DryadLINQ: a system for general-purpose distributed data-parallel computing using a high-level language

DryadLINQ is a system and a set of language extensions that enable a new programming model for large scale dis-tributed computing. It generalizes previous execution en-vironments such as SQL, MapReduce, and Dryad in two ways: by adopting an expressive data model of strongly typed.NET objects; and by supporting general-purpose imperative and declarative operations on datasets within a traditional high-level programming language. A DryadLINQ program is a sequential program com-posed of LINQ expressions performing arbitrary side-effect-free transformations on datasets, and can be writ-ten and debugged using standard.NET development tools. The DryadLINQ system automatically and trans-parently translates the data-parallel portions of the pro-gram into a distributed execution plan which is passed to the Dryad execution platform. Dryad, which has been in continuous operation for several years on production clusters made up of thousands of computers, ensures ef-ficient, reliable execution of this plan. We describe the implementation of the DryadLINQ compiler and runtime. We evaluate DryadLINQ on a varied set of programs drawn from domains such as web-graph analysis, large-scale log mining, and machine learning. We show that excellent absolute performance can be attained—a general-purpose sort of 1012 Bytes of data executes in 319 seconds on a 240-computer, 960-disk cluster—as well as demonstrating near-linear scal-ing of execution time on representative applications as we vary the number of computers used for a job. 1