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lrzip

github.com

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Spec score 7.7 #16 of 36 in File Compression Software

What it is

lrzip is a free GPL-2.0 command-line utility for compressing large files, usually above 10–50 MB. It first applies an extended rzip pass to reduce long-distance redundancy, then uses a backend such as LZMA, ZPAQ, LZO, GZIP, or BZIP2. Users can favor speed or smaller output, but not both simultaneously. Compression and decompression are heavily multithreaded, with CPU detection, and compression windows can use available RAM. Sliding mmap can exceed physical RAM but may be extremely slow. Encryption uses AES-256-GCM with PBKDF2-HMAC-SHA512 and HKDF; forgotten passwords cannot be recovered. The main program handles single files, while lrztar packages directories. It targets 64-bit Unix-like systems and supports macOS 10.5 or later from version 0.43. Building uses configure and make. Archive output is LRZ or TAR-compressed LRZ, with stable source releases on GitHub.

Spec sheet

Purpose
lrzip is a compression utility designed to compress large files, usually larger than 10–50 MB.Read 9 Oct 2026 · github.com
Compression backends
Available backends include LZMA, ZPAQ, LZO, GZIP, BZIP2, and an option to leave data uncompressed after rzip preparation.Read 9 Oct 2026 · github.com
Downloads and installation
The project README points to GitHub releases for stable source tarballs and documents building from source with configure and make.Read 9 Oct 2026 · github.com
Encryption
The README says the default encryption option uses AES-256-GCM with PBKDF2-HMAC-SHA512 and HKDF, and forgotten passwords cannot be recovered.Read 9 Oct 2026 · github.com
File handling limit
The main lrzip program works on single files; the lrztar wrapper can package directories for compression.Read 9 Oct 2026 · github.com
Integrity check
The README's example decompression command uses an integrity check before decompressing.Read 9 Oct 2026 · github.com
Large-file memory use
lrzip uses available RAM for compression windows and offers a sliding mmap mode that can use windows larger than RAM, though this can be extremely slow.Read 9 Oct 2026 · github.com
License and support
The repository lists a GPL-2.0 license and directs users to GitHub Issues to report bugs.Read 9 Oct 2026 · github.com
Long-range reduction
It uses an extended version of rzip for a first pass that reduces long-distance redundancy before compression.Read 9 Oct 2026 · github.com
macOS support
The FAQ says version 0.43 and later should build on Mac OS X 10.5 or later.Read 9 Oct 2026 · github.com
Parallel processing
The README says lrzip is heavily multithreaded for backend compression and decompression and detects the number of CPUs to use.Read 9 Oct 2026 · github.com
Platform requirement
lrzip targets 64-bit platforms and the build guide calls for an operating system with typical Unix headers and libraries.Read 9 Oct 2026 · github.com
Resource considerations
The README says compression needs substantial memory and a modern processor for best performance, and warns against production server use without CPU and RAM limits.Read 9 Oct 2026 · github.com
Speed and size
The README says users can optimize for compression speed or output size, but not both at once.Read 9 Oct 2026 · github.com

Plans and prices

lrzip Free GPL-2.0 licensed compression utility Read 9 Oct 2026 · github.com

Every lrzip plan and price →

Strong points and limits

Strong points

  • Optimized for large files
  • Multiple compression backends
  • Multithreaded processing
  • AES-256-GCM encryption
  • Directory packaging through lrztar

Limits

  • Speed and size cannot both be optimized
  • Compression needs substantial memory
  • Sliding mmap can be extremely slow
  • Main program handles single files

Who it suits

lrzip suits command-line users handling large files on Linux, POSIX-like systems, or supported macOS versions. It also fits workflows needing encrypted archives or directory packaging through lrztar.

Verdict

lrzip offers large-file compression, encryption, and multithreaded backends as free command-line software. Plan around its memory demands and the speed-versus-size tradeoff.

Where Specifiction places it

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