MongoDB: The New Default Database for Giant Ideas Gary Taylor, MongoDB Solutions Architect MongoDB Evenings DC April 12, 2016 at 1776

1. Tweet tonight using #MongoDB to be
entered to win a t-shirt!

2. Welcome to MongoDB Evenings DC!
Agenda
6:00pm: Pizza, Beer & Soft Drinks
6:30pm: Welcome
Andrew Lockman, Enterprise Account Executive, MongoDB
6:40pm: MongoDB: The New Default Database for Giant Ideas
Gary Taylor, Senior Solutions Architect, MongoDB
7:30pm: Get MEAN and Lean with Docker and Kubernetes
Vadim Polyakov, Director of Enterprise Architecture, Inovalon
8:15pm: Announcements
Q&A
#MongoDB
@1776

3. MongoDB World
Pre-conference training: June 27th
Conference: June 28th-29th
Hilton Midtown
1335 6th Avenue
New York, NY 10019

4. Who’s Speaking (partial list)

5. Who’s Speaking

6. Code “AndrewLockman” gets 25% off
Early Bird Registration Ends May 13, 2016
June 29 - 29, 2016
New York, NY
www.mongodbworld.com

7. MongoDB
The New Default Database for GIANT Ideas
Gary W.Taylor
Senior SolutionsArchitect
gary.taylor@mongodb.com

8. Agenda
• The Changing World
• The Relational Approach
• Challenging the Relational Model
• The MongoDB Way
• When to use MongoDB
• Enterprise Advanced Features
• Use Cases Demo (time permitting)

9. The Changing World

10. Remember these?

11. Or these?

12. “Internet” in 1973

13. Internet Today

14. Software Has Changed
•Higher up-front costs
•Higher TCO
• Lower up-front costs
• Lower TCO

15. Data Has Changed
• 90% of the world’s data was
created in the last two years
• 80% of enterprise data is
unstructured
• Unstructured data growing 2x
faster than structured

16. What These Changes Mean
YEARS
ITERATIVE

17. Database Popularity in Today’s World
http://db-engines.com/en/ranking

18. 2015 Gartner Magic Quadrant
for
Operational Database Management Systems

19. Technical Skills Trend
http://www.indeed.com/jobtrends

20. The Relational Approach

21. Modern Relational Underpinnings
Expressive Query Language
& Secondary Indexes
Strong Consistency
Enterprise Management
& Integrations

22. Relational In Today’s World

23. Relational Data Model
This is hard…
Long time to
develop
Queries are
complex
Difficult to
change

25. Car – A Relational View

26. How we really want to view the car

27. Challenging the Relational
Model

29. Database Timeline
Magnetic tape
“flat” (sequential) files
Pre-computer
technologies:
Printing press
Dewey decimal
system
Punched cards
Magnetic Disk
IMS
Relational
Model
defined
Indexed-Sequential Access
Mechanism (ISAM)
Network Model
IDMS
ADABAS
System R
Oracle V2
Ingres
dBase
DB2
Informix
Sybase
SQL Server
Access
Postgres
MySQL
Cassandra
Hadoop
Vertica
Riak
HBase
Dynamo
MongoDB
Redis
VoltDB
Hana
Neo4J
Aerospike
Hierarchical model
1960-701940-50 1950-60 1970-80 1980-90 1990-2000 2000-2010
Dell Software Group
40 Years Ago Today
Relational (SQL)
Non-Relational (NoSQL)

31. NoSQL Challenger’s Underpinnings
Scalability
& Performance
Always On,
Global Deployments
FlexibilityExpressive Query Language
& Secondary Indexes
Strong Consistency
Enterprise Management
& Integrations

32. The MongoDB Way

33. What’s MongoDB? (short version)
• MongoDB (from “humongous”) is a full-featured scalable, high-performance and
developer friendly open source document-oriented database.
• Instead of storing your data in tables and rows as you would with a relational database, in
MongoDB you store JSON-like documents with dynamic schemas.
• MongoDB bridges the gap between key-value stores (which are fast and
scalable) and relational databases (which have rich functionality).
• Using BSON (binary JSON), developers can easily map to modern object-oriented languages
without a complicated ORM layer.
• This new data model simplifies coding significantly, and also improves performance by
grouping relevant data together internally.

