel error values. They all fall into a category of expected errors, and because they are expected, you’re expected to check for them.

To compare the error you have with the one you were expecting, you need to import the package that defines that error. Because, by definition, sentinel errors are exported public variables, any code that imports, for example, the io package could change the value of io.EOF.

package nelson

import "io"

func init() {
        io.EOF = nil // haha!

I’ll say that again. If I know the name of io.EOF I can import the package that declares it, which I must if I want to compare it to my error, and thus I could change io.EOF‘s value. Historically convention and a bit of dumb luck discourages people from writing code that does this, but technically there is nothing to prevent you from doing so.

Replacing io.EOF is probably going to be detected almost immediately. But replacing a less frequently used sentinel error may cause some interesting side effects:

package innocent

import "crypto/rsa"

func init() {
        rsa.ErrVerification = nil // 🤔

If you were hoping the race detector will spot this subterfuge, I suggest you talk to the folks writing testing frameworks who replace os.Stdout without it triggering the race detector.


I want to digress for a moment to talk about the most important property of constants. Constants aren’t just immutable, its not enough that we cannot overwrite their declaration,
Constants are fungible. This is a tremendously important property that doesn’t get nearly enough attention.

Fungible means identical. Money is a great example of fungibility. If you were to lend me 10 bucks, and I later pay you back, the fact that you gave me a 10 dollar note and I returned to you 10 one dollar bills, with respect to its operation as a financial instrument, is irrelevant. Things which are fungible are by definition equal and equality is a powerful property we can leverage for our programs.

var myEOF = errors.New("EOF") // io/io.go line 38
fmt.Println(myEOF == io.EOF)  // false

Putting aside the effect of malicious actors in your code base the key design challenge with sentinel errors is they behave like singletons, not constants. Even if we follow the exact procedure used by the io package to create our own EOF value, myEOF and io.EOF are not equal. myEOF and io.EOF are not fungible, they cannot be interchanged. Programs can spot the difference.

When you combine the lack of immutability, the lack of fungibility, the lack of equality, you have a set of weird behaviours stemming from the fact that sentinel error values in Go are not constant expressions. But what if they were?

Constant errors

Ideally a sentinel error value should behave as a constant. It should be immutable and fungible. Let’s recap how the built in error interface works in Go.

type error interface {
        Error() string

Any type with an Error() string method fulfils the error interface. This includes user defined types, it includes types derived from primitives like string, and it includes constant strings. With that background, consider this error implementation:

type Error string

func (e Error) Error() string {
        return string(e)

We can use this error type as a constant expression:

const err = Error("EOF")

Unlike errors.errorString, which is a struct, a compact struct literal initialiser is not a constant expression and cannot be used.

const err2 = errors.errorString{"EOF"} // doesn't compile

As constants of this Error type are not variables, they are immutable.

const err = Error("EOF")
err = Error("not EOF")   // doesn't compile

Additionally, two constant strings are always equal if their contents are equal:

const str1 = "EOF"
const str2 = "EOF"
fmt.Println(str1 == str2) // true

which means two constants of a type derived from string with the same contents are also equal.

type Error string

const err1 = Error("EOF")
const err2 = Error("EOF")
fmt.Println(err1 == err2) // true```

Said another way, equal constant Error values are the same, in the way that the literal constant 1 is the same as every other literal constant 1.

Now we have all the pieces we need to make sentinel errors, like io.EOF, and rsa.ErrVerfication, immutable, fungible, constant expressions.

% git diff
diff --git a/src/io/io.go b/src/io/io.go
index 2010770e6a..355653b4b8 100644
--- a/src/io/io.go
+++ b/src/io/io.go
@@ -35,7 +35,12 @@ var ErrShortBuffer = errors.New("short buffer")
 // If the EOF occurs unexpectedly in a structured data stream,
 // the appropriate error is either ErrUnexpectedEOF or some other error
 // giving more detail.
-var EOF = errors.New("EOF")
+const EOF = ioError("EOF")
+type ioError string
+func (e ioError) Error() string { return string(e) }

This change is probably a bit of a stretch for the Go 1 contract, but there is no reason you cannot adopt a constant error pattern for your sentinel errors in the packages that you write.

