|uglify-js||228.72 kB||BSD-2-Clause||12 Years||23 Oct 2022|
|archiver||10.14 kB||MIT||11 Years||4 Sep 2023|
|terser-webpack-plugin||18.21 kB||MIT||5 Years||17 May 2023|
|yauzl||18.26 kB||MIT||9 Years||3 Jul 2018|
|extract-zip||4.26 kB||BSD-2-Clause||9 Years||10 Jun 2020|
|cssnano||2.81 kB||MIT||8 Years||30 Apr 2023|
|file-type||21.57 kB||MIT||9 Years||4 Jun 2023|
|adm-zip||22.37 kB||MIT||11 Years||20 Dec 2022|
|jszip||190.51 kB||(MIT OR GPL-3.0-or-later)||10 Years||2 Aug 2022|
|compressible||3 kB||MIT||9 Years||6 Jan 2020|
|csso||164.22 kB||MIT||12 Years||10 Aug 2022|
|lz-string||36.04 kB||MIT||10 Years||4 Mar 2023|
|html-minifier||26.12 kB||MIT||12 Years||1 Apr 2019|
|decompress||3.2 kB||MIT||10 Years||1 Apr 2020|
|imagemin||3.18 kB||MIT||10 Years||11 Aug 2021|
Data compression libraries are particularly useful in scenarios where large amounts of data need to be transferred over a network or stored. Here's a quick rundown of major use cases:
Efficient Storage: Data compression libraries allow developers to reduce the size of large data files, permitting more efficient storage utilization.
Faster Data Transfer: Reduced data size also contributes to more efficient network data transfer, minimizing bandwidth utilization and decreasing the time it takes to transmit data across networks.
Data compression libraries essentially perform two main functions: compression and decompression. The level of sophistication and methods used can vary. Here's a generic overview of these functionalities:
Compression: The primary function of these libraries is to compress data into a format that takes up less digital space, this could be lossless compression where data can be perfectly reconstructed or lossy compression where some data is lost for even smaller sizes.
Decompression: This is the process of reversing the compression, taking a compressed file, and returning it to its original form.
Common algorithms used by these libraries include Huffman encoding, Run length encoding, and Block sorting.
There are some pitfalls and points to consider when working with data compression libraries:
Lossy vs. Lossless: The type of compression the library uses matters. Lossless is the go-to for most data as it preserves data integrity, while Lossy may be a choice where absolute data fidelity is not essential, like in image or audio compression.
Compression Ratio and Time: There is a trade-off between compression ratio (how small the data can be made) and how fast the compression occurs. Higher compression ratios will take longer to process.
Memory Usage: Some data compression libraries might consume more memory, thus negatively impacting the performance of the application.
It's vital to have a thorough understanding of what you're trying to achieve with data compression and the limitations of the libraries you're considering, to prevent potential issues down the line.