Abstract
Big Data systems manage and process huge volumes of data constantly generated by various technologies in a myriad of formats. Big Data advocates (and preachers) have claimed that, relative to classical, relational/SQL Data Base Management Systems, Big Data technologies such as NoSQL, Hadoop and in-memory data stores perform better. This paper compares data processing performance of two systems belonging to SQL (PostgreSQL/Postgres XL) and Big Data (Hadoop/Hive) camps on a distributed five-node cluster deployed in cloud. Unlike benchmarks in use (YCSB, TPC), a series of R modules were devised for generating random non-aggregate queries on different subschema (with increasing data size) of TPC-H database. Overall performance of the two systems was compared. Subsequently a number of models were developed for relating performance on the system and also on various query parameters such as the number of attributes in SELECT and WHERE clause, number of joins, number of processing rows etc.
APPROXIMATION TECHNIQUES FOR EXECUTION OF AGGREGATE QUERIES ON BIG DATA
