The document discusses the evolution of databases from flat files to relational databases to NoSQL databases like MongoDB. It provides an overview of MongoDB, describing it as a free, open-source, cross-platform, document-oriented database designed for scalability. Some key features of MongoDB are that it uses dynamic schemas, is horizontally scalable, and supports replication and sharding for high availability. The document also compares MongoDB to relational databases and provides examples of CRUD operations and data modeling in MongoDB.

1. MongoDB
1

2. Database Evolution
Flat Files RDBMS NoSQL MongoDB
1970s: data stored in flat
file system
1979: a standard
way of query the
data - what do we
want to query,
how to process
that query but
1998:
1. Key-value Store
2. Tabular
3. Document oriented
(MongoDB, CouchDB,
etc.)
2009: Free & Open
Source, Cross-
Platform, Document
Oriented
Problem:
no standard
implementation
Problem: with the
big n huge amount
data
Problem: Less support
for Complex Queries
Solution: Relational
Database
Solution: NoSQL Solution: Use Third-
Party GUIs
2

3. 1. RDBMS’s do not well-scaling with very large size of data
2. RDBMS’s do not scale well to massive transaction rates
3. RDBMS’s cannot handle semi-structured data
4. RDBMS’s cannot handle unstructured data
5. RDBMS’s do not play well in a cloud environment
6. RDBMS’s cannot cope with changing or fuzzy schema specifications
and it is costly/impossible/slow to manage schema change
7. RDBMS’s cannot deal well with non-standard data like time series,
geospatial, or graph related data
3
NoSQL is a good solution to deal with these problems.
The problems with Relational Database:

4. What is NoSQL:
 NoSQL = Non SQL or Not only SQL
 Wikipedia’s definition:
4
A NoSQL database provides a mechanism for storage and retrieval of data
that is modeled in means other than the tabular relations used in relational
databases.

5. NoSQL Family 5

6. Knee Curve 6

7. What is MongoDB?
 Defination: MongoDB is an open source, document-
oriented database designed with both scalability and developer
agility in mind.
 Instead of storing your data in tables and rows as you would with
a relational database, in MongoDB you store JSON-like
documents(generally BSON) with dynamic schemas
(schema-free, schemaless).
7

8. Features
• Scalable High-Performance Open-source, Document-orientated
database.
• Built for Speed
• Rich Document based queries for Easy readability.
• Full Index Support for High Performance.
• Replication and Failover for High Availability.
• Auto Sharding for Easy Scalability.
• Map / Reduce for Aggregation.
8

9. WHAT IS MONGODB GREAT FOR?
• RDBMS replacement for Web Applications.
• Semi-structured Content Management.
• Real-time Analytics & High-Speed Logging.
• Caching and High Scalability
9

10. NOT GREAT FOR?
 Highly Transactional Applications.
 Problems requiring SQL.
 No referential integrity
 High degree of denormalization means updating
something in many places instead of one
10

11. Comparisons
RDBMS MongoDB
Relational database Document-oriented database, non relational
Schema based database, follow joins Schema-less database, references and
$lookup
Data stored in tables (tables contain
collection of rows)
Data stored in form of collection (collection
contain group of object document)
No horizontal scaling
Eg. 1st row has 5 column then 2nd row must
have 5 column for the given table
Horizontal scaling
Eg. 1st row has 5 column then 2nd row can
have any no of column for the given table
11

12. Data Format
JSON XML
{"employees“ :[
{"firstName":“Anju",
"lastName":“Shah"},
{"firstName":“Sumit Kumar",
"lastName":“Shah"},
{"firstName":“Shiva K.",
"lastName":“Shrestha"}
]}
<employees>
<employee>
<firstName>Anju</firstName>
<lastName>Shah</lastName>
</employee>
<employee>
<firstName>Sumit Kumar</firstName>
<lastName>Shah</lastName>
</employee>
<employee> <firstName>Shiva
K.</firstName>
<lastName>Shrestha</lastName>
</employee>
</employees>
12

13. Terminology Translation 13
SQL Terms/Concepts MongoDB Terms/Concepts
database database
table collection
row document
column field
index index
table joins (e.g. select queries) embedded documents and linking
Primary keys _id field is always the primary key
Aggregation (e.g. group by) aggregation pipeline

14. MongoDB Data Model 14
A collection includes documents.

15. MongoDB Data Model 15
Structure of a JSON-document:
The value of field:
 Native data types
 Arrays
 Other documents

16. MongoDB Data Model 16
Embedded documents:
The primary key

17. Installation
• Download and install suitable package for each platform [Windows, Linux,
Mac OSX, Solaris]
• Create a folder e.g. C:mongodb
• Go to bin of installation folder.
• Type following command: mongod --dbpath=C:/mongodb
• Run another command: mongo.exe
• The mongodb server is running.
17

18. CRUD
 Create
 db.collection.insert( <document> )
 db.collection.save( <document> )
 db.collection.update( <query>, <update>, { upsert: true } )
 Read
 db.collection.find( <query>, <projection> )
 db.collection.findOne( <query>, <projection> )
 Update
 db.collection.update( <query>, <update>, <options> )
 Delete
 db.collection.remove( <query>, <justOne> )
18

19. CRUD example
> db.user.insert({
first: "John",
last : "Doe",
age: 39
})
> db.user.find ()
{
"_id" : ObjectId("51…"),
"first" : "John",
"last" : "Doe",
"age" : 39
}
> db.user.update(
{"_id" : ObjectId("51…")},
{
$set: {
age: 40,
salary: 7000}
}
)
> db.user.remove({
"first": /^J/
})
19

20. Conclusions
 MongoDB is fast
 Very little CPU overhead
 Implemented in C++ for best performance
 Free & Open Source, Cross-Platform, Document Oriented
 Easier and faster integration of data
 useful when working with a huge quantity of data when the data's
nature does not require a relational model
 used when what really matters is the ability to store and retrieve great
quantities of data, not the relationships between the elements.
 Works on many platforms and there are many language drivers
20

21. Thank You!
21