I'm trying to understand a bit more about what happens under the surface during various blocking/waiting types of operations in Go. Take the following example:
otherChan = make(chan int)
t = time.NewTicker(time.Second)
for {
doThings()
// OPTION A: Sleep
time.Sleep(time.Second)
// OPTION B: Blocking ticker
<- t.C
// OPTION C: Select multiple
select {
case <- otherChan:
case <- t.C:
}
}
From a low level view (system calls, cpu scheduling) what is the difference between these while waiting?
My understanding is that time.Sleep leaves the CPU free to perform other tasks until the specified time has elapsed. Does the blocking ticker <- t.C do the same? Is the processor polling the channel or is there an interrupt involved? Does having multiple channels in a select change anything?
In other words, assuming that otherChan never had anything put into it, would these three options execute in an identical way, or would one be less resource intensive than the others?
That's a very interesting question, so I did cd into my Go source to start looking.
time.Sleep
time.Sleep is defined like this:
// src/time/sleep.go
// Sleep pauses the current goroutine for at least the duration d.
// A negative or zero duration causes Sleep to return immediately.
func Sleep(d Duration)
No body, no definition in an OS-specific time_unix.go!?! A little search and the answer is because time.Sleep is actually defined in the runtime:
// src/runtime/time.go
// timeSleep puts the current goroutine to sleep for at least ns nanoseconds.
//go:linkname timeSleep time.Sleep
func timeSleep(ns int64) {
// ...
}
Which in retrospect makes a lot of sense, as it has to interact with the goroutine scheduler. It ends up calling goparkunlock, which "puts the goroutine into a waiting state". time.Sleep creates a runtime.timer with a callback function that is called when the timer expires - that callback function wakes up the goroutine by calling goready. See next section for more details on the runtime.timer.
time.NewTicker
time.NewTicker creates a *Ticker (and time.Tick is a helper function that does the same thing but directly returns *Ticker.C, the ticker's receive channel, instead of *Ticker, so you could've written your code with it instead) has similar hooks into the runtime: a ticker is a struct that holds a runtimeTimer and a channel on which to signal the ticks.
runtimeTimer is defined in the time package but it must be kept in sync with timer in src/runtime/time.go, so it is effectively a runtime.timer. Remember that in time.Sleep, the timer had a callback function to wake up the sleeping goroutine? In the case of *Ticker, the timer's callback function sends the current time on the ticker's channel.
Then, the real waiting/scheduling happens on the receive from the channel, which is essentially the same as the select statement unless otherChan sends something before the tick, so let's look at what happens on a blocking receive.
<- chan
Channels are implemented (now in Go!) in src/runtime/chan.go, by the hchan struct. Channel operations have matching functions, and a receive is implemented by chanrecv:
// chanrecv receives on channel c and writes the received data to ep.
// ep may be nil, in which case received data is ignored.
// If block == false and no elements are available, returns (false, false).
// Otherwise, if c is closed, zeros *ep and returns (true, false).
// Otherwise, fills in *ep with an element and returns (true, true).
func chanrecv(t *chantype, c *hchan, ep unsafe.Pointer, block bool) (selected, received bool) {
// ...
}
This part has a lot of different cases, but in your example, it is a blocking receive from an asynchronous channel (time.NewTicker creates a channel with a buffer of 1), but anyway it ends up calling... goparkunlock, again to allow other goroutines to proceed while this one is stuck waiting.
So...
In all cases, the goroutine ends up being parked (which is not really shocking - it can't make progress, so it has to leave its thread available for a different goroutine if there's any available). A glance at the code seems to suggest that the channel has a bit more overhead than a straight-up time.Sleep. However, it allows far more powerful patterns, such as the last one in your example: the goroutine can be woken up by another channel, whichever comes first.
To answer your other questions, regarding polling, the timers are managed by a goroutine that sleeps until the next timer in its queue, so it's working only when it knows a timer has to be triggered. When the next timer has expired, it wakes up the goroutine that called time.Sleep (or sends the value on the ticker's channel, it does whatever the callback function does).
