Part one an Introduction to MongoDB. Learn how easy it is to start building applications with MongoDB. This session covers key features and functionality of MongoDB and sets out the course of building an application.

1. Application Development Series
Back to Basics – Introduction
Daniel Roberts
@dmroberts
#MongoDBBasics

2. Introduction
• About the Webinar Series
• Data Model
• Query Model
• Scalability
• Availability
• Deployment Architectures
• Performance
• Next Session
2

3. Series Outline & Approach
• Split into 2 sections
– Application Development (4 parts)
•
•
•
•
Schema Design
Interacting with the database query and update operators
Indexing
Reporting
– Operations (3 parts)
• Deployment – scale out and high availability
• Monitoring and performance tuning
• Backup and recovery
3

4. Application Overview
• Content Management System
– Will utilise :
•
•
•
•
•
•
Query & update operators
Aggregation Framework
Geospatial queries
Pre Aggregated reports for fast analytics
Polymorphic documents
And more…
• Take away framework
• An approach that you can reuse in your own
applications
4

5. Q&A
• Virtual Genius Bar
– Use the chat to post
questions
– EMEA Solution
Architecture team are
on hand
– Make use of them
during the sessions!!!
5

6. MongoDB

7. Operational Database
7

8. Document Data Model
Document - Collections
Relational - Tables
8
{ first_name: „Paul‟,
surname: „Miller‟,
city: „London‟,
location: {
type: “Point”,
coordinates :
[-0.128, 51.507]
},
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}

9. Document Model
• Agility and flexibility – dynamic schema
– Data models can evolve easily
– Companies can adapt to changes quickly
• Intuitive, natural data representation
– Remove impedance mismatch
– Many types of applications are a good fit
• Reduces the need for joins, disk seeks
– Programming is more simple
– Performance can be delivered at scale
9

10. Simplify development
10

11. Simplify development
11

12. Rich database interaction
12

13. Query Model

14. Shell and Drivers
Drivers
Drivers for most popular
programming languages and
frameworks
Java
Ruby
JavaScript
Python
Shell
Command-line shell for
interacting directly with
database
14
Perl
Haskell
> db.collection.insert({company:“10gen”,
product:“MongoDB”})
>
> db.collection.findOne()
{
“_id”
: ObjectId(“5106c1c2fc629bfe52792e86”),
“company”
: “10gen”
“product”
: “MongoDB”
}

15. MongoDB is full featured
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?)
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{ first_name: „Paul‟,
surname: „Miller‟,
city: „London‟,
location: {
type: “Point”,
coordinates :
[-0.128, 51.507]
},
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}

16. Query Example
Rich Queries
• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980
db.cars.find({
first_name: „Paul‟
})
db.cars.find({
city: „London‟,
”cars.year" : {
$gte : 1970,
$lte : 1980
}
})
16
{ first_name: „Paul‟,
surname: „Miller‟,
city: „London‟,
location: {
type: “Point”,
coordinates :
[-0.128, 51.507]
},
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}

17. Geo Spatial Example
Geospatial
• Find all of the car owners within 5km of
Trafalgar Sq.
db.cars.find( {
location:
{ $near :
{ $geometry :
{
type: 'Point' ,
coordinates :
[-0.128, 51.507]
}
},
$maxDistance :5000
}
})
17
{ first_name: „Paul‟,
surname: „Miller‟,
city: „London‟,
location: {
type: “Point”,
coordinates :
[-0.128, 51.507]
},
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}

18. Aggregation Framework Example
Aggregation
• Calculate the average value of Paul’s
car collection
db.cars.aggregate( [
{$match : {"first_name" : "Paul"}},
{$project : {"first_name":1,"cars":1}},
{$unwind : "$cars"},
{ $group : {_id:"$first_name",
average : {
$avg : "$cars.value"}}}
])
{ "_id" : "Paul", "average" : 215000 }
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{ first_name: „Paul‟,
surname: „Miller‟,
city: „London‟,
location: {
type: “Point”,
coordinates :
[-0.128, 51.507]
},
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}

19. Scalability

20. Automatic Sharding
• Three types of sharding: hash-based, range-based, tagaware
• Increase or decrease capacity as you go
• Automatic balancing
20

21. Query Routing
• Multiple query optimization models
• Each sharding option appropriate for different apps
21

22. Availability

23. Availability Considerations
• High Availability – Ensure application availability during
many types of failures
• Disaster Recovery – Address the RTO and RPO goals
for business continuity
• Maintenance – Perform upgrades and other maintenance
operations with no application downtime
23

24. Replica Sets
• Replica Set – two or more copies
• “Self-healing” shard
• Addresses many concerns:
- High Availability
- Disaster Recovery
- Maintenance
24

25. Replica Set Benefits
Business Needs
High Availability
Automated failover
Disaster Recovery
Hot backups offsite
Maintenance
Rolling upgrades
Low Latency
Locate data near users
Workload Isolation
Read from non-primary replicas
Data Privacy
Restrict data to physical location
Data Consistency
25
Replica Set Benefits
Tunable Consistency

26. Performance

27. Performance
Better Data
Locality
27
In-Memory
Caching
In-Place
Updates

28. Summary
• Document Model
– Simplify development
– Simplify scale out
– Improve performance
• MongoDB
– Rich general purpose database
– Built in High Availability and Failover
– Built in scale out
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29. Next Week – 6th February
• Matt Bates
– Schema design for the CMS application
• Collections
• Design decisions
– Application architecture
• Example technologies
• RESTful interface
• We‟ve chosen python for the examples
– Code Examples
29