34. What’s MongoDB for
•We’re for modern, mission-critical and
the previously impossible
•We’re for #GIANTideas

35. MongoDB’s Nexus Architecture
Scalability
& Performance
Always On,
Global Deployments
FlexibilityExpressive Query Language
& Secondary Indexes
Strong Consistency
Enterprise Management
& Integrations

36. {
first_name: "Paul",
surname: "Miller",
cell: 447557505611,
city: "London",
{
"type": "Point”, "location”: [-
0.145280,51.510907]
},
Profession: ["banking", "finance",
"trader"],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000, … },
{ model: "Rolls Royce",
year: 1965,
value: 330000, … }
]
}
MongoDB
Document Model
RDBMS

37. MongoDB vs. SQL terminology
MongoDB SQL
Database Database
Collection Table/View
Document Record/Tuple
PK: _id Field PK: Any Attribute(s)
Dynamic Schema Fixed Schema
Index Index
Embedded Document Joins
Shard Partition

38. Documents are Rich Data Structures{
first_name: "Paul",
surname: "Miller",
cell: 447557505611,
city: "London",
loc: {
"type": "Point”, ”coordinates”: [-0.145280,51.510907]
},
Profession: ["banking", "finance", "trader"],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000, … },
{ model: "Rolls Royce",
year: 1965,
value: 330000, … }
]
}
Fields can contain an array of sub-
documents
Fields
Typed field values
Fields can contain
arrays

39. Document Model Benefits
{
first_name: "Paul",
surname: "Miller",
cell: 447557505611,
city: "London",
loc: {
"type": "Point”, ”coordinates”: [-
0.145280,51.510907]
},
Profession: ["banking", "finance", "trader"],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000, … },
{ model: "Rolls Royce",
year: 1965,
value: 330000, … }
]
}
Agility and flexibility
Data model supports business change
Rapidly iterate to meet new requirements
Intuitive, natural data representation
Eliminates ORM layer
Developers are more productive
Reduces the need for joins, disk seeks
Programming is simpler
Performance delivered at scale

40. Dynamic Schema (polymorphic)
{
policyNum: 123,
type: “auto”,
customerId: “abc”,
payment: 1200,
deductible: 500,
make: “Taurus”,
model: “Ford”,
VIN: “123ABC456”,
miles_driven_yr: 15000,
start: “jan, 2016”,
end: “jul, 2016”
}
Insurance Policy Collection
{
policyNum: 456,
type: “life”,
customerId: “efg”,
payment: 240,
policyValue: 125000,
smoker: “no”,
beneficiaries: [
{spouse: “jane”, percent:
80},
{child: “jack”, percent: 20}
],
start: “jan, 2016”,
end: “jul, 2016”
}
{
policyNum: 789,
type: “home”,
customerId: “hij”,
payment: 2300,
deductible: 1000,
square_footage: 3000,
floodCoverage: “no”,
street: “10 Maple Lane”,
city: “Springfield”,
state: “Maryland”,
start: “jan, 2016”,
end: “jul, 2016”
}

41. Rich
Queries
• Find Paul’s cars
• Find everybody in London with a car built
between 1970 and 1980
db.vehicles.find({
first_name: “Paul”
})
db.vehicles.find({
city: “London”,
“cars.year” : {
$gte : 1970,
$lte : 1980
}
})
{
first_name: "Paul",
surname: "Miller",
cell: 447557505611,
city: "London",
loc: {
"type": "Point”, ”coordinates”: [-
0.145280,51.510907]
},
Profession: ["banking", "finance",
"trader"],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000, … },
{ model: "Rolls Royce",
year: 1965,
value: 330000, … }
]
}
Fully Featured .

42. Geospatial
• Find all of the car owners within 5 miles of
Trafalgar Square
{
first_name: "Paul",
surname: "Miller",
cell: 447557505611,
city: "London",
loc: {
"type": "Point”, ”coordinates”:
[-0.145280,51.510907]
},
Profession: ["banking",
"finance", "trader"],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000, … },
{ model: "Rolls Royce",
year: 1965,
value: 330000, … }
]
}
var milesToRadian = function(miles) {
var earthRadiusInMiles = 3963.2;
return miles / earthRadiusInMiles;
};
var landmark = db.landmarks.findOne({name: ”Trafalgar Square"});
var query = {
"loc" : {
$geoWithin : {
$centerSphere : [landmark.loc.coordinates, milesToRadian(5) ]
}
}
};
// Query points.
db.vehicles.find(query).pretty();
Fully Featured ..

43. Fully Featured ...
Text Search
• Find all the cars with ‘leather seats’ in their
description
Aggregation
• Calculate average value of every owner’s car
collection & show the top 10 sorted
descending by average value
{
first_name: "Paul",
surname: "Miller",
cell: 447557505611,
city: "London",
loc: {
"type": "Point”, ”coordinates”:
[-0.145280,51.510907]
},
Profession: ["banking",
"finance", "trader"],
cars: [
{ model: "Bentley",
year: 1973,
value: 100000, … },
{ model: "Rolls Royce",
year: 1965,
value: 330000, … }
]
}
Native Binary
JSON Support
• Add other vehicle classes (boats,
motorcycles) to Paul’s vehicle collection.
Left Outer Join
($lookup)
• Query for Paul’s cell # and lookup all calls
made in the last 30 days.

