MongoDB: What, why, when.
Solutions Architect, MongoDB Inc.
Massimo Brignoli
#mongodb
Who Am I?
•Solutions Architect/Evangelist in MongoDB Inc.
•24 years of experience in databases and softwaredevelopment
•Former MySQL employee
•Previous life: web, web, web
Innovation
Understanding Big Data – It’s Not Very“Big”
64% - Ingest diverse,new data in real-time
15% - More than 100TBof data
20% - Less than 100TB(average of all? <20TB)
“I have not failed. I've just found 10,000 ways that won't work.”
― Thomas A. Edison
Back in 1970…Cars Were Great!
Lots of Great Innovations Since1970
Would you use thesetechnologies for yourbusiness today?
Including the Relational Database
For which computers therelational model hasbeen designed for?
So Were Computers!
And Storage!
RDBMS Makes Development Hard
RelationalDatabase
Object RelationalMapping
Application
Code
Code
XML Config
XML Config
DB Schema
DB Schema
And Even Harder To Iterate
NewTable
NewTable
NewColumn
Name
Name
Pet
Pet
Phone
Phone
Email
Email
NewColumn
3 months later…
RDBMS
From Complexity to Simplicity
MongoDB
{
_id : ObjectId("4c4ba5e5e8aabf3"),
employee_name: "Dunham, Justin",
department : "Marketing",
title : "Product Manager, Web",
report_up: "Neray, Graham",
pay_band: “C",
benefits : [
{ type : "Health",
plan : "PPO Plus" },
{ type : "Dental",
plan : "Standard" }
]
}
MongoDB
The leading NoSQL database
DocumentDatabase
Open-Source
GeneralPurpose
7,000,000+7,000,000+
MongoDB DownloadsMongoDB Downloads
150,000+150,000+
Online Education RegistrantsOnline Education Registrants
25,000+25,000+
MongoDB User Group MembersMongoDB User Group Members
25,000+25,000+
MongoDB Days AttendeesMongoDB Days Attendees
20,000+20,000+
MongoDB Management Service (MMS) UsersMongoDB Management Service (MMS) Users
Global Community
To provide the best database for how we build andrun apps today
MongoDB Vision
Build
–New and complex data
–Flexible
–New languages
–Faster development
Run
–Big Data scalability
–Real-time
–Commodity hardware
–Cloud
Enterprise Big Data Stack
EDW
Hadoop
Management & Monitoring
Security & Auditing
RDBMS
CRM, ERP, Collaboration, Mobile, BI
OS & Virtualization, Compute, Storage, Network
RDBMS
Applications
Infrastructure
Data Management
Online Data
Offline Data
Agile
MongoDB Overview
Scalable
Operational Database Landscape
Document Data Model
Relational
MongoDB
{
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, … }
]
}
Document Model Benefits
•Agility and flexibility
–Data models can evolve easily
–Companies can adapt to changes quickly
•Intuitive, natural data representation
–Developers are more productive
–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
Developers are more productive
Developers are more productive
Automatic Sharding
•Three types of sharding: hash-based, range-based, tag-aware
•Increase or decrease capacity as you go
•Automatic balancing
Query Routing
•Multiple query optimization models
•Each sharding option appropriate for different apps
High Availability – Ensure application availability during manytypes of failures
Disaster Recovery – Address the RTO and RPO goals forbusiness continuity
Maintenance – Perform upgrades and other maintenanceoperations with no application downtime
Availability Considerations
Replica Sets
•Replica Set – two or more copies
•“Self-healing” shard
•Addresses many concerns:
-High Availability
-Disaster Recovery
-Maintenance
Single Data Center
•Automated failover
•Tolerates server failures
•Tolerates rack failures
•Number of replicasdefines failure tolerance
Primary – A
Primary – B
Primary – C
Secondary – A
Secondary – A
Secondary – B
Secondary – B
Secondary – C
Secondary – C
Active/Standby Data Center
•Tolerates server and rack failure
•Standby data center
Data Center - West
Primary – A
Primary – B
Primary – C
Secondary – A
Secondary – B
Secondary – C
Data Center - East
Secondary – A
Secondary – B
Secondary – C
Active/Active Data Center
•Tolerates server, rack, data center failures, networkpartitions
Data Center - West
Primary – A
Primary – B
Primary – C
Secondary – A
Secondary – B
Secondary – C
Data Center - East
Secondary – A
Secondary – B
Secondary – C
Secondary – B
Secondary – C
Secondary – A
Data Center - Central
Arbiter – A
Arbiter – B
Arbiter – C
Global Data Distribution
Real-time
Real-time
Real-time
Real-time
Real-time
Real-time
Real-time
Primary
Secondary
Secondary
Secondary
Secondary
Secondary
Secondary
Secondary
Read Global/Write Local
Primary:NYC
Secondary:NYC
Primary:LON
Primary:SYD
Secondary:LON
Secondary:NYC
Secondary:SYD
Secondary:LON
Secondary:SYD
Common Use Cases
High Volume Data Feeds
•More machine forms, sensors & data
•Variably structured
MachineGeneratedData
•High frequency trading
•Daily closing price
Securities Data
•Multiple data sources
•Each changes their format consistently
•Student Scores, ISP logs
Social Media /
General Public
Operational Intelligence
•Large volume of users
•Very strict latency requirements
•Sentiment Analysis
Ad Targeting
•Expose data to millions of customers
•Reports on large volumes of data
•Reports that update in real time
Real timedashboards
•Join the conversation
•Catered Games
•Customized Surveys
Social MediaMonitoring
Metadata
•Diverse product portfolio
•Complex querying and filtering
•Multi-faceted product attributes
ProductCatalogue
•Data mining
•Call records
•Insurance Claims
Data analysis
•Retina Scans
•Fingerprints
Biometric
Content Management
•Comments and user generatedcontent
•Personalization of content and layout
News Site
•Generate layout on the fly
•No need to cache static pages
Multi-devicerendering
•Store large objects
•Simpler modeling of metadata
Sharing
Questions?
Thanks!
@massimobrignoli
Massimo Brignoli
#MongoDB
Solutions Architect, MongoDB Inc.
massimo@mongodb.com