Keap is a heap data structure written in Kotlin similar to binary heap. It maintains separately the queue of elements and the tournament tree atop the queue.
Keap is stable, that is, it keeps initial order of equal elements.
It's faster than binary heap in terms of number of comparisons. For any kind of input (random or ordered) of size n,
the heapify procedure requires exactly n - 1 comparisons. Binary heap reaches this number of comparisons
only for ordered input. For random input, keap compared to binary heap does approximately 1.9 times less comparisons
to heapify, 2.7 times less comparisons to offer, and 3.5 times less comparisons to poll.
Though, array-backed keap on the average consumes 1.5 times more memory than array-backed binary heap.
Performance summary of keap and binary heap (both array-backed) is as follows:
| Keap average | Keap worst case | Binary heap average | Binary heap worst case | |
|---|---|---|---|---|
| Heapify | exactly n - 1 | exactly n - 1 | Θ(n) | Θ(n) |
| Peek | Θ(1) | Θ(1) | Θ(1) | Θ(1) |
| Poll | Θ(log n) | Θ(log n) | Θ(log n) | Θ(log n) |
| Offer | Θ(1) | Θ(log n) | Θ(1) | Θ(log n) |
| Memory used | Θ(n) | Θ(n) | exactly n | exactly n |
Here are two applications: keap-based PriorityQueue as a replacement of java.util.PriorityQueue and Keapsort
sorting algorithm. Both might be useful in two cases:
- stability is a must;
- comparing elements is rather heavyweight operation (e.g., it requires a database access).
PriorityQueue is a keap-based stable replacement of java.util.PriorityQueue.
Keapsort is a Heapsort with keap used instead of binary heap. Keapsort is a comparison-based sorting algorithm with a worst-case O(n log n) runtime. As keap is a stable priority queue, Keapsort is stable. Unlike Heapsort, Keapsort doesn't have an in-place version.
Like Heapsort, Keapsort produces first output after Θ(n) comparisons.
For Keapsort, this Θ(n) estimate is just equal to n - 1. To sort random input completely, Keapsort does almost
two times less comparisons than Heapsort, nearly 1% less comparisons than
Mergesort and 1-2% more comparisons than
Timsort.
Like Heapsort, Keapsort is not a modern CPU-friendly algorithm since it has poor data cache performance.
Like Heapsort, Keapsort doesn't seem to parallelize well.
JMH Gradle Plugin is used to build and run benchmarks.
Benchmark results are obtained on a PC running under Windows 7 with Intel(R) Core(TM) i7-3770 3.4 GHz CPU
and 64-bit JRE build 1.8.0_112-b15 with the following java parameters: -Xms1g -Xmx1g. To get results in your
environment, run:
./gradlew clean jar jmh
For both java.util.PriorityQueue and keap-based PriorityQueue, there are two types of benchmarks:
- Benchmarks examining operations with random elements: heapify (building the queue from a collection), offer, peek, and poll. The queue elements are random strings of length ~30 characters with constant 10-characters prefix. Queue size is 10000.
- Benchmarks examining offering ordered elements: offerIncreasing (successively increasing elements) and offerDecreasing (successively decreasing elements). The queue elements are strings of length ~30 characters with constant 10-characters prefix. Queue size is 10000.
Current results grouped for easy review are as follows. Building the queue from collections of random elements:
Benchmark Mode Cnt Score Error Units
JavaQueueRandomBenchmark.heapify thrpt 20 1.810 ± 0.039 ops/ms
KeapQueueRandomBenchmark.heapify thrpt 20 3.487 ± 0.013 ops/ms
Basic queue operations with random elements:
Benchmark Mode Cnt Score Error Units
JavaQueueRandomBenchmark.offer thrpt 20 3.649 ± 0.041 ops/us
JavaQueueRandomBenchmark.peek thrpt 20 79.458 ± 0.626 ops/us
JavaQueueRandomBenchmark.poll thrpt 20 4.250 ± 0.070 ops/us
KeapQueueRandomBenchmark.offer thrpt 20 3.727 ± 0.073 ops/us
KeapQueueRandomBenchmark.peek thrpt 20 84.371 ± 0.499 ops/us
KeapQueueRandomBenchmark.poll thrpt 20 11.993 ± 0.239 ops/us
Offering ordered elements:
Benchmark Mode Cnt Score Error Units
JavaQueueOrderedBenchmark.offerDecreasing thrpt 20 5.432 ± 0.177 ops/us
JavaQueueOrderedBenchmark.offerIncreasing thrpt 20 30.795 ± 0.588 ops/us
KeapQueueOrderedBenchmark.offerDecreasing thrpt 20 7.959 ± 0.033 ops/us
KeapQueueOrderedBenchmark.offerIncreasing thrpt 20 8.860 ± 0.044 ops/us
The scores above are numbers of operations per microsecond, for heapify - per millisecond. So the greater the score, the better performance.
<!-- in Maven project -->
<dependency>
<groupId>com.github.penemue</groupId>
<artifactId>keap</artifactId>
<version>0.3.0</version>
</dependency>// in Gradle project
dependencies {
compile 'com.github.penemue:keap:0.3.0'
}Gradle is used to build, test, and run benchmarks. JDK 1.8 and Kotlin 1.3.10 are required. To build the project, run:
./gradlew