After (briefly) reviewing the Go language spec, effective Go, and the Go memory model, I'm still a little unclear as to how Go channels work under the hood.
What kind of structure are they? They act kind of like a thread-safe queue /array.
Does their implementation depend on the architecture?
The source file for channels is (from your go source code root) in /src/pkg/runtime/chan.go.
hchan is the central data structure for a channel, with send and receive linked lists (holding a pointer to their goroutine and the data element) and a closed flag. There's a Lock embedded structure that is defined in runtime2.go and that serves as a mutex (futex) or semaphore depending on the OS. The locking implementation is in lock_futex.go (Linux/Dragonfly/Some BSD) or lock_sema.go (Windows/OSX/Plan9/Some BSD), based on the build tags.
Channel operations are all implemented in this chan.go file, so you can see the makechan, send and receive operations, as well as the select construct, close, len and cap built-ins.
For a great in-depth explanation on the inner workings of channels, you have to read Go channels on steroids by Dmitry Vyukov himself (Go core dev, goroutines, scheduler and channels among other things).
Here is a good talk that describes roughly how channels are implemented:
https://youtu.be/KBZlN0izeiY
Talk description:
GopherCon 2017: Kavya Joshi - Understanding Channels
Channels provide a simple mechanism for goroutines to communicate, and a powerful construct to build sophisticated concurrency patterns. We will delve into the inner workings of channels and channel operations, including how they're supported by the runtime scheduler and memory management systems.
You asked two questions:
What kind of structure are they?
Channels in go are indeed "kind of like a thread-safe queue", to be more precise, channels in Go have the following properties:
goroutine-safe
Provide FIFO semantics
Can store and pass values between goroutines
Cause goroutines to block and unblock
Every time you create a channel, an hchan struct is allocated on the heap, and a pointer to the hchan memory location is returned represented as a channel, this is how go-routines can share it.
The first two properties described above are implemented similarly to a queue with a lock.
The elements that the channel can pass to different go-routines are implemented as a circular queue (ring buffer) with indices in the hchan struct, the indices account for the position of elements in the buffer.
Circular queue:
qcount uint // total data in the queue
dataqsiz uint // size of the circular queue
buf unsafe.Pointer // points to an array of dataqsiz elements
And the indices:
sendx uint // send index
recvx uint // receive index
Every time a go-routine needs to access the channel structure and modify it's state it holds the lock, e.g: copy elements to/ from the buffer, update lists or an index. Some operations are optimized to be lock-free, but this is out of the scope for this answer.
The block and un-block property of go channels is achieved using two queues (linked lists) that hold the blocked go-routines
recvq waitq // list of recv waiters
sendq waitq // list of send waiters
Every time a go-routine wants to add a task to a full channel (buffer is full), or to take a task from an empty channel (buffer is empty), a pseudo go-routine sudog struct is allocated and the go-routine adds the sudog as a node to the send or receive waiters list accordingly. Then the go-routine updates the go runtime scheduler using special calls, which hints when they should be taken out of execution (gopark) or ready to run (goready).
Notice this is a very simplified explanations that hides some complexities.
Does their implementation depend on the architecture?
Besides the lock implementation that is OS specific as #mna already explained, I'm not aware of any architecture specific constraints optimizations or differences.
A simpler way to look at channels is as such, in that you may like to hold a program up while waiting for a condition to complete, typically used to prevent RACE condition, which means a thread might not finish before another, and then something your later thread or code depends on sometimes does not complete.
An example could be, you have a thread to retrieve some data from a database or other server and place the data into a variable, slice or map, and for some reason it gets delayed. then you have a process that uses that variable, but since it hasn't been initialised, or its not got its data yet. the program fails.
So a simple way to look at it in code is as follows:
package main
import "fmt"
var doneA = make(chan bool)
var doneB = make(chan bool)
var doneC = make(chan bool)
func init() { // this runs when you program starts.
go func() {
doneA <- true //Give donA true
}()
}
func initB() { //blocking
go func() {
a := <- doneA //will wait here until doneA is true
// Do somthing here
fmt.Print(a)
doneB <- true //State you finished
}()
}
func initC() {
go func() {
<-doneB // still blocking, but dont care about the value
// some code here
doneC <- true // Indicate finished this function
}()
}
func main() {
initB()
initC()
}
So hope this helps. not the selected answer above, but i believe should help to remove the mystery. I wonder if I should make a question and self answer?
