User:CedarMint/test/test02

Lempel–Ziv–Oberhumer (LZO) is a lossless data compression algorithm that is focused on speed. The classic LZO is available under the terms of the GNU General Public License (GPL v2+). If a better compression is needed or if it is not possible to fulfill any term of the GPL, commercial licenses of LZO versions are obtainable.

LZO is widespread in use, in medical technology, real-time financial services, cars, OTA-solutions, mobile telephones, aircrafts, games, operating systems, databases and by NASA, even on planet Mars.

Design
LZO was developed by Markus Franz Xaver Johannes Oberhumer, based on earlier algorithms by Abraham Lempel and Jacob Ziv. The LZO library implements a number of algorithms with the following characteristics:
 * compression comparable in speed to DEFLATE compression
 * extremly fast decompression
 * requires an additional buffer during compression (of size 8 kB or 64 kB, depending on compression level)
 * requires no additional memory for decompression other than the source and destination buffers
 * allows the user to adjust the balance between compression ratio and compression speed, without affecting the speed of decompression

LZO supports overlapping compression and in-place decompression. As a block compression algorithm, it compresses and decompresses blocks of data. Block size must be the same for compression and decompression. LZO compresses a block of data into matches (a sliding dictionary) and runs of non-matching literals to produce good results on highly redundant data and deals acceptably with non-compressible data, only expanding incompressible data by a maximum of 1/64 of the original size when measured over a block size of at least 1 kB.

See also bouchez.info

Implementations
The original library was written in ANSI C, and it has been made available under the GNU General Public License. Versions of LZO are available for the Perl, Python and Java languages. Various LZO implementations are reported to work under Win32, AIX, ConvexOS, IRIX, Mac OS, Palm OS, PlayStation, Nintendo 64, Wii, Solaris, SunOS, TOS (Atari ST), Linux and VxWorks. LZO is an option for transparent compression in the btrfs and zfs filesystems.

LZO Classic
The LZO Classic (also known as LZO GPL) is the open source variant of the LZO Library. LZO Classic contains a lot of compression algorithms named, LZO1-1, LZO1-A, LZO1-B, LZO1-C, LZO1-F, LZO1-X, LZO1-Y, LZO1-Z, LZO2-A. The most known algorithm is:
 * LZO1-X: due to its ability for easy implementation using miniLZO.

miniLZO
Is a subset of the LZO Classic, containing only the LZO1-X compressor and decompressor functions. You just have to include a single file (no extra linking) to implement a fully working compressor/decompressor.

LZO Professional, LZO Professional Plus, LZO Ultimate
Details of the commercial versions are only available when interested parties sign a strictly non-disclosure agreement.

Superset of LZO Classic which contains all the functions of the LZO Classic and additonally fully speed optmized functions for compression and decompression. LZO Professional also contains proprietary compression algorithms extremly optimized for speed at nearly(?) same compression ratio than the LZO CLassic algorithms.

Users

 * Amazon Redshift (AWS) [2]  [2]  [2]  [2]  [2]  [2]
 * Apache Hadoop [3] [[User:CedarMint
 * BTRFS
 * GRUB
 * HP Vertica [4]  [4]  [4]  [4]  [4]  [4]
 * Linux Kernel [5]  [5]  [5]  [5]  [5]  [5]
 * NASA's FOSS System
 * NASA Mars Rovers ( Spirit,  Opportunity  )


 * NEC Storage - HYDRAstor [9]  [9]  [9]  [9]  [9]  [9]
 * OpenVPN
 * Oracle Database [10] [[User:CedarMint/test/test02#cite%20note-
 * Pure Storage Flash Array [11]  [11]  [11]  [11]  [11]  [11]
 * UBoot
 * UltraVNC

Literature
In books and scientific works LZO is mentioned, used or is a part of investigation – some examples are below.


 * Enes, Jonatan; Expósito, Roberto R.; Tourino, Juan; (2018-02-02) BDWatchdog: real-time monitoring and profiling of Big Data applications and frameworks; in: Future Generation Computer Systems, Volume 87, October 2018
 * Pujara, H.D.; Sharma, Manika; Loss less real-time data compression based on LZO for steady-state Tokamak DAS; in: 6. IAEA technical meeting on control, data acquisition, and remote participation for fusion research; Inuyama (Japan); 4-8 Jun 2007
 * Shirvani, Mirsaeid Hosseini; Rahmani, Amir Masoud; Sahafi, Amir; (2018-07-07) A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: Taxonomy and challenges; in: Journal of King Saud University - Computer and Information Sciences, Volume 32, Issue 3, March 2020
 * Kane, Jason; Yang, Qing; Compression Speed Enhancements to LZO for Multi-Core Systems; in: 2012 IEEE 24th International Symposium on Computer Architecture and High Performance Computing, 24-26 Oct. 2012