  SQL-on-Hadoop systems, query optimization, data distribution over multiple
nodes and parallelization techniques are few of the areas under extreme
research these days. Big names like Amazon, Google, Microsoft and many more are
working on implementing systems for faster access of data from multiple nodes
reducing data mobility and increasing the parallelization. Queries are
retrieved and reviewed by the database systems in an efficient way in the least
amount of time by the introduction of various parallelization techniques by
running the same query in parallel over different nodes carrying the data.
Apart from multi-threading parallelization, there is another way of
parallelization that can be performed in order to further reduce retrieval time
hence improving the efficiency of the system; parallelization on user queries
on top of a DBMS/RDBMS. In this paper, we will study one such technique of how
multiple queries can run simultaneously on a system in order to increase the
efficiency by reducing the data retrieval from the storage. Maximum sharing of
workload has been performed by generating optimal and ubiquitous join plans for
a set of queries and then fed them to GLADE (Generalized Linear Aggregate
Distribution Engine), a scalable distributed system for large scale data
analytics. Our main work is centered on generating GLADE join plans for a
Multi-Query satisfying maximum number of queries in order to maximize data
sharing and minimize data retrieval for each individual query.
