[Demo] Low-latency Spark Queries on Updatable Data
Alexandru Uta, Bogdan Ghiț, Ankur Dave, Peter A. Boncz · 2019
As data science gets deployed more and more into operational applications, it becomes important for data science frameworks to be able to perform computations in interactive, sub-second time. Indexing and caching are two key techniques that can make interactive query processing on large datasets possible. In this demo, we show the design, implementation and performance of a new indexing abstraction in Apache Spark, called the Indexed DataFrame. This is a cached DataFrame that incorporates an index to support fast lookup and join operations, and supports updates with multi-version concurrency. We demonstrate the Indexed Dataframe on a social network dataset using microbenchmarks and real-world graph processing queries, in datasets that are continuously growing.