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@mongodb

What, When and Why of
MongoDB
Massimo Brignoli
Solution Architect, MongoDB Inc.
Agenda
About MongoDB Inc.
Data and Query Model
Scalability

Availability
Deployment Architectures
Schema Design Challenges
Use Cases
About MongoDB
MongoDB Inc. Overview

300+ employees

Offices in New York, Palo Alto, Washington
DC, London, Dublin, Barcelona and Sydney

600+ customers

Over $231 million in funding
Global Community
6,000,000+
MongoDB Downloads

100,000+
Online Education Registrants

20,000+
MongoDB User Group Members

20,000+
MongoDB Days Attendees

15,000+
MongoDB Management Service (MMS) Users
MongoDB Inc. Products and
Services
Subscriptions
MongoDB Enterprise, On-Prem Monitoring, Professional Support
and Commercial License

Consulting
Expert Resources for All Phases of MongoDB Implementations

Training
Online and In-Person for Developers and Administrators

MongoDB Monitoring Service
Cloud-Based Service for Monitoring, Alerts, Backup and Restore
Data & Query Model
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
Developers are more productive
MongoDB is full featured
Rich Queries

• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980

MongoDB
{

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, … }
}
}
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

Perl

Haskell

> db.collection.insert({company:“10gen”,
product:“MongoDB”})
>
> db.collection.findOne()
{
“_id”
: ObjectId(“5106c1c2fc629bfe52792e86”),
“company”
: “10gen”
“product”
: “MongoDB”
}
Scalability
Automatic Sharding

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

• Multiple query optimization models
• Each sharding option appropriate for different apps
Availability
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
Replica Sets
• Replica Set – two or more copies
• “Self-healing” shard
• Addresses many concerns:
- High Availability
- Disaster Recovery
- Maintenance
Replica Set Benefits
Business Needs

Replica Set Benefits

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

Tunable Consistency
Deployment Architectures
Single Data Center

Primary – A

Primary – B

Primary – C

Secondary – B

Secondary – A

Secondary – A

• Automated failover
• Tolerates server failures
• Tolerates rack failures

Secondary – C

Secondary – C

Secondary – B

• Number of replicas
defines failure tolerance
Active/Standby Data Center
Primary – A

Primary – B

Primary – C

Secondary – B

Secondary – C

Secondary – A
Secondary – A

Data Center - West

• Tolerates server and rack failure
• Standby data center

Secondary – B

Secondary – C

Data Center - East
Active/Active Data Center

Primary – A

Primary – B

Primary – C

Secondary – A

Secondary – B

Secondary – C

Secondary – C

Secondary – A

Secondary – B

Secondary – B

Secondary – C

Secondary – A

Arbiter – A

Data Center - West

Arbiter – B

Arbiter – C

Data Center - Central

Data Center - East

• Tolerates server, rack, data center failures, network
partitions
Global Data Distribution

Real-time

Real-time
Secondary

Secondary

Secondary

Real-time

Real-time

Primary

Secondary

Real-time
Secondary

Real-time
Secondary

Real-time
Secondary
Read Global/Write Local
Primary:LON

Secondary:NYC

Primary:NYC

Secondary:SYD

Secondary:LON
Secondary:SYD

Primary:SYD
Secondary:LON
Secondary:NYC
Schema Design Challenges
First a story:
Once upon a time there was a
medical records company…
tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When
tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When
tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When
tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When
tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When
Schema Design Challenge
• Flexibility
– Easily adapt to new requirements

• Agility
– Rapid application development

• Scalability
– Support large data and query volumes
Schema Design:
MongoDB vs. Relational
MongoDB versus Relational
MongoDB

Relational

Collections

Tables

Documents

Rows

Data Use

Data Storage

What questions do I have?

