go-re2 is a drop-in replacement for the standard library regexp package which uses the C++ re2 library for improved performance with large inputs or complex expressions. By default, re2 is packaged as a WebAssembly module and accessed with the pure Go runtime, wazero. This means that it is compatible with any Go application, regardless of availability of cgo.
The library can also be used in a TinyGo application being compiled to WebAssembly. Currently,
regexp when compiled with TinyGo always has very slow performance and sometimes fails to
compile expressions completely.
Note that if your regular expressions or input are small, this library is slower than the standard library. You will generally "know" if your application requires high performance for complex regular expressions, for example in security filtering software. If you do not know your app has such needs and are not using TinyGo, you should turn away now.
The library is almost fully compatible with the standard library regexp package, with just a few behavior differences. These are likely corner cases that don't affect typical applications. It is best to confirm them before proceeding.
Invalid utf-8 strings are treated differently. The standard library silently replaces invalid utf-8 with the unicode replacement character. This library will stop consuming strings when encountering invalid utf-8.
experimental.CompileLatin1 can be used to match against non-utf8 stringsreflect.DeepEqual cannot compare Regexp objects.
Continue to use the standard library if your usage would match any of these.
Searching this codebase for // GAP will allow finding tests that have been tweaked to demonstrate
behavior differences.
All APIs found in regexp are available except
*Reader: re2 does not support streaming inputNote that unlike many packages that wrap C++ libraries, there is no added Close type of method.
See the rationale for more details.
The experimental package contains APIs not part of standard regexp that are
incubating. They may in the future be moved to stable packages. The experimental package does not
provide any guarantee of API stability even across minor version updates.
go-re2 is a standard Go library package and can be added to a go.mod file. It will work fine in Go or TinyGo projects.
go get github.com/wasilibs/go-re2Because the library is a drop-in replacement for the standard library, an import alias can make migrating code to use it simple.
import "regexp"can be changed to
import regexp "github.com/wasilibs/go-re2"This library also supports opting into using cgo to wrap re2 instead of using WebAssembly. This
requires having re2 installed and available via pkg-config on the system. The build tag re2_cgo
can be used to enable cgo support.
On Ubuntu install the gcc tool chain and the re2 library as follows:
sudo apt install build-essential
sudo apt-get install -y libre2-devOn Windows start by installing MSYS2. Then open the MINGW64 terminal and install the gcc toolchain and re2 via pacman:
pacman -S mingw-w64-x86_64-gcc
pacman -S mingw-w64-x86_64-re2
pacman -S mingw-w64-x86_64-pkg-configIf you want to run the resulting exe program outside the MINGW64 terminal you need to add a path to the MinGW-w64 libraries to the PATH environmental variable (adjust as needed for your system):
SET PATH=C:\msys64\mingw64\bin;%PATH%On Mac start by installing homebrew including installation of the command line tools. Then install re2 via brew:
brew install re2Benchmarks are run against every commit in the bench workflow. GitHub action runners are highly virtualized and do not have stable performance across runs, but the relative numbers within a run should still be somewhat, though not precisely, informative.
wafbench tests the performance of replacing the regex operator of the OWASP CoreRuleSet and Coraza implementation with this library. This benchmark is considered a real world performance test, with the regular expressions being real ones used in production. Security filtering rules often have highly complex expressions.
