Algorithmic Aspects of Parallel Data Processing

In the last decade or so we have witnessed a growing interest in processing large data sets on large distributed clusters. The idea was pioneered by the MapReduce framework, and has been widely adopted by several other systems, including PigLatin, Hive, Scope, U-SQL, Dremmel, Spark and Myria. A large part of the complex data analysis performed by these systems consists of a sequence of relatively simple query operations, such as joining two or more tables. This survey discusses recent algorithmic developments for distributed data processing. It uses a theoretical model of parallel processing called the Massively Parallel Computation (MPC) model, which is a simplification of the BSP model where the only cost is given by the amount of communication and the number of communication rounds. The survey studies several algorithms for multi-join queries, for sorting, and for matrix multiplication, and discusses their relationships and common techniques applied across the different data processing tasks.