While traditional relational databases are still used in a large scope of applications, we have seen recently an explosion in the number of a new data bases technologies developed in particular for Big Data serving. Currently the main alternatives to RDMBS are NoSQL databases.

1. Today 26th
April 2016
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2.  Introduction
 Relational
 Non-Relational
 MongoDB
 MySQL
 STS Soft SC Benchmark
 Result and Analysis
 Conclusion
 References
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While traditional relational databases are still used in a large scope of
applications, we have seen recently an explosion in the number of a new
data bases technologies developed in particular for Big Data serving.
Currently the main alternatives to RDMBS are NoSQL databases.
The primary focus of this thesis is to compare and evaluate a
NoSQL(MongoDB) and SQL(MySQL) databases in terms of response time
while performing write, read, and secondary read operations and answer
the question of whether one perform better than the other.
The NoSQL databases were created as a mean to offer high availability at
the price of losing the ACID (Atomic, Consistent, Isolated, Durable)
guarantees of the traditional databases in exchange with keeping a weaker
BASE (Basic Availability, Soft state, Eventual consistency) feature

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5. The idea of the relational model presented by Codd in 1970,
where the relational model emphasized on the idea of
organizing data in the form of two-dimensional table,
"relationships"
SQL is a programming language for special purposes have
been designed to manage data in a relational database
management system.
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6.  The NoSQL database is a whole new approach to manage
data for very large sets of distributed data.
 The data created today is so large and complex it is very
difficult to process in traditional RDBMS.
 NoSQL is widely used for exploring big data. Many Internet
giants, such as Amazon Dynamo, Google BigTable,
LinkedIn Voldemort, Twitter FlockDB and Facebook
Cassandra
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8. MongoDB is an open source non-relational document
oriented (NoSQL) database management system
designed by MongoDB.
In MongoDB the objects are represented by documents
(with JSON like format). The documents with the same
characteristics/properties are grouped together in a
collection. Data in MongoDB has a flexible schema.
Unlike relational databases, MongoDB’s collections do
not enforce document structure.
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9.  MySQL was included as a baseline to represent conventional
RDBMS performance. MySQL, the second most widely used
RDBMS in the world, has been covered heavily in prior
literature and will be left to the reader to pursue.
 MySQL is a commercial relational database management
system designed by Oracle. In order to capture the semantics
of the database, the data (objects) in SQL is represented by a
table (schema).
 The objects with the same number of properties, type and
format are grouped together in a table with a column for each
property, making it structured data. Each row in such a table
represents a different object.
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10. MongoDB
Adobe: uses Mongodb to store petabytes of data.
 e-bay uses mongodb in the search suggestion.
Linkedln uses mongo as its backend DB
MySQL
Booking.com: one of Europe's online hotel travel
reservation agency
Telenor
Friendstar
Motorola
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11. mysql mongodb
Tables and rows Collection of JSON
Column Field
Join operation Embedded document
Sql Mongodb quering language
Table and column required No define schema
Select*from user Db.user.find()
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12. The benchmarks to collect performance results were run
using the STS Soft SC version 3.0.0.
STS Soft Database Benchmark is an open source tools
designed to stress test database indexing technologies with
large data flows.
The test engine of Database Benchmark performs three
tests:
Write
Read
Secondary Read
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14. The benchmark results analyzed here reveals how
the two databases respond to write, read and
secondary read operations in terms of INSERT
and READ.
As mentioned earlier, the primary method of
analyzing the results is through a graph, the time
taken to complete an operation.
The graph is plotted against the varied number of
INSERTED records and READ the records.
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Records MongoDB Write Time(s) MySQL Write Time(s)
100
0 94
50000
14 103
100000
18 105
200000
49 109
300000
77 119
1000000
259 166
2000000
508 227
3000000
674 278
4000000
922 316
5000000
1064 415

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Records MongoDB Read Time(s) MySQL Read Time(s)
100
0 0
50000
0 1
100000
4 2
200000
10 4
300000
13 5
1000000
47 70
2000000
87 133
3000000
114 570
4000000
151 882
5000000
163 1050

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19. Records MongoDB Secondary Read
Time(s)
MySQL Secondary Read Time(s)
100
0 0
50000
1 0
100000
4 2
200000
10 4
300000
12 5
1000000
46 53
2000000
88 114
3000000
114 614
4000000
140 839
5000000
170 1079
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21. Records MongoDB Size in (MB) MySQL Size in (MB)
100
0 0
50000
13.5 3.4
100000
27.1 6.8
200000
54.1 13.6
300000
82.5 20.4
1000000
273.2 68.1
2000000
546.3 130.1
3000000
819.2 201.1
4000000
1092.4 272.1
5000000
1370.1 340.1
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23. The experiments performed in the project tested and
compared how each of the three databases responded to
operation loads per time. The operations used in analyzing
the performance and scaling abilities were WRITE and
READ operations.
The times spent by each database to complete different
magnitude of the operations were recorded. The
measurements and charts presented in the experiment
revealed MongoDB’s READ performance advantage over
MySQL in both read and secondary operations due to its
simple and flexible schema, which made it cope faster with
queries.
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24.  [1] Berg K., Seymour T., and Coel R. History of Databases. International
Journal of Management and Information Services, 17, 2013
 [2] IBM. Big data: Why it Matters to The Midmarket.
http://http://www.ibm.com/midmarket/us/en/article_BusinessAnalytics4_121
2.html.
 [3] Jatana N., Puri S., Ahuja M., Kathuria I., Gosain D., (2012), “A Survey
and Comparison of Relational and Non-Relational Database”, International
Journal of Engineering Re-search & Technology (IJERT), vol. 1, no. 6, pp.1-
5.
 [4] Beynon-Davies P. Database Systems. Palgrave Macmillan, 3rd
edition.
 [5] Cory Nance, Reenu Iype, Travis Losser and Gary Harmon. NOSQL
VS RDBMS - WHY THERE IS ROOM FOR BOTH. In Proceedings of the
Southern Association for Information Systems Conference, paper 27, SAIS,
2013
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25. THANK YOU
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