Open AccessData Partitioning in Mongo DB with Cloud
Author(s) -
Aakanksha Jumle,
Swati Ahirrao
Publication year - 2018
Publication title -
international journal of advances in applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2722-2594
pISSN - 2252-8814
DOI - 10.11591/ijaas.v7.i1.pp21-28
Subject(s) - scalability , cloud computing , computer science , nosql , workload , benchmark (surveying) , throughput , database , distributed computing , operating system , geodesy , wireless , geography
Cloud computing offers various and useful services like IAAS, PAAS SAAS for deploying the applications at low cost. Making it available anytime anywhere with the expectation to be it scalable and consistent. One of the technique to improve the scalability is Data partitioning. The alive techniques which are used are not that capable to track the data access pattern. This paper implements the scalable workload-driven technique for polishing the scalability of web applications. The experiments are carried out over cloud using NoSQL data store MongoDB to scale out. This approach offers low response time, high throughput and less number of distributed transaction. The results of partitioning technique is conducted and evaluated using TPC-C benchmark.