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This is a cgo wrapper around the LZO real-time compression library.lzopack.go is a sample program that demonstrates using the library. It is reimplementation of, and compatible with, the 'lzopack.c' example distributed with LZO.
Python library for indexing block offsets within LZO compressed files. The implementation is largely based on that of the Hadoop Library. Index files are used to allow Hadoop to split a single file compressed with LZO into several chunks for parallel processing.Since LZO is a block based compression algorithm, we can split the file along the lines of blocks and decompress each block on it's own. The index is a file containing byte offsets for each block in the original LZO file.
Consumes records from an [Amazon Kinesis][kinesis] stream, compresses them using [splittable LZO][hadoop-lzo], and writes them to S3.The records are treated as raw byte arrays. [Elephant Bird's][elephant-bird] `BinaryBlockWriter` class is used to serialize them as a [Protocol Buffers][protobufs] array (so it is clear where one record ends and the next begins) before compressing them.The compression process generates both compressed .lzo files and small .lzo.index files. Each index file contain t
A research library with integer compression schemes. It is broadly applicable to the compression of arrays of 32-bit integers where most integers are small. The library seeks to exploit SIMD instructions (SSE) whenever possible.This library can decode at least 4 billions of compressed integers per second on most desktop or laptop processors. That is, it can decompress data at a rate of 15 GB/s. This is significantly faster than generic codecs like gzip, LZO, Snappy or LZ4.
As the name suggests, this is a C/C++ library for fast compression and intersection of lists of sorted integers using SIMD instructions. The library focuses on innovative techniques and very fast schemes, with particular attention to differential coding. It introduces new SIMD intersections schemes such as SIMD Galloping.This library can decode at least 4 billions of compressed integers per second on most desktop or laptop processors. That is, it can decompress data at a rate of 15 GB/s. This is significantly faster than generic codecs like gzip, LZO, Snappy or LZ4.