MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling for MongoDB

#MDBlocal
A Complete Methodology of
Data Modeling for MongoDB
Daniel Coupal
Education, MongoDB
SANFRANCISCO

@
#MDBlocal
Daniel Coupal
Senior Curriculum Engineer, Education, MongoDB
danielcoupal
SANFRANCISCO

Goals of the Presentation
Document vs Tabular
Recognize the
differences
Methodology
Summarize the steps
when modeling for
MongoDB
Patterns
Recognize when to apply

Document versus
Tabular
Recognize the differences when modeling for a Document
Database versus a Relational/Tabular Database

#MDBLocal
Car Stored in a Tabular/Relational Database
SELECT * FROM Cars
WHERE Cars.owner = "Daniel"
INNER JOIN Wheels Cars.id = Wheels.car_id
INNER JOIN Seats Cars.id = Seats.car_id
INNER JOIN Brakes Cars.id = Brakes.car_id
...

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Car Stored in a Document Database
db.cars.find( {"owner":"Daniel"} )
What goes together is stored together

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Arrays in the Document Model
Use to represents One-to-Many relationships

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Arrays in the Document Model
{
owner: "Daniel",
make: Ferrari,
wheels: [
partNo: 234812819289,
partNo:,
partNo: 392838,
partNo: 928038
],
...
}
Use to represents One-to-Many relationships

#MDBLocal
Sub-documents in the Document Model
Use to represents One-to-One relationships

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{
owner: "Daniel",
make: "Ferrari",
engine: {
power: 660hp,
consumption: 10mpg
}
…
}

#MDBLocal
{
owner: "Daniel",
make: "Ferrari",
engine: {
power: 660hp,
consumption: 10mpg
}
…
}
db.cars.find(
{"owner":"Daniel"},
{"engine":1}
)
Projection

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Example 1: Modeling a blog

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CRDs: A few Collection-Relationship-Diagrams Solutions
Solution A
Queries by
users
Simple

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Solution A
Queries by
articles
Queries by
users
Duplication
of users
information
Simple
Solution B

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Solution A Solution C
Queries by
articles
Queries by
users
Duplication
of users
information
Simple
Solution B

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Example 2: Modeling a Social Network

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Solution A
write read
Images
Collection

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Solution B
write
read
Profiles
Collection

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Solution B
Pre-aggregated
Data
ü Slower writes
ü More storage space
ü Duplication
ü Faster reads
write
read
Profiles
Collection

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Differences: Tabular vs Document
Tabular MongoDB
Steps to create the
model
1 – define schema
2 – develop app and queries
1 – identifying the queries
2 – define schema

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Tabular MongoDB
Steps to create the
model
1 – define schema
2 – define schema
Initial schema • 3rd normal form
• one possible solution
• many possible solutions

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Tabular MongoDB
Steps to create the
model
1 – define schema
2 – define schema
Final schema • likely denormalized • few changes

#MDBLocal
Tabular MongoDB
Steps to create the
model
1 – define schema
2 – define schema
Schema evolution • difficult and not optimal
• likely downtime
• easy
• no downtime

#MDBLocal
Tabular MongoDB
Steps to create the
model
1 – define schema
2 – define schema
Schema evolution • difficult and not optimal
• likely downtime
• easy
• no downtime
Performance • mediocre • optimized

Methodology
Summarize the steps of a methodology when modeling for
MongoDB

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Main Tradeoff in Modeling

Methodology
1. Describe the
Workload

Methodology
1. Describe the
Workload
2. Identify and Model
the Relationships

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Actors, Movies and Reviews
actor_name
date_of_birth
movie_title
revenues
reviewer_name
rating

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Actors, Movies and Reviews
actor_name
date_of_birth
movie_title
revenues
reviewer
rating

Methodology
1. Describe the
Workload
2. Identify and Model
the Relationships
3. Apply Patterns

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Flexible Methodology

Use Case
Let's start a franchise of coffee shops…

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Case Study: Coffee Shop Franchises
Name: Beyond the Stars Coffee

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Objective:
• 10 000 stores in North America

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Objective:
• … then we expend to the rest of the World

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Objective:
• … then we expand to the rest of the World
Keys to success:
1. Best coffee in the world

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Objective:
• … then we expand to the rest of the World
Keys to success:
1. Best coffee in the world
2. Best Technology

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First Key to Success: Make the Best Coffee in the World
23g of ground coffee in, 20g of extracted coffee
out, in approximately 20 seconds
1. Fill a small or regular cup with 80% hot
water (not boiling but pretty hot). Your cup
should be 150ml to 200ml in total volume,
80% of which will be hot water.
2. Grind 23g of coffee into your portafilter
using the double basket. We use a scale that
you can get here.
3. Draw 20g of coffee over the hot water by
placing your cup on a scale, press tare and
extract your shot.

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Second Key to Success: Use the Best Technology
a) Intelligent Coffee Machines
• Weightings, temperature, time to produce, …
• Coffee perfection

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Key to Success 2: Best Technology
b) Intelligent Shelves
• Measure inventory in real time

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Key to Success 2: Best Technology
b) Intelligent Shelves
• Measure inventory in real time
c) Intelligent Data Storage
• MongoDB

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1 – Workload: List Queries
Query Operation Description
1. Coffee weight on the shelves write A shelf send information when coffee bags are
added or removed

#MDBLocal
added or removed
2. Coffee to deliver to stores read How much coffee do we have to ship to the store in
the next days

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added or removed
the next days
3. Anomalies in the inventory read Analytics

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added or removed
the next days
4. Making a cup of coffee write A coffee machine reporting on the production of a
coffee cup

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added or removed
the next days
coffee cup
5. Analysis of cups of coffee read Analytics