44. Morphia
MEAN Stack
Java Python PerlRuby
Support for the most popular languages and frameworks
Drivers & Ecosystem

45. When To Use MongoDB

46. The important aspect of MongoDB
• MongoDB was not designed for niche use cases
• MongoDB strives to have excellent characteristics applicable to
a very broad range of use cases
MongoDB: the most balanced general purpose database for
Enterprise applications and performance

47. MongoDB is good for
• Single View
• Internet of Things – sensor data
• Mobile apps – geospatial
• Real-time analytics
• Catalog
• Personalization
• Content management
• Inventory management
• Personalization engines
• Shopping cart
• Dependent datamarts
• Archiving for fast lookup
• Collaboration tools
• Messaging applications
• Log file aggregation
• Caching
• Adserving
• …
Use Cases where MongoDB shines
Mixture of analytics and archiving
Build information from data as it comes in
Extract from DW for analysis
Large volume, targeted queries
Sharing in near real time
Twitter-like apps
e.g., SPLUNK
Enable massive reads on consolidated data

48. MongoDB Use Cases
Single View Internet of Things Mobile Real-Time Analytics
Catalog Personalization Content Management

49. Single View
Internet of Things /
Sensor Data
Mobile Engagement Real-Time Analytics
Open Data Personalization Content Management
Department of Energy
Customs and
Immigration Service
Veterans Affairs
Intelligence
Community
Immigration and
Customs Enforcement
Federal
Communications
Commission
Intelligence
Community
US Geological
Survey
Department of
Homeland Security
Defense
Information
Systems Agency
Federal
Aviation
Administration
Intelligence
Community
National Institutes of HealthConsumer Financial
Protection Bureau
Federal
Law Enforcement
Veterans Affairs
US ArmyOffice of the
Secretary of Defense
MongoDB US Federal Use Cases

50. Best Fit for MongoDB over RDBMSs
Data
 Variably or unstructured
 Hierarchical objects
 Geo-coordinates
 Disparate sources
 Schema changes often
Querying
 Real-time analytics &
aggregations
 Location-based
 Lowest latency
 Performance affects user
experience
 Known relationships between
entities
 Local reading/writing globally
Other requirements
 Agile development
 Fastest time-to-market
 Cloud infrastructure
 Data will grow quickly
 Highest throughput
 Always on (~99.999%) availability
 Lowest TCO
 Challenges today with RDBMS

51. Best Fit for MongoDB over NoSQL
Data
 Hierarchical objects
 Geo-coordinates
 Disparate sources
 Schema changes often
Querying
 Secondary indexes useful
 Strong consistency desired
 In-DB analytics & aggregations
 Geospatial (location-based)
 SQL-based access & BI
Other requirements
 Robust management tools
 Highest read/write concurrency
 Lowest TCO
 Full application DB
 Largest ecosystem
 Future proofing & recruiting
 Want to influence roadmap
 Commercial license desired

52. MongoDB does well
• Straightforward replication
• High performance on mixed workloads of reads,
inserts, and updates
• Scaling on demand
• Location based deployment
• Geospatial queries
• High Availability and auto failover
• Flexible schema & secondary indexing
• Agile development in most programming
languages
• Commodity infrastructure
• Real time analytics
• Text indexing
• Data consistency
• Compression
• …
As a database, where does MongoDB shine?
Easy to initiate
All reads, mixed, and mostly writes
No expensive overprovisioning
One cluster can span the globe
Easy to build relevant mobile apps
Low stress operations
No need for complex data modeling
No need to give up your favorite development language
No vendor lock-in through hardware
Get value from data right away !
Basic search feature
Simpler app design
With new version 3.X

53. Enterprise Advanced
Features

54. Only With Enterprise Advanced
Storage Engines
 Encrypted Storage Engine
 In-Memory Storage Engine
Tools
 MongoDB Compass
 MongoDB Connector for BI
 Ops Manager or Cloud Manager
Premium
Integrations
 Kerberos & LDAP Authentication
 Auditing
 SNMP Support
 Red Hat Identity Management
Certification
Platform Certification
 Windows,
 RedHat/CentOS,
 Ubuntu,
 Amazon Linux
And …
 Private, On-Demand Training
 Support SLA 1 hour
 Support Availability 24 x 365
 Emergency Patches
 Commercial License, Warranty,
and Indemnification

55. Encrypted & In-memory Storage
Engines
In-memory storage engine offers the
benefits of in-memory computing without
sacrificing functionality or durability
Encrypted storage engine natively encrypts
database files on disk – for sensitive data,
i.e., in regulated industries

56. MongoDB Compass
For fast schema discovery and
visual construction of ad-hoc
queries
• Visualize schema
– Frequency of fields
– Frequency of types
– Determine validator rules
• View Documents
• Graphically build queries
• Authenticated access