In summary

Go’s constants are powerful. If you only think of them as immutable numbers, you’re missing out. Go’s constants let us compose programs that are more correct and harder to misuse.

Today I’ve outlined three ways to use constants that are more than your typical immutable number.

Now it’s over to you, I’m excited to see where you can take these ideas.

Prefer table driven tests

I’m a big fan of testing, specifically unit testing and TDD (done correctly, of course). A practice that has grown around Go projects is the idea of a table driven test. This post explores the how and why of writing a table driven test.

Let’s say we have a function that splits strings:

// Split slices s into all substrings separated by sep and
// returns a slice of the substrings between those separators.
func Split(s, sep string) []string {
var result []string
i := strings.Index(s, sep)
for i > -1 {
result = append(result, s[:i])
s = s[i+len(sep):]
i = strings.Index(s, sep)
return append(result, s)

In Go, unit tests are just regular Go functions (with a few rules) so we write a unit test for this function starting with a file in the same directory, with the same package name, strings.

package split

import (

func TestSplit(t *testing.T) {
got := Split("a/b/c", "/")
want := []string{"a", "b", "c"}
if !reflect.DeepEqual(want, got) {
t.Fatalf("expected: %v, got: %v", want, got)

Tests are just regular Go functions with a few rules:

  1. The name of the test function must start with Test.
  2. The test function must take one argument of type *testing.T. A *testing.T is a type injected by the testing package itself, to provide ways to print, skip, and fail the test.

In our test we call Split with some inputs, then compare it to the result we expected.

Code coverage

The next question is, what is the coverage of this package? Luckily the go tool has a built in branch coverage. We can invoke it like this:

% go test -coverprofile=c.out
coverage: 100.0% of statements
ok split 0.010s

Which tells us we have 100% branch coverage, which isn’t really surprising, there’s only one branch in this code.

If we want to dig in to the coverage report the go tool has several options to print the coverage report. We can use go tool cover -func to break down the coverage per function:

% go tool cover -func=c.out
split/split.go:8: Split 100.0%
total: (statements) 100.0%

Which isn’t that exciting as we only have one function in this package, but I’m sure you’ll find more exciting packages to test.

Spray some .bashrc on that

This pair of commands is so useful for me I have a shell alias which runs the test coverage and the report in one command:

cover () {
local t=$(mktemp -t cover)
go test $COVERFLAGS -coverprofile=$t $@ \
&& go tool cover -func=$t \
&& unlink $t

Going beyond 100% coverage

So, we wrote one test case, got 100% coverage, but this isn’t really the end of the story. We have good branch coverage but we probably need to test some of the boundary conditions. For example, what happens if we try to split it on comma?

func TestSplitWrongSep(t *testing.T) {
got := Split("a/b/c", ",")
want := []string{"a/b/c"}
if !reflect.DeepEqual(want, got) {
t.Fatalf("expected: %v, got: %v", want, got)

Or, what happens if there are no separators in the source string?

func TestSplitNoSep(t *testing.T) {
got := Split("abc", "/")
want := []string{"abc"}
if !reflect.DeepEqual(want, got) {
t.Fatalf("expected: %v, got: %v", want, got)

We’re starting build a set of test cases that exercise boundary conditions. This is good.

Introducing table driven tests

However the there is a lot of duplication in our tests. For each test case only the input, the expected output, and name of the test case change. Everything else is boilerplate. What we’d like to to set up all the inputs and expected outputs and feel them to a single test harness. This is a great time to introduce table driven testing.

func TestSplit(t *testing.T) {
type test struct {
input string
sep string
want []string

tests := []test{
{input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{input: "abc", sep: "/", want: []string{"abc"}},

for _, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %v, got: %v", tc.want, got)

We declare a structure to hold our test inputs and expected outputs. This is our table. The tests structure is usually a local declaration because we want to reuse this name for other tests in this package.

