Description
Modern data analysis is undergoing a "Big Data" transformation: organizations are generating and gathering more data than ever before, in a variety of formats covering both structured and unstructured data, and employing increasingly sophisticated techniques such as machine learning and graph computation beyond the traditional roll-up and drill-down capabilities provided by SQL. To cope with the big data challenges, we believe that data processing systems will need to provide fine-grained fault recovery across a larger cluster of machines, support both SQL and complex analytics efficiently, and enable real-time computation.
This dissertation builds on Apache Spark, a distributed dataflow engine, and creates three related systems: Spark SQL, Structured Streaming, and GraphX. Spark SQL combines relational and procedural processing through a new API called DataFrame. It also includes an extensible query optimizer to support a wide variety of data sources and analytic workloads. Structured Streaming extends Spark SQL's DataFrame API and query optimizer to automatically incrementalize queries, so users can reason about real-time stream data as batch datasets, and have the same application operate over both stream data and batch data. GraphX recasts graph specific system optimizations as dataflow optimizations, and provides an efficient framework for graph computation on top of Spark.
The three systems have enjoyed wide adoption in industry and academia, and together they laid the foundation for Spark's 2.0 release. They demonstrate the feasibility and advantages of unifying disparate, specialized data systems on top of distributed dataflow systems.