The BSD TAR (Tape Archive) format is a widely-used file format for archiving and compressing collections of files and directories. It was originally developed for backing up data to sequential access devices like magnetic tapes, but is now commonly used for distributing software packages and creating backup archives on various storage media. The TAR format allows multiple files to be bundled into a single archive file while preserving directory structures, file attributes, and permissions.
A TAR archive consists of a series of file headers and file data blocks concatenated together. Each file in the archive is represented by a 512-byte header block followed by the file's data, which is padded to a multiple of 512 bytes. The header block contains metadata about the file, such as its name, size, ownership, permissions, and modification timestamps.
The file header block has a fixed structure with fields of predefined sizes. Some of the key fields include:
- File name (100 bytes): The name of the file, typically limited to 255 characters, terminated by a null byte.
- File mode (8 bytes): The file's permissions and type, stored as an octal number.
- Owner's user ID (8 bytes): The numeric user ID of the file's owner.
- Group's user ID (8 bytes): The numeric group ID of the file's owner.
- File size (12 bytes): The size of the file in bytes, stored as an octal number.
- Modification time (12 bytes): The timestamp of the file's last modification, stored as the number of seconds since January 1, 1970, in octal.
- Header checksum (8 bytes): A checksum of the header block, used to detect corruption.
Following the header block, the file's data is stored in contiguous 512-byte blocks. If the file size is not a multiple of 512 bytes, the last block is padded with null bytes. The end of the archive is marked by two consecutive 512-byte blocks filled with null bytes.
One of the limitations of the original TAR format is that it does not support file sizes larger than 8 GB due to the 12-byte file size field. To overcome this limitation, later extensions like the POSIX.1-2001 (pax) format introduced additional header fields to support larger file sizes.
The TAR format itself does not provide data compression. However, it is common practice to compress TAR archives using compression algorithms like gzip, bzip2, or xz. The resulting files are often given extensions like .tar.gz, .tgz, .tar.bz2, .tbz2, .tar.xz, or .txz to indicate the compression method used.
Creating and extracting TAR archives is supported by most operating systems and can be done using command-line tools or graphical user interfaces. On Unix-like systems, the tar command is commonly used. For example:
- To create a TAR archive: `tar -cf archive.tar file1 file2 directory/`
- To extract a TAR archive: `tar -xf archive.tar`
- To create a compressed TAR archive: `tar -czf archive.tar.gz file1 file2 directory/`
In addition to the basic TAR format, there are several variations and extensions, such as the GNU TAR format, which adds support for sparse files, long file names, and extended attributes. These extensions provide additional functionality while maintaining compatibility with the basic TAR format.
The simplicity and portability of the TAR format have contributed to its widespread adoption across different platforms and use cases. It remains a popular choice for archiving, backup, and software distribution, often in combination with compression methods to reduce storage requirements and transmission times.
File compression reduces redundancy so the same information takes fewer bits. The upper bound on how far you can go is governed by information theory: for lossless compression, the limit is the entropy of the source (see Shannon’s source coding theorem and his original 1948 paper “A Mathematical Theory of Communication”). For lossy compression, the trade-off between rate and quality is captured by rate–distortion theory.
Most compressors have two stages. First, a model predicts or exposes structure in the data. Second, a coder turns those predictions into near-optimal bit patterns. A classic modeling family is Lempel–Ziv: LZ77 (1977) and LZ78 (1978) detect repeated substrings and emit references instead of raw bytes. On the coding side, Huffman coding (see the original paper 1952) assigns shorter codes to more likely symbols. Arithmetic coding and range coding are finer-grained alternatives that squeeze closer to the entropy limit, while modern Asymmetric Numeral Systems (ANS) achieves similar compression with fast table-driven implementations.
DEFLATE (used by gzip, zlib, and ZIP) combines LZ77 with Huffman coding. Its specs are public: DEFLATE RFC 1951, zlib wrapper RFC 1950, and gzip file format RFC 1952. Gzip is framed for streaming and explicitly does not attempt to provide random access. PNG images standardize DEFLATE as their only compression method (with a max 32 KiB window), per the PNG spec “Compression method 0… deflate/inflate… at most 32768 bytes” and W3C/ISO PNG 2nd Edition.
Zstandard (zstd): a newer general-purpose compressor designed for high ratios with very fast decompression. The format is documented in RFC 8878 (also HTML mirror) and the reference spec on GitHub. Like gzip, the basic frame doesn’t aim for random access. One of zstd’s superpowers is dictionaries: small samples from your corpus that dramatically improve compression on many tiny or similar files (see python-zstandard dictionary docs and Nigel Tao’s worked example). Implementations accept both “unstructured” and “structured” dictionaries (discussion).