57. MongoDB Connector for BI
Visualize and explore multi-
dimensional documents using SQL-
based BI tools. The connector does
the following:
• Provides the BI tool with the schema of the
MongoDB collection to be visualized
• Translates SQL statements issued by the
BI tool into equivalent MongoDB queries
that are sent to MongoDB for processing
• Converts the results into the tabular format
expected by the BI tool, which can then
visualize the data based on user
requirements

58. Monitoring & Alerting - Charts,
dashboards & alerts on 100+ metrics
Backup - Backup and restore, with point-in-
time recovery
Automation - Single-click provisioning,
scaling out/back & rolling upgrades without
downtime
MongoDB Ops Manager
The Best Way to Manage MongoDB In Your Data Center
Typically around 95% Reduction in Operational Overhead

59. Integrates with Existing Infrastructure

60. MongoDB Enterprise Server
MongoDB Enterprise Advanced24x7Support
CommercialLicense
Platform
Certifications
Emergency
Patches
On-Demand
Online Training
Customer
Success
Program
MongoDB CompassMongoDB Ops Manager
Monitoring &
Alerting
Query
Optimization
Backup &
Recovery
Automation &
Configuration
Schema Visualization
Data Exploration
Ad-Hoc Queries
MongoDB
Connector for BI
Visualization
Analysis
Reporting
LDAP & Kerberos Auditing FIPS 140-2Encryption at Rest
REST API

61. Technical: Why MongoDB
High performance (1000’s – millions
queries / sec) - reads & writes
Need flexible schema, rich querying with
any number of secondary indexes
Need for replication across multiple data
centers, even globally
Need to deploy rapidly and scale on
demand (start small and fast, grow easily)
99.999% availability
Real time analysis in the database, under
load
Geospatial querying
Processing in real time, not in batch
Need to promote agile coding
methodologies
Deploy over commodity computing and
storage architectures
Point in Time recovery
Need strong data consistency
Advanced security

62. Business: Why MongoDB
Management tooling and services
Ease of hiring
Commercial license
Ease of developer adoption
Global Support
Global Professional Services
IT ecosystem integration
Company stability
De facto standard for next generation database

63. Summary
• Wide range of use cases – and that’s the core point !
• Excellent across many possible use cases, not just a few
• Recognized by Gartner and Forrester
• De facto standard for next generation database
• Enterprise maturity and integration
• Built for GIANT Ideas!

65. Government DBaaS
Government agency provides a centralized data store to
manage veterans’ electronic records (VLER DAS)
Problem Why MongoDB ResultsProblem Solution Results
Internal and external systems need to
exchange and store data through
trusted connections to provide a full
range of services to the veteran
Clinicians needs accurate information to
ensure quality patient treatment
Benefits users needs accurate
information for benefits adjudication
Leverage flexible data model to save
all types of electronic records via one
centralized data service
Scales easily using sharding to
manage electronic records for the
lifetime (and beyond) of all veterans
Provides expressive query
capabilities to meet the needs of each
line of business
Succeeded in rolling out system in 9
months, meeting Congressionally
mandated deadline
Common access mechanism to
exchange and store veteran electronic
records
One place to store and manage veteran
electronic records for the lifetime of the
agency

66. Real-Time Geospatial Platform for
Innovation
Using MongoDB to create a smarter and safer city
Problem Why MongoDB ResultsProblem Solution Results
Siloed data across city departments
made it difficult for the City of Chicago
to intelligently analyze situations deliver
services to its citizens
City needed a system that could not
only handle 7 million pieces of data /
day from 30+ departments, but also
run analytics across it to deliver insight
Used MongoDB’s flexible data model to
build the WindyGrid, a unified view of the
city’s operations that brings together
disparate datasets from 30 departments
Leveraged MongoDB’s rich analytics
features (aggregation framework,
geospatial indexes, etc.) to create maps
that deliver real-time insight
Horizonal scalability with automatic
sharding across commodity servers
ensures the city can continue to cost
effectively deliver real-time results
A single view of the city’s operations
on a map of Chicago is now available to
all managers to help them better analyze
and respond to incidents in real-time
New predictive analytics system is
planned that will help prevent crimes
before they happen
450 data sets have been published to the
public, sparking even further innovation,
e.g., an app that alerts citizens when
street sweepers are coming

67. What is Chicago’s WindyGrid?
• A real-time situational
awareness system that
brings over a dozen data
sources together into a
single application.
• Is built using open source
software including
MongoDB

68. What is Chicago’s OpenGrid?
• A new project by the City of
Chicago
• An open-source, next
generation version of
WindyGrid
• That will allow the public
to navigate Chicago’s
open data
• Has a more intuitive
interface.

71. DEMO
http://opengrid.io/