Compression Options in Hadoop - A Tale of Tradeoffs
Yahoo! is one of the most-visited web sites in the world. It runs one of the largest private cloud infrastructures, one that operates on petabytes of data every day. Being able to store and manage that data well is essential to the efficient functioning of Yahoo!`s Hadoop clusters. A key component that enables this efficient operation is data compression. With regard to compression algorithms, the
Quick Benchmark: Gzip vs Bzip2 vs LZMA vs XZ vs LZ4 vs LZO - CatchChallenger wiki
Selected archives I have selected: Source of the kernel to test source compression Stream protocol with flush Test conditions Tests were run on a desktop: Intel Core i5 CPU 750 at 2.67GHz 8GB of DDR3 memory tmpfs as ram disk is used Linux kernel 3.3.2, gentoo amd64 CFLAGS: -pipe -O2 -g -floop-block -floop-interchange -fgraphite bzip2-1.0.6-r3, xz-utils-5.0.3, gzip-1.4 Only normal mode will be test
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