In fact, we don’t even need to give the type a name, we can use an anonymous struct literal to reduce the boilerplate like this:

func TestSplit(t *testing.T) {
tests := []struct {
input string
sep string
want []string
{input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{input: "abc", sep: "/", want: []string{"abc"}},

for _, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %v, got: %v", tc.want, got)

Now, adding a new test is a straight forward matter; simply add another line the tests structure. For example, what will happen if our input string has a trailing separator?

{input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{input: "abc", sep: "/", want: []string{"abc"}},
{input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}}, // trailing sep

But, when we run go test, we get

% go test
--- FAIL: TestSplit (0.00s)
split_test.go:24: expected: [a b c], got: [a b c ]

Putting aside the test failure, there are a few problems to talk about.

The first is by rewriting each test from a function to a row in a table we’ve lost the name of the failing test. We added a comment in the test file to call out this case, but we don’t have access to that comment in the go test output.

There are a few ways to resolve this. You’ll see a mix of styles in use in Go code bases because the table testing idiom is evolving as people continue to experiment with the form.

Enumerating test cases

As tests are stored in a slice we can print out the index of the test case in the failure message:

func TestSplit(t *testing.T) {
    tests := []struct {
        input string
        sep   string
        want  []string
        {input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
        {input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
        {input: "abc", sep: "/", want: []string{"abc"}},
        {input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},

    for i, tc := range tests {
        got := Split(tc.input, tc.sep)
        if !reflect.DeepEqual(tc.want, got) {
            t.Fatalf("test %d: expected: %v, got: %v", i+1, tc.want, got)

Now when we run go test we get this

% go test
--- FAIL: TestSplit (0.00s)
split_test.go:24: test 4: expected: [a b c], got: [a b c ]

Which is a little better. Now we know that the fourth test is failing, although we have to do a little bit of fudging because slice indexing—​and range iteration—​is zero based. This requires consistency across your test cases; if some use zero base reporting and others use one based, it’s going to be confusing. And, if the list of test cases is long, it could be difficult to count braces to figure out exactly which fixture constitutes test case number four.

Give your test cases names

Another common pattern is to include a name field in the test fixture.

func TestSplit(t *testing.T) {
tests := []struct {
name string
input string
sep string
want []string
{name: "simple", input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{name: "wrong sep", input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{name: "no sep", input: "abc", sep: "/", want: []string{"abc"}},
{name: "trailing sep", input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},

for _, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("%s: expected: %v, got: %v",, tc.want, got)

Now when the test fails we have a descriptive name for what the test was doing. We no longer have to try to figure it out from the output—​also, now have a string we can search on.

% go test
--- FAIL: TestSplit (0.00s)
split_test.go:25: trailing sep: expected: [a b c], got: [a b c ]

We can dry this up even more using a map literal syntax:

func TestSplit(t *testing.T) {
tests := map[string]struct {
input string
sep string
want []string
"simple": {input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
"wrong sep": {input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
"no sep": {input: "abc", sep: "/", want: []string{"abc"}},
"trailing sep": {input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},

for name, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("%s: expected: %v, got: %v", name, tc.want, got)

Using a map literal syntax we define our test cases not as a slice of structs, but as map of test names to test fixtures. There’s also a side benefit of using a map that is going to potentially improve the utility of our tests.

Map iteration order is undefined 1 This means each time we run go test, our tests are going to be potentially run in a different order.

This is super useful for spotting conditions where test pass when run in statement order, but not otherwise. If you find that happens you probably have some global state that is being mutated by one test with subsequent tests depending on that modification.

Introducing sub tests

Before we fix the failing test there are a few other issues to address in our table driven test harness.

The first is we’re calling t.Fatalf when one of the test cases fails. This means after the first failing test case we stop testing the other cases. Because test cases are run in an undefined order, if there is a test failure, it would be nice to know if it was the only failure or just the first.

The testing package would do this for us if we go to the effort to write out each test case as its own function, but that’s quite verbose. The good news is since Go 1.7 a new feature was added that lets us do this easily for table driven tests. They’re called sub tests.

func TestSplit(t *testing.T) {
tests := map[string]struct {
input string
sep string
want []string
"simple": {input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
"wrong sep": {input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
"no sep": {input: "abc", sep: "/", want: []string{"abc"}},
"trailing sep": {input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},

for name, tc := range tests {
t.Run(name, func(t *testing.T) {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %v, got: %v", tc.want, got)


As each sub test now has a name we get that name automatically printed out in any test runs.

