![]() In such a system, if your server is able to handle 500 concurrent requests and then it stops accepting more, that's how much load you can plan for. Then, the regulating factor for all the requests is how much concurrency can be had by design or limitation. This, of course, remains true in Erlang, but only as long as all the calls for a given request happen within a single process. You only move forward or return once a computation has ended, and if there is contention, the overall systematic symptom is that things become slow. The unavoidable backpressureīy default, people tend to think of control flow as blocking first there tends to be far more cases we can intuitively think of where "late is better than never", and therefore, forcing requests into a waiting line makes more sense than just shedding load by ignoring and dropping them.īack-pressure is actually the default way to do things in most programming languages, since function and method calls are inherently synchronous in most cases. That's when people complain of the need for bounded queues in Erlang: process in charge of transform b could be configured with a max queue size, which would then drop or block and fix our problem. A harder scenario is one of those that tend to happen in the real world:īecause the process in charge of transform b cannot be parallelized, it is a bottleneck, and the flow of all tasks depend on its success and the average sojourn time of a request through the system shoots up.īecause the sojourn time shoots up, a continuous influx of requests to the system can increase its load (each request making things worse for all other ones) until the system runs out of memory and fails. This generally would mean you get quite the linear scaling going on, and each incoming request can be allocated all the resources it needs capacity is fairly easy to predict by little's law and rating your system for a maximal capacity is simple. If the 3 transforms above are processes, any level of concurrency on the system may look like: Even then, the internal flow of information would still not be synchronous. Now it's possible that the input and output bits are made synchronous by the observer, which could poll for system changes until the desired modifications are noticed. ![]() ![]() You can only know that things worked by looking at changes in the overall system, or streaming logs. ![]() The naive and 'fast' system is one where all the tasks are done asynchronously: the only feedback you get is that the request entered the system, and then you trust things to be fine for the rest of processing: Mechanism by which you drop tasks on the floor instead of handling them. Mechanism by which you can resist to a given input, usually by blocking. Increasing capacity or speeding up processing times will be the only way to help things if the average load does not subside. Anything above that will mean that you will sooner or later find yourself in a situation of overload. This essentially says that what really matters to the capacity of your system is going to be how long a task takes to go through it, with how many can be handled at once (or concurrently) in the system. The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W or expressed algebraically: L = λW. I'm gonna try to go at it from a fairly generic manner, but to do this, I'll have to establish a few concepts. Cooperative ones (like Akka or node.js) will tend to have implicit mechanisms due to heavy workload blocking the CPU earlier, but may still encounter the same issues depending on workloads. Do note, however, that the overall criticism of flow-control remain true in other concurrent platforms, specifically the preemptive ones such as Go. In this text, I'm going to dig through the common overload patterns that can be encountered in Erlang with the regular workarounds available today for your systems. The general consensus is that this is bad because the mailbox, if unbound via the language, are therefore bound by memory limitations of the computer, and all control is lost (this is no longer true, since OTP 19, added a max_heap_size flag that can be set per process to force an early death if its memory usage is too high, including its mailbox). This is a long-ish entry posted after multiple discussions were had on the nature of having or not having bounded mailbox in Erlang. Handling Overload “My bad opinions ” 4 Handling Overload
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