![]() How about expressing the limits in Combine's pipeline terms. You can make up to 5,000 requests per hour. ![]() ![]() The API is limited to 5 requests per second per base By the way, let's check out some real-world limit examples: So, rate limiting is an omnipresent system design pattern that keeps clients' traffic at bay. That's why most public APIs are often rate-limited. Whatever it is, you certainly want to keep your appetites in check and don't accidentally DDoS your data sources. It might be for the sake of data scrapping or a part of Extract, transform, load process. Let's actually build the rate limiter :) By the way, what the heck is a rate limiter, and where can it be used? Say that you are ingesting large portions of data. But this time, a smiling sergeant is marching ahead of you. Don't get me wrong, it still is a minefield walk, with explosion leftovers, body parts scattered here and there, smokes from the production builds. It would be a minefield walk if not for OpenCombine and CombineExt. For example, we still don't know how to build thread-safe Publishers □ Only a handful of people know its internals. Secondly, Combine is (surprise-surprise) a closed-source project.First of all, there are so many moving parts: Publisher, Publishers, Subscriber, Subscription, Cancellable, Scheduler.It's much more pleasant to interact with a well-polished facade and never dig to the pipeline's internals for sure: If the orchestration process sounds like an intimidating chore, well, it is. Combine goes even further and orchestrates the data flow itself, twirls a pipeline's valves, keeps the system safe from overflows. So, Combine not only helps to build message pipelines. Imagine an intelligent Combine's map that would parallelize workload if given 20 or more items (like AsyncSequence □). But backpressure is not only a safeguard from overflow but also introduces the point of concurrency and parallelization. Though, the before-mentioned examples are mostly from the backend side of things. ![]() Frameworks like GenStage are purely demand-driven. Take for example Fred Hebert's classics Queues Don't Fix Overload and Handling Overload. In system design, backpressure is a well-known and honored concept. Aside from User Interface, there are a lot of cases where you'll find backpressure useful. That's somewhat ironic and also makes backpressure a bit underrepresented citizen.īut don't forget that Combine is a general framework. However, your best friends - throttle and debounce, are here to carry the weight when it comes to that.Īnd indeed, all shipped Combine consumers ( sink and assign) are rolling with unlimited demand. UI is rarely demand-bounded and generally concerned only about the latest values. Thus, you can't send more events than consumers can, well, consume.įrom the UI standpoint, backpressure doesn't seem like a groundbreaking concept. The backpressure concept coordinates a consumer's demand (values processing) and a producer's supply (values generating). Combine included the backpressure mechanism. Though, with one, and significant, distinction. The good ol' asynchronous stream of values over time concept. By mere coincidence, Combine looks suspiciously Rx-like. It will take another 20 years and a retired Apple veteran to spin a tale of Combine's origins and why 2019 was the year.įor now, let's distract ourselves by scrutinizing the framework. Here are the honorable mentions: ReactiveCocoa/ ReactiveSwift has been here since 2012, ReactiveSwift's initial release dates to 2015.īut in 2019, Apple comes off with Combine. There're tons and tons of reactive and reactive-like frameworks available for it. The Apple platform is not an exception here. Heck, Rx specification will celebrate its 10th birthday this year. Reactive programming is one of those well-brewed paradigms that outgrew initial hype and became classics.
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