The document provides an overview of MongoDB, highlighting its features such as the aggregation framework, sharding, and replica sets, focusing on their scalability, performance, and flexibility. It illustrates MongoDB's capabilities through real-life examples and describes various query operators and their usage in managing data. The emphasis is on MongoDB's advantages over traditional RDBMS, especially in handling large datasets and high throughput applications.

1. MongoDB“Walking on water and developing software from a
specification are easy if both are frozen.”
BY :
NklMish
@nklmish

2. Why & What?
Features
Aggregation framework overview
Sharding and Replica Set
Summary
Agenda

3. This talk is not about :
NoSql
Relational vs Non-Relational
Comparison of other NoSql flavors
NOTE

4. “One size fits all” approach no longer applies
NoSql Database
SchemaLess != NoSchema
Document based Approach
Non-Relational Dbs scale more easily, especially
horizontally
In a nutshell focus on speed, performance, flexibility and
scalability.
What is MongoDB?
“Nothing endures but change”

5. Agility.
High Scalability => horizontal => thousand of nodes or clouds or across multiple data centers
Rich Indexing
Real life example:
Server Density
OTTO
Expedia, Forbes, MetLife,Bosch, etc.
Why?
“Luck is not a factor. Hope is not a strategy. Fear is not an option”

6. RDBMS
Tables
Records/rows
Queries return
records
Mapping
“You improvise. You adapt. You overcome.”
MongoDB
Collections
Documents/objects
Queries return a
cursor ???? Because
of performance,
efficiency

7. Standard Db Features
Docs are stored in BSON => Mongo understands JSON
natively => Any Valid JSON can be imported and
queried(E.g. mongoimport -f foo.json).
Map Reduce
Aggregation Framework
GridFS (for Efficient binary large objects )
GeoNear
Features
“Stable Velocity. Sustainable Pace.”

8. $match – filter docs
$project – reshape docs
$group – Summarize docs
$unwind – Expand docs
$sort – Order docs
$limit/$skip – paginate docs
$redact – Restrict docs
$geoNear – Proximity sort docs
$let, $map – Define variables
Aggregation In Nutshell
“Talk is cheap. Show me the code”

9. {
name : “Java”,
price : 250,
Type : “ebook”
}
{
name : “Php”,
price : 200,
Type : “ebook”
}
{
name : “Javascript”,
price : 150,
Type : “hardCopy”
}
Matching
{$match : {
Type : “ebook”
}}
{
name : “Java”,
price : 200,
Type : “ebook”
}
{
name : “Php”,
price : 200,
Type : “ebook”
}

10. {
name : “Java”,
price : 250,
Type : “ebook”
}
{
name : “Php”,
price : 200,
Type : “ebook”
}
{
name : “Javascript”,
price : 150,
Type : “hardCopy”
}
Query Operator
{$match : {
price : {$gt : 200}
}}
{
name : “Java”,
price : 250,
Type : “ebook”
}
{
name : “Php”,
price : 200,
Type : “ebook”
}

11. {
name : “Java”,
price : 250,
Type : “ebook”
}
{
name : “Php”,
price : 200,
Type : “ebook”
}
Including Excluding part of document
{$project : {
name :1,
price : 1,
Type : 0
}}
{
name : “Java”,
price : 200”
}
{
name : “Php”,
price : 200
}

12. {
name : “Java”,
price : 250,
Type : “ebook”,
quantity : 3
}
{
name : “Php”,
price : 200,
Type : “ebook”,
quantity : 2
}
Custom Field Computation
{$project : {
fullStock : {
$mul : [“$price”, “$quantity”]
},
Title : “$name”
}}
{
fullStock : 750,
Title: “Java”
}
{
price : 400,
Title: “Php”
}

13. {
name : “Java”,
price : 250,
Type : “ebook”,
quantity : 3
}
{
name : “Php”,
price : 200,
Type : “ebook”,
quantity : 2
}
Generating Sub-Document
{$project : {
fullStock : {
$mul : [“$price”, “$quantity”]
},
details : {Title : “$name”, quantity : “$quantity” }
}}
{
fullStock : 750,
details : {Title: “Java”, quantity : 3}
},
{,
fullStock : 400,
details : {Title: “Php”, quantity : 2}
}

14. $group
Group Docs by value
By default in memory processing
Helpful operators that go with:
$max, $min, $avg, $sum
$addToSet, $push
$first, $last

15. {
name : “Java Fun”,
publisher : “manning”,
price : 1000
}
{
name : “Oracle Fun”,
publisher : “manning”,
price : 1000
}
{
name : “Php Fun”,
publisher : “Orelly”,
price : 400
}
Compute Average
{$group : {
_id : “$publisher”,
avgPrice : {$avg : “$price” }
}}
{
_id: manning,
avgPrice :1000
},
{
_id : Php,,
avgPrice: 400
}

16. $unwind
Useful for doc containing array fields:
Create docs from array entries
Entries can be replace by value

17. {
publisher :“manning”,
title: [“java”, “Php”],
discount : 50%
}
$unwind
{$unwind : $category}
{
publisher : manning,
title: java,
discount :50%
},
{
publisher : manning,
title: Php,
discount :50%
}

18. $redact
Restrict access to docs based on doc fields
to define privileges
Useful terminology $$DESCEND, $$PRUNE,
$$KEEP

19. {
_id :123,
name : logo,
security : “ANYONE”,
Profit : {
security : “MARKETING”,
revenue : 500%,
ProfitByCountry : {
security : “BOARD_OF_DIRECTOR”
PL : 800 %
NO : 700 %
DK : 600%
SW : 500 %
}
}
}
$redact
db.products.aggregate([
{$match : {name : “logo”}},
$redact : {
$cond : {
if : {$eq : …},
then : “$$DESCEND”,
else : “$PRUNE
}
}
])
{...}

20. Sharding(Horizontal scaling)

21. Sharding
Allow to store data across multiple machines.
Ok but what for ?
Database systems with large data sets and high throughput applications
can challenge the capacity of a single server.
High query rates can exhaust the CPU capacity of the server.
Larger data sets exceed the storage capacity of a single machine
Working set sizes larger than the system’s RAM stress the I/O capacity of
disk drives.

22. Replica Set

23. Replica Set
Group of mongod processes that maintain the same data set. Provides redundancy and high
availability, in a nutshell basis for all production deployments.
Min 3 nodes required E.g.

24. Document oriented db.
Scale and performs well
Provide powerful aggregration framework
Tested on massive datasets.
Support for map reduce.
Summary

25. Questions ????