What answers do I have?
Attribute

MongoDB

Relational

Storage

N-dimensional

Two-dimensional

Field Values

0, 1, many, or
embed

Single value

Query

Any field or level

Any field

Schema

Flexible

Very structured

Updates

In line

In place
With relational, this is hard

Long development times
Inflexible
Doesn’t scale
Document model is much easier
{
"patient_id": "1177099",
"first_name": "John",
"last_name": "Doe",
"middle_initial": "A",
"dob": "2000-01-25",
"gender": "Male",
"blood_type": "B+",
"address": "123 Elm St., Chicago, IL 59923",
"height": "66",
"weight": "110",
"allergies": ["Nuts", "Penicillin", "Pet Dander"],
"current_medications": [{"name": "Zoloft",
"dosage": "2mg",
"frequency": "daily",
"route": "orally"}],
"complaint" : [{"entered": "2000-11-03",
"onset": "2000-11-03",
"prob_desc": "",
"icd" : 250.00,
"status" : "Active"},
{"entered": "2000-02-04",
"onset": "2000-02-04",
"prob_desc": "in spite of regular exercise, ...",
"icd" : 401.9,
"status" : "Active"}],
"diagnosis" : [{"visit" : "2005-07-22" ,
"narrative" : "Fractured femur",
"icd" : "9999",
"priority" : "Primary"},
{"visit" : "2005-07-22" ,
"narrative" : "Type II Diabetes",
"icd" : "250.00",
"priority" : "Secondary"}]

Shorter development times
Flexible

}

Scalable
Let’s model something together
How about a business card?
Business Card
Twitters
•
•
•
•

name
location
web
bio

Groups
• name
N
N

1

Thumbnail
s

• mime_type 1
• data

Portraits
• mime_type 1
• data

Addresses

1

Contacts

N
1

•
•
•
•
•

type
street
city
state
zip_code

Phones

• name
1 • company
• title

1

• type
N
• number

1

1

Emails
N • type
• address

Address Book Entity-Relationship
Referencing

Contact
•
•
•
•

name
company
title
phone

Address
•
•
•
•

street
city
state
zip_code

Use two collections with a reference
Similar to relational
Embedding

Contact
•
•
•

name
company
address
• Street
street
• City
city
• State
• Zip
zip_code
• title
• phone

Document Schema
Referencing
Contacts

Addresses

{

{
“_id”: 2,
“name”: “Steven Jobs”,
“title”: “VP, New Product Development”,
“company”: “Apple Computer”,
“phone”: “408-996-1010”,
“address_id”: 1

}

}

“_id”: 1,
“street”: “10260 Bandley Dr”,
“city”: “Cupertino”,
“state”: “CA”,
“zip_code”: ”95014”,
“country”: “USA”
Embedding
Contacts
{
“_id”: 2,
“name”: “Steven Jobs”,
“title”: “VP, New Product Development”,
“company”: “Apple Computer”,
“address”: {“street”: “10260 Bandley Dr”,
“city”: “Cupertino”,
“state”: “CA”,
“zip_code”: ”95014”,
“country”: “USA”},
“phone”: “408-996-1010”
}
How are they different? Why?

Contact
Contact
•
•
•
•

name
company
title
phone

Address
•
•
•
•

street
city
state
zip_code

• name
• company
• adress
address
• Street
street
• City
city
• State
state
• Zip
zip_code
• title
• phone
Schema Flexibility
{

{
“name”: “Larry Page,
“url”: “http://google.com”,
“title”: “CEO”,
“company”: “Google!”,
“address”: {
“street”: 555 Bryant, #106”,
“city”: “Palo Alto”,
“state”: “CA”,
“zip_code”: “94301”
},
“phone”: “650-330-0100”
“fax”: ”650-330-1499”

“name”: “Steven Jobs”,
“title”: “VP, New Product Development”,
“company”: “Apple Computer”,
“address”: {
“street”: 10260 Bandley Dr”,
“city”: “Cupertino”,
“state”: “CA”,
“zip_code”: “95014”
},
“phone”: “408-996-1010”
}

}
{

One-to-many embedding vs.
referencing
“name”: “Larry Page”,
“url”: “http://google.com/”,
“title”: “CEO”,
“company”: “Google!”,
“email”: “larry@google.com”,
“address”: [{
“street”: “555 Bryant, #106”,
“city”: “Palo Alto”,
“state”: “CA”,
“zip_code”: “94301”
}]
“phones”: [{“type”: “Office”,
“number”: “650-618-1499”},
{“type”: “fax”,
“number”: “650-330-0100”}]

