alt Hacker NewsThe max-heap algorithm alluded to above is correct. You fill it with the first k values scanned, then peek at the max element for each subsequent value. If the current value is smaller than the max element, you evict the max element and insert the new element. This streaming top-k algorithm is ubiquitous in both leetcode interviews and applications. (The standard quickselect top-k algorithm is not useful in the streaming context because it requires random access and in-place mutation.)
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Akronymus • last Tuesday at 5:29 PM
My failure was misreading it as most common k rather than max k.
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To be fair to quickselect, I can imagine a lazy data processing framework having a concept of a lazily sorted data column where the actual data has been materialized but it’s not in sorted order yet. Then someone does “LIMIT k” to it, and the framework can go to town with quickselect.
As noted a couple times in this thread, there are all kinds of tradeoffs here, and I can’t imagine quickselect being even close to competitive for k that is small enough to fit in cache. Quickselect will, in general, scan a large input approximately twice. For k = 3, the answer fits in general-purpose registers or even in a single SIMD register, and a single scan with brute force accumulation of the answer will beat quickselect handily and will also beat any sort of log-time heap.
(In general, more advanced and asymptotically better algorithms often lose to simpler brute force algorithms when the parameters in question are smallish.)