471–480 of 772 posts
I remember when I first discovered it, it drove home the point that choice of data structure can really matter - it made a bunch of problems I'd been working on much easier to solve, as it gave a different way of looking at things.
Not a very deep CS-y one, but still one of my favourite data structures: Promise Maps. It only works in languages where promises/futures/tasks are a first-class citizen. Eg JavaScript. When caching the result of an expensive computation or a network call, don't actually cache the result, but cache the promise that awaits the result. Ie don't make a Map but a Map > This way, if a new, uncached key gets requested twice…
Can someone explain to me why the second example is better. To me it seems to be the same thing. Replace result with int and I literally do not see a problem with the first one. Also why is a mutex or lock needed for Result in javascript? As far as I know... In a single threaded application, mutexes and locks are only needed for memory operations on two or more results. With a single value, say an int, within javascr…
So if you get two requests for the computation that computes Result in close succession the Map doesn't have the result in place for the second call and as a result you get no caching effect and make 2 expensive requests.
By caching the Promise instead the second request is caught by the cache and simply awaits the Promise resolving so now you only get 1 expensive request.
Not a very deep CS-y one, but still one of my favourite data structures: Promise Maps. It only works in languages where promises/futures/tasks are a first-class citizen. Eg JavaScript. When caching the result of an expensive computation or a network call, don't actually cache the result, but cache the promise that awaits the result. Ie don't make a Map but a Map > This way, if a new, uncached key gets requested twice…
Can someone explain to me why the second example is better. To me it seems to be the same thing. Replace result with int and I literally do not see a problem with the first one. Also why is a mutex or lock needed for Result in javascript? As far as I know... In a single threaded application, mutexes and locks are only needed for memory operations on two or more results. With a single value, say an int, within javascr…
If you store the promise, then not only does the second attempt know that it doesn't need to make a new request, but it can also await on the promise it finds in the map directly.
Not a very deep CS-y one, but still one of my favourite data structures: Promise Maps. It only works in languages where promises/futures/tasks are a first-class citizen. Eg JavaScript. When caching the result of an expensive computation or a network call, don't actually cache the result, but cache the promise that awaits the result. Ie don't make a Map but a Map > This way, if a new, uncached key gets requested twice…
Can someone explain to me why the second example is better. To me it seems to be the same thing. Replace result with int and I literally do not see a problem with the first one. Also why is a mutex or lock needed for Result in javascript? As far as I know... In a single threaded application, mutexes and locks are only needed for memory operations on two or more results. With a single value, say an int, within javascr…
Not a very deep CS-y one, but still one of my favourite data structures: Promise Maps. It only works in languages where promises/futures/tasks are a first-class citizen. Eg JavaScript. When caching the result of an expensive computation or a network call, don't actually cache the result, but cache the promise that awaits the result. Ie don't make a Map but a Map > This way, if a new, uncached key gets requested twice…
Can someone explain to me why the second example is better. To me it seems to be the same thing. Replace result with int and I literally do not see a problem with the first one. Also why is a mutex or lock needed for Result in javascript? As far as I know... In a single threaded application, mutexes and locks are only needed for memory operations on two or more results. With a single value, say an int, within javascr…
Not a very deep CS-y one, but still one of my favourite data structures: Promise Maps. It only works in languages where promises/futures/tasks are a first-class citizen. Eg JavaScript. When caching the result of an expensive computation or a network call, don't actually cache the result, but cache the promise that awaits the result. Ie don't make a Map but a Map > This way, if a new, uncached key gets requested twice…
Just make sure Map access is thread safe - awaiting promises is - but updating/reading map keys usually isn't.
The union-find data structure / algorithm is useful and a lot of fun. The goal is a data structure where you can perform operations like "a and b are in the same set", "b and c are in the same set" and then get answers to questions like "are a and c in the same set?" (yes, in this example.) The implementation starts out pretty obvious - a tree where every element either points at itself or some thing it was merged wi…
Not a very deep CS-y one, but still one of my favourite data structures: Promise Maps. It only works in languages where promises/futures/tasks are a first-class citizen. Eg JavaScript. When caching the result of an expensive computation or a network call, don't actually cache the result, but cache the promise that awaits the result. Ie don't make a Map but a Map > This way, if a new, uncached key gets requested twice…
I love this approach and have used it many times in JavaScript. I often end up adding an additional map in front with the resolved values to check first, because awaiting or then'ing a Promise always means you will wait until the next microtask for the value, instead of getting it immediately. With a framework like React, this means you'll have a flash of missing content even when it is already cached.