One run looks like this
name \ time/op build/wafbench_stdlib.txt build/wafbench.txt build/wafbench_cgo.txt
WAF/FTW-4 21.72 ± ∞ ¹ 21.17 ± ∞ ¹ -2.55% (p=0.008 n=5) 19.51 ± ∞ ¹ -10.19% (p=0.008 n=5)
WAF/POST/1-4 2.308m ± ∞ ¹ 2.727m ± ∞ ¹ +18.18% (p=0.008 n=5) 2.479m ± ∞ ¹ +7.44% (p=0.008 n=5)
WAF/POST/1000-4 15.399m ± ∞ ¹ 4.734m ± ∞ ¹ -69.26% (p=0.008 n=5) 3.742m ± ∞ ¹ -75.70% (p=0.008 n=5)
WAF/POST/10000-4 150.65m ± ∞ ¹ 19.90m ± ∞ ¹ -86.79% (p=0.008 n=5) 13.47m ± ∞ ¹ -91.06% (p=0.008 n=5)
WAF/POST/100000-4 1511.3m ± ∞ ¹ 171.7m ± ∞ ¹ -88.64% (p=0.008 n=5) 110.4m ± ∞ ¹ -92.69% (p=0.008 n=5)FTW is the time to run the standard CoreRuleSet test framework. The performance of this
library with WebAssembly, wafbench.txt, shows a slight improvement over the standard library
in this baseline test case.
The FTW test suite will issue many requests with various payloads, generally somewhat small.
The POST tests show the same ruleset applied to requests with payload sizes as shown, in bytes.
We see that only with the absolute smallest payload of 1 byte does the standard library perform
a bit better than this library. For any larger size, even a fairly typical 1KB, go-re2
greatly outperforms.
cgo seems to offer about a 30% improvement on WebAssembly in this library. Many apps may accept the somewhat slower performance in exchange for the build and deployment flexibility of WebAssembly but either option will work with no changes to the codebase.
Microbenchmarks are the same as included in the Go standard library. Full results can be viewed in the workflow, a sample of results for one run looks like this
name \ time/op build/bench_stdlib.txt build/bench.txt build/bench_cgo.txt
Find-4 162.3n ± ∞ ¹ 1057.0n ± ∞ ¹ +551.26% (p=0.008 n=5) 397.5n ± ∞ ¹ +144.92% (p=0.008 n=5)
Compile/Onepass-4 4.017µ ± ∞ ¹ 23.585µ ± ∞ ¹ +487.13% (p=0.008 n=5) 7.803µ ± ∞ ¹ +94.25% (p=0.008 n=5)
Compile/Medium-4 9.042µ ± ∞ ¹ 45.105µ ± ∞ ¹ +398.84% (p=0.008 n=5) 13.346µ ± ∞ ¹ +47.60% (p=0.008 n=5)
Compile/Hard-4 66.52µ ± ∞ ¹ 272.58µ ± ∞ ¹ +309.80% (p=0.008 n=5) 94.49µ ± ∞ ¹ +42.06% (p=0.008 n=5)
Match/Easy0/16-4 3.721n ± ∞ ¹ 615.300n ± ∞ ¹ +16435.88% (p=0.008 n=5) 155.900n ± ∞ ¹ +4089.73% (p=0.008 n=5)
Match/Easy0/32-4 40.41n ± ∞ ¹ 620.10n ± ∞ ¹ +1434.52% (p=0.008 n=5) 155.00n ± ∞ ¹ +283.57% (p=0.008 n=5)