{
“name”: “Larry Page”,
“url”: “http://google.com/”,
“title”: “CEO”,
“company”: “Google!”,
“email”: “larry@google.com”,
“address”: [“addr99”],
“phones”: [“ph23”, “ph49”]}
{

{

}
{

“_id”: “addr99”,
“street”: “555 Bryant, #106”,
“city”: “Palo Alto”,
“state”: “CA”,
“zip_code”: “94301”}
“_id”: “ph23”,
“type”: “Office”,
“number”: “650-618-1499”},
“_id”: “ph49”,
“type”: “fax”,
“number”: “650-330-0100”}
Many to Many
Traditional Relational Association

Join table
Groups
name

X

GroupContacts
group_id
contact_id

Use arrays instead

Contacts
name
company
title
phone
Twitters
•
•
•
•

name
location
web
bio

Groups
• name
N
N

1

Thumbnail
s

• mime_type 1
• data

Portraits
• mime_type 1
• data

Addresses

1

Contacts

N
1

•
•
•
•
•

type
street
city
state
zip_code

Phones

• name
1 • company
• title

1

• type
N
• number

1

1

Emails
N • type
• address

Address Book Entity-Relationship
Groups

Contacts

• name

• name
• company
• title

N
N
1
1
Portraits
• mime_type
• data

twitter
•
•
•
•

addresses N

1

name
location
web
bio

thumbnail 1
• mime_type
• data

•
•
•
•
•

type
street
city
state
zip_code

phones N

• type
• number

emails N
• type
• address

Document model - holistic and efficient representation
Contact document example
{

}

“name” : “Gary J. Murakami, Ph.D.”,
“company” : “MongoDB, Inc”,
“title” : “Lead Engineer and Ruby Evangelist”,
“twitter” : {
“name” : “GaryMurakami”, “location” : “New Providence, NJ”,
“web” : “http://www.nobell.org”
},
“portrait_id” : 1,
“addresses” : [
{ “type” : “work”, “street” : ”229 W 43rd St.”, “city” : “New York”, “zip_code” : “10036” }
],
“phones” : [
{ “type” : “work”, “number” : “1-866-237-8815 x8015” }
],
“emails” : [
{ “type” : “work”, “address” : “gary.murakami@mongodb.com” },
{ “type” : “home”, “address” : “gjm@nobell.org” }
]
Health Care Use Cases
360-Degree Patient View
• Healthcare provider networks have massive

amounts of patient data
–
–
–
–

Both structured and unstructured
Basic patient informations
Lab results
MRI images

• Centralization of data needed
– Aggregation of all the data in one repository
• Analytics
Population Management for At-Risk
Demographics
• Certain populations are known to be prone to certain

diseases.
• Analyzing data insurers help people take

preventative measures
– reminding them to get regularly scheduled colonoscopies

• Help insurers to reduce costs and to expand

margins,
Lab Data Management and
Analytics
• Strain on traditional technological systems:
– Rise of number of tests conducted
– Rise of variety of data collected
– Lack of flexibility
• With MongoDB‟s flexible data model, providers of

lab testing, genomics and clinical pathology can:
– Ingest, store and analyze a variety of data types
– Coming from numerous sources all in a single data store

• enables these companies to generate new insights

and revenue streams
Other use cases for MongoDB in
healthcare include:
• Fraud Detection
• Remote Monitoring and Body Area Networks
• Mobile Apps for Doctors and Nurses

• Pandemic Detection with Real-Time Geospatial

Analytics
• Electronic Healthcare Records (EHR)

• Advanced Auditing Systems for Compliance
• Hospital Equipment Management and Optimization
#MongoDB

Thank You
Massimo Brignoli
massimo@mongodb.com
@massimobrignoli
Solutions Architect, MongoDB
tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When

