内容简介:之前经常使用golang测试框架中的单元测试,一直没用性能测试,今天想熟悉一下golang的Benchmark顺便给堆排和快排做个性能测试,测试非常简单,源代码如下:测试文件为:测试命令:
之前经常使用golang测试框架中的单元测试,一直没用性能测试,今天想熟悉一下golang的Benchmark顺便给堆排和快排做个性能测试,测试非常简单,源代码如下:
//sort.go package mysort import ( "math/rand" "time" ) func swap(nums []int, i, j int) { nums[i], nums[j] = nums[j], nums[i] } func parition(nums []int, start, end int) int { idx := rand.Int()%(end-start) + 1 + start swap(nums, idx, end) idx = end for start < end { for nums[start] <= nums[idx] && start < end { start++ } for nums[end] >= nums[idx] && start < end { end-- } swap(nums, start, end) } swap(nums, start, idx) return start } //quick sort func QSort(nums []int, start, end int) { rand.Seed(time.Now().UnixNano()) if start < end { p := parition(nums, start, end) QSort(nums, start, p-1) QSort(nums, p+1, end) } } //生成一个随机的数组,长度为len, 元素最大值不超过max func GenRandSlice(len, max int) []int { rand.Seed(time.Now().UnixNano()) a := make([]int, 0) for i := 0; i < len; i++ { a = append(a, rand.Int()%max) } return a } //堆排序 func left(i int) int { return i << 1 } func right(i int) int { return i<<1 + 1 } func maxHeapify(a []int, i int) { l := left(i) r := right(i) max := i aLen := len(a) if l < aLen && a[l] > a[max] { max = l } if r < aLen && a[r] > a[max] { max = r } if max != i { swap(a, i, max) maxHeapify(a, max) } } func BuildMaxHeap(a []int) { aLen := len(a) if aLen == 0 { return } for i := aLen/2 - 1; i >= 0; i-- { maxHeapify(a, i) } } func HeapSort(a []int) { BuildMaxHeap(a) aLen := len(a) tmp := a[:] for i := aLen - 1; i >= 1; i-- { swap(tmp, 0, i) tmp = tmp[:len(tmp)-1] maxHeapify(tmp, 0) } }
测试文件为:
//sort_test.go import ( "testing" ) func BenchmarkHeapSort(b *testing.B) { a := GenRandSlice(10000, 10000) for i := 0; i < b.N; i++ { HeapSort(a) } } func BenchmarkQSort(b *testing.B) { a := GenRandSlice(10000, 10000) for i := 0; i < b.N; i++ { QSort(a, 0, len(a)-1) } }
测试命令:
go test -bench=. goos: darwin goarch: amd64 pkg: go_practice/algorithm/mysort BenchmarkHeapSort-4 2000 914686 ns/op BenchmarkQSort-4 10 120658646 ns/op PASS ok go_practice/algorithm/mysort 3.269s
每ns快速 排序 执行的操作远远高于堆排,相比较来说,快排确实高效。另外,goalng的testing真是好用,各种想要的功能都有。性能测试了,还可以对cpu和mem做进一步分析,详细的指令可查看:
go test -h
这里只截取一部分
-cpuprofile cpu.out Write a CPU profile to the specified file before exiting. Writes test binary as -c would. -memprofile mem.out Write an allocation profile to the file after all tests have passed. Writes test binary as -c would. -memprofilerate n Enable more precise (and expensive) memory allocation profiles by setting runtime.MemProfileRate. See 'go doc runtime.MemProfileRate'. To profile all memory allocations, use -test.memprofilerate=1. -mutexprofile mutex.out Write a mutex contention profile to the specified file when all tests are complete. Writes test binary as -c would. -mutexprofilefraction n Sample 1 in n stack traces of goroutines holding a contended mutex. -outputdir directory Place output files from profiling in the specified directory, by default the directory in which "go test" is running. -trace trace.out Write an execution trace to the specified file before exiting.
如执行命令 go test -test.bench="BenchmarkHeapSort" -cpuprofile cpu.out
,会得到两个文件:
cpu.out mysort.test
cpu.out是cpu采样结果,mysort.test是测试的二进制文件,使用命令 go tool pprof mysort.test cpu.out
可得到如下结果:
File: mysort.test Type: cpu Time: Feb 17, 2019 at 12:55pm (CST) Duration: 2.06s, Total samples = 1.67s (80.90%) Entering interactive mode (type "help" for commands, "o" for options) (pprof) top10 Showing nodes accounting for 1.67s, 100% of 1.67s total Showing top 10 nodes out of 16 flat flat% sum% cum cum% 1.06s 63.47% 63.47% 1.38s 82.63% go_practice/algorithm/mysort.maxHeapify 0.30s 17.96% 81.44% 0.30s 17.96% go_practice/algorithm/mysort.swap (inline) 0.12s 7.19% 88.62% 0.12s 7.19% runtime.newstack 0.08s 4.79% 93.41% 0.08s 4.79% go_practice/algorithm/mysort.left (inline) 0.05s 2.99% 96.41% 1.50s 89.82% go_practice/algorithm/mysort.HeapSort 0.04s 2.40% 98.80% 0.04s 2.40% runtime.nanotime 0.01s 0.6% 99.40% 0.14s 8.38% go_practice/algorithm/mysort.BuildMaxHeap 0.01s 0.6% 100% 0.01s 0.6% runtime.kevent 0 0% 100% 1.50s 89.82% go_practice/algorithm/mysort.BenchmarkHeapSort 0 0% 100% 0.12s 7.19% runtime.morestack
再对 QSort
做测试:
go test -test.bench="BenchmarkQSort" -cpuprofile cpu.out go tool pprof mysort.test cpu.out File: mysort.test Type: cpu Time: Feb 17, 2019 at 12:58pm (CST) Duration: 1.45s, Total samples = 1.16s (79.90%) Entering interactive mode (type "help" for commands, "o" for options) (pprof) top10 Showing nodes accounting for 1.16s, 100% of 1.16s total Showing top 10 nodes out of 20 flat flat% sum% cum cum% 0.80s 68.97% 68.97% 0.80s 68.97% math/rand.seedrand (inline) 0.25s 21.55% 90.52% 1.05s 90.52% math/rand.(*rngSource).Seed 0.05s 4.31% 94.83% 0.05s 4.31% runtime.nanotime 0.03s 2.59% 97.41% 0.03s 2.59% runtime.walltime 0.02s 1.72% 99.14% 0.02s 1.72% runtime.usleep 0.01s 0.86% 100% 0.01s 0.86% runtime.kevent 0 0% 100% 1.08s 93.10% go_practice/algorithm/mysort.BenchmarkQSort 0 0% 100% 1.08s 93.10% go_practice/algorithm/mysort.QSort 0 0% 100% 1.05s 90.52% math/rand.(*Rand).Seed 0 0% 100% 1.05s 90.52% math/rand.(*lockedSource).seedPos
以上所述就是小编给大家介绍的《快排和堆排性能对比》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!
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