Reynold Xin

2014 has been the most active year of Spark development to date, with major improvements across the entire engine. One particular area where it made great strides was performance: Spark set a new world record in 100TB sorting, beating the previous record held by Hadoop MapReduce by three times, using only one-tenth of the resources; it received a new SQL query engine with a state-of-the-art optimizer; and many of its built-in algorithms became five times faster. In this post, I’ll cover some of the technology behind these improvements as well as new performance work the Apache Spark developer community has done to speed up Spark.

Back in 2010, we at the AMPLab at UC Berkeley designed Spark for interactive queries and iterative algorithms, as these were two major use cases not well served by batch frameworks like MapReduce. As a result, early users were drawn to Spark because of the significant performance improvements in these workloads. However, performance optimization is a never-ending process, and as Spark’s use cases have grown, so have the areas looked at for further improvement. User feedback and detailed measurements helped the Apache Spark developer community to prioritize areas to work in. Starting with the core engine, I’ll cover some of the recent optimizations that have been made. Read more…