I was wondering if there is already a library to do that or maybe a suggestion which way to go for the following problem:
Client A makes request for resource A, this is a long running request since resource A is expensive and it results in a cache miss. In the meantime client B makes request for resource A, now it's still a cache miss since client A's request hasn't returned and populated the cache yet. so instead of making a new request to generate resource A, client B should block and be notified when client A's request is complete and has populated the cache.
I think the group cache library has something along those lines, but I haven't been able to browse through the code to figure out how they do it, I also don't wanna tie the implementation to it and use it as a dependency.
The only solution I had so far is a pub-sub type of thing, where we have a global map of the current in-flight requests with the reqID as a key. When req1 comes it sets its ID in the map, req2 comes and checks if its id is in the map, since its requesting the same resource it is, so we block on a notifier channel. When req1 finishes it does 3 things:
Since go doesn't have built in support for broadcasts, theres probably 1 grouting listening on the broadcast channel and then keeping a list of subscribers to broadcast to for each request, or maybe we change the map to reqId => list(broadcastChannelSubscribers). Something along those lines.
If you think there is a better way to do it with Go's primitives, any input would be appreciated. The only piece of this solution that bothers me is this global map, surrounded by locks, I assume it quickly is going to become a bottleneck. IF you have some non-locking ideas, even if they are probabilistic Im happy to hear them.
It reminds me of one question where someone was implementing a similar thing:
I gave an answer with an example of implementing such a middle layer. I think this is in line with your ideas: have a routine keeping track of requests for the same resource and prevent them from being recalculating in parallel.
If you have a separate routine responsible for taking requests and managing access to cache, you don't need an explicit lock (there is one buried in a channel though). Anyhow, I don't know specifics of your application, but considering you need to check cache (probably locked) and (occasionally) perform an expensive calculation of missing entry – lock on map lookups doesn't seem like a massive problem to me. You can also always span more such middle layer routines if you think this would help, but you would need a deterministic way of routing the requests (so each cache entry is managed by a single routine).
Sorry for not bringing you a silver bullet solution, but it sounds like you're on a good way of solving your problem anyway.
Caching and perfomance problems are always tricky and you should always make a basic solution to benchmark against to ensure that your assumptions are correct. But if we know that the bottleneck is fetching the resource and that caching will give significant returns you could use Go's channels to implement queuing. Assuming that response is the type of your resource.
type request struct {
back chan *response
}
func main() {
c := make(chan request,10) // non-blocking
go func(input chan request){
var cached *response
for _,i := range input {
if cached == nil { // only make request once
cached = makeLongRunningRequest()
}
i.back <- cached
}
}(c)
resp := make(chan *response)
c <- request{resp} // cache miss
c <- request{resp} // will get queued
c <- request{resp} // will get queued
for _,r := range resp {
// do something with response
}
}
Here we're only fetching one resource but you could start one goroutine for each resource you want to fetch. Goroutines are cheap so unless you need millions of resources cached at the same time you should be ok. You could of course also kill your goroutines after a while.
To keep track of which resource id belongs to which channel, I'd use a map
map[resourceId]chan request
with a mutex. Again, if fetching the resource is the bottle neck then the cost of locking the map should be negligible. If locking the map turns out to be a problem, consider using a sharded map.
In general you seem to be well on your way. I'd advise to try to keep your design as simple as possible and use channels instead of locks when possible. They do protect from terrible concurrency bugs.
One solution is a concurrent non-blocking cache as discussed in detail in The Go Programming Language, chapter 9.
The code samples are well worth a look because the authors take you through several versions (memo1, memo2, etc), illustrating problems of race conditions, using mutexes to protect maps, and a version using just channels.
Also consider https://blog.golang.org/context as it has similar concepts and deals with cancellation of in flight requests.
It's impractical to copy the content into this answer, so hopefully the links are of use.
This is already provided by Golang as a feature single flight.
For your use case just use some extra logic on top of single flight. Consider the code snippet below:
func main() {
http.HandleFunc("/github", func(w http.ResponseWriter, r *http.Request) {
var key = "facebook"
var requestGroup singleflight.Group
// Search The Cache, if found in cache return from cache, else make single flight request
if res, err := searchCache(); err != nil{
return res
}
// Cache Miss-> Make Single Flight Request, and Cache it
v, err, shared := requestGroup.Do(key, func() (interface{}, error) {
// companyStatus() returns string, error, which statifies interface{}, error, so we can return the result directly.
if err != nil {
return interface{}, err
}
return companyStatus(), nil
})
if err != nil {
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
//Set the Cache Here
setCache(key, v)
status := v.(string)
log.Printf("/Company handler requst: status %q, shared result %t", status, shared)
fmt.Fprintf(w, "Company Status: %q", status)
})
http.ListenAndServe("127.0.0.1:8080", nil)
}
// companyStatus retrieves Comapny's API status
func getCompanyStatus() (string, error) {
log.Println("Making request to Some API")
defer log.Println("Request to Some API Complete")
time.Sleep(1 * time.Second)
resp, err := http.Get("Get URL")
if err != nil {
return "", err
}
defer resp.Body.Close()
if resp.StatusCode != 200 {
return "", fmt.Errorf("Upstream response: %s", resp.Status)
}
r := struct{ Status string }{}
err = json.NewDecoder(resp.Body).Decode(&r)
return r.Status, err
}
I hope the code snippet is self explanatory and you can refer to Single Flight Official Docs to delve deep into single flight.