1. MongoDB Er. Shiva K. Shrestha ME Computer, NCIT
6/20/2016
Er. Shiva K. Shrestha
ME Computer, NCIT
Distributed Operating System

2. Introduction MongoDB = “Humongous DB” Open-source & Cross-platform
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MongoDB = “Humongous DB”
Open-source & Cross-platform
Document-based
“High Performance, High Availability”
Automatic Scaling
10gen => MongoDB Inc.
2009
Stable Release: 8th March, 2016
High Performance: JSON Format, Less CPU Overhead, Data assessable from any Node
Scaling:
-> Cluster Scale
-> Performance Scale
-> Data Scale

3. 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
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4. Features Expressive Query Language & Secondary Indexes
Strong Consistency
Flexibility
Scalability & Performance
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Easy Assess
Provide up-to-date copy of data
Easily added extra column without affecting others
Secured Monitored, automated & integrated with our existing infrastructure
Millions ops/sec, 100 Billions Documents, Peta Bytes of Data

5. MongoDB is: General Purpose Easy to Use Fast & Scalable
Rich data model
Full featured indexes
Sophisticated query language
Easy to Use
Easy mapping to object oriented code
Native language drivers in all popular languages
Simple to setup and manage
Rich Data Model: Any type of data can be stored
Fast & Scalable
Operates at in-memory speed wherever possible
Auto-sharding built in
Dynamically add / remove capacity with no downtime

6. 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
SQL queries are used
JSON, BSON format
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7. 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>
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8. Advantages Schema less Structure of single object is clear
No complex joins
Document-based Query language
Conversion/Mapping is not needed
Use internal memory, hence faster
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9. Why and Where to use Object oriented storage style (JSON)
Index on any attributes
Replication and Availability
Auto-sharding
Big data
Content management system
Mobile and social infrastructure
Data Hub
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10. Terminology Translation
SQL Terms/Concepts
MongoDB Terms/Concepts
Database
Table
Collection
Row
Document or BSON document
Column
Field
Index
Table joins
Embedded documents & linking
Unique column or column combination as primary key.
Primary key is automatically set to the _id field.
Aggregation (e.g. group by)
Aggregation pipeline
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11. CRUD Operations Insertion: Update: Delete: SQL MongoDB Command
INSERT INTO t (fn, ln) VALUES (‘Saroj', ‘Shakya')
db.t.insert({fn:‘Saroj', ln: ‘Shakya'})
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SQL
MongoDB Command
UPDATE t SET ln='Nepal' WHERE ln='Shakya'
db.t.update({ln:‘Shakya'},{$set:{ln:‘Nepal'}})
SQL
MongoDB Command
DELETE FROM t WHERE fn=‘Saroj'
db.t.remove({fn:‘Saroj'})

12. Retrieving Data SQL Statement MongoDB Commands SELECT * FROM table
db.collection.find()
WHERE artist = ‘Rekha’
db.collection.find({Artist:”Rekha”})
SELECT*
ORDER BY Title
db.collection.find().sort(Title:1)
DISTINCT
.distinct()
GROUP BY
.group()
>=, <
$gte, $lt
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13. Replication
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14. Knee Curve
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15. Drivers & Frameworks
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MEAN Stack
Morphia

16. 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
Very rapid development
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17. Q/A Session:
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Thank You!