What was the original vision of Spark? How did it hold up? Matei Zaharia speaks to this, and new developments like DataFrames and Spark SQL, on November 2nd at IBM Research, Yorktown NY.
“The Spark project started at UC Berkeley to provide a general engine for distributed data processing, capable of combining many types of analytics into complex workflows. It was one of the first projects to offer a functional API for big data processing, and it has grown to include the largest integrated standard library for big data, with support for relational queries, streaming, machine learning and graph processing. We discuss lessons learned bringing Spark to developers and building out its library.
In particular, although Spark’s functional API led to concise code, we found that it could limit opportunities for optimization, both in CPU time and memory usage. We have developed a new API called DataFrames that gives significantly more information about data and computations to the engine, through an interface based on records with a known schema. A further extension, Datasets, offers a typed API on top of DataFrames that integrates into Java and Scala in the same way the original Spark API did. Finally, more and more of Spark’s standard libraries are written to take DataFrames / Datasets as input, enabling rich optimizations such as loop fusion and join reordering across libraries. We describe ongoing research at MIT to use this new interface for optimizations. Together, these changes are one of the first attempts go beyond the functional APIs proposed for big data processing while maintaining ease of programming and composabilty.” – Matei Zaharia
Matei Zaharia is an assistant professor of computer science at MIT and CTO of Databricks, the company commercializing Apache Spark. He started the Spark project during his PhD at UC Berkeley. He is broadly interested in large-scale computer systems and networks, and has also contributed to projects including Mesos, Hadoop, Tachyon and Shark.
Things Matei is too modest to tell you: ACM gave Matei the best doctoral dissertation award for 2015, and he received two Best Paper awards at NSDI 2012 and SIGCOMM 2012.