There's no polling in channels, the receive is unlocked when a send is made on the channel, in chansend of the chan.go file:
// wake up a waiting receiver
sg := c.recvq.dequeue()
if sg != nil {
recvg := sg.g
unlock(&c.lock)
if sg.releasetime != 0 {
sg.releasetime = cputicks()
}
goready(recvg, 3)
} else {
unlock(&c.lock)
}
That was an interesting dive into Go's source code, very interesting question! Hope I answered at least part of it!
Related
I'm just getting into concurrency in Go and trying to create a dispatch go routine that will send jobs to a worker pool listening on the jobchan channel. If a message comes into my dispatch function via the dispatchchan channel and my other go routines are busy, the message is appended onto the stack slice in the dispatcher and the dispatcher will try to send again later when a worker becomes available, and/or no more messages are received on the dispatchchan. This is because the dispatchchan and the jobchan are unbuffered, and the go routine the workers are running will append other messages to the dispatcher up to a certain point and I don't want the workers blocked waiting on the dispatcher and creating a deadlock. Here's the dispatcher code I've come up with so far:
func dispatch() {
var stack []string
acount := 0
for {
select {
case d := <-dispatchchan:
stack = append(stack, d)
case c := <-mw:
acount = acount + c
case jobchan <-stack[0]:
if len(stack) > 1 {
stack[0] = stack[len(stack)-1]
stack = stack[:len(stack)-1]
} else {
stack = nil
}
default:
if acount == 0 && len(stack) == 0 {
close(jobchan)
close(dispatchchan)
close(mw)
wg.Done()
return
}
}
}
Complete example at https://play.golang.wiki/p/X6kXVNUn5N7
The mw channel is a buffered channel the same length as the number of worker go routines. It acts as a semaphore for the worker pool. If the worker routine is doing [m]eaningful [w]ork it throws int 1 on the mw channel and when it finishes its work and goes back into the for loop listening to the jobchan it throws int -1 on the mw. This way the dispatcher knows if there's any work being done by the worker pool, or if the pool is idle. If the pool is idle and there are no more messages on the stack, then the dispatcher closes the channels and return control to the main func.
This is all good but the issue I have is that the stack itself could be zero length so the case where I attempt to send stack[0] to the jobchan, if the stack is empty, I get an out of bounds error. What I'm trying to figure out is how to ensure that when I hit that case, either stack[0] has a value in it or not. I don't want that case to send an empty string to the jobchan.
Any help is greatly appreciated. If there's a more idomatic concurrency pattern I should consider, I'd love to hear about it. I'm not 100% sold on this solution but this is the farthest I've gotten so far.
This is all good but the issue I have is that the stack itself could be zero length so the case where I attempt to send stack[0] to the jobchan, if the stack is empty, I get an out of bounds error.
I can't reproduce it with your playground link, but it's believable, because at lest one gofunc worker might have been ready to receive on that channel.
My output has been Msgcnt: 0, which is also easily explained, because gofunc might not have been ready to receive on jobschan when dispatch() runs its select. The order of these operations is not defined.
trying to create a dispatch go routine that will send jobs to a worker pool listening on the jobchan channel
A channel needs no dispatcher. A channel is the dispatcher.
If a message comes into my dispatch function via the dispatchchan channel and my other go routines are busy, the message is [...] will [...] send again later when a worker becomes available, [...] or no more messages are received on the dispatchchan.
With a few creative edits, it was easy to turn that into something close to the definition of a buffered channel. It can be read from immediately, or it can take up to some "limit" of messages that can't be immediately dispatched. You do define limit, though it's not used elsewhere within your code.
In any function, defining a variable you don't read will result in a compile time error like limit declared but not used. This stricture improves code quality and helps identify typeos. But at package scope, you've gotten away with defining the unused limit as a "global" and thus avoided a useful error - you haven't limited anything.
Don't use globals. Use passed parameters to define scope, because the definition of scope is tantamount to functional concurrency as expressed with the go keyword. Pass the relevant channels defined in local scope to functions defined at package scope so that you can easily track their relationships. And use directional channels to enforce the producer/consumer relationship between your functions. More on this later.
Going back to "limit", it makes sense to limit the quantity of jobs you're queueing because all resources are limited, and accepting more messages than you have any expectation of processing requires more durable storage than process memory provides. If you don't feel obligated to fulfill those requests no matter what, don't accept "too many" of them in the first place.
