With pthread's rwlock (reader-writer lock) implementation, I got 23.4 million reads in five seconds. With read-copy-update (RCU), I had 49.2 million reads, a one hundred ten percent improvement with zero changes to the workload.
Ever since writing about them, the generator in JavaScript has become my favorite hammer. I'll wield it nearly any chance I can get it. Usually, that looks like rolling through a finite batch of items over time. For example, doing something with a bunch of leap years: ...or lazily processing some files: In both examples, the pool of items is exhausted once and never replenished. The for loop stops, and the final item returned by the iterator contains done: true. C'est fini.
Which Algorithm Is This? If you step back, this maps almost perfectly to the Top K Frequent Elements problem.We usually solve it for integers in a list. Here, the "elements" are audience profiles age and body-type combinations. First, define what an audience profile looks like: case class Profile(age: Int, height: Int, weight: Int) What we want is a function like this:
Edge computing is a type of IT infrastructure in which data is collected, stored, and processed near the "edge" or on the device itself instead of being transmitted to a centralized processor. Edge computing systems usually involve a network of devices, sensors, or machinery capable of data processing and interconnection. A main benefit of edge computing is its low latency. Since each endpoint processes information near the source, it can be easier to process data, respond to requests, and produce detailed analytics.
At that point, backpressure and load shedding are the only things that retain a system that can still operate. If you have ever been in a Starbucks overwhelmed by mobile orders, you know the feeling. The in-store experience breaks down. You no longer know how many orders are ahead of you. There is no clear line, no reliable wait estimate, and often no real cancellation path unless you escalate and make noise.
When I manage infrastructure for major events (whether it is the Olympics, a Premier League match or a season finale) I am dealing with a "thundering herd" problem that few systems ever face. Millions of users log in, browse and hit "play" within the same three-minute window. But this challenge isn't unique to media. It is the same nightmare that keeps e-commerce CTOs awake before Black Friday or financial systems architects up during a market crash. The fundamental problem is always the same: How do you survive when demand exceeds capacity by an order of magnitude?