dsJSON: A Distributed SQL JSON Processor

Abstract

The popularity of JSON as a data interchange format resulted in big amounts of datasets available for processing. Users would like to analyze this data using SQL queries but existing distributed systems limit their users to only two specific formats, JSONLine and GeoJSON. The complexity of JSON schema makes it challenging to parse arbitrary files in a modern distributed system while producing records with unified schema that can be processed with SQL. To address these challenges, this paper introduces dsJSON, a state-of-the-art distributed JSON processor that overcomes limitations in existing systems and scales to big and complex data. dsJSON introduces the projection tree, a novel data structure that applies selective parsing of nested attributes to produce records that are ready for SQL processors. The key objective of the projection tree is to parse a big JSON file in parallel to produce records with a unified schema that can be processed with SQL. dsJSON is integrated into SparkSQL which enables users to run arbitrary SQL queries on complex JSON files. It also pushes projection and filter down into the parser for full integration between the parser and the processor. Experiments on up-to two terabytes of real data show that dsJSON performs several times faster than existing systems. It can also efficiently parse extremely large files not supported by existing distributed parsers