This looks like it would be a very useful design, however the article doesn't discuss the implementation of the Promise.
This is not something memcached or redis support out of the box, as far as I know. It would seem to imply a cache manager service that has its own in-memory table of Promises.
Yeah, some details on implementation would have been welcome.
I'm not sure how often "thundering herd" is actually a problem for your cache, but I could definitely see this being a useful feature to add to a caching system. Even just a way to tell Redis that something will exist there soon and to delay response until it arrives would be nice. (In essence, a Promise.)
dogpile.cache [1] implements this pattern (or at least a very similar pattern) for both memcache and redis using locks. If the cache value doesn't exist, attempt to acquire the lock and generate the value. If the lock can't be acquired, wait until it frees then check for the value again.
> however the article doesn't discuss the implementation of the Promise.
Its not a "thundering herd" problem either. The Thundering herd is classically a scheduling problem.
You have 100-threads waiting on a resource (classically: a Mutex). The Mutex unlocks, which causes all 100-threads to wakeup. You KNOW that only one thread will win the Mutex, so 99 of the threads wasted CPU-time as they wokeup. When the next thread is done, 98 threads will wakeup (again, all wasting CPU time because only one can win).
Solving the thundering herd requires your scheduler to know all the resources that could be blocking a thread. The scheduler then only wakes ONE thread up at a time in these cases.
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I'm not entirely sure what the problem should be named in the blog, but it definitely isn't a "thundering herd". I will admit that its a similar looking problem though.
I agree with you, the article could have omitted the mention of the thundering herd and would still not feel any more incomplete.
I think it becomes a thundering herd problem when every request that's a potential cache miss tries to obtain a lock on the Promise for that request. Likely what the author was trying to get at which was lost due to overly generalising the problem.
Hmm, the Netflix presentation there seems to make sense superficially though.
The key attribute of the "Thundering Herd" problem is the LOOP. The Thundering Herd causes another Thundering Herd... which later causes another Thundering Herd. In the Netflix presentation, the "Thundering Herd" causes all of the requests to time out, which causes two new servers to be added ("automatic scale up"), then everyone tries again.
When everyone tries again, there's more people waiting, so everyone times-out AGAIN, which causes everything to shutdown, add two more servers to scale up, and start over. Etc. etc. Its a cascading problem that gets worse and worse each loop. You solve the Thundering Herd not by adding more resources (that actually makes the problem worse!!), but by cutting off the feedback loop somehow.
The problem discussed in the blog post has no feedback loop. Its simply a problem that happens once on startup.
System start is indeed a synchronization point, and for limited resources like here, painful and now clpser to the vernacular
Thundering herds can cause escalating and successive failures. That is very much an issue with service start/restart. A bad restart will cause a timeout, another restart, and eventually, restarts on further layers. Imagine all this running above k8s. So yes, this pattern is indeed about one of the failure modes that happen with thundering herds.
Though if your cache needs another cache, that feels like a bad cache. The promise pattern can be done transparently by the cache, coalescing GETs, instead of requiring a user protocol. We do app level caching to stay process-local because latency is fun in GPU land and we are a visual analytics tool... But that is not for the problem shown here.
FWIW I've always heard it described as a thundering herd. Though, your description is spot on, according to Wikipedia [1]. The problem the article discusses is called a cache stampede or dog pile [2].
It's also possible the Wikipedia article is maintained by someone with a stronger opinion about the definition than would be reflected in the typical person using the term.
My reading is that the cache is just another JavaScript service - a proxy that says "on: URL, if (in cache) return val, else fetch, add val to cache, return val."
Changing the cache to store promises is indeed an easy way to deduplicate most requests that occur roughly at the same time.
The logic now is:
"on URL: if (in cache) return promise.resolve(), else add promise to cache, fetch, return promise.resolve()"
But what does it mean for a promise to be in the cache? If you're using an external cache, like redis or memcached, you can't just put a JavaScript promise in there and expect everything to work. Neither of those services understands what it means for the promise to resolve so that all the clients waiting on it can continue.
At best you could serialize the promise and then have everyone who was waiting on it poll the cache to see if the promise was resolved yet, but that is pretty inefficient.
You probably want another layer in there that handles the conversion of a JavaScript promise to something that can be awaited on from your cache, like a redis pub/sub. Then in JavaScript land your services just await on a promise as normal but under the covers your service is doing something else which is specific to the cache you're using.
"This is not something memcached or redis support out of the box, as far as I know. It would seem to imply a cache manager service that has its own in-memory table of Promises."
Note that it isn't even meaningful to suggest that memcache or Redis should support this, because they aren't responsible for filling cache values for misses. Only something actually generating the value upon the miss can implement this "promise".
And in the general case, you can't serialize promises either, so you can't be "putting a promise into memcached", because memcached only stores bytes. You'd have to wrap a lot of machinery around it, and even then, I personally would say it's the machinery doing it, not memcached.
(I think Redis does have some features that could be pressed into service here for notification, but Redis still wouldn't be doing the actual filling in of the value, since it can't.)