#MDBLocal
added or removed
the next days
coffee cup
5. Analysis of cups of coffee read Analytics
6. Technical Support read Helping our franchisees

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1 – Workload: quantify/qualify the queries
Query Quantification Qualification
1. Coffee weight on the shelves 10/day*shelf*store
=> 1/sec
<1s
critical write
2. Coffee to deliver to stores 1/day*store
=> 0.1/sec
<60s
3. Anomalies in the inventory 24 reads/day <5mins
"collection scan"
4. Making a cup of coffee 10 000 000 writes/day
115 writes/sec
<100ms
non-critical write
… cups of coffee at rush hour 3 000 000 writes/hr
833 writes/sec
<100ms
non-critical write
5. Analysis of cups of coffee 24 reads/day stale data is fine
"collection scan"
6. Technical Support 1000 reads/day <1s

#MDBLocal
Disk Space
Cups of coffee
• one year of data
• 10000 x 1000/day x 365
• 3.7 billions/year
• 370 GB (100 bytes/cup of
coffee)
Weighings
• one year of data
• 10000 x 10/day x 365
• 365 billions/year
• 3.7 GB (100 bytes/weighings)

#MDBLocal
2 - Relations are still important
Type of Relation -> one-to-one/1-1 one-to-many/1-N many-to-many/N-N
Document
embedded in the
parent document
• one read
• no joins
• one read
• no joins
• one read
• no joins
• duplication of
information
Document
referenced in the
parent document
• smaller reads
• many reads
• smaller reads
• many reads
• smaller reads
• many reads

#MDBLocal
2 - Entities for Beyond the Stars Coffee
Entities:
• Coffee cups
• Stores
• Coffee machines
• Shelves
• Weighings
• Coffee bags

Patterns
Recognize the need and when to apply Schema Design Patterns

#MDBLocal
Schema Design Patterns Resources
A. Advanced Schema Design Patterns, Daniel Coupal
• MongoDB World 2017
B. Blogs on Patterns, Ken Alger & Daniel Coupal
• https://www.mongodb.com/blog/post/building-
with-patterns-a-summary
C. MongoDB University: M320 – Data Modeling
• https://university.mongodb.com/courses/M320/about

#MDBLocal
Bucket Pattern
{
"device_id": 000123456,
"type": "2A",
"date": ISODate("2018-03-02"),
"temp": [ [ 20.0, 20.1, 20.2, ... ],
[ 22.1, 22.1, 22.0, ... ],
...
]
}
{
"device_id": 000123456,
"type": "2A",
"date": ISODate("2018-03-03"),
"temp": [ [ 20.1, 20.2, 20.3, ... ],
[ 22.4, 22.4, 22.3, ... ],
...
]
}
{
"device_id": 000123456,
"type": "2A",
"date": ISODate("2018-03-02T13"),
"temp": { 1: 20.0, 2: 20.1, 3: 20.2, ... }
}
{
"device_id": 000123456,
"type": "2A",
"date": ISODate("2018-03-02T14"),
"temp": { 1: 22.1, 2: 22.1, 3: 22.0, ... }
}
Bucket per
Day
Bucket per
Hour

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Solution with Patterns
• Schema Versioning
• Computed
• Subset
• Bucket

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https://university.mongodb.com/courses/M320/about
Data Modeling Patterns Use Cases

Takeaways from the Presentation
Document vs Tabular
Recognize the
differences
Methodology
Summarize the steps
when modeling for
MongoDB
Patterns
Recognize when to apply

Thank you for taking our FREE
MongoDB classes at
university.mongodb.com

Register Now!
https://university.mongodb.com/courses/M320/about

Appendix A
Schema Versioning Pattern

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Nightmare: Alter Table

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This is what your dreams should be when
thinking about a schema upgrade !

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Schema Revision
Relational MongoDB
Versioned Unit Schema Document
Migration Procedure Difficult Easy
Service Uptime Interrupted No interruption
Rollback Difficult to
nightmare-ish
Easy

#MDBLocal
Application Lifecycle
Modify Application
• Can read/process all versions of documents
• Have different handler per version
• Reshape the document before processing
it
Update all Application servers
• Install updated application
• Remove old processes
Once migration completed
• remove the code to process old versions.

#MDBLocal
Document Lifecycle
New Documents:
• Application writes them in latest version
Existing Documents
A) Use updates to documents
• to transform to latest version
• keep forever documents that never
need an update
B) or transform all documents in batch
• no worry even if process takes days

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Timeline of the migration

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Problem Solution
Use Cases Examples Benefits and Trade-Offs
Schema Versioning Pattern
• Avoid downtime while doing schema
upgrades
• Upgrading all documents can take hours,
days or even weeks when dealing with big
data
• Don't want to update all documents
No downtime needed
Feel in control of the migration
Less future technical debt
! May need 2 indexes for same field while
in migration period
• Each document gets a "schema_version"
field
• Application can handle all versions
• Choose your strategy to migrate the
documents
• Every application that use a database,
deployed in production and heavily used.
• System with a lot of legacy data

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Mathematical Operations

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"Fan Out" Operations

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"Roll Up" Operations

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Problem Solution
Use Cases Examples Benefits and Trade-Offs
Computed Pattern
• Costly computation or manipulation of
data
• Executed frequently on the same data,
producing the same result
Read queries are faster
Saving on resources like CPU and Disk
! May be difficult to identify the need
! Avoid applying or overusing it unless
needed
• Perform the operation and store the
result in the appropriate document and
collection
• If need to redo the operations, keep the
source of them
• Internet Of Things (IOT)
• Event Sourcing
• Time Series Data
• Frequent Aggregation Framework
queries

MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling for MongoDB

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