Match/Easy0/1K-4 242.3n ± ∞ ¹ 689.7n ± ∞ ¹ +184.65% (p=0.008 n=5) 154.8n ± ∞ ¹ -36.11% (p=0.008 n=5)
Match/Easy0/32K-4 3279.0n ± ∞ ¹ 1384.0n ± ∞ ¹ -57.79% (p=0.008 n=5) 154.0n ± ∞ ¹ -95.30% (p=0.008 n=5)
Match/Easy0/1M-4 240196.0n ± ∞ ¹ 42954.0n ± ∞ ¹ -82.12% (p=0.008 n=5) 155.0n ± ∞ ¹ -99.94% (p=0.008 n=5)
Match/Easy0/32M-4 7942195.0n ± ∞ ¹ 1359859.0n ± ∞ ¹ -82.88% (p=0.008 n=5) 155.4n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Easy0i/16-4 3.723n ± ∞ ¹ 589.600n ± ∞ ¹ +15736.69% (p=0.008 n=5) 155.800n ± ∞ ¹ +4084.80% (p=0.008 n=5)
Match/Easy0i/32-4 681.2n ± ∞ ¹ 593.3n ± ∞ ¹ -12.90% (p=0.008 n=5) 154.3n ± ∞ ¹ -77.35% (p=0.008 n=5)
Match/Easy0i/1K-4 20325.0n ± ∞ ¹ 667.6n ± ∞ ¹ -96.72% (p=0.008 n=5) 155.4n ± ∞ ¹ -99.24% (p=0.008 n=5)
Match/Easy0i/32K-4 913391.0n ± ∞ ¹ 1368.0n ± ∞ ¹ -99.85% (p=0.008 n=5) 155.2n ± ∞ ¹ -99.98% (p=0.008 n=5)
Match/Easy0i/1M-4 29087522.0n ± ∞ ¹ 42975.0n ± ∞ ¹ -99.85% (p=0.008 n=5) 155.5n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Easy0i/32M-4 929572314.0n ± ∞ ¹ 1362975.0n ± ∞ ¹ -99.85% (p=0.008 n=5) 155.7n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Easy1/16-4 3.721n ± ∞ ¹ 591.000n ± ∞ ¹ +15782.83% (p=0.008 n=5) 156.600n ± ∞ ¹ +4108.55% (p=0.008 n=5)
Match/Easy1/32-4 37.39n ± ∞ ¹ 595.30n ± ∞ ¹ +1492.14% (p=0.008 n=5) 155.40n ± ∞ ¹ +315.62% (p=0.008 n=5)
Match/Easy1/1K-4 517.5n ± ∞ ¹ 685.1n ± ∞ ¹ +32.39% (p=0.008 n=5) 156.4n ± ∞ ¹ -69.78% (p=0.008 n=5)
Match/Easy1/32K-4 25905.0n ± ∞ ¹ 1342.0n ± ∞ ¹ -94.82% (p=0.008 n=5) 158.2n ± ∞ ¹ -99.39% (p=0.008 n=5)
Match/Easy1/1M-4 929177.0n ± ∞ ¹ 42940.0n ± ∞ ¹ -95.38% (p=0.008 n=5) 155.1n ± ∞ ¹ -99.98% (p=0.008 n=5)
Match/Easy1/32M-4 29798707.0n ± ∞ ¹ 1362212.0n ± ∞ ¹ -95.43% (p=0.008 n=5) 155.6n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Medium/16-4 3.723n ± ∞ ¹ 590.900n ± ∞ ¹ +15771.61% (p=0.008 n=5) 155.000n ± ∞ ¹ +4063.31% (p=0.008 n=5)
Match/Medium/32-4 566.8n ± ∞ ¹ 595.2n ± ∞ ¹ +5.01% (p=0.008 n=5) 154.9n ± ∞ ¹ -72.67% (p=0.008 n=5)
Match/Medium/1K-4 20063.0n ± ∞ ¹ 669.7n ± ∞ ¹ -96.66% (p=0.008 n=5) 155.3n ± ∞ ¹ -99.23% (p=0.008 n=5)
Match/Medium/32K-4 924929.0n ± ∞ ¹ 1340.0n ± ∞ ¹ -99.86% (p=0.008 n=5) 155.8n ± ∞ ¹ -99.98% (p=0.008 n=5)
