Qqwy
How to interface with a pool to handle request throttling?
We are building an application where we have a lot of import requests, which each spawn one or multiple processes, one per requested resource we want to import.
The concern of a resource-worker is:
- get data from external API.
- transform data into our internal data format.
- return data to batch (which bundles all resources in a request together, before returning all of them at once to the requester).
The actual resources are requested from an external API. However, on a single API-account, you’re only allowed to do n API requests to the server per second, or do m queries every 15 minutes, etc.
What I was thinking, was to create a pool of n HTTP-client-workers, and have all resource-workers request data through this pool.
But here is the catch: What is the best way to communicate between the resource-workers and the pool?
- Should all resource-workers try to open a pool-connection continuously? (i.e. a resource-worker loops until one can be established). This seems to result in an insane amount of message-passing in the application, as all resource-workers will send ‘can I be helped yet?’ messages all the time.
- Should resource-workers be put in some sort of queue in the pool-server, and then get a worker-PID returned to them as soon as they are next in line? Caveat: What should a resource do while waiting? Should the resource monitor the pool to ensure that it will still be helped somewhere in the future? (because when the pool-server crashes, the queue will be gone)
- Maybe there is even another alternative?
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sasajuric
Rather than conflating rate limiter with the pool, you could have a simple rate limiter which responds with yes/no. Then, each client process (in your case resource worker) would ask the limiter for permission. If the permission is given, the client can safely issue the API request (or any other limited job).
This means you don’t need a pool at all. Instead, the rate limiter ensures that at most n API requests are issued in a given time frame. You won’t be able to do more, since limiter won’t allow it.
There are a couple of libraries for this available. Quick googling revealed this one. I never tried it so can’t comment how it works. For my blog, I implemented a naive solution based on ETS counters. This one’s nice because permission questions are not going through the single process, so it should have better throughput. However, since it’ based on ETS tables, it can’t work as a cluster-wide (global) limiter.
It’s worth mentioning that these rate limiters are not 100% consistent, because things happen concurrently. Namely, when a limiter gives you permission, you still need to make the request. So I think it’s theoretically possible that in one second you end up issuing a bit more requests than allowed. A simple remedy for this would be to set the internal limit a bit lower than the one imposed by the external API. If you really want strong consistency I think the only option is to forward all calls through the single process. This would provide stronger guarantees, but might affect the throughput.
fishcakez
GitHub - jlouis/safetyvalve: A safety valve for your erlang node · GitHub provides exactly these features.
However sbroker master also provides a framework for building custom job regulators, there is even a property testing framework for the testing it. Its Erlang code but the simplest one limits the number of concurrent tasks: sbroker/src/sregulator_open_valve.erl at master · fishcakez/sbroker · GitHub. The regulator process plugs together a valve, a queue and a meter, and has 3 different ways to request to run, blocking with queue, blocking without queueing and asynchronous with queue. The queues and meters come with sbroker but you would need to write the valve. I would really like a PR adding this feature and can help with the property based testing.
sasajuric
If you use GenServer.call to issue a request to the rate limiter it will simplify some things. In particular, you won’t need to monitor R from C (GenServer.call does that for you). You also don’t need to monitor C from R if you can immediately respond.
To sketch the idea, handle_call could look something like:
def handle_call(:request_permission, from, state) do
if can_grant?(state) do
{:reply, :ok, inc_issued_count(state)}
else
{:noreply, enqueue_caller(state, from)}
end
end
Where enqueue_caller would have to store the from tuple and setup a monitor to the caller, whose pid is the first element of the from tuple (see here).
Then, when you want to respond to the caller at the later point, you can use GenServer.reply, passing the dequeued from tuple. When doing this, you should also demonitor the corresponding caller.
I think you could also make it work without using Process.send_after, by relying on timeout values in response tuples and some juggling with monotonic time. This would allow you to reset the counter even when you’re highly loaded, because you could also reset it when handling an incoming request. However, that would probably be a more complex solution, so I’d start with send_after, and then maybe refine once everything else is in place.
I think that’s the general idea ![]()
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Qqwy
@swennemans In the end, as projects in companies tend to go, the project moved a lot slower because of other work that was deemed more important at the time. So above idea still is mostly theoretical; I have not implemented it yet.
GenStage seems like a perfect fit for this problem. I have not used it a lot myself yet, so I cannot be 100% sure, but it looks like it simplifies constructing these kinds of concurrent pipelines a lot.
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