So then, what function has dispatchchan and dispatch()? To store a limited number of pending requests, if any, before they can be processed, and then to send them to the next available worker? That's exactly what a buffered channel is for.
Circular Logic
Who "knows" when your program is done? main() provides the initial input, but you close all 3 channels in `dispatch():
close(jobchan)
close(dispatchchan)
close(mw)
Your workers write to their own job queue so only when the workers are done writing to it can the incoming job queue be closed. However, individual workers also don't know when to close the jobs queue because other workers are writing to it. Nobody knows when your algorithm is done. There's your circular logic.
The mw channel is a buffered channel the same length as the number of worker go routines. It acts as a semaphore for the worker pool.
There's a race condition here. Consider the case where all n workers have just received the last n jobs. They've each read from jobschan and they're checking the value of ok. disptatcher proceeds to run its select. Nobody is writing to dispatchchan or reading from jobschan right now so the default case is immediately matched. len(stack) is 0 and there's no current job so dispatcher closes all channels including mw. At some point thereafter, a worker tries to write to a closed channel and panics.
So finally I'm ready to provide some code, but I have one more problem: I don't have a clear problem statement to write code around.
I'm just getting into concurrency in Go and trying to create a dispatch go routine that will send jobs to a worker pool listening on the jobchan channel.
Channels between goroutines are like the teeth that synchronize gears. But to what end do the gears turn? You're not trying to keep time, nor construct a wind-up toy. Your gears could be made to turn, but what would success look like? Their turning?
Let's try to define a more specific use case for channels: given an arbitrarily long set of durations coming in as strings on standard input*, sleep that many seconds in one of n workers. So that we actually have a result to return, we'll say each worker will return the start and end time the duration was run for.
So that it can run in the playground, I'll simulate standard input with a hard-coded byte buffer.
package main
import (
"bufio"
"bytes"
"fmt"
"os"
"strings"
"sync"
"time"
)
type SleepResult struct {
worker_id int
duration time.Duration
start time.Time
end time.Time
}
func main() {
var num_workers = 2
workchan := make(chan time.Duration)
resultschan := make(chan SleepResult)
var wg sync.WaitGroup
var resultswg sync.WaitGroup
resultswg.Add(1)
go results(&resultswg, resultschan)
for i := 0; i < num_workers; i++ {
wg.Add(1)
go worker(i, &wg, workchan, resultschan)
}
// playground doesn't have stdin
var input = bytes.NewBufferString(
strings.Join([]string{
"3ms",
"1 seconds",
"3600ms",
"300 ms",
"5s",
"0.05min"}, "\n") + "\n")
var scanner = bufio.NewScanner(input)
for scanner.Scan() {
text := scanner.Text()
if dur, err := time.ParseDuration(text); err != nil {
fmt.Fprintln(os.Stderr, "Invalid duration", text)
} else {
workchan <- dur
}
}
close(workchan) // we know when our inputs are done
wg.Wait() // and when our jobs are done
close(resultschan)
resultswg.Wait()
}
func results(wg *sync.WaitGroup, resultschan <-chan SleepResult) {
for res := range resultschan {
fmt.Printf("Worker %d: %s : %s => %s\n",
res.worker_id, res.duration,
res.start.Format(time.RFC3339Nano), res.end.Format(time.RFC3339Nano))
}
wg.Done()
}
func worker(id int, wg *sync.WaitGroup, jobchan <-chan time.Duration, resultschan chan<- SleepResult) {
var res = SleepResult{worker_id: id}
for dur := range jobchan {
res.duration = dur
res.start = time.Now()
time.Sleep(res.duration)
res.end = time.Now()
resultschan <- res
}
wg.Done()
}
Here I use 2 wait groups, one for the workers, one for the results. This makes sure Im done writing all the results before main() ends. I keep my functions simple by having each function do exactly one thing at a time: main reads inputs, parses durations from them, and sends them off to the next worker. The results function collects results and prints them to standard output. The worker does the sleeping, reading from jobchan and writing to resultschan.
workchan can be buffered (or not, as in this case); it doesn't matter because the input will be read at the rate it can be processed. We can buffer as much input as we want, but we can't buffer an infinite amount. I've set channel sizes as big as 1e6 - but a million is a lot less than infinite. For my use case, I don't need to do any buffering at all.
main knows when the input is done and can close the jobschan. main also knows when jobs are done (wg.Wait()) and can close the results channel. Closing these channels is an important signal to the worker and results goroutines - they can distinguish between a channel that is empty and a channel that is guaranteed not to have any new additions.
for job := range jobchan {...} is shorthand for your more verbose:
for {
job, ok := <- jobchan
if !ok {
wg.Done()
return
}
...