Match/Medium/1M-4 29406989.0n ± ∞ ¹ 42947.0n ± ∞ ¹ -99.85% (p=0.008 n=5) 154.6n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Medium/32M-4 963966642.0n ± ∞ ¹ 1363441.0n ± ∞ ¹ -99.86% (p=0.008 n=5) 154.6n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Hard/16-4 3.744n ± ∞ ¹ 596.000n ± ∞ ¹ +15818.80% (p=0.008 n=5) 155.600n ± ∞ ¹ +4055.98% (p=0.008 n=5)
Match/Hard/32-4 997.3n ± ∞ ¹ 598.1n ± ∞ ¹ -40.03% (p=0.008 n=5) 155.6n ± ∞ ¹ -84.40% (p=0.008 n=5)
Match/Hard/1K-4 30435.0n ± ∞ ¹ 686.5n ± ∞ ¹ -97.74% (p=0.008 n=5) 154.7n ± ∞ ¹ -99.49% (p=0.008 n=5)
Match/Hard/32K-4 1348825.0n ± ∞ ¹ 1342.0n ± ∞ ¹ -99.90% (p=0.008 n=5) 155.7n ± ∞ ¹ -99.99% (p=0.008 n=5)
Match/Hard/1M-4 43023861.0n ± ∞ ¹ 42891.0n ± ∞ ¹ -99.90% (p=0.008 n=5) 155.9n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Hard/32M-4 1380076363.0n ± ∞ ¹ 1362504.0n ± ∞ ¹ -99.90% (p=0.008 n=5) 155.7n ± ∞ ¹ -100.00% (p=0.008 n=5)
Match/Hard1/16-4 2661.0n ± ∞ ¹ 697.6n ± ∞ ¹ -73.78% (p=0.008 n=5) 169.0n ± ∞ ¹ -93.65% (p=0.008 n=5)
Match/Hard1/32-4 5084.0n ± ∞ ¹ 796.9n ± ∞ ¹ -84.33% (p=0.008 n=5) 201.5n ± ∞ ¹ -96.04% (p=0.008 n=5)
Match/Hard1/1K-4 157.089µ ± ∞ ¹ 6.838µ ± ∞ ¹ -95.65% (p=0.008 n=5) 2.092µ ± ∞ ¹ -98.67% (p=0.008 n=5)
Match/Hard1/32K-4 6748.74µ ± ∞ ¹ 196.99µ ± ∞ ¹ -97.08% (p=0.008 n=5) 62.52µ ± ∞ ¹ -99.07% (p=0.008 n=5)
Match/Hard1/1M-4 214.239m ± ∞ ¹ 6.290m ± ∞ ¹ -97.06% (p=0.008 n=5) 1.895m ± ∞ ¹ -99.12% (p=0.008 n=5)
Match/Hard1/32M-4 6936.54m ± ∞ ¹ 201.05m ± ∞ ¹ -97.10% (p=0.008 n=5) 59.88m ± ∞ ¹ -99.14% (p=0.008 n=5)
MatchParallel/Easy0/16-4 1.710n ± ∞ ¹ 942.200n ± ∞ ¹ +54999.42% (p=0.008 n=5) 81.700n ± ∞ ¹ +4677.78% (p=0.008 n=5)
MatchParallel/Easy0/32-4 19.10n ± ∞ ¹ 944.80n ± ∞ ¹ +4846.60% (p=0.008 n=5) 82.03n ± ∞ ¹ +329.48% (p=0.008 n=5)
MatchParallel/Easy0/1K-4 96.72n ± ∞ ¹ 978.40n ± ∞ ¹ +911.58% (p=0.008 n=5) 80.82n ± ∞ ¹ -16.44% (p=0.008 n=5)
MatchParallel/Easy0/32K-4 1285.00n ± ∞ ¹ 1425.00n ± ∞ ¹ +10.89% (p=0.008 n=5) 80.82n ± ∞ ¹ -93.71% (p=0.008 n=5)
MatchParallel/Easy0/1M-4 74284.00n ± ∞ ¹ 41354.00n ± ∞ ¹ -44.33% (p=0.008 n=5) 79.92n ± ∞ ¹ -99.89% (p=0.008 n=5)
MatchParallel/Easy0/32M-4 2382580.00n ± ∞ ¹ 1320813.00n ± ∞ ¹ -44.56% (p=0.008 n=5) 80.95n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Easy0i/16-4 1.711n ± ∞ ¹ 937.600n ± ∞ ¹ +54698.36% (p=0.008 n=5) 81.650n ± ∞ ¹ +4672.06% (p=0.008 n=5)
MatchParallel/Easy0i/32-4 339.90n ± ∞ ¹ 945.70n ± ∞ ¹ +178.23% (p=0.008 n=5) 81.28n ± ∞ ¹ -76.09% (p=0.008 n=5)
MatchParallel/Easy0i/1K-4 10107.00n ± ∞ ¹ 992.20n ± ∞ ¹ -90.18% (p=0.008 n=5) 81.21n ± ∞ ¹ -99.20% (p=0.008 n=5)
