A Review: Vertical Partitioning Algorithms to Handle Dataload on Distributed Databases

Abstract

Relational databases are widely used in many applications to store data. But there are many problems with relational databases like scalability, handling real time data and handling unstructured data like data on web is not properly structured, it is semi structured or unstructured. To overcome these problems non-relational databases come in to existence. Non-relational databases are growing these days.. Non relational databases deals with the concept of partitioning to handle the different data load on distributed machines. Non relational databases deals with vertical partitioning method which is based on hash partitioning, range partitioning, list partitioning to handle the better data load into some extent. This paper deals with vertical partitioning method as vertical partitioning is applied in three contexts: a database stored on devices of a single type, a database stored in different memory levels, and a distributed database. In distributed databases, fragment allocation should maximize the amount of local transaction process. In this paper, we study on distributed databases and summarizes the problems of data fragmentation, allocation and replication in distributed database.

Keywords- Relational databases, vertical partitioning method, distributed database, fragment allocation, dataload.