Related
Let’s say I use a WaitGroup to make the main thread of an application wait until all the goroutines I have launched from said main have completed.
Is there a safe, straightforward, way to assess at any point in time how many goroutines associated with said WaitGroup are still running?
The internal state of the WaitGroup is not exposed, and it won't be: https://github.com/golang/go/issues/7202
I don't think we're likely to make this API more complex. I don't see any way to use
counter and waiters that is not subject to race conditions, other than simply printing
them out. And for that you can maintain your own counts.
You could implement a counter yourself:
type WaitGroupCount struct {
sync.WaitGroup
count int64
}
func (wg *WaitGroupCount) Add(delta int) {
atomic.AddInt64(&wg.count, int64(delta))
wg.WaitGroup.Add(delta)
}
func (wg *WaitGroupCount) Done() {
atomic.AddInt64(&wg.count, -1)
wg.WaitGroup.Done()
}
func (wg *WaitGroupCount) GetCount() int {
return int(atomic.LoadInt64(&wg.count))
}
// Wait() promoted from the embedded field
However, even if the counter access is synchronized, it will become stale immediately after you read it, since other goroutines may go on and call Add or Done irrespective of what you are doing with the count — unless you synchronize the entire operation that depends on the count. But in that case, you might need a more complex data structure altogether.
Is there a safe, straightforward, way to assess at any point in time how many goroutines associated with said waitgroup are still running?
No, there isn't.
Simply because Go (the language) has no notion of "goroutines associated with [a] waitgroup".
I'm trying to understand best practices for Golang concurrency. I read O'Reilly's book on Go's concurrency and then came back to the Golang Codewalks, specifically this example:
https://golang.org/doc/codewalk/sharemem/
This is the code I was hoping to review with you in order to learn a little bit more about Go. My first impression is that this code is breaking some best practices. This is of course my (very) unexperienced opinion and I wanted to discuss and gain some insight on the process. This isn't about who's right or wrong, please be nice, I just want to share my views and get some feedback on them. Maybe this discussion will help other people see why I'm wrong and teach them something.
I'm fully aware that the purpose of this code is to teach beginners, not to be perfect code.
Issue 1 - No Goroutine cleanup logic
func main() {
// Create our input and output channels.
pending, complete := make(chan *Resource), make(chan *Resource)
// Launch the StateMonitor.
status := StateMonitor(statusInterval)
// Launch some Poller goroutines.
for i := 0; i < numPollers; i++ {
go Poller(pending, complete, status)
}
// Send some Resources to the pending queue.
go func() {
for _, url := range urls {
pending <- &Resource{url: url}
}
}()
for r := range complete {
go r.Sleep(pending)
}
}
The main method has no way to cleanup the Goroutines, which means if this was part of a library, they would be leaked.
Issue 2 - Writers aren't spawning the channels
I read that as a best practice, the logic to create, write and cleanup a channel should be controlled by a single entity (or group of entities). The reason behind this is that writers will panic when writing to a closed channel. So, it is best for the writer(s) to create the channel, write to it and control when it should be closed. If there are multiple writers, they can be synced with a WaitGroup.
func StateMonitor(updateInterval time.Duration) chan<- State {
updates := make(chan State)
urlStatus := make(map[string]string)
ticker := time.NewTicker(updateInterval)
go func() {
for {
select {
case <-ticker.C:
logState(urlStatus)
case s := <-updates:
urlStatus[s.url] = s.status
}
}
}()
return updates
}
This function shouldn't be in charge of creating the updates channel because it is the reader of the channel, not the writer. The writer of this channel should create it and pass it to this function. Basically saying to the function "I will pass updates to you via this channel". But instead, this function is creating a channel and it isn't clear who is responsible of cleaning it up.
Issue 3 - Writing to a channel asynchronously
This function:
func (r *Resource) Sleep(done chan<- *Resource) {
time.Sleep(pollInterval + errTimeout*time.Duration(r.errCount))
done <- r
}
Is being referenced here:
for r := range complete {
go r.Sleep(pending)
}
And it seems like an awful idea. When this channel is closed, we'll have a goroutine sleeping somewhere out of our reach waiting to write to that channel. Let's say this goroutine sleeps for 1h, when it wakes up, it will try to write to a channel that was closed in the cleanup process. This is another example of why the writters of the channels should be in charge of the cleanup process. Here we have a writer who's completely free and unaware of when the channel was closed.
