This document compares different compression options for Hadoop clusters including bzip2, gzip, zlib, LZO, LZ4, and Snappy. It discusses the tradeoffs between compression speed, output file size, ability to split files for parallel decompression, and CPU overhead. The optimal compression choice depends on the phase of the MapReduce job and factors like data transfer overhead and hardware capabilities. Compression can reduce storage usage and network load during shuffling and sorting, while slower algorithms may be preferable for initial mapping to enable parallelism.