conc is your toolbelt for structured concurrency in go, making common tasks
easier and safer.
go get github.com/sourcegraph/conc- Use
conc.WaitGroupif you just want a safer version ofsync.WaitGroup - Use
pool.Poolif you want a concurrency-limited task runner - Use
pool.ResultPoolif you want a concurrent task runner that collects task results - Use
pool.(Result)?ErrorPoolif your tasks are fallible - Use
pool.(Result)?ContextPoolif your tasks should be canceled on failure - Use
stream.Streamif you want to process an ordered stream of tasks in parallel with serial callbacks - Use
iter.Mapif you want to concurrently map a slice - Use
iter.ForEachif you want to concurrently iterate over a slice - Use
panics.Catcherif you want to catch panics in your own goroutines
All pools are created with
pool.New()
or
pool.NewWithResults[T](),
then configured with methods:
p.WithMaxGoroutines()configures the maximum number of goroutines in the poolp.WithErrors()configures the pool to run tasks that return errorsp.WithContext(ctx)configures the pool to run tasks that should be canceled on first errorp.WithFirstError()configures error pools to only keep the first returned error rather than an aggregated errorp.WithCollectErrored()configures result pools to collect results even when the task errored
The main goals of the package are:
- Make it harder to leak goroutines
- Handle panics gracefully
- Make concurrent code easier to read
A common pain point when working with goroutines is cleaning them up. It's
really easy to fire off a go statement and fail to properly wait for it to
complete.
conc takes the opinionated stance that all concurrency should be scoped.
That is, goroutines should have an owner and that owner should always
ensure that its owned goroutines exit properly.
In conc, the owner of a goroutine is always a conc.WaitGroup. Goroutines
are spawned in a WaitGroup with (*WaitGroup).Go(), and
(*WaitGroup).Wait() should always be called before the WaitGroup goes out
of scope.
In some cases, you might want a spawned goroutine to outlast the scope of the
caller. In that case, you could pass a WaitGroup into the spawning function.
func main() {
var wg conc.WaitGroup
defer wg.Wait()
startTheThing(&wg)
}
func startTheThing(wg *conc.WaitGroup) {
wg.Go(func() { ... })
}For some more discussion on why scoped concurrency is nice, check out this blog post.
A frequent problem with goroutines in long-running applications is handling panics. A goroutine spawned without a panic handler will crash the whole process on panic. This is usually undesirable.
However, if you do add a panic handler to a goroutine, what do you do with the panic once you catch it? Some options:
- Ignore it
- Log it
- Turn it into an error and return that to the goroutine spawner
- Propagate the panic to the goroutine spawner
Ignoring panics is a bad idea since panics usually mean there is actually something wrong and someone should fix it.
Just logging panics isn't great either because then there is no indication to the spawner that something bad happened, and it might just continue on as normal even though your program is in a really bad state.
Both (3) and (4) are reasonable options, but both require the goroutine to have
an owner that can actually receive the message that something went wrong. This
is generally not true with a goroutine spawned with go, but in the conc
package, all goroutines have an owner that must collect the spawned goroutine.
In the conc package, any call to Wait() will panic if any of the spawned goroutines
panicked. Additionally, it decorates the panic value with a stacktrace from the child
goroutine so that you don't lose information about what caused the panic.
Doing this all correctly every time you spawn something with go is not
trivial and it requires a lot of boilerplate that makes the important parts of
the code more difficult to read, so conc does this for you.
stdlib |
conc |
|---|---|
type caughtPanicError struct {
val any
stack []byte
}
func (e *caughtPanicError) Error() string {
return fmt.Sprintf(
"panic: %q\n%s",
e.val,
string(e.stack)
)
}
func main() {
done := make(chan error)
go func() {
defer func() {
if v := recover(); v != nil {
done <- &caughtPanicError{
val: v,
stack: debug.Stack()
}
} else {
done <- nil
}
}()
doSomethingThatMightPanic()
}()
err := <-done
if err != nil {
panic(err)
}
} |
func main() {
var wg conc.WaitGroup
wg.Go(doSomethingThatMightPanic)
// panics with a nice stacktrace
wg.Wait()
} |
Doing concurrency correctly is difficult. Doing it in a way that doesn't
obfuscate what the code is actually doing is more difficult. The conc package
attempts to make common operations easier by abstracting as much boilerplate
complexity as possible.