Plain-old-caches and even concurrent-aware maps do this too--several are the cases where I've slapped a Guava or Caffeine cache into place for quick 'n dirty concurrency control and key coalescing, even with a short TTL.
We do the same thing on client side when lazy-loading assets (typically textures) from the disk, cause you don’t want to hold multiple copies of the same asset in memory.
In practice there's a bit more to it than what the article describes, especially for a distributed cache: you need to have the Promise auto-expire so that if the machine that's performing the backend read disappears the other readers don't stay stuck forever waiting for it. It's also useful to have a feature in the cache itself that blocks the caller until the Promise has been fulfilled, so as to avoid repeated requests asking if the data is finally there.
As an aside, Guava loading caches[1] implement per-key locking so that multiple threads accessing a missing key simultaneously[2] would only lead to a single load with all other readers waiting for the loading thread to complete the fetch and populate the cache.
> so that multiple threads accessing a missing key simultaneously[2] would only lead to a single load with all other readers waiting for the loading thread to complete
For anyone using Rails, Rails internal cache does this on expiration, but might not on initial load.
Because then your caller will need to implement something that understands what a promise is instead of just getting a data object it already has to understand. Not only this, but the caller will need to also implement a polling mechanism to keep trying. Put into code:
func get_value(key):
value = backend.get(key)
return value
Is way better than something along these lines:
func get_value(key):
while true:
response = backend.get(key)
if response.type == "promise":
sleep duration
continue
return response.value
If your service looks like the former, then suddenly your unit tests can use a postgres database, a sqlite database, a rest client... that all implement the same backend.get(key) interface.
Guava cache's successor, Caffeine [1], handles the asynchronous case. In your scenario, you could set a timeout on the future as the entry will be discarded if the future results in an error.
In an old apartment I had a lot of stuff on the same extension plug. I had to turn on the devices one by one to prevent power loss ... There's also the same problem/solution when boarding airplanes. Even if it feels backwards, it's actually faster to let one third of the requests go through first, then to let all requests go through at once.
"instead of caching the actual value, we cached a Promise that will eventually provide the value"
I did this exact thing recently in a client-side HTTP caching system for frequently-duplicated API requests from within a single page. Cool to see it pop up elsewhere.
Came up with something along these lines at my last place - not actually the hardest thing to do as long as you've got a robust & convenient locking system available to you. In my case I abused the common db instance that all the clients were connected to anyway to synchronize the callers on postgres advisory locks. Sure, this isn't the infinitely scalable, Netflix-scale solution that everyone is convinced they need for everything, but it will probably work absolutely fine for >90% of development scenarios.
Back when I worked on a similar problem 10 years ago, we solved it by having a quickly expiring memcached key for hitting the database. So if the value wasn't cached, and if that key wasn't there, it would attempt to add that key. If it was added, it would hit the database and cache the result. Otherwise, if that key was there or it didn't successfully add it, it would wait for a short period of time, then re-try the whole process again.
There's other similar problems elsewhere too though. A cold MySQL start is a bitch when you have and rely upon huge amounts of memory for MySQL to service your requests - this is especially noticeable if you have so much traffic you need to cache some results. Back then it would take us about an hour before a freshly spun up MySQL instance could keep up with our regular traffic, even accounting for stuff being cached.
Instead – and more usefully given how slow some of our backend APIs are – we cache each value twice, under e.g. `key` with a short TTL and `key_backup` with a long TTL.
The first process to miss on `key` renames `key_backup` to `key` (which is atomic and fast on Redis) and goes to the backend for a new value to cache twice and return, while the rest of the herd reads the renamed backup.
Yes, this doubles the total cache size, or equivalently halves the number of keys we have room for. That's a price we're OK with paying to avoid blocking reads while a value is recalculated.
I'm not a developer, but to be honest, thought that's how all non-trivial caching implementations worked -- instead of going directly to the back end or having each one trigger a read from the back end for a cache-miss, all of the threads that want that resource just waited on it to appear in the cache.
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[ 2.9 ms ] story [ 60.4 ms ] threadThis is not something memcached or redis support out of the box, as far as I know. It would seem to imply a cache manager service that has its own in-memory table of Promises.
I'm not sure how often "thundering herd" is actually a problem for your cache, but I could definitely see this being a useful feature to add to a caching system. Even just a way to tell Redis that something will exist there soon and to delay response until it arrives would be nice. (In essence, a Promise.)
[1] https://dogpilecache.sqlalchemy.org/en/latest/
Its not a "thundering herd" problem either. The Thundering herd is classically a scheduling problem.
You have 100-threads waiting on a resource (classically: a Mutex). The Mutex unlocks, which causes all 100-threads to wakeup. You KNOW that only one thread will win the Mutex, so 99 of the threads wasted CPU-time as they wokeup. When the next thread is done, 98 threads will wakeup (again, all wasting CPU time because only one can win).
Solving the thundering herd requires your scheduler to know all the resources that could be blocking a thread. The scheduler then only wakes ONE thread up at a time in these cases.
