Almost three years ago we announced results of the first ever "petasort" (sorting a petabyte-worth of 100-byte records, following the Sort Benchmark rules). It completed in just over six hours on 4000 computers. Recently we repeated the experiment using 8000 computers. The execution time was 33 minutes, an order of magnitude improvement.
Our sorting code is based on MapReduce, which is a key framework for running multiple processes simultaneously at Google. Thousands of applications, supporting most services offered by Google, have been expressed in MapReduce. While not many MapReduce applications operate at a petabyte scale, some do. Their scale is likely to continue growing quickly. The need to help such applications scale motivated us to experiment with data sets larger than one petabyte. In particular, sorting a ten petabyte input set took 6 hours and 27 minutes to complete on 8000 computers. We are not aware of any other sorting experiment successfully completed at this scale.
We are excited by these results. While internal improvements to the MapReduce framework contributed significantly, a large part of the credit goes to numerous advances in Google's hardware, cluster management system, and storage stack.
What would it take to scale MapReduce by further orders of magnitude and make processing of such large data sets efficient and easy? One way to find out is to join Google’s systems infrastructure team. If you have a passion for distributed computing, are an expert or plan to become one, and feel excited about the challenges of exascale then definitely consider applying for a software engineering position with Google.