MatchParallel/Easy0i/32K-4 451362.00n ± ∞ ¹ 1422.00n ± ∞ ¹ -99.68% (p=0.008 n=5) 81.38n ± ∞ ¹ -99.98% (p=0.008 n=5)
MatchParallel/Easy0i/1M-4 14439204.00n ± ∞ ¹ 41325.00n ± ∞ ¹ -99.71% (p=0.008 n=5) 80.67n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Easy0i/32M-4 563552821.00n ± ∞ ¹ 1322234.00n ± ∞ ¹ -99.77% (p=0.008 n=5) 80.94n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Easy1/16-4 1.712n ± ∞ ¹ 940.700n ± ∞ ¹ +54847.43% (p=0.008 n=5) 82.050n ± ∞ ¹ +4692.64% (p=0.008 n=5)
MatchParallel/Easy1/32-4 18.04n ± ∞ ¹ 944.10n ± ∞ ¹ +5133.37% (p=0.008 n=5) 81.89n ± ∞ ¹ +353.94% (p=0.008 n=5)
MatchParallel/Easy1/1K-4 256.50n ± ∞ ¹ 1007.00n ± ∞ ¹ +292.59% (p=0.008 n=5) 81.64n ± ∞ ¹ -68.17% (p=0.008 n=5)
MatchParallel/Easy1/32K-4 11781.00n ± ∞ ¹ 1424.00n ± ∞ ¹ -87.91% (p=0.008 n=5) 81.45n ± ∞ ¹ -99.31% (p=0.008 n=5)
MatchParallel/Easy1/1M-4 407922.00n ± ∞ ¹ 41413.00n ± ∞ ¹ -89.85% (p=0.008 n=5) 81.57n ± ∞ ¹ -99.98% (p=0.008 n=5)
MatchParallel/Easy1/32M-4 13077618.00n ± ∞ ¹ 1326004.00n ± ∞ ¹ -89.86% (p=0.008 n=5) 81.48n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Medium/16-4 1.719n ± ∞ ¹ 937.100n ± ∞ ¹ +54414.25% (p=0.008 n=5) 79.930n ± ∞ ¹ +4549.80% (p=0.008 n=5)
MatchParallel/Medium/32-4 284.90n ± ∞ ¹ 948.90n ± ∞ ¹ +233.06% (p=0.008 n=5) 79.97n ± ∞ ¹ -71.93% (p=0.008 n=5)
MatchParallel/Medium/1K-4 9223.00n ± ∞ ¹ 1003.00n ± ∞ ¹ -89.13% (p=0.008 n=5) 80.01n ± ∞ ¹ -99.13% (p=0.008 n=5)
MatchParallel/Medium/32K-4 469468.00n ± ∞ ¹ 1418.00n ± ∞ ¹ -99.70% (p=0.008 n=5) 79.45n ± ∞ ¹ -99.98% (p=0.008 n=5)
MatchParallel/Medium/1M-4 14414208.00n ± ∞ ¹ 41372.00n ± ∞ ¹ -99.71% (p=0.008 n=5) 80.16n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Medium/32M-4 571771553.00n ± ∞ ¹ 1320563.00n ± ∞ ¹ -99.77% (p=0.008 n=5) 79.68n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Hard/16-4 1.717n ± ∞ ¹ 940.800n ± ∞ ¹ +54693.24% (p=0.008 n=5) 81.440n ± ∞ ¹ +4643.16% (p=0.008 n=5)
MatchParallel/Hard/32-4 487.00n ± ∞ ¹ 949.10n ± ∞ ¹ +94.89% (p=0.008 n=5) 81.31n ± ∞ ¹ -83.30% (p=0.008 n=5)
MatchParallel/Hard/1K-4 14814.00n ± ∞ ¹ 1002.00n ± ∞ ¹ -93.24% (p=0.008 n=5) 81.13n ± ∞ ¹ -99.45% (p=0.008 n=5)
MatchParallel/Hard/32K-4 685271.00n ± ∞ ¹ 1423.00n ± ∞ ¹ -99.79% (p=0.008 n=5) 81.37n ± ∞ ¹ -99.99% (p=0.008 n=5)
