Gzip

program to compress multiple files. A gzip file (described in the table below) consists of a 10-byte header, optional extra header fields, Deflate-compressed data, and an 8-byte trailer. Although multiple streams may be concatenated (gzipped files are simply decompressed concatenated as if they were originally one file), normally only a single file is compressed. Compressed archives are typically created by assembling collections of files into a single tar archive (also called tarball), and then compressing that archive with gzip. The final compressed file usually has the extension .tar.gz, .tgz, .gz, or .gzip. gzip is not to be confused with the ZIP archive format, which also uses DEFLATE. The ZIP format can hold collections of files without an external archiver, but is less compact than compressed tarballs holding the same data, because it compresses files individually and cannot take advantage of redundancy between files (solid compression). The gzip file format is also not to be confused with that of the compress utility, based on LZW, with extension .Z; however, the gunzip utility is able to decompress .Z files. File structure Endianness is little-endian. ==Implementations==

Various implementations of the program have been written. The most commonly known is the GNU Project's implementation using Lempel-Ziv coding (LZ77). OpenBSD's version of gzip is actually the compress program, to which support for the gzip format was added in OpenBSD 3.4. The "g" in this specific version stands for gratis. FreeBSD, DragonFly BSD and NetBSD use a BSD-licensed implementation instead of the GNU version; it is actually a command-line interface for zlib intended to be compatible with the GNU implementations' options. These implementations originally come from NetBSD, and support decompression of bzip2 and the Unix pack format. An alternative compression program achieving 3-8% better compression is Zopfli. It achieves gzip-compatible compression using more exhaustive algorithms, at the expense of compression time required. It does not affect decompression time. pigz, written by Mark Adler, is compatible with gzip and speeds up compression by using all available CPU cores and threads. Damage recovery Data in blocks prior to the first damaged part of the archive is usually fully readable. Data from blocks not demolished by damage that are located afterward may be recoverable through difficult workarounds. == Derivatives and other uses ==

The tar utility included in most Linux distributions can extract .tar.gz files by passing the option, e.g., , where -z instructs decompression, -x means extraction, and -f specifies the name of the compressed archive file to extract from. Optionally, -v (verbose) lists files as they are being extracted. The zlib library supports the gzip file format. The gzip format is used in HTTP compression, a technique used to speed up the sending of HTML and other content on the World Wide Web. It is one of the three standard formats for HTTP compression as specified in RFC 2616. This RFC also specifies a zlib format (called "DEFLATE"), which is equal to the gzip format except that gzip adds eleven bytes of overhead in the form of headers and trailers. Still, the gzip format is sometimes recommended over zlib because Internet Explorer does not implement the standard correctly and cannot handle the zlib format as specified in RFC 1950. Since the late 1990s, bzip2, a file compression utility based on a block-sorting algorithm, has gained some popularity as a gzip replacement. It produces considerably smaller files (especially for source code and other structured text), but at the cost of memory and processing time (up to a factor of 4). AdvanceCOMP, Zopfli, libdeflate and 7-Zip can produce gzip-compatible files, using an internal DEFLATE implementation with better compression ratios than gzip itself—at the cost of more processor time compared to the reference implementation. Research published in 2023 showed that simple lossless compression techniques such as gzip could be combined with a k-nearest-neighbor classifier to create an attractive alternative to deep neural networks for text classification in natural language processing. This approach has been shown to equal and in some cases outperform conventional approaches such as BERT due to low resource requirements, e.g. no requirement for GPU hardware. ==See also==