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MongoDB
Er. Shiva K. Shrestha
ME Computer, NCIT
Distributed Operating System
1
Introduction
 MongoDB = “Humongous DB”
Open-source & Cross-platform
Document-based
“High Performance, High Availability”
Automatic Scaling
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
3
Features
 Expressive Query Language & Secondary Indexes
 Strong Consistency
 Flexibility
 Scalability & Performance
4
General
Purpose
MongoDB is:
Easy to Use
Fast &
Scalable
Sophisticated query
language
Full featured indexesRich data model
Simple to setup and
manage
Native language drivers
in all popular languages
Easy mapping to
object oriented code
Dynamically add /
remove capacity with no
downtime
Auto-sharding built in
Operates at in-memory
speed wherever
possible
Comparisons
RDBMS MongoDB
Relational database Document-oriented database, non
relational
Schema based database, follow joins Schema-less database, references and
$lookup
Data stored in tables (tables contain
collection of rows)
Data stored in form of collection (collection
contain group of object document)
No horizontal scaling
Eg. 1st row has 5 column then 2nd row
must have 5 column for the given table
Horizontal scaling
Eg. 1st row has 5 column then 2nd row can
have any no of column for the given table
SQL queries are used JSON, BSON format
6
Data Format
JSON XML
{"employees“ :[
{"firstName":“Anju",
"lastName":“Shah"},
{"firstName":“Sumit Kumar",
"lastName":“Shah"},
{"firstName":“Shiva K.",
"lastName":“Shrestha"}
]}
<employees>
<employee>
<firstName>Anju</firstName>
<lastName>Shah</lastName>
</employee>
<employee>
<firstName>Sumit Kumar</firstName>
<lastName>Shah</lastName>
</employee>
<employee> <firstName>Shiva
K.</firstName>
<lastName>Shrestha</lastName>
</employee>
</employees>
7
Advantages
 Schema less
 Structure of single object is clear
 No complex joins
 Document-based Query language
 Conversion/Mapping is not needed
 Use internal memory, hence faster
8
Why and Where to use
 Object oriented storage style (JSON)
 Index on any attributes
 Replication and Availability
 Auto-sharding
 Big data
 Content management system
 Mobile and social infrastructure
 Data Hub
9
Terminology Translation 10
SQL Terms/Concepts MongoDB Terms/Concepts
Database Database
Table Collection
Row Document or BSON document
Column Field
Index Index
Table joins Embedded documents & linking
Unique column or column combination as
primary key.
Primary key is automatically set to the
_id field.
Aggregation (e.g. group by) Aggregation pipeline
CRUD Operations
Insertion:
Update:
Delete:
11
SQL MongoDB Command
INSERT INTO t (fn, ln) VALUES (‘Saroj',
‘Shakya') db.t.insert({fn:‘Saroj', ln: ‘Shakya'})
SQL MongoDB Command
UPDATE t SET ln='Nepal' WHERE
ln='Shakya' db.t.update({ln:‘Shakya'},{$set:{ln:‘Nepal'}})
SQL MongoDB Command
DELETE FROM t WHERE fn=‘Saroj' db.t.remove({fn:‘Saroj'})
Retrieving Data 12
SQL Statement MongoDB Commands
SELECT *
FROM table
db.collection.find()
SELECT *
FROM table
WHERE artist = ‘Rekha’
db.collection.find({Artist:”Rekha”})
SELECT*
FROM table
ORDER BY Title
db.collection.find().sort(Title:1)
DISTINCT .distinct()
GROUP BY .group()
>=, < $gte, $lt
Replication 13
Knee Curve 14
Drivers & Frameworks
Morphia
MEAN Stack
15
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
16
Thank You!
 Q/A Session: 17

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Mongo DB

  • 1. MongoDB Er. Shiva K. Shrestha ME Computer, NCIT Distributed Operating System 1
  • 2. Introduction  MongoDB = “Humongous DB” Open-source & Cross-platform Document-based “High Performance, High Availability” Automatic Scaling 2
  • 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 3
  • 4. Features  Expressive Query Language & Secondary Indexes  Strong Consistency  Flexibility  Scalability & Performance 4
  • 5. General Purpose MongoDB is: Easy to Use Fast & Scalable Sophisticated query language Full featured indexesRich data model Simple to setup and manage Native language drivers in all popular languages Easy mapping to object oriented code Dynamically add / remove capacity with no downtime Auto-sharding built in Operates at in-memory speed wherever possible
  • 6. Comparisons RDBMS MongoDB Relational database Document-oriented database, non relational Schema based database, follow joins Schema-less database, references and $lookup Data stored in tables (tables contain collection of rows) Data stored in form of collection (collection contain group of object document) No horizontal scaling Eg. 1st row has 5 column then 2nd row must have 5 column for the given table Horizontal scaling Eg. 1st row has 5 column then 2nd row can have any no of column for the given table SQL queries are used JSON, BSON format 6
  • 7. Data Format JSON XML {"employees“ :[ {"firstName":“Anju", "lastName":“Shah"}, {"firstName":“Sumit Kumar", "lastName":“Shah"}, {"firstName":“Shiva K.", "lastName":“Shrestha"} ]} <employees> <employee> <firstName>Anju</firstName> <lastName>Shah</lastName> </employee> <employee> <firstName>Sumit Kumar</firstName> <lastName>Shah</lastName> </employee> <employee> <firstName>Shiva K.</firstName> <lastName>Shrestha</lastName> </employee> </employees> 7
  • 8. Advantages  Schema less  Structure of single object is clear  No complex joins  Document-based Query language  Conversion/Mapping is not needed  Use internal memory, hence faster 8
  • 9. Why and Where to use  Object oriented storage style (JSON)  Index on any attributes  Replication and Availability  Auto-sharding  Big data  Content management system  Mobile and social infrastructure  Data Hub 9
  • 10. Terminology Translation 10 SQL Terms/Concepts MongoDB Terms/Concepts Database Database Table Collection Row Document or BSON document Column Field Index Index Table joins Embedded documents & linking Unique column or column combination as primary key. Primary key is automatically set to the _id field. Aggregation (e.g. group by) Aggregation pipeline
  • 11. CRUD Operations Insertion: Update: Delete: 11 SQL MongoDB Command INSERT INTO t (fn, ln) VALUES (‘Saroj', ‘Shakya') db.t.insert({fn:‘Saroj', ln: ‘Shakya'}) SQL MongoDB Command UPDATE t SET ln='Nepal' WHERE ln='Shakya' db.t.update({ln:‘Shakya'},{$set:{ln:‘Nepal'}}) SQL MongoDB Command DELETE FROM t WHERE fn=‘Saroj' db.t.remove({fn:‘Saroj'})
  • 12. Retrieving Data 12 SQL Statement MongoDB Commands SELECT * FROM table db.collection.find() SELECT * FROM table WHERE artist = ‘Rekha’ db.collection.find({Artist:”Rekha”}) SELECT* FROM table ORDER BY Title db.collection.find().sort(Title:1) DISTINCT .distinct() GROUP BY .group() >=, < $gte, $lt
  • 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 16
  • 17. Thank You!  Q/A Session: 17

Editor's Notes

  1. 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
  2. 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
  3. Rich Data Model: Any type of data can be stored