}
Note that this code creates 2 workers, but it could create 20 or 2000, or even 1. The program functions regardless of how many workers are in the pool. It can handle any volume of input (though interminable input of course leads to an interminable program). It does not create a cyclic loop of output to input. If your use case requires jobs to create more jobs, that's a more challenging scenario that can typically be avoided with careful planning.
I hope this gives you some good ideas about how you can better use concurrency in your Go applications.
https://play.golang.wiki/p/cZuI9YXypxI
I want to compute the inverse element of a prime in modular arithmetic.
In order to speed things up I start a few goroutines which try to find the element in a certain range. When the first one finds the element, it sends it to the main goroutine and at this point I want to terminate the program. So I call close in the main goroutine, but I don't know if the goroutines will finish their execution (I guess not). So a few questions arise:
1) Is this a bad style, should I have something like a WaitGroup?
2) Is there a more idiomatic way to do this computation?
package main
import "fmt"
const (
Procs = 8
P = 1000099
Base = 1<<31 - 1
)
func compute(start, end uint64, finished chan struct{}, output chan uint64) {
for i := start; i < end; i++ {
select {
case <-finished:
return
default:
break
}
if i*P%Base == 1 {
output <- i
}
}
}
func main() {
finished := make(chan struct{})
output := make(chan uint64)
for i := uint64(0); i < Procs; i++ {
start := i * (Base / Procs)
end := (i + 1) * (Base / Procs)
go compute(start, end, finished, output)
}
fmt.Println(<-output)
close(finished)
}
Is there a more idiomatic way to do this computation?
You don't actually need a loop to compute this.
If you use the GCD function (part of the standard library), you get returned numbers x and y such that:
x*P+y*Base=1
this means that x is the answer you want (because x*P = 1 modulo Base):
package main
import (
"fmt"
"math/big"
)
const (
P = 1000099
Base = 1<<31 - 1
)
func main() {
bigP := big.NewInt(P)
bigBase := big.NewInt(Base)
// Compute inverse of bigP modulo bigBase
bigGcd := big.NewInt(0)
bigX := big.NewInt(0)
bigGcd.GCD(bigX,nil,bigP,bigBase)
// x*bigP+y*bigBase=1
// => x*bigP = 1 modulo bigBase
fmt.Println(bigX)
}
Is this a bad style, should I have something like a WaitGroup?
A wait group solves a different problem.
In general, to be a responsible go citizen here and ensure your code runs and tidies up behind itself, you may need to do a combination of:
Signal to the spawned goroutines to stop their calculations when the result of the computation has been found elsewhere.
Ensure a synchronous process waits for the goroutines to stop before returning. This is not mandatory if they properly respond to the signal in #1, but if you don't wait, there will be no guarantee they have terminated before the parent goroutine continues.
In your example program, which performs this task and then quits, there is strictly no need to do either. As this comment indicates, your program's main method terminates upon a satisfactory answer being found, at which point the program will end, any goroutines will be summarily terminated, and the operating system will tidy up any consumed resources. Waiting for goroutines to stop is unnecessary.
However, if you wrapped this code up into a library or it became part of a long running "inverse prime calculation" service, it would be desirable to tidy up the goroutines you spawned to avoid wasting cycles unnecessarily. Additionally, in general, you may have other scenarios in which goroutines store state, hold handles to external resources, or hold handles to internal objects which you risk leaking if not properly tidied away – it is desirable to properly close these.
Communicating the requirement to stop working
There are several approaches to communicate this. I don't claim this is an exhaustive list! (Please do suggest other general-purpose methods in the comments or by proposing edits to the post.)
