For the first time in four decades something new is happening in the database world. Five years ago a multitude of so called NoSQL databases were born to address the Big Data challenge. Data volumes and often also user volumes have grown a hundred or thousand times larger compared to what we were dealing with just one decade ago. Also the data has evolved to be more complex and more heterogeneous, and it doesn't easily fit into rows and columns anymore. MongoDB is the leading NoSQL database. The hundreds of thousands of MongoDB clusters out there range from startups and their server side JavaScript architectures to bankers and government officials with their Java stacks. In this talk we will explore 5 features that made MongoDB stand out from the NoSQL crowd. Henrik Ingo - About the Speaker Henrik Ingo is a MongoDB Solutions Architect based in Helsinki and leads the Telecom Solution Architecture team. Prior to MongoDB he worked as a database performance architect at Nokia, and in various roles in MySQL, Sun Microsystems, MariaDB and Galera.

1. 5 Reasons that made MongoDB
the Leading NoSQL Database
Henrik Ingo
Solutions Architect, MongoDB

2. 2
Hi, I am Henrik Ingo
@h_ingo

3. 5 Reasons that made MongoDB
the Leading NoSQL Database
Henrik Ingo
Solutions Architect, MongoDB

4. 4
1. Open Source
2. Replication
3. Sharding
4. Schemaless
5. Cloud, Big Data...
5 MongoDB features that are
NOT reasons to choose MongoDB

5. 5
1. Open Source
2. Replication
3. Sharding
4. Schemaless
5. Cloud, Big Data...
Reasons to move FROM relational TO NoSQL
...all NoSQL Databases do this.

6. Ok, so what ARE the
Top 5 reasons to choose MongoDB?

7. 1. General purpose

8. 8
1. General Purpose Database
Big Data Product & Asset
Catalogs
Security &
Fraud
Internet of
Things
Database-as-a-
Service
Mobile
Apps
Customer Data
Management
Data
Hub
Social &
Collaboration
Content
Management
Intelligence Agencies
Top Investment and
Retail Banks
Top US Retailer
Top Global Shipping
Company
Top Industrial Equipment
Manufacturer
Top Media Company
Top Investment and
Retail Banks

9. 9
5 NoSQL categories
Key Value Wide Column Document
Graph Map Reduce
Redis, Riak Cassandra
Neo4j Hadoop

10. 10
MongoDB is a Document Database
MongoDB
Rich Queries
• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980
Geospatial
• Find all of the car owners within 5km of
Trafalgar Sq.
Text Search
• Find all the cars described as having
leather seats
Aggregation
• Calculate the average value of Paul’s
car collection
Map Reduce
• What is the ownership pattern of colors
by geography over time? (is purple
trending up in China?)
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location:
[45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
}
}

11. 11
Most popular NoSQL database

12. 12
Most popular NoSQL database
Document
Wide
Column
Wide
Column
KV
KV
Graph DocumentKV KV

13. 13
Hadoop
Document
Right tool for the right job
Column
KV
Graph

14. 14
Hadoop
Right tool for the right job
RDBMS .
Document
Column
KV
Graph

15. 15
Operational Database Landscape

16. 2. Agile

17. 17
Ease of use
Paul Downey (17419636)
Easy to use

18. 18
Integrated single binary
Chalon Handmade (6206864791)
Integrated single binary

19. 19
Replication & sharding
Replication & Sharding
Daniel Oines (7390222182)

20. 20
mms.mongodb.com/learn-more/automation
MMS Automation

21. 21
Schemaless iterative development

22. 22
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location:
[45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
}
}
Flexible Schema

23. 23
Rich query language = less code
MongoDB
Rich Queries
• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980
Geospatial
• Find all of the car owners within 5km of
Trafalgar Sq.
Text Search
• Find all the cars described as having
leather seats
Aggregation
• Calculate the average value of Paul’s
car collection
Map Reduce
• What is the ownership pattern of colors
by geography over time? (is purple
trending up in China?)
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location:
[45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
}
}