-----------
I'm not entirely sure what the problem should be named in the blog, but it definitely isn't a "thundering herd". I will admit that its a similar looking problem though.
I think it becomes a thundering herd problem when every request that's a potential cache miss tries to obtain a lock on the Promise for that request. Likely what the author was trying to get at which was lost due to overly generalising the problem.
0. https://qconsf.com/sf2011/dl/qcon-sanfran-2011/slides/Siddha...
The key attribute of the "Thundering Herd" problem is the LOOP. The Thundering Herd causes another Thundering Herd... which later causes another Thundering Herd. In the Netflix presentation, the "Thundering Herd" causes all of the requests to time out, which causes two new servers to be added ("automatic scale up"), then everyone tries again.
When everyone tries again, there's more people waiting, so everyone times-out AGAIN, which causes everything to shutdown, add two more servers to scale up, and start over. Etc. etc. Its a cascading problem that gets worse and worse each loop. You solve the Thundering Herd not by adding more resources (that actually makes the problem worse!!), but by cutting off the feedback loop somehow.
The problem discussed in the blog post has no feedback loop. Its simply a problem that happens once on startup.
Thundering herds can cause escalating and successive failures. That is very much an issue with service start/restart. A bad restart will cause a timeout, another restart, and eventually, restarts on further layers. Imagine all this running above k8s. So yes, this pattern is indeed about one of the failure modes that happen with thundering herds.
Though if your cache needs another cache, that feels like a bad cache. The promise pattern can be done transparently by the cache, coalescing GETs, instead of requiring a user protocol. We do app level caching to stay process-local because latency is fun in GPU land and we are a visual analytics tool... But that is not for the problem shown here.
[1] https://en.wikipedia.org/wiki/Thundering_herd_problem
[2] https://en.wikipedia.org/wiki/Cache_stampede
I wouldn't fault anyone for getting these similar names mixed up though.
https://github.com/golang/groupcache
https://news.ycombinator.com/item?id=6121501
https://talks.golang.org/2013/oscon-dl.slide#43
Changing the cache to store promises is indeed an easy way to deduplicate most requests that occur roughly at the same time.
The logic now is: "on URL: if (in cache) return promise.resolve(), else add promise to cache, fetch, return promise.resolve()"
At best you could serialize the promise and then have everyone who was waiting on it poll the cache to see if the promise was resolved yet, but that is pretty inefficient.
You probably want another layer in there that handles the conversion of a JavaScript promise to something that can be awaited on from your cache, like a redis pub/sub. Then in JavaScript land your services just await on a promise as normal but under the covers your service is doing something else which is specific to the cache you're using.
Note that it isn't even meaningful to suggest that memcache or Redis should support this, because they aren't responsible for filling cache values for misses. Only something actually generating the value upon the miss can implement this "promise".
And in the general case, you can't serialize promises either, so you can't be "putting a promise into memcached", because memcached only stores bytes. You'd have to wrap a lot of machinery around it, and even then, I personally would say it's the machinery doing it, not memcached.
(I think Redis does have some features that could be pressed into service here for notification, but Redis still wouldn't be doing the actual filling in of the value, since it can't.)
Some examples:
* varnish: https://info.varnish-software.com/blog/hit-for-pass-varnish-...
* nginx: http://nginx.org/en/docs/http/ngx_http_proxy_module.html#pro...
* fastly: https://docs.fastly.com/guides/performance-tuning/request-co...
* cloudfront: https://docs.aws.amazon.com/AmazonCloudFront/latest/Develope...
For TypeScripters out there, this is what my team wrote for our static analysis framework: https://github.com/Polymer/tools/blob/master/packages/analyz...
As an aside, Guava loading caches[1] implement per-key locking so that multiple threads accessing a missing key simultaneously[2] would only lead to a single load with all other readers waiting for the loading thread to complete the fetch and populate the cache.
[1] https://github.com/google/guava/wiki/CachesExplained
[2] in the sense of "in the time it takes for the first accessor to complete the backend read"
For anyone using Rails, Rails internal cache does this on expiration, but might not on initial load.
[1] https://github.com/ben-manes/caffeine/wiki/Population
I did this exact thing recently in a client-side HTTP caching system for frequently-duplicated API requests from within a single page. Cool to see it pop up elsewhere.
https://gist.github.com/risicle/f4807bd706c9862f69aa
There's other similar problems elsewhere too though. A cold MySQL start is a bitch when you have and rely upon huge amounts of memory for MySQL to service your requests - this is especially noticeable if you have so much traffic you need to cache some results. Back then it would take us about an hour before a freshly spun up MySQL instance could keep up with our regular traffic, even accounting for stuff being cached.
The first process to miss on `key` renames `key_backup` to `key` (which is atomic and fast on Redis) and goes to the backend for a new value to cache twice and return, while the rest of the herd reads the renamed backup.
Yes, this doubles the total cache size, or equivalently halves the number of keys we have room for. That's a price we're OK with paying to avoid blocking reads while a value is recalculated.
What happens if I have a new request come in for which I have no key OR key_backup?