% go test
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: [a b c], got: [a b c ]

Each subtest is its own anonymous function, therefore we can use t.Fatalft.Skipf, and all the other testing.Thelpers, while retaining the compactness of a table driven test.

Individual sub test cases can be executed directly

Because sub tests have a name, you can run a selection of sub tests by name using the go test -run flag.

% go test -run=.*/trailing -v
=== RUN TestSplit
=== RUN TestSplit/trailing_sep
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: [a b c], got: [a b c ]

Comparing what we got with what we wanted

Now we’re ready to fix the test case. Let’s look at the error.

--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: [a b c], got: [a b c ]

Can you spot the problem? Clearly the slices are different, that’s what reflect.DeepEqual is upset about. But spotting the actual difference isn’t easy, you have to spot that extra space after c. This might look simple in this simple example, but it is any thing but when you’re comparing two complicated deeply nested gRPC structures.

We can improve the output if we switch to the %#v syntax to view the value as a Go(ish) declaration:

got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %#v, got: %#v", tc.want, got)

Now when we run our test it’s clear that the problem is there is an extra blank element in the slice.

% go test
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: []string{"a", "b", "c"}, got: []string{"a", "b", "c", ""}

But before we go to fix our test failure I want to talk a little bit more about choosing the right way to present test failures. Our Split function is simple, it takes a primitive string and returns a slice of strings, but what if it worked with structs, or worse, pointers to structs?

Here is an example where %#v does not work as well:

func main() {
type T struct {
I int
x := []*T{{1}, {2}, {3}}
y := []*T{{1}, {2}, {4}}
fmt.Printf("%v %v\n", x, y)
fmt.Printf("%#v %#v\n", x, y)

The first fmt.Printfprints the unhelpful, but expected slice of addresses; [0xc000096000 0xc000096008 0xc000096010] [0xc000096018 0xc000096020 0xc000096028]. However our %#v version doesn’t fare any better, printing a slice of addresses cast to *main.T;[]*main.T{(*main.T)(0xc000096000), (*main.T)(0xc000096008), (*main.T)(0xc000096010)} []*main.T{(*main.T)(0xc000096018), (*main.T)(0xc000096020), (*main.T)(0xc000096028)}

Because of the limitations in using any fmt.Printf verb, I want to introduce the go-cmp library from Google.

The goal of the cmp library is it is specifically to compare two values. This is similar to reflect.DeepEqual, but it has more capabilities. Using the cmp pacakge you can, of course, write:

func main() {
type T struct {
I int
x := []*T{{1}, {2}, {3}}
y := []*T{{1}, {2}, {4}}
fmt.Println(cmp.Equal(x, y)) // false

But far more useful for us with our test function is the cmp.Diff function which will produce a textual description of what is different between the two values, recursively.

func main() {
type T struct {
I int
x := []*T{{1}, {2}, {3}}
y := []*T{{1}, {2}, {4}}
diff := cmp.Diff(x, y)

Which instead produces:

% go run
-: 3
+: 4

Telling us that at element 2 of the slice of Ts the Ifield was expected to be 3, but was actually 4.

Putting this all together we have our table driven go-cmp test

func TestSplit(t *testing.T) {
tests := map[string]struct {
input string
sep string
want []string
"simple": {input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
"wrong sep": {input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
"no sep": {input: "abc", sep: "/", want: []string{"abc"}},
"trailing sep": {input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},

for name, tc := range tests {
t.Run(name, func(t *testing.T) {
got := Split(tc.input, tc.sep)
diff := cmp.Diff(tc.want, got)
if diff != "" {


Running this we get

% go test
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:27: {[]string}[?->3]:
-: <non-existent>
+: ""
exit status 1
FAIL split 0.006s

Using cmp.Diff our test harness isn’t just telling us that what we got and what we wanted were different. Our test is telling us that the strings are different lengths, the third index in the fixture shouldn’t exist, but the actual output we got an empty string, “”. From here fixing the test failure is straight forward.