# min-max-pqueue A min-max priority queue provides efficient access to both its least element and its greatest element. Also known as [double-ended priority queue](https://en.wikipedia.org/wiki/Double-ended_priority_queue). This library provides two variants of min-max priority queues: - [`MinMaxQueue prio a`](https://hackage.haskell.org/package/min-max-pqueue/docs/Data-MinMaxQueue.html), a general-purpose min-max priority queue. - [`IntMinMaxQueue a`](https://hackage.haskell.org/package/min-max-pqueue/docs/Data-IntMinMaxQueue.html), a min-max priority queue where priority values are integers. A min-max priority queue can be configured with a maximum size. Each time an insertion causes the queue to grow beyond the size limit, the greatest element will be automatically removed (rather than rejecting the insertion). Their implementations are backed by `Map prio (NonEmpty a)` and `IntMap (NonEmpty a)`, respectively. This means that certain operations are asymptotically more expensive than implementations [backed by mutable arrays](https://dl.acm.org/citation.cfm?id=6621), e.g., `peekMin` and `peekMax` is *O(n* log *n)* vs. *O(n)*, `fromList` is also *O(n* log *n)* vs. *O(n)*. In a pure language like Haskell, a mutable array based implementation would be impure and need to operate inside monads. And in many applications, regardless of language, the additional time complexity would be a small or negligible price to pay to avoid destructive updates anyway. If you only access one end of the queue (i.e., you need a regular priority queue), an implementation based on a kind of heap that is more amenable to purely functional implementations, such as binomial heap and pairing heap, is *potentially* more efficient. But always benchmark if performance is important; in my experience `Map` *always* wins, even for regular priority queues. ## Advantages over Using Maps Directly - `size` is *O(1)*, vs. *O(n)* for maps. Note that `Data.Map.size` is *O(1)* but it returns the number of keys, which is not the same as the number of elements in the queue. `Data.IntMap.size`, on the other hand, is *O(k)* where *k* is the number of keys. - A queue can have a size limit, and it is guaranteed that its size does not grow beyond the limit. - Many useful operations, such as `takeMin`, `dropMin`, are non-trivial to implement with `Map prio (NonEmpty a)` and `IntMap (NonEmpty a)`. - The queue's fold operations operate on individual elements, as opposed to `NonEmpty a`. ## Alternative Implementation In Haskell, an alternative to the mutable array based implementation is to use immutable, general purpose arrays such as `Seq`. This would achieve *O(1)* `peekMin` and `peekMax`, but since `lookup` and `update` for `Seq` cost *O(n* log *n)*, the cost of `insert`, `deleteMin` and `deleteMax` would become *O(n* log2 *n)*. [A `Seq`-based implementation](https://github.com/zliu41/min-max-pqueue/blob/master/benchmark/SeqQueue.hs) is provided for benchmarking purposes, which, as shown below, is more than an order of magnitude slower than the `Map`-based implementation for enqueuing and dequeuing 200,000 elements, proving that the improved time complexity of `peekMin` and `peekMax` is not worth the cost. In fact, if you perform `peekMin` and `peekMax` much more often than enqueuing and dequeuing operations, which means you perform `peekMin` and `peekMax` many times on the same queue, you should simply memoize the results. ## Benchmarks Benchmarking was done on my laptop in which 200,000 elements (which are integers) are inserted into the queue and subsequently removed one after another. - `pq`, `intpq` and `sq` represents `MinMaxQueue`, `IntMinMaxQueue` and `SeqQueue`. - `asc`, `desc` and `rand` represents inserting the elements in ascending, descending and random order. - `min` and `max` represents removing elements from the min-end and max-end. As seen in the following result, `IntMinMaxQueue` is twice as fast as `MinMaxQueue` for integer keys, whereas `SeqQueue` is more than an order of magnitude slower. ``` benchmarking intpq-asc-min time 27.15 ms (23.85 ms .. 29.31 ms) 0.972 R² (0.927 R² .. 0.997 R²) mean 30.84 ms (29.67 ms .. 35.07 ms) std dev 4.308 ms (1.160 ms .. 7.915 ms) variance introduced by outliers: 57% (severely inflated) benchmarking intpq-desc-max time 29.70 ms (29.23 ms .. 30.41 ms) 0.998 R² (0.995 R² .. 1.000 R²) mean 30.62 ms (30.29 ms .. 31.02 ms) std dev 803.7 μs (548.0 μs .. 1.190 ms) benchmarking intpq-rand-min time 31.00 ms (29.10 ms .. 33.05 ms) 0.985 R² (0.973 R² .. 0.994 R²) mean 28.39 ms (27.43 ms .. 29.46 ms) std dev 2.216 ms (1.968 ms .. 2.591 ms) variance introduced by outliers: 32% (moderately inflated) benchmarking intpq-rand-max time 30.96 ms (28.98 ms .. 32.96 ms) 0.987 R² (0.976 R² .. 0.996 R²) mean 33.66 ms (32.71 ms .. 34.49 ms) std dev 1.820 ms (1.473 ms .. 2.388 ms) variance introduced by outliers: 18% (moderately inflated) benchmarking pq-asc-min time 69.02 ms (61.94 ms .. 72.95 ms) 0.988 R² (0.968 R² .. 0.997 R²) mean 71.41 ms (68.99 ms .. 74.35 ms) std dev 4.799 ms (3.401 ms .. 6.974 ms) variance introduced by outliers: 17% (moderately inflated) benchmarking pq-desc-max time 80.90 ms (78.68 ms .. 85.06 ms) 0.997 R² (0.994 R² .. 0.999 R²) mean 83.20 ms (80.91 ms .. 89.15 ms) std dev 5.853 ms (2.234 ms .. 9.957 ms) variance introduced by outliers: 19% (moderately inflated) benchmarking pq-rand-min time 65.80 ms (60.01 ms .. 69.62 ms) 0.987 R² (0.965 R² .. 0.996 R²) mean 74.17 ms (70.93 ms .. 79.86 ms) std dev 7.495 ms (4.557 ms .. 12.39 ms) variance introduced by outliers: 35% (moderately inflated) benchmarking pq-rand-max time 68.29 ms (65.07 ms .. 70.84 ms) 0.997 R² (0.995 R² .. 1.000 R²) mean 74.03 ms (71.64 ms .. 77.51 ms) std dev 5.016 ms (3.110 ms .. 7.556 ms) variance introduced by outliers: 17% (moderately inflated) benchmarking sq-asc-min time 1.954 s (1.369 s .. 2.838 s) 0.971 R² (NaN R² .. 1.000 R²) mean 1.733 s (1.592 s .. 1.861 s) std dev 160.1 ms (28.78 ms .. 203.2 ms) variance introduced by outliers: 22% (moderately inflated) benchmarking sq-rand-min time 2.889 s (2.000 s .. 3.658 s) 0.989 R² (0.959 R² .. 1.000 R²) mean 2.915 s (2.828 s .. 3.054 s) std dev 130.7 ms (442.1 μs .. 159.9 ms) variance introduced by outliers: 19% (moderately inflated) ```