Recently I’ve been dabling in Go. I was lured in by the lore of a purposefully simple language with amazing concurrency features. While that is true, I have to admit that I stayed around for the documentation. Golang.org has a this great Tour which walks you through the basics of the language through interactive examples and exercises. Secondly, the godoc command has a --server flag which spins up essentially a local copy of golang.org but the docs pages also include any locale packages you have pulled down, or that you’ve developed. Ok, enough gushing about Go documentation.

The end of the tour talks about concurrency and has some exercises to help you learn by doing. I found this method very compelling and came up with two different methods for handling concurrency in Go; each suited for slightly different use cases.


If you aren’t familiar with concurrency in Go, this section is for you. I will touch on some key points which are necessary to understand the examples below. This is not an in-depth guide. I, myself, am still learning the ropes, but this should cover us enough.

The idomatic way to handle concurrency in Go is through something called a goroutine. A goroutine is something like a lightweight thread. They are cheap to create and are ideal for small little jobs. Think of using goroutines as if to say “Someone else go do this real quick.” A thread will go do your bidding and your program can continue to execute.

func timer() {
    time.Sleep(100 * time.Millisecond)

func main() {
  go timer()
  fmt.Println("Timer started")

  // wait for timer to complete
  time.Sleep(2000 * time.Millisecond)

However, what about results from a goroutine? Many uses of concurrency involve a collection phase where results from the concurrent tasks are reduced down to a final result. This is where channels come in. Think of a channel like a typed Unix pipe. Data written in at one end can be read from the other end.

Say we wanted to add all the digits from one to ten. We could let two goroutines work on the first and half last each, then combine the individual results.

func summer(start int, end int, c chan int) {
  sum := 0
  for i := start; i <= end; i++ {
    sum += i
  c <- sum

func main() {
  c := make(chan int)

  go summer(1, 5, c)
  go summer(6, 10, c)

  // read the values from the channel
  first_half := <- c
  last_half :=  <- c

  fmt.Println(first_half, last_half, first_half + last_half)

Worker driven concurrency

Concurrency with a known amount of return values, like the above example, is fairly straightforward, but also quite contrived. A (slightly) more realistic example would include a case where the exact number of values to be sent through a channel is unknown. Reads from channels will wait until a value is available (similarly writes also wait for an empty space in a channel). The range function will wait-read on a channel until the channel is closed. Closing a channel should only be done on the writing end as trying to write to a closed channel will panic. This wait-read cycle allows us to setup a loop waiting for values on a channel and break out of the loop once the channel is closed.

In the example below we are calculating the decimal part of division in a goroutine with the decimal_divide function. Since we won’t know exactly how many digits the decimal result will be we can wait on the digits and print them as we get them. Once decimal_divide completes the division, it signals main by closing the channel.

func decimal_divide(numerator, denominator int, c chan int) {
  // do long division "by hand"
  for ; numerator != 0; numerator = numerator % denominator {
    numerator = numerator * 10
    // send the next calculated digit back to main
    c <- numerator / denominator

  // all done; close the channel

func main() {
  numerator := 22
  denominator := 7

  // print the integer part of the result
  fmt.Print(numerator / denominator, ".")

  // calculate the decimal part digit by digit
  c := make(chan int)
  go decimal_divide(numerator, denominator, c)
  for digit := range c {

Multi-worker concurrency

In the example above, is fairly “linear” since the input of the next step is the output of the previous. It isn’t straightforward to add additional working in that case. However, let’s look at a “fan out” style of problem where we won’t know the number of workers, nor the number of expected values. These kinds of problems can be thought of as graph traversal problems.

Suppose we had a list of cities each with their own list of immediately neighboring cities which were connected by roads.

type cityList map[string][]string

var list = cityList{

Using this information and a starting city, you could generate a list all the cities which could be visited by car. Each time a city’s neighbors are found, a new goroutine can be used to fetch each neighbor’s neighbors. Since, we won’t know, in advance, how many neighbors each city has or how many neighbors each neighbor will have. We’ll have to do some housekeeping of on our own to make sure everyone is accounted. Further, after we see a city for the first time, there’s no reason to re-visit its neighbors again (and avoid graph loops).

func main() {
    startingCity := "Cleveland";

    // keep track of cities we've visited to avoid infinite loops
    fetchedCities := map[string]bool{startingCity: true}

    // pass the neighbors after fetching
    c := make(chan []string)

    var fetchNeighbors = func(c chan []string, city string, list cityList) {
        if neighbors, ok := list[city]; ok {
          // if we've found the city on the map, send back the neighbors
          c <- neighbors;
        } else {
          // otherwise send no neighbors back
          // could be a good place for an error message, etc.
          c <- []string{};

    // track how many cities' neighbors are still to be fetched
    // start at one because of the starting city
    stillToFetch := 1;

    // go fetch the first set of neighbors
    go fetchNeighbors(c, startingCity, cityMap);

    // while we are still fetching neighbors
    for stillToFetch > 0 {

        // read the neighbors from the channel
        neighbors := <- c

        // track that we've fetched a set of neighbors

        for _, city := range neighbors {
            // if we haven't seen this city before
            if !fetchedCities[city] {
                // mark it as seen
                fetchedCities[city] = true

                // now we have one more city to fetch
                stillToFetch ++

                // fetch
                go fetchNeighbors(c, city, cityMap);

                // print out the name of the neighbor as visitable

As you can see we’ve had to use the stillToFetch variable to keep track of how many sets of neighbors to read from the channel. Each time we get a new set of neighbors, we can subtract one to say that we’ve fetched neighbors. And on the flip side, each new neighbor that we haven’t seen yet, we add one to say that we have another set of neighbors to fetch.

These examples show some basic techniques to work with concurrency in go and many more techniques exist to create performant, robust applications. For full, working examples please see the gist to accompany this post. With the knowledge above, I hope you are inspired to take advantage of the wonderful power amd simplicity of go concurrency. Please link to any such work in the comments below.


Christopher R Marshall


Enjoys programming web applications; especially in Ruby and Go. Also enjoys playing ice hockey as a goalie and playing the guitar.