Multi Query Optimization in GLADE

TL;DR

This paper proposes a method for multi-query optimization in GLADE by generating shared join plans to maximize data sharing and reduce retrieval time in distributed data analytics systems.

Contribution

It introduces a technique for creating optimal join plans that enable multiple queries to be executed simultaneously, enhancing efficiency in GLADE.

Findings

Shared join plans increase query processing efficiency.

Maximized data sharing reduces overall data retrieval.

Improved parallelization in distributed systems.

Abstract

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.