Please
If I missed any issues from that code (related to concurrency), please list them. It doesn't have to be an objective issue, if you'd have designed the code in a different way for any reason, I'm also interested in learning about it.
Biggest lesson from this code
For me the biggest lesson I take from reviewing this code is that the cleanup of channels and the writing to them has to be synchronized. They have to be in the same for{} or at least communicate somehow (maybe via other channels or primitives) to avoid writing to a closed channel.
It is the main method, so there is no need to cleanup. When main returns, the program exits. If this wasn't the main, then you would be correct.
There is no best practice that fits all use cases. The code you show here is a very common pattern. The function creates a goroutine, and returns a channel so that others can communicate with that goroutine. There is no rule that governs how channels must be created. There is no way to terminate that goroutine though. One use case this pattern fits well is reading a large resultset from a
database. The channel allows streaming data as it is read from the
database. In that case usually there are other means of terminating the
goroutine though, like passing a context.
Again, there are no hard rules on how channels should be created/closed. A channel can be left open, and it will be garbage collected when it is no longer used. If the use case demands so, the channel can be left open indefinitely, and the scenario you worry about will never happen.
As you are asking about if this code was part of a library, yes it would be poor practice to spawn goroutines with no cleanup inside a library function. If those goroutines carry out documented behaviour of the library, it's problematic that the caller doesn't know when that behaviour is going to happen. If you have any behaviour that is typically "fire and forget", it should be the caller who chooses when to forget about it. For example:
func doAfter5Minutes(f func()) {
go func() {
time.Sleep(5 * time.Minute)
f()
log.Println("done!")
}()
}
Makes sense, right? When you call the function, it does something 5 minutes later. The problem is that it's easy to misuse this function like this:
// do the important task every 5 minutes
for {
doAfter5Minutes(importantTaskFunction)
}
At first glance, this might seem fine. We're doing the important task every 5 minutes, right? In reality, we're spawning many goroutines very quickly, probably consuming all available memory before they start dropping off.
We could implement some kind of callback or channel to signal when the task is done, but really, the function should be simplified like so:
func doAfter5Minutes(f func()) {
time.Sleep(5 * time.Minute)
f()
log.Println("done!")
}
Now the caller has the choice of how to use it:
// call synchronously
doAfter5Minutes(importantTaskFunction)
// fire and forget
go doAfter5Minutes(importantTaskFunction)
This function arguably should also be changed. As you say, the writer should effectively own the channel, as they should be the one closing it. The fact that this channel-reading function insists on creating the channel it reads from actually coerces itself into this poor "fire and forget" pattern mentioned above. Notice how the function needs to read from the channel, but it also needs to return the channel before reading. It therefore had to put the reading behaviour in a new, un-managed goroutine to allow itself to return the channel right away.
func StateMonitor(updates chan State, updateInterval time.Duration) {
urlStatus := make(map[string]string)
ticker := time.NewTicker(updateInterval)
defer ticker.Stop() // not stopping the ticker is also a resource leak
for {
select {
case <-ticker.C:
logState(urlStatus)
case s := <-updates:
urlStatus[s.url] = s.status
}
}
}
Notice that the function is now simpler, more flexible and synchronous. The only thing that the previous version really accomplishes, is that it (mostly) guarantees that each instance of StateMonitor will have a channel all to itself, and you won't have a situation where multiple monitors are competing for reads on the same channel. While this may help you avoid a certain class of bugs, it also makes the function a lot less flexible and more likely to have resource leaks.
I'm not sure I really understand this example, but the golden rule for channel closing is that the writer should always be responsible for closing the channel. Keep this rule in mind, and notice a few points about this code:
The Sleep method writes to r
The Sleep method is executed concurrently, with no method of tracking how many instances are running, what state they are in, etc.
Based on these points alone, we can say that there probably isn't anywhere in the program where it would be safe to close r, because there's seemingly no way of knowing if it will be used again.
"What?" you ask, "That title doesn't make any sense."
Consider the following:
Jobs with different ids may be processed asynchronously but jobs with the same id should be processed synchronously and in order from the queue.
