Google snappy compression example code#
Google snappy compression example series#

The first data is unchanged during encoding, and other data are converted into the delta of the previous data. The algorithm is greatly affected by data fluctuation, the more severe the fluctuation, the worse the compression effect.ĭifferential encoding is also called incremental encoding. If the result is the same, only the storage A 0 to store the XORed result if the result is different. The first value is not compressed, and the following values are the result of XOR (exclusive OR) with the first value. This algorithm is a specific algorithm designed in combination with the data characteristics that follow the IEEE754 standard floating-point storage format.

The disadvantage is also obvious, the data compression effect with low repetition is not good. Its advantages are simplicity, high speed, and the ability to compress continuous and highly repetitive data into small units. After RLE, the string can be compressed into 4A3B2C1D4E. For example, a set of string “AAAABBBCCDEEEE” consists of 4 A, 3 B, 2 C, 1 D, and 4 E.

Google snappy compression example code#

This mechanism is also useful because the prefix code makes decoding of the bitstream unambiguous.Ī lossless data compression technique independent of the nature of the data, based on “replacing the original data that recurs continuously with a code of varying length” to achieve compression. For example, if 0 is the prefix code for our first symbol, A, then no other symbol in the alphabet can start with 0. It should be noted that a prefix code here represents a value, and it is necessary to ensure that the prefix code of each symbol in the alphabet does not become the prefix of another symbol prefix code. Huffman coding works by finding the optimal prefix code for a given alphabet. This is one of the most widespread compression schemes, first described in the 1850s by David Huffman in his paper “A Method for Constructing Minimal Redundancy Codes”.

Google snappy compression example series#

Time Series CompressionĬommonly used compression methods for time series data are described as follows: Time series compression is particularly important due to the massive scale of datasets combined with the highly compressible nature of the data, making it a smart investment. The importance of data compression is in saving storage resources and reducing the associated costs. Modern datasets grow quickly in size, requiring increasingly large storage systems to contain them. Common lossy compression methods include predictive codecs, fractal compression, wavelet compression, JPEG, and MPEG. However, lossy compression enables high compression ratios. This compression method is used in scenarios where data accuracy is not important, such as compressing multimedia files.

On the other hand, with lossy compression, the compressed data cannot be reconstructed into the original data – only into an approximation of that data.

Common lossless compression methods include differential coding, run-length encoding (RLE), Huffman coding, LZW compression, and arithmetic coding. However, lossless methods have relatively low compression ratios. It may also be used for multimedia compression. This compression method is used in scenarios where data accuracy is important, such as compressing entire hard drives or compressing executable files.

Losslessly compressed data can be completely reconstructed after decompression into the original raw data.

A compression method that alters data is considered lossy. If the original data and decompressed data are exactly the same, the compression method can be considered lossless. These rules can be based on content – the similarities between adjacent frames in a video, for example on representation, as in entropy coding and transform coding or on bitrate, as in differential compression and deep compression. There is no single compression method or algorithm that is optimal for all data instead, an appropriate method is chosen based on the characteristics of the data being compressed.ĭata compression is a summary of the trends and rules found in data. In essence, this process trades compute time for storage space – while data compression enables more data to be stored on a storage medium, more compute resources are required to read and write compressed data. The purpose of compression is to represent the given data in as few bits as possible.