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  • 1. @mongodb What, When and Why of MongoDB Massimo Brignoli Solution Architect, MongoDB Inc.
  • 2. Agenda About MongoDB Inc. Data and Query Model Scalability Availability Deployment Architectures Schema Design Challenges Use Cases
  • 4. MongoDB Inc. Overview 300+ employees Offices in New York, Palo Alto, Washington DC, London, Dublin, Barcelona and Sydney 600+ customers Over $231 million in funding
  • 5. Global Community 6,000,000+ MongoDB Downloads 100,000+ Online Education Registrants 20,000+ MongoDB User Group Members 20,000+ MongoDB Days Attendees 15,000+ MongoDB Management Service (MMS) Users
  • 6. MongoDB Inc. Products and Services Subscriptions MongoDB Enterprise, On-Prem Monitoring, Professional Support and Commercial License Consulting Expert Resources for All Phases of MongoDB Implementations Training Online and In-Person for Developers and Administrators MongoDB Monitoring Service Cloud-Based Service for Monitoring, Alerts, Backup and Restore
  • 7. Data & Query Model
  • 9. 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, … } } }
  • 10. 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
  • 11. Developers are more productive
  • 12. Developers are more productive
  • 13. Developers are more productive
  • 14. MongoDB is full featured Rich Queries • Find Paul’s cars • Find everybody in London with a car built between 1970 and 1980 MongoDB { 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, … } } }
  • 15. 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 Perl Haskell > db.collection.insert({company:“10gen”, product:“MongoDB”}) > > db.collection.findOne() { “_id” : ObjectId(“5106c1c2fc629bfe52792e86”), “company” : “10gen” “product” : “MongoDB” }
  • 17. Automatic Sharding • Three types of sharding: hash-based, range-based, tagaware • Increase or decrease capacity as you go • Automatic balancing
  • 18. Query Routing • Multiple query optimization models • Each sharding option appropriate for different apps
  • 20. 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
  • 21. Replica Sets • Replica Set – two or more copies • “Self-healing” shard • Addresses many concerns: - High Availability - Disaster Recovery - Maintenance
  • 22. Replica Set Benefits Business Needs Replica Set Benefits 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 Tunable Consistency
  • 24. Single Data Center Primary – A Primary – B Primary – C Secondary – B Secondary – A Secondary – A • Automated failover • Tolerates server failures • Tolerates rack failures Secondary – C Secondary – C Secondary – B • Number of replicas defines failure tolerance
  • 25. Active/Standby Data Center Primary – A Primary – B Primary – C Secondary – B Secondary – C Secondary – A Secondary – A Data Center - West • Tolerates server and rack failure • Standby data center Secondary – B Secondary – C Data Center - East
  • 26. Active/Active Data Center Primary – A Primary – B Primary – C Secondary – A Secondary – B Secondary – C Secondary – C Secondary – A Secondary – B Secondary – B Secondary – C Secondary – A Arbiter – A Data Center - West Arbiter – B Arbiter – C Data Center - Central Data Center - East • Tolerates server, rack, data center failures, network partitions
  • 30. First a story: Once upon a time there was a medical records company…
  • 36. Schema Design Challenge • Flexibility – Easily adapt to new requirements • Agility – Rapid application development • Scalability – Support large data and query volumes
  • 38. MongoDB versus Relational MongoDB Relational Collections Tables Documents Rows Data Use Data Storage What questions do I have? What answers do I have?
  • 39. Attribute MongoDB Relational Storage N-dimensional Two-dimensional Field Values 0, 1, many, or embed Single value Query Any field or level Any field Schema Flexible Very structured Updates In line In place
  • 40. With relational, this is hard Long development times Inflexible Doesn’t scale
  • 41. Document model is much easier { "patient_id": "1177099", "first_name": "John", "last_name": "Doe", "middle_initial": "A", "dob": "2000-01-25", "gender": "Male", "blood_type": "B+", "address": "123 Elm St., Chicago, IL 59923", "height": "66", "weight": "110", "allergies": ["Nuts", "Penicillin", "Pet Dander"], "current_medications": [{"name": "Zoloft", "dosage": "2mg", "frequency": "daily", "route": "orally"}], "complaint" : [{"entered": "2000-11-03", "onset": "2000-11-03", "prob_desc": "", "icd" : 250.00, "status" : "Active"}, {"entered": "2000-02-04", "onset": "2000-02-04", "prob_desc": "in spite of regular exercise, ...", "icd" : 401.9, "status" : "Active"}], "diagnosis" : [{"visit" : "2005-07-22" , "narrative" : "Fractured femur", "icd" : "9999", "priority" : "Primary"}, {"visit" : "2005-07-22" , "narrative" : "Type II Diabetes", "icd" : "250.00", "priority" : "Secondary"}] Shorter development times Flexible } Scalable
  • 42. Let’s model something together How about a business card?