My current implementation creates a go routine to handle the jobs for each specific id and looks something like this:
func FanOut() chan<- *Job {
channel := make(chan *Job)
routines = make(map[string]chan<- *Job)
go func() {
for j := range channel {
r, found := routines[j.id]
if !found {
r = Routine()
routines[j.id] = r
}
r <- j
}
}()
return channel
}
This appears to work well (in current testing) but the creation of thousands of go routines might not be the best approach? Additionally the fan out code blocks unless a buffered channel is used.
Rather than a collection of go routines (above) I'm considering using a collection of sync.Mutex. The idea would be to have a pool of go routines which must first establish a lock on the mutex corresponding to the job id.
Are there any existing Go patterns suited to handling these requirements?
Is there a better approach?
Create a channel for each ID - perhaps a slice of channels or a map (indexed by ID). Each channel would have a go-routine that processes the jobs for that ID in order. Simple.
I wouldn't worry about creating too many go-routines. And I wouldn't use mutex - without getting into too much detail using channels and go-routines allows each job to only be processed by one go-routine at a time and avoids possibility of data races.
BTW I only added this as an answer as I am not permitted to add comments (yet?).
I've wrapped a queue to implement the Writer and Reader interfaces (for pushing and popping, respectively).
I need to continuously listen to the queue, and handle every message that comes through. This is simple when the queue is represented as a channel, but more difficult otherwise:
loop:
for {
var data []byte
select {
case <-done:
break loop
case _, err := queue.Read(data):
fmt.Println(string(data))
}
}
What's the proper way to do this? Read here is blocking - it waits until the queue has a message.
Is there a better, more idiomatic way to achieve this?
It’s harder to take a synchronous API (like queue.Read as you described above) and make it asynchronous than it is to do the opposite.
The idea would be to create a new goroutine (using, for example go func() {...}) and have that goroutine execute the read and write the output to a channel.
Then the first goroutine would block on that channel and the one it’s already blocking on.
This has the potentially to leave orphaned resources for a little while if the read takes to long but if you have a synchronous API, it’s the best you can do.
According to the Go blog,
Maps are not safe for concurrent use: it's not defined what happens when you read and write to them simultaneously. If you need to read from and write to a map from concurrently executing goroutines, the accesses must be mediated by some kind of synchronization mechanism.
(source: https://blog.golang.org/go-maps-in-action)
Can anyone elaborate on this? Concurrent read operations seem permissible across routines, but concurrent read/write operations may generate a race condition if one attempts to read from and write to the same key.
Can this last risk be reduced in some cases? For example:
Function A generates k and sets m[k]=0. This is the only time A writes to map m. k is known to not be in m.
A passes k to function B running concurrently
A then reads m[k]. If m[k]==0, it waits, continuing only when m[k]!=0
B looks for k in the map. If it finds it, B sets m[k] to some positive integer. If it doesn't it waits until k is in m.
This isn't code (obviously) but I think it shows the outlines of a case where even if A and B both try to access m there won't be a race condition, or if there is it won't matter because of the additional constraints.
Before Golang 1.6, concurrent read is OK, concurrent write is not OK, but write and concurrent read is OK. Since Golang 1.6, map cannot be read when it's being written.
So After Golang 1.6, concurrent access map should be like:
package main
import (
"sync"
"time"
)
var m = map[string]int{"a": 1}
var lock = sync.RWMutex{}
func main() {
go Read()
time.Sleep(1 * time.Second)
go Write()
time.Sleep(1 * time.Minute)
}
func Read() {
for {
read()
}
}
func Write() {
for {
write()
}
}
func read() {
lock.RLock()
defer lock.RUnlock()
_ = m["a"]
}
func write() {
lock.Lock()
defer lock.Unlock()
m["b"] = 2
}
Or you will get the error below:
ADDED:
You can detect the race by using go run -race race.go
Change the read function:
func read() {
// lock.RLock()
// defer lock.RUnlock()
_ = m["a"]
}
Another choise:
As we known, map was implemented by buckets and sync.RWMutex will lock all the buckets. concurrent-map use fnv32 to shard the key and every bucket use one sync.RWMutex.
Concurrent read (read only) is ok. Concurrent write and/or read is not ok.
Multiple goroutines can only write and/or read the same map if access is synchronized, e.g. via the sync package, with channels or via other means.