Using a special channel
Signal the child goroutines by closing a special "shutdown" channel reserved for the purpose. This exploits the channel axiom:
A receive from a closed channel returns the zero value immediately
On receiving from the shutdown channel, the goroutine should immediately arrange to tidy any local state and return from the function. Your earlier question had example code which implemented this; a version of the pattern is:
func myGoRoutine(shutdownChan <-chan struct{}) {
select {
case <-shutdownChan:
// tidy up behaviour goes here
return
// You may choose to listen on other channels here to implement
// the primary behaviour of the goroutine.
}
}
func main() {
shutdownChan := make(chan struct{})
go myGoRoutine(shutdownChan)
// some time later
close(shutdownChan)
}
In this instance, the shutdown logic is wasted because the main() method will immediately return after the call to close. This will race with the shutdown of the goroutine, but we should assume it will not properly execute its tidy-up behaviour. Point 2 addresses ways to fix this.
Using a context
The context package provides the option to create a context which can be cancelled. On cancellation, a channel exposed by the context's Done() method will be closed, which signals time to return from the goroutine.
This approach is approximately the same as the previous method, with the exception of neater encapsulation and the availability of a context to pass to downstream calls in your goroutine to cancel nested calls where desired. Example:
func myGoRoutine(ctx context.Context) {
select {
case <-ctx.Done():
// tidy up behaviour goes here
return
// Put real behaviour for the goroutine here.
}
}
func main() {
// Get a context (or use an existing one if you are provided with one
// outside a `main` method:
ctx := context.Background()
// Create a derived context with a cancellation method
ctx, cancel := context.WithCancel(ctx)
go myGoRoutine(ctx)
// Later, when ready to quit
cancel()
}
This has the same bug as the other case in that the main method will not wait for the child goroutines to quit before returning.
Waiting (or "join"ing) for child goroutines to stop
The code which closes the shutdown channel or closes the context in the above examples will not wait for child goroutines to stop working before continuing. This may be acceptable in some instances, while in others you may require the guarantee that goroutines have stopped before continuing.
sync.WaitGroup can be used to implement this requirement. The documentation is comprehensive. A wait group is a counter which should be incremented using its Add method on starting a goroutine and decremented using its Done method when a goroutine completes. Code can wait for the counter to return to zero by calling its Wait method, which blocks until the condition is true. All calls to Add must occur before a call to Wait.
Example code:
func main() {
var wg sync.WaitGroup
// Increment the WaitGroup with the number of goroutines we're
// spawning.
wg.Add(1)
// It is common to wrap a goroutine in a function which performs
// the decrement on the WaitGroup once the called function returns
// to avoid passing references of this control logic to the
// downstream consumer.
go func() {
// TODO: implement a method to communicate shutdown.
callMyFunction()
wg.Done()
}()
// Indicate shutdown, e.g. by closing a channel or cancelling a
// context.
// Wait for goroutines to stop
wg.Wait()
}
Is there a more idiomatic way to do this computation?
This algorithm is certainly parallelizable through use of goroutines in the manner you have defined. As the work is CPU-bound, the limitation of goroutines to the number of available CPUs makes sense (in the absence of other work on the machine) to benefit from the available compute resource.
See peterSO's answer for a bug fix.
func First(query string, replicas ...Search) Result {
c := make(chan Result)
searchReplica := func(i int) {
c <- replicas[i](query)
}
for i := range replicas {
go searchReplica(i)
}
return <-c
}
This function is from the slides of Rob Pike on go concurrency patterns in 2012. I think there is a resource leak in this function. As the function return after the first send & receive pair happens on channel c, the other go routines try to send on channel c. So there is a resource leak here. Anyone knows golang well can confirm this? And how can I detect this leak using what kind of golang tooling?
Yes, you are right (for reference, here's the link to the slide). In the above code only one launched goroutine will terminate, the rest will hang on attempting to send on channel c.