  • 44. Twitters • • • • name location web bio Groups • name N N 1 Thumbnail s • mime_type 1 • data Portraits • mime_type 1 • data Addresses 1 Contacts N 1 • • • • • type street city state zip_code Phones • name 1 • company • title 1 • type N • number 1 1 Emails N • type • address Address Book Entity-Relationship
  • 46. Embedding Contact • • • name company address • Street street • City city • State • Zip zip_code • title • phone Document Schema
  • 47. Referencing Contacts Addresses { { “_id”: 2, “name”: “Steven Jobs”, “title”: “VP, New Product Development”, “company”: “Apple Computer”, “phone”: “408-996-1010”, “address_id”: 1 } } “_id”: 1, “street”: “10260 Bandley Dr”, “city”: “Cupertino”, “state”: “CA”, “zip_code”: ”95014”, “country”: “USA”
  • 48. Embedding Contacts { “_id”: 2, “name”: “Steven Jobs”, “title”: “VP, New Product Development”, “company”: “Apple Computer”, “address”: {“street”: “10260 Bandley Dr”, “city”: “Cupertino”, “state”: “CA”, “zip_code”: ”95014”, “country”: “USA”}, “phone”: “408-996-1010” }
  • 49. How are they different? Why? Contact Contact • • • • name company title phone Address • • • • street city state zip_code • name • company • adress address • Street street • City city • State state • Zip zip_code • title • phone
  • 50. Schema Flexibility { { “name”: “Larry Page, “url”: “http://google.com”, “title”: “CEO”, “company”: “Google!”, “address”: { “street”: 555 Bryant, #106”, “city”: “Palo Alto”, “state”: “CA”, “zip_code”: “94301” }, “phone”: “650-330-0100” “fax”: ”650-330-1499” “name”: “Steven Jobs”, “title”: “VP, New Product Development”, “company”: “Apple Computer”, “address”: { “street”: 10260 Bandley Dr”, “city”: “Cupertino”, “state”: “CA”, “zip_code”: “95014” }, “phone”: “408-996-1010” } }
  • 51. { One-to-many embedding vs. referencing “name”: “Larry Page”, “url”: “http://google.com/”, “title”: “CEO”, “company”: “Google!”, “email”: “larry@google.com”, “address”: [{ “street”: “555 Bryant, #106”, “city”: “Palo Alto”, “state”: “CA”, “zip_code”: “94301” }] “phones”: [{“type”: “Office”, “number”: “650-618-1499”}, {“type”: “fax”, “number”: “650-330-0100”}] { “name”: “Larry Page”, “url”: “http://google.com/”, “title”: “CEO”, “company”: “Google!”, “email”: “larry@google.com”, “address”: [“addr99”], “phones”: [“ph23”, “ph49”]} { { } { “_id”: “addr99”, “street”: “555 Bryant, #106”, “city”: “Palo Alto”, “state”: “CA”, “zip_code”: “94301”} “_id”: “ph23”, “type”: “Office”, “number”: “650-618-1499”}, “_id”: “ph49”, “type”: “fax”, “number”: “650-330-0100”}
  • 52. Many to Many Traditional Relational Association Join table Groups name X GroupContacts group_id contact_id Use arrays instead Contacts name company title phone
  • 53. Twitters • • • • name location web bio Groups • name N N 1 Thumbnail s • mime_type 1 • data Portraits • mime_type 1 • data Addresses 1 Contacts N 1 • • • • • type street city state zip_code Phones • name 1 • company • title 1 • type N • number 1 1 Emails N • type • address Address Book Entity-Relationship
  • 54. Groups Contacts • name • name • company • title N N 1 1 Portraits • mime_type • data twitter • • • • addresses N 1 name location web bio thumbnail 1 • mime_type • data • • • • • type street city state zip_code phones N • type • number emails N • type • address Document model - holistic and efficient representation
  • 55. Contact document example { } “name” : “Gary J. Murakami, Ph.D.”, “company” : “MongoDB, Inc”, “title” : “Lead Engineer and Ruby Evangelist”, “twitter” : { “name” : “GaryMurakami”, “location” : “New Providence, NJ”, “web” : “http://www.nobell.org” }, “portrait_id” : 1, “addresses” : [ { “type” : “work”, “street” : ”229 W 43rd St.”, “city” : “New York”, “zip_code” : “10036” } ], “phones” : [ { “type” : “work”, “number” : “1-866-237-8815 x8015” } ], “emails” : [ { “type” : “work”, “address” : “gary.murakami@mongodb.com” }, { “type” : “home”, “address” : “gjm@nobell.org” } ]
  • 57. 360-Degree Patient View • Healthcare provider networks have massive amounts of patient data – – – – Both structured and unstructured Basic patient informations Lab results MRI images • Centralization of data needed – Aggregation of all the data in one repository • Analytics
  • 58. Population Management for At-Risk Demographics • Certain populations are known to be prone to certain diseases. • Analyzing data insurers help people take preventative measures – reminding them to get regularly scheduled colonoscopies • Help insurers to reduce costs and to expand margins,
  • 59. Lab Data Management and Analytics • Strain on traditional technological systems: – Rise of number of tests conducted – Rise of variety of data collected – Lack of flexibility • With MongoDB‟s flexible data model, providers of lab testing, genomics and clinical pathology can: – Ingest, store and analyze a variety of data types – Coming from numerous sources all in a single data store • enables these companies to generate new insights and revenue streams
  • 60. Other use cases for MongoDB in healthcare include: • Fraud Detection • Remote Monitoring and Body Area Networks • Mobile Apps for Doctors and Nurses • Pandemic Detection with Real-Time Geospatial Analytics • Electronic Healthcare Records (EHR) • Advanced Auditing Systems for Compliance • Hospital Equipment Management and Optimization