Your example:
Function A generates k and sets m[k]=0. This is the only time A writes to map m. k is known to not be in m.
A passes k to function B running concurrently
A then reads m[k]. If m[k]==0, it waits, continuing only when m[k]!=0
B looks for k in the map. If it finds it, B sets m[k] to some positive integer. If it doesn't it waits until k is in m.
Your example has 2 goroutines: A and B, and A tries to read m (in step 3) and B tries to write it (in step 4) concurrently. There is no synchronization (you didn't mention any), so this alone is not permitted / not determined.
What does it mean? Not determined means even though B writes m, A may never observe the change. Or A may observe a change that didn't even happen. Or a panic may occur. Or the Earth may explode due to this non-synchronized concurrent access (although the chance of this latter case is extremely small, maybe even less than 1e-40).
Related questions:
Map with concurrent access
what does not being thread safe means about maps in Go?
What is the danger of neglecting goroutine/thread-safety when using a map in Go?
Go 1.6 Release Notes
The runtime has added lightweight, best-effort detection of concurrent
misuse of maps. As always, if one goroutine is writing to a map, no
other goroutine should be reading or writing the map concurrently. If
the runtime detects this condition, it prints a diagnosis and crashes
the program. The best way to find out more about the problem is to run
the program under the race detector, which will more reliably identify
the race and give more detail.
Maps are complex, self-reorganizing data structures. Concurrent read and write access is undefined.
Without code, there's not much else to say.
After long discussion it was decided that the typical use of maps did not require safe access from multiple goroutines, and in those cases where it did, the map was probably part of some larger data structure or computation that was already synchronized. Therefore requiring that all map operations grab a mutex would slow down most programs and add safety to few. This was not an easy decision, however, since it means uncontrolled map access can crash the program.
The language does not preclude atomic map updates. When required, such as when hosting an untrusted program, the implementation could interlock map access.
Map access is unsafe only when updates are occurring. As long as all goroutines are only reading—looking up elements in the map, including iterating through it using a for range loop—and not changing the map by assigning to elements or doing deletions, it is safe for them to access the map concurrently without synchronization.
As an aid to correct map use, some implementations of the language contain a special check that automatically reports at run time when a map is modified unsafely by concurrent execution.
You can use sync.Map which is safe for concurrent use. The only caveat is that you are gonna give up on type safety and change all the reads and writes to your map to use the methods defined for this type
You can store a pointer to an int in the map, and have multiple goroutines read the int being pointed to while another writes a new value to the int. The map is not being updated in this case.
This wouldn't be idiomatic for Go and not what you were asking.
Or instead of passing a key to a map, you could pass the index to an array, and have that updated by one goroutine while others read the location.
But you're probably just wondering why a map's value can't be updated with a new value when the key is already in the map. Presumably nothing about the map's hashing scheme is being changed - at least not given their current implementation. It would seem the Go authors don't want to make allowances for such special cases. Generally they want code to be easy to read and understand, and a rule like not allowing map writes when other goroutines could be reading keeps things simple and now in 1.6 they can even start to catch misuse during normal runtimes - saving many people many hours of debugging.
As the other answers here stated, the native map type is not goroutine-safe. A couple of notes after reading the current answers:
Do not use defer to unlock, it has some overhead that affects performance (see this nice post). Call unlock directly.
You can achieve better performance by reducing time spent between locks. For example, by sharding the map.
There is a common package (approaching 400 stars on GitHub) used to solve this called concurrent-map here which has performance and usability in mind. You could use it to handle the concurrency issues for you.
Map is concurrent safe for read only in Golang. Let's say, your map is written first and never be written again then you don't need any mutex type of thing to make sure that only one go routine is accessing your map. I have given an example below about map concurrent safe reading.
package main
import (
"fmt"
"sync"
)
var freq map[int]int
// An example of concurrent read from a map
func main() {
// Map is written before accessing from go routines
freq = make(map[int]int)
freq[1] = 1
freq[2] = 2
wg := sync.WaitGroup{}
wg.Add(10)
for i:=1;i<=10;i++ {
// In go routine we are only reading val from map
go func(id int, loop int) {
defer wg.Done()
fmt.Println("In loop ", loop)
fmt.Println("Freq of 1: ", freq[id])
}(1, i)
}
wg.Wait()
}