Brotli: optimized for web content (e.g., WOFF2 fonts, HTTP). It mixes a static dictionary with a DEFLATE-like LZ+entropy core. The spec is RFC 7932, which also notes a sliding window of 2WBITS−16 with WBITS in [10, 24] (1 KiB−16 B up to 16 MiB−16 B) and that it does not attempt random access. Brotli often beats gzip on web text while decoding quickly.
ZIP container: ZIP is a file archive that can store entries with various compression methods (deflate, store, zstd, etc.). The de facto standard is PKWARE’s APPNOTE (see APPNOTE portal, a hosted copy, and LC overviews ZIP File Format (PKWARE) / ZIP 6.3.3).
LZ4 targets raw speed with modest ratios. See its project page (“extremely fast compression”) and frame format. It’s ideal for in-memory caches, telemetry, or hot paths where decompression must be near RAM speed.
XZ / LZMA push for density (great ratios) with relatively slow compression. XZ is a container; the heavy lifting is typically LZMA/LZMA2 (LZ77-like modeling + range coding). See .xz file format, the LZMA spec (Pavlov), and Linux kernel notes on XZ Embedded. XZ usually out-compresses gzip and often competes with high-ratio modern codecs, but with slower encode times.
bzip2 applies the Burrows–Wheeler Transform (BWT), move-to-front, RLE, and Huffman coding. It’s typically smaller than gzip but slower; see the official manual and man pages (Linux).
“Window size” matters. DEFLATE references can only look back 32 KiB (RFC 1951 and PNG’s 32 KiB cap noted here). Brotli’s window ranges from about 1 KiB to 16 MiB (RFC 7932). Zstd tunes window and search depth by level (RFC 8878). Basic gzip/zstd/brotli streams are designed for sequential decoding; the base formats don’t promise random access, though containers (e.g., tar indexes, chunked framing, or format-specific indexes) can layer it on.
The formats above are lossless: you can reconstruct exact bytes. Media codecs are often lossy: they discard imperceptible detail to hit lower bitrates. In images, classic JPEG (DCT, quantization, entropy coding) is standardized in ITU-T T.81 / ISO/IEC 10918-1. In audio, MP3 (MPEG-1 Layer III) and AAC (MPEG-2/4) rely on perceptual models and MDCT transforms (see ISO/IEC 11172-3, ISO/IEC 13818-7, and an MDCT overview here). Lossy and lossless can coexist (e.g., PNG for UI assets; Web codecs for images/video/audio).
Theory: Shannon 1948 · Rate–distortion · Coding: Huffman 1952 · Arithmetic coding · Range coding · ANS. Formats: DEFLATE · zlib · gzip · Zstandard · Brotli · LZ4 frame · XZ format. BWT stack: Burrows–Wheeler (1994) · bzip2 manual. Media: JPEG T.81 · MP3 ISO/IEC 11172-3 · AAC ISO/IEC 13818-7 · MDCT.
Bottom line: choose a compressor that matches your data and constraints, measure on real inputs, and don’t forget the gains from dictionaries and smart framing. With the right pairing, you can get smaller files, faster transfers, and snappier apps — without sacrificing correctness or portability.
File compression is a process that reduces the size of a file or files, typically to save storage space or speed up transmission over a network.
File compression works by identifying and removing redundancy in the data. It uses algorithms to encode the original data in a smaller space.
The two primary types of file compression are lossless and lossy compression. Lossless compression allows the original file to be perfectly restored, while lossy compression enables more significant size reduction at the cost of some loss in data quality.
A popular example of a file compression tool is WinZip, which supports multiple compression formats including ZIP and RAR.
With lossless compression, the quality remains unchanged. However, with lossy compression, there can be a noticeable decrease in quality since it eliminates less-important data to reduce file size more significantly.
Yes, file compression is safe in terms of data integrity, especially with lossless compression. However, like any files, compressed files can be targeted by malware or viruses, so it's always important to have reputable security software in place.
Almost all types of files can be compressed, including text files, images, audio, video, and software files. However, the level of compression achievable can significantly vary between file types.
A ZIP file is a type of file format that uses lossless compression to reduce the size of one or more files. Multiple files in a ZIP file are effectively bundled together into a single file, which also makes sharing easier.
Technically, yes, although the additional size reduction might be minimal or even counterproductive. Compressing an already compressed file might sometimes increase its size due to metadata added by the compression algorithm.
To decompress a file, you typically need a decompression or unzipping tool, like WinZip or 7-Zip. These tools can extract the original files from the compressed format.