Package: hypergrep
HyperGrepNote: This project had been replaced byVectorGrep.
No additional features or enhancements will be made to this library. Due to licensing changes in
Intel Hyperscan starting in 5.5, all future development besides bug fixes will be dedicated to maintaining
theVectorscanversion of this library. Vectorscan/VectorGrep
also provides more options for increasing portability and supporting multiple architectures.HyperGrep is a fast (Hyperspeed) Global Regular Expression Processing library for Python. It uses Intel Hyperscan
to maximize performance, and can be used with multi-threaded or multi-processed applications. While a standard grep
if designed to print, this is designed to allow full control over processing matches. The library supports scanning
plaintext, gzip, and ztsd compressed files for regular expressions, and customizing the action to take when matched.For full information on the amazing performance that can be obtained through Intel Hyperscan with, refer to:HyperscanTable Of ContentsKey FeaturesCompatibilityGetting StartedInstallationExamplesContributeAdvanced GuidesFAQKey FeaturesSimplicityNo experience with Hyperscan required. Provides "grep" styled interfaces.No external dependencies, and no building required (on natively supported platforms).Built in support for compressed and uncompressed files.SpeedUses Hyperscan, a high-performance multiple regex matching library.Performs read and regex operations outside Python.Batches results for Python, reducing overhead (customizable).ParallelismBypasses GIL (Global Interpreter Lock) during read and regex operations to allow proper multithreading.Python consumer threads (callbacks) are able to handle many producer threads (readers).CompatibilitySupports Python 3.10+Supports Linux systems with x86_64 architectureTested on Ubuntu Trusty (14.04) and aboveOther Linux distros may work, but are not guaranteedMay be able to be built on Windows/OSX manuallyMore platforms are planned to be supported (natively) in the futureSome regex constructs are not supported by Hyperscan in order to guarantee stable performanceFor more information refer to:Unsupported ConstructsGetting StartedInstallationInstall HyperGrep via pip:pipinstallhypergrepOr via git clone:gitclone<pathtofork>cdhypergrep
pipinstall.Or build and install from wheel:# Build locally.gitclone<pathtofork>cdhypergrep
makewheel# Push dist/hypergrep*.tar.gz to environment where it will be installed.pipinstalldist/hypergrep*.tar.gzExamplesRead one file with the example single threaded command:# hypergrep/scanner.py <regex> <file>hypergrep/scanner.pypattern./hypergrep/scanner.pyRead multiple files with the multithreaded command (drop in replacement forgrepwhere patterns are compatible):# From install:# hypergrep <regex> <file(s)>hypergreppattern./hypergrep/scanner.py# From package:# hypergrep/multiscanner.py <regex> <file>hypergrep/multiscanner.pypattern./hypergrep/scanner.pyCollect all matches from a file, similar to grep, and perform a custom operation on results:importhypergrepfile="./hypergrep/scanner.py"pattern='pattern'results,return_code=hypergrep.grep(file,[pattern])forindex,lineinresults:print(f'{index}:{line}')Manually scan a file and perform a custom operation on match:importhypergrepfile="./hypergrep/scanner.py"pattern='pattern'defon_match(matches:list,count:int)->None:forindexinrange(count):match=matches[index]line=match.line.decode(errors='ignore')print(f'Custom print:{line.rstrip()}')hypergrep.scan(file,[pattern],on_match)Override thelibhsand/orlibzstdlibraries to use files outside the package.
Must be called before any other usage ofhypergrep:importhypergrephypergrep.configure_libraries(libhs='/home/myuser/libhs.so.mybuild',libzstd='/home/myuser/libzstd.so.mybuild',)ContributingRefer to theContributing Guidefor information on how to contribute to this project.Advanced GuidesRefer toHow Tosfor more advanced topics, such as building the shared library objects.FAQQ: How does HyperGrep compare to other Hyperscan python libraries?A:HyperGrep has a specific goal: provide a high performance "grep" like interface in python,
but with more control. It is not intended to be a full set of bindings to Hyperscan. If you need
full control over the low level backend, there are other python libraries intended for that use case. Here are
a few of the reasons for the focused goal of this library:Simplify developer integration.No experience with Hyperscan required.Familiarity withgrepvariants beneficial, but not required.Avoid messy subprocess chains common in "parallel grep" implementations.Commands likezgrepare actually azcat+grep. This can lead to 3+ processes per file read.Subprocessing is messy in general, best to minimize its use as much as possible.Optimize performance.Reduce callbacks to/from python to reduce overhead.Allow true multithreading during read and regex matching.Provide the pattern matched in multi-regex searches, without having to repeat the search in Python.When it comes to performance, here is an example of the benefit of this design. Due to the performance of
Hyperscan, it is also often faster than nativegrepvariants, even while using python. Scenario setup:2.10GHz Intel x86_64 Processor~17M line file (~300M gzip compressed, ~3G uncompressed).~800 PCRE patterns.Counting only, no extra processing of lines.Each job run 5 times and averaged (lower is better).Scenario (Uncompressed timings in parenthesis)HyperGrepFull bindingszgrep (grep)1~90K matches, 1 pattern8.2s (2.5s)22.8s (15.5s)12.5s (5.2s)2~900K matches, 10 patterns9.7s (3.8s)25.7s (16.8s)19.8s (17.3s)3~15M matches, ~800 patterns44.2s (38.1s)73.5s (57.7s)*4Scenario #3 (x4 files), 1 process (4 threads)49.6s (46.8s)1432.6s (1302.2s)*GNU grep does not allow multiple PCRE patterns natively, and concatenation via "or" failed.