Want to run a set of concurrent tasks with a bounded set of goroutines? Use
pool.New(). Want to process an ordered stream of results concurrently, but
still maintain order? Try stream.New(). What about a concurrent map over
a slice? Take a peek at iter.Map().
Browse some examples below for some comparisons with doing these by hand.
Each of these examples forgoes propagating panics for simplicity. To see what kind of complexity that would add, check out the "Goal #2" header above.
Spawn a set of goroutines and waiting for them to finish:
stdlib |
conc |
|---|---|
func main() {
var wg sync.WaitGroup
for i := 0; i < 10; i++ {
wg.Add(1)
go func() {
defer wg.Done()
// crashes on panic!
doSomething()
}()
}
wg.Wait()
} |
func main() {
var wg conc.WaitGroup
for i := 0; i < 10; i++ {
wg.Go(doSomething)
}
wg.Wait()
} |
Process each element of a stream in a static pool of goroutines:
stdlib |
conc |
|---|---|
func process(stream chan int) {
var wg sync.WaitGroup
for i := 0; i < 10; i++ {
wg.Add(1)
go func() {
defer wg.Done()
for elem := range stream {
handle(elem)
}
}()
}
wg.Wait()
} |
func process(stream chan int) {
p := pool.New().WithMaxGoroutines(10)
for elem := range stream {
elem := elem
p.Go(func() {
handle(elem)
})
}
p.Wait()
} |
Process each element of a slice in a static pool of goroutines:
stdlib |
conc |
|---|---|
func process(values []int) {
feeder := make(chan int, 8)
var wg sync.WaitGroup
for i := 0; i < 10; i++ {
wg.Add(1)
go func() {
defer wg.Done()
for elem := range feeder {
handle(elem)
}
}()
}
for _, value := range values {
feeder <- value
}
close(feeder)
wg.Wait()
} |
func process(values []int) {
iter.ForEach(values, handle)
} |
Concurrently map a slice:
stdlib |
conc |
|---|---|
func concMap(
input []int,
f func(int) int,
) []int {
res := make([]int, len(input))
var idx atomic.Int64
var wg sync.WaitGroup
for i := 0; i < 10; i++ {
wg.Add(1)
go func() {
defer wg.Done()
for {
i := int(idx.Add(1) - 1)
if i >= len(input) {
return
}
res[i] = f(input[i])
}
}()
}
wg.Wait()
return res
} |
func concMap(
input []int,
f func(int) int,
) []int {
return iter.Map(input, f)
} |
Process an ordered stream concurrently:
stdlib |
conc |
|---|---|
func mapStream
in chan int,
out chan int,
f func(int) int,
) {
tasks := make(chan func())
taskResults := make(chan chan int)
// Worker goroutines
var workerWg sync.WaitGroup
for i := 0; i < 10; i++ {
workerWg.Add(1)
go func() {
defer workerWg.Done()
for task := range tasks {
task()
}
}()
}
// Ordered reader goroutines
var readerWg sync.WaitGroup
readerWg.Add(1)
go func() {
defer readerWg.Done()
for result := range taskResults {
out <- result
}
}
// Feed the workers with tasks
for elem := range in {
resultCh := make(chan int, 1)
taskResults <- resultCh
tasks <- func() {
resultCh <- f(elem)
}
}
// We've exhausted input.
// Wait for everything to finish
close(tasks)
workerWg.Wait()
close(taskResults)
readerWg.Wait()
} |
func mapStream(
in chan int,
out chan int,
f func(int) int,
) {
s := stream.New().WithMaxGoroutines(10)
for elem := range in {
elem := elem
s.Go(func() stream.Callback {
res := f(elem)
return func() { out <- res }
})
}
s.Wait()
} |
This package is currently pre-1.0. There are likely to be minor breaking changes before a 1.0 release as we stabilize the APIs and tweak defaults. Please open an issue if you have questions, concerns, or requests that you'd like addressed before the 1.0 release. Currently, a 1.0 is targeted for March 2023.