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Parallel Aggregation on Sharded Clusters

T. Mothilal1 , P. Anil Kumar2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-9 , Page no. 39-43, Sep-2015

Online published on Oct 01, 2015

Copyright © T. Mothilal , P. Anil Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: T. Mothilal , P. Anil Kumar, “Parallel Aggregation on Sharded Clusters,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.39-43, 2015.

MLA Style Citation: T. Mothilal , P. Anil Kumar "Parallel Aggregation on Sharded Clusters." International Journal of Computer Sciences and Engineering 3.9 (2015): 39-43.

APA Style Citation: T. Mothilal , P. Anil Kumar, (2015). Parallel Aggregation on Sharded Clusters. International Journal of Computer Sciences and Engineering, 3(9), 39-43.

BibTex Style Citation:
@article{Mothilal_2015,
author = {T. Mothilal , P. Anil Kumar},
title = {Parallel Aggregation on Sharded Clusters},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2015},
volume = {3},
Issue = {9},
month = {9},
year = {2015},
issn = {2347-2693},
pages = {39-43},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=637},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=637
TI - Parallel Aggregation on Sharded Clusters
T2 - International Journal of Computer Sciences and Engineering
AU - T. Mothilal , P. Anil Kumar
PY - 2015
DA - 2015/10/01
PB - IJCSE, Indore, INDIA
SP - 39-43
IS - 9
VL - 3
SN - 2347-2693
ER -

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Abstract

The data is organized by databases with the help of database management systems. DBMS is the collection of schemas, queries and other objects. To aggregate the data DBMS used Cartesian products between two or more tables and produce a result in a logical table. Where data is increasing rapidly day by day, so writing joins on large tables is difficult to data analysts and manage complex queries on large scale table is quite difficult to DBMS. To reduce complexity of manipulating large data schemaless databases are introduced. MongoDB process schemaless data and having more use cases to achieve parallel processing on data. Aggregation is one of the function which is applying on the data. To get fastest aggregation results use mongodb sharded cluster and mareduce.

Key-Words / Index Term

NOSQL, MongoDB, Sharding, Parallelism, MapReduce

References

[1] J. M. Hellerstein, “The case for online aggregation”, Technical Report UCB//CSD-96-908, EECS Computer Science Division, University of California, Berkeley, CA,1996
[2] Jeffrey Dean and Sanjay Ghemawat “MapReduce: Simplified Data Processing on Large Clusters”, OSDI 2014
[3] Anju abraha,"A Dynamic Query Form System for Mongodb", SSRG-IJCSE, volume-1 issue-9, Nov 2014.
[4] MongoDB, “http://docs.mongodb.org/manual/”, Thursday, April 30, 2015.
[5] MongoDB, http://www.tutorialspoint.com/mongodb/, Monday, July 6, 2015
[6] Replication, http://stackoverflow.com, Tuesday, August 11, 2015
[7] Sharding, http://gist.github.com, Monday, August 17, 2015.