Roman Khavronenko 9dc7358869
allow exposing meta information for registered metrics (#61)
* allow exposing meta information for registered metrics

New public method `ExposeMetadata` allows enabling exposition
of dummy meta-info for all exposed metrics across all Sets.

This feature is needed to improve compatibility
with 3rd-party scrapers that require meta information to be present.

This commit doesn't update exposition of default system/process
metrics to keep the list of changes small. This change should
be added in a follow-up commit.

https://github.com/VictoriaMetrics/metrics/issues/48

* cleanup

* wip

* wip

* wip

* wip

---------

Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
2023-12-19 02:36:54 +02:00
..

Build Status GoDoc Go Report

fastrand

Fast pseudorandom number generator.

Features

  • Optimized for speed.
  • Performance scales on multiple CPUs.

How does it work?

It abuses sync.Pool for maintaining "per-CPU" pseudorandom number generators.

TODO: firgure out how to use real per-CPU pseudorandom number generators.

Benchmark results

$ GOMAXPROCS=1 go test -bench=. github.com/valyala/fastrand
goos: linux
goarch: amd64
pkg: github.com/valyala/fastrand
BenchmarkUint32n                   	50000000	        29.7 ns/op
BenchmarkRNGUint32n                	200000000	         6.50 ns/op
BenchmarkRNGUint32nWithLock        	100000000	        21.5 ns/op
BenchmarkMathRandInt31n            	50000000	        31.8 ns/op
BenchmarkMathRandRNGInt31n         	100000000	        17.9 ns/op
BenchmarkMathRandRNGInt31nWithLock 	50000000	        30.2 ns/op
PASS
ok  	github.com/valyala/fastrand	10.634s
$ GOMAXPROCS=2 go test -bench=. github.com/valyala/fastrand
goos: linux
goarch: amd64
pkg: github.com/valyala/fastrand
BenchmarkUint32n-2                     	100000000	        17.6 ns/op
BenchmarkRNGUint32n-2                  	500000000	         3.36 ns/op
BenchmarkRNGUint32nWithLock-2          	50000000	        32.0 ns/op
BenchmarkMathRandInt31n-2              	20000000	        51.2 ns/op
BenchmarkMathRandRNGInt31n-2           	100000000	        11.0 ns/op
BenchmarkMathRandRNGInt31nWithLock-2   	20000000	        91.0 ns/op
PASS
ok  	github.com/valyala/fastrand	9.543s
$ GOMAXPROCS=4 go test -bench=. github.com/valyala/fastrand
goos: linux
goarch: amd64
pkg: github.com/valyala/fastrand
BenchmarkUint32n-4                     	100000000	        14.2 ns/op
BenchmarkRNGUint32n-4                  	500000000	         3.30 ns/op
BenchmarkRNGUint32nWithLock-4          	20000000	        88.7 ns/op
BenchmarkMathRandInt31n-4              	10000000	       145 ns/op
BenchmarkMathRandRNGInt31n-4           	200000000	         8.35 ns/op
BenchmarkMathRandRNGInt31nWithLock-4   	20000000	       102 ns/op
PASS
ok  	github.com/valyala/fastrand	11.534s

As you can see, fastrand.Uint32n scales on multiple CPUs, while rand.Int31n doesn't scale. Their performance is comparable on GOMAXPROCS=1, but fastrand.Uint32n runs 3x faster than rand.Int31n on GOMAXPROCS=2 and 10x faster than rand.Int31n on GOMAXPROCS=4.