Detailing:
c is an unbuffered channel
there is only a single receive operation, in the return statement
A new goroutine is launched for each element of replicas
each launched goroutine sends a value on channel c
since there is only 1 receive from it, one goroutine will be able to send a value on it, the rest will block forever
Note that depending on the number of elements of replicas (which is len(replicas)):
if it's 0: First() would block forever (no one sends anything on c)
if it's 1: would work as expected
if it's > 1: then it leaks resources
The following modified version will not leak goroutines, by using a non-blocking send (with the help of select with default branch):
searchReplica := func(i int) {
select {
case c <- replicas[i](query):
default:
}
}
The first goroutine ready with the result will send it on channel c which will be received by the goroutine running First(), in the return statement. All other goroutines when they have the result will attempt to send on the channel, and "seeing" that it's not ready (send would block because nobody is ready to receive from it), the default branch will be chosen, and thus the goroutine will end normally.
Another way to fix it would be to use a buffered channel:
c := make(chan Result, len(replicas))
And this way the send operations would not block. And of course only one (the first sent) value will be received from the channel and returned.
Note that the solution with any of the above fixes would still block if len(replicas) is 0. To avoid that, First() should check this explicitly, e.g.:
func First(query string, replicas ...Search) Result {
if len(replicas) == 0 {
return Result{}
}
// ...rest of the code...
}
Some tools / resources to detect leaks:
https://github.com/fortytw2/leaktest
https://github.com/zimmski/go-leak
https://medium.com/golangspec/goroutine-leak-400063aef468
https://blog.minio.io/debugging-go-routine-leaks-a1220142d32c
I've written a code snipped that creates a timer with a 0 length time, and it does not immediately expire (which is what I expected). A very short sleep call does make it expire, but I'm confused as to why.
The reason I care is that the code using this idea has a snippet that returns 0 on a low probability error, with the idea that the timer should be set to immediately expire, and retry a function. I do not believe that the nanosecond sleep needed here will affect my implementation, but it bothers me.
Did I make a mistake, is this expected behaviour?
Thanks!
package main
import (
"fmt"
"time"
)
func main() {
testTimer := time.NewTimer(time.Duration(0) * time.Millisecond)
fmt.Println(Expired(testTimer))
time.Sleep(time.Nanosecond)
fmt.Println(Expired(testTimer))
}
func Expired(T *time.Timer) bool {
select {
case <-T.C:
return true
default:
return false
}
}
Playground link: https://play.golang.org/p/xLLHoR8aKq
Prints
false
true
time.NewTimer() does not guarantee maximum wait time. It only guarantees a minimum wait time. Quoting from its doc:
NewTimer creates a new Timer that will send the current time on its channel after at least duration d.
So passing a zero duration to time.NewTimer(), it's not a surprise the returned time.Timer is not "expired" immediately.
The returned timer could be "expired" immediately if the implementation would check if the passed duration is zero, and would send a value on the timer's channel before returning it, but it does not. Instead it starts an internal timer normally as it does for any given duration, which will take care of sending a value on its channel, but only some time in the future.
Note that with multiple CPU cores and with runtime.GOMAXPROCS() being greater than 1 there is a slight chance that another goroutine (internal to the time package) sends a value on the timer's channel before NewTimer() returns, but this is a very small chance... Also since this is implementation detail, a future version might add this "optimization" to check for 0 passed duration, and act as you expected it, but as with all implementation details, don't count on it. Count on what's documented, and expect no more.
Go's timer functions guarantee to sleep at least the specified time. See the docs for Sleep and NewTimer respectively:
Sleep pauses the current goroutine for at least the duration d. A negative or zero duration causes Sleep to return immediately.
NewTimer creates a new Timer that will send the current time on its channel after at least duration d.
(emphasis added)
In your situation, you should probably just not use a timer in the situation that you don't want to sleep at all.
This is due to the internal time it takes to set up the timer object. If you'll note in the playground link below the timer does expire at the proper time, but the internal go routine that sets it up and starts it takes longer than your Expire function does to check it.
When the Timer expires, the current time will be sent on C (the channel)
So you'll notice that after it expires, it still sends the original time, because it has expired even before the nanosecond Sleep finished.
https://play.golang.org/p/Ghwq9kJq3J
package main
import (
"fmt"
"time"
)
func main() {
testTimer := time.NewTimer(0 * time.Millisecond)
Expired(testTimer)
time.Sleep(time.Nanosecond)
Expired(testTimer)
n := time.Now()
fmt.Printf("after waiting: %d\n", n.UnixNano())
}
func Expired(T *time.Timer) bool {
select {
case t:= <-T.C:
fmt.Printf("expired %d\n", t.UnixNano())
return true
default:
n := time.Now()
fmt.Printf("not expired: %d\n", n.UnixNano())
return false
}
}
For some reason, when I remove the fmt.Printlns then the code is blocking.