24. 24
Query language = JSON & JavaScript
db.collection.find( { "firstname" : "Henrik" } )

25. 25
Real life agility
Customer Project RDBMS MongoDB
360O customer view
$22M
2 years
Failed
Minimal
90 days
In production
Subscriber database
20 tables
35 joins
login = 5 joins
high latencies
15 months
20 persons
5 collections
2 queries
login = 1 query
10x faster
4 months
10 persons
eCommerce analytics
prototyped only
perf challenges
prototype in 1 week
good perf
http://www.mongodb.com/lp/whitepaper/quantifying-business-advantage

26. 3. JSON & JavaScript

27. 27
MEAN stack
Express.js

28. 28
MEAN stack
Express.js
JSON
JSON

29. 29
JavaScript
JVM stack
JVM
RDBMS
JDBC Spring, Morphia
JSON
Tomcat, Jetty, Play, Spring...
JSON

30. 4. Aggregation Framework

31. 31
OpenStreetMap data as JSON
db.collection.find( { "firstname" : "Henrik" } )
{ "_id" : 98684990,
"addr:housenumber" : "4",
"website" : "http://www.ravintolapohjanpoika.fi/",
"amenity" : "pub",
"addr:city" : "Helsinki",
"addr:postcode" : "00770",
"location" : {
"type" : "Point",
"coordinates" : [
25.0760622,
60.261145
]
},
"addr:street" : "Jakomäenkuja",
"addr:country" : "FI",
"name" : "Pohjanpoika"
}
http://rosslawley.co.uk/posts/the-most-popular-pub-name/

32. 32
OpenStreetMap data as JSON
db.collection.find( { "firstname" : "Henrik" } )
db.pubs.aggregate( [
{ $match : { "addr:city" : { $in :
[ "Helsinki", "Turku", "Tampere" ] } } },
{ $group : { "_id" : "$addr:city", "count" : { "$sum" : 1 } } },
{ $sort : { "count" : -1 } }
] )
{ "_id" : "Helsinki", "count" : 150 }
{ "_id" : "Tampere", "count" : 53 }
{ "_id" : "Turku", "count" : 23 }

33. 33
Big Data warning: data may be dirty
db.collection.find( { "firstname" : "Henrik" } )
db.pubs.find( { "addr:city" : { $exists : false } } ).count()
2291
db.pubs.find( { "addr:city" : { $exists : true } } ).count()
701

34. 34
Sharding is a first class citizen
db.collection.find( { "firstname" : "Henrik" } )
{ $match : ... }
{ $group : ... }
{ $sort : ... }
DB DB DB
Query Router

35. 35
Sharding is a first class citizen
db.collection.find( { "firstname" : "Henrik" } )
{ results }
{ $match : ... }
{ $group : ... }
{ $sort : ... }
{ $match : ... }
{ $group : ... }
{ $sort : ... }
{ $match : ... }
{ $group : ... }
{ $sort : ... }
{ $group : ... }
{ $sort : ... }

36. 5. Ecosystem

37. 37
7,000,000+
MongoDB Downloads
150,000+
Online Education Registrants
35,000+
MongoDB Management Service (MMS) Users
30,000+
MongoDB User Group Members
20,000+
MongoDB Days Attendees
Global Community

38. 38
MongoDB Partners (500)
Software & Services
Cloud & Channel Hardware

39. 39
MongoDB Inc
350+ employees 1,000+ customers
Over $231 million in funding13 offices around the world

40. Summary

41. 41
1. Open Source
2. Replication
3. Sharding
4. Schemaless
5. Agile, Cloud, Big Data...
5 MongoDB features that are
NOT reasons to choose MongoDB

42. 42
1. General purpose
2. Agile
3. JSON & JavaScript
4. Aggregation framework
5. Ecosystem
Top 5 reasons to choose MongoDB