MatchParallel/Hard/1M-4 21979040.00n ± ∞ ¹ 41376.00n ± ∞ ¹ -99.81% (p=0.008 n=5) 81.13n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Hard/32M-4 1382894393.00n ± ∞ ¹ 1321072.00n ± ∞ ¹ -99.90% (p=0.008 n=5) 81.32n ± ∞ ¹ -100.00% (p=0.008 n=5)
MatchParallel/Hard1/16-4 1303.00n ± ∞ ¹ 1020.00n ± ∞ ¹ -21.72% (p=0.008 n=5) 86.51n ± ∞ ¹ -93.36% (p=0.008 n=5)
MatchParallel/Hard1/32-4 2526.00n ± ∞ ¹ 1094.00n ± ∞ ¹ -56.69% (p=0.008 n=5) 97.66n ± ∞ ¹ -96.13% (p=0.008 n=5)
MatchParallel/Hard1/1K-4 77806.0n ± ∞ ¹ 3413.0n ± ∞ ¹ -95.61% (p=0.008 n=5) 633.1n ± ∞ ¹ -99.19% (p=0.008 n=5)
MatchParallel/Hard1/32K-4 3519.23µ ± ∞ ¹ 97.97µ ± ∞ ¹ -97.22% (p=0.008 n=5) 17.12µ ± ∞ ¹ -99.51% (p=0.008 n=5)
MatchParallel/Hard1/1M-4 121966.5µ ± ∞ ¹ 3122.6µ ± ∞ ¹ -97.44% (p=0.008 n=5) 517.1µ ± ∞ ¹ -99.58% (p=0.008 n=5)
MatchParallel/Hard1/32M-4 6725.29m ± ∞ ¹ 100.80m ± ∞ ¹ -98.50% (p=0.008 n=5) 16.86m ± ∞ ¹ -99.75% (p=0.008 n=5)Most benchmarks from the standard library are similar to Find, testing simple expressions with small input.
In all of these, the standard library performs much better. To reiterate the guidance at the top of this README,
if you only use simple expressions with small input, you should not use this library.
The compilation benchmarks show that re2 is much slower to compile expressions than the standard library - this is more than just the overhead of foreign function invocation. This likely results in the improved performance at runtime in other cases. Because memory allocated in Wasm or cgo is essentially "off-heap", it is difficult to measure allocation overhead compared to standard library. More investigation needs to be done, but we expect the memory overhead for all approaches to be similar.
The match benchmarks show the performance tradeoffs for complexity vs input size. We see the standard
library perform the best with low complexity and size, but for high complexity or high input size,
go-re2 with WebAssembly outperforms, often significantly. Notable is Hard1, where even on the smallest
size this library outperforms. The expression is ABCD|CDEF|EFGH|GHIJ|IJKL|KLMN|MNOP|OPQR|QRST|STUV|UVWX|WXYZ,
a simple OR of literals - re2 has the concept of regex sets and likely is able to optimize this in a
special way. The CoreRuleSet contains many expressions of a form like this - this possibly indicates good
performance in real world use cases.