I've got no idea why it happens. All I want to do is to implement a simple concurrency limiter...
I've never experienced such a weird thing. It's like that fmt flushes the variables or something and makes it work.
Also, when I use a regular function instead of a goroutine then it works too.
Here's the following code -
package main
import "fmt"
type ConcurrencyLimit struct {
active int
Limit int
}
func (c *ConcurrencyLimit) Block() {
for {
fmt.Println(c.active, c.Limit)
// If should block
if c.active == c.Limit {
continue
}
c.active++
break
}
}
func (c *ConcurrencyLimit) Decrease() int {
fmt.Println("decrease")
if c.active > 0 {
c.active--
}
return c.active
}
func main() {
c := ConcurrencyLimit{Limit: 1}
c.Block()
go func() {
c.Decrease()
}()
c.Block()
}
Clarification: Even though I've accepted #kaedys 's answer(here) a solution was answered by #Kaveh Shahbazian (here)
You're not giving c.Decrease() a chance to run. c.Block() runs an infinite for loop, but it never blocks in that for loop, just calling continue over and over on every iteration. The main thread spins at 100% usage endlessly.
However, when you add an fmt.Print() call, that makes a syscall, which allows the other goroutine to run.
This post has details on how exactly goroutines yield or are pre-empted. Note, however, that it's slightly out of date, as entering a function now has a random chance to yield that thread to another goroutine, to prevent similar style flooding of threads.
As others have pointed out, Block() will never yield; a goroutine is not a thread. You could use Gosched() in the runtime package to force a yield -- but note that spinning this way in Block() is a pretty terrible idea.
There are much better ways to do concurrency limiting. See http://jmoiron.net/blog/limiting-concurrency-in-go/ for one example
What you are looking for is called a semaphore. You can apply this pattern using channels
http://www.golangpatterns.info/concurrency/semaphores
The idea is that you create a buffered channel of a desired length. Then you make callers acquire the resource by putting a value into the channel and reading it back out when they want to free the resource. Doing so creates proper synchronization points in your program so that the Go scheduler runs correctly.
What you are doing now is spinning the cpu and blocking the Go scheduler. It depends on how many cpus you have available, the version of Go, and the value of GOMAXPROCS. Given the right combination, there may not be another available thread to service other goroutines while you infinitely spin that particular thread.
While other answers pretty much covered the reason (not giving a chance for the goroutine to run) - and I'm not sure what you intend to achieve here - you are mutating a value concurrently without proper synchronization. A rewrite of above code with synchronization considered; would be:
type ConcurrencyLimit struct {
active int
Limit int
cond *sync.Cond
}
func (c *ConcurrencyLimit) Block() {
c.cond.L.Lock()
for c.active == c.Limit {
c.cond.Wait()
}
c.active++
c.cond.L.Unlock()
c.cond.Signal()
}
func (c *ConcurrencyLimit) Decrease() int {
defer c.cond.Signal()
c.cond.L.Lock()
defer c.cond.L.Unlock()
fmt.Println("decrease")
if c.active > 0 {
c.active--
}
return c.active
}
func main() {
c := ConcurrencyLimit{
Limit: 1,
cond: &sync.Cond{L: &sync.Mutex{}},
}
c.Block()
go func() {
c.Decrease()
}()
c.Block()
fmt.Println(c.active, c.Limit)
}
sync.Cond is a synchronization utility designed for times that you want to check if a condition is met, concurrently; while other workers are mutating the data of the condition.
The Lock and Unlock functions work as we expect from a lock. When we are done with checking or mutating, we can call Signal to awake one goroutine (or call Broadcast to awake more than one), so the goroutine knows that is free to act upon the data (or check a condition).
The only part that may seem unusual is the Wait function. It is actually very simple. It is like calling Unlock and instantly call Lock again - with the exception that Wait would not try to lock again, unless triggered by Signal (or Broadcast) in other goroutines; like the workers that are mutating the data (of the condition).