Mongo db

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

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:

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.

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

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

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

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

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

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

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

MongoDB Data Model 14
A collection includes documents.

Structure of a JSON-document:
The value of field:
 Native data types
 Arrays
 Other documents

Embedded documents:
The primary key

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

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

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/
})
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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

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