Notas del editor

  1. Dotted line is the natural boundary of what is possible today. Eg, ORCL lives far out on the right and does things nosql vendors will ever do. These things come at the expense of some degree of scale and performance.NoSQL born out of wanting greater scalability and performance, but we think they overreacted by giving up some things. Eg, caching layers give up many things, key value stores are super fast, but give up rich data model and rich query model.MongoDB tries to give up some features of a relational database (joins, complex transactions) to enable greater scalability and performance. You get most of the functionality – 80% - with much better scalability and performance. Start with rdbms, ask what could we do to scale – take out complex transactions and joins. How? Change the data model. >> segue to data model section.May need to revise the graphic – either remove the line or all points should be on the line.To enable horizontal scalability, reduce coordination between nodes (joins and transactions). Traditionally in rdbms you would denormalize the data or tell the system more about how data relates to one another. Another way, a more intuitive way, is to use a document data model. More intuitive b/c closer to the way we develop applications today with object oriented languages, like java,.net, ruby, node.js, etc. Document data model is good segue to next section >> Data Model
  2. Here we have greatly reduced the relational data model for this application to two tables. In reality no database has two tables. It is much more common to have hundreds or thousands of tables. And as a developer where do you begin when you have a complex data model?? If you’re building an app you’re really thinking about just a hand full of common things, like products, and these can be represented in a document much more easily that a complex relational model where the data is broken up in a way that doesn’t really reflect the way you think about the data or write an application.
  3. Segue – Rich queries, text search, geospatial, aggregation, mapreduce are types of things you can build based on the richness of the query model. More on that in just a moment.
  4. Replicas in DC East configured as secondary-only.