Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Speaker: Daniel Coupal, Senior Curriculum Engineer, MongoDB
Level: 200 (Intermediate)
Track: Developer
At this point, you may be familiar with the design of MongoDB databases and collections, however what are the frequent patterns you may have to model?
This presentation will build on the knowledge of how to represent common relationships (1-1, 1-N, N-N) into MongoDB. Going further than relationships, this presentation aims at identifying a set of common patterns in a similar way the Gang of Four did for Object Oriented Design. Finally, this presentation will guide you through the steps of modeling those patterns into MongoDB collections.
What You Will Learn:
- How to create the appropriate MongoDB collections for some of the patterns discussed.
- The different relationships from the relational databases world, and understand how those translate to MongoDB collections.
- The patterns that are frequently seen in developing applications with MongoDB, and a specific vocabulary with which to refer to them. For example, “Subset”, “Attributes” and “Rolled Up” are among some of the patterns explored.
MongoDB.local Sydney 2019: Data Modeling for MongoDBMongoDB
At this point, you may be familiar with MongoDB and its Document Model.
However, what are the methods you can use to create an efficient database schema quickly and effectively?
This presentation will explore the different phases of a methodology to create a database schema. This methodology covers the description of your workload, the identification of the relationships between the elements (one-to-one, one-to-many and many-to-many) and an introduction to design patterns. Those patterns present practical solutions to different problems observed while helping our customers over the last 10 years.
In this session, you will learn about:
The differences between modeling for MongoDB versus a relational database.
A flexible methodology to model for MongoDB, which can be applied to simple projects, agile ones or more complex ones.
Overview of some common design patterns that help improve the performance of systems.
Speaker: Daniel Coupal, Senior Curriculum Engineer, MongoDB
Level: 200 (Intermediate)
Track: Developer
At this point, you may be familiar with the design of MongoDB databases and collections, however what are the frequent patterns you may have to model?
This presentation will build on the knowledge of how to represent common relationships (1-1, 1-N, N-N) into MongoDB. Going further than relationships, this presentation aims at identifying a set of common patterns in a similar way the Gang of Four did for Object Oriented Design. Finally, this presentation will guide you through the steps of modeling those patterns into MongoDB collections.
What You Will Learn:
- How to create the appropriate MongoDB collections for some of the patterns discussed.
- The different relationships from the relational databases world, and understand how those translate to MongoDB collections.
- The patterns that are frequently seen in developing applications with MongoDB, and a specific vocabulary with which to refer to them. For example, “Subset”, “Attributes” and “Rolled Up” are among some of the patterns explored.
MongoDB.local Sydney 2019: Data Modeling for MongoDBMongoDB
At this point, you may be familiar with MongoDB and its Document Model.
However, what are the methods you can use to create an efficient database schema quickly and effectively?
This presentation will explore the different phases of a methodology to create a database schema. This methodology covers the description of your workload, the identification of the relationships between the elements (one-to-one, one-to-many and many-to-many) and an introduction to design patterns. Those patterns present practical solutions to different problems observed while helping our customers over the last 10 years.
In this session, you will learn about:
The differences between modeling for MongoDB versus a relational database.
A flexible methodology to model for MongoDB, which can be applied to simple projects, agile ones or more complex ones.
Overview of some common design patterns that help improve the performance of systems.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Whether you're a MongoDB professional or totally new to document databases, our MongoDB performance success factors & evaluation framework has something for you,
Curious about MongoDB performance?
Mydbops CTO, Manosh Malai illustrates the secret sauce for MongoDB performance best practices & analysis tool.
Speaker: Alex Komyagin
MongoDB replica sets allow you to make the database highly available so that you can keep your applications running even when some of the database nodes are down. In a distributed system, local durability of writes with journaling is no longer enough to guarantee system-wide durability, as the node might go down just before any other node replicates new write operations from it. As such, we need a new concept of cluster-wide durability.
How do you make sure that your write operations are durable within a replica set? How do you make sure that your read operations do not see those writes that are not yet durable? This talk will cover the mechanics of ensuring durability of writes via write concern and how to prevent reading of stale data in MongoDB using read concern. We will discuss the decision flow for selecting an appropriate level of write concern, as well as associated tradeoffs and several practical use cases and examples."
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: http://bit.ly/2gFPTi8
MongoDB for Coder Training (Coding Serbia 2013)Uwe Printz
Slides of my MongoDB Training given at Coding Serbia Conference on 18.10.2013
Agenda:
1. Introduction to NoSQL & MongoDB
2. Data manipulation: Learn how to CRUD with MongoDB
3. Indexing: Speed up your queries with MongoDB
4. MapReduce: Data aggregation with MongoDB
5. Aggregation Framework: Data aggregation done the MongoDB way
6. Replication: High Availability with MongoDB
7. Sharding: Scaling with MongoDB
Webinar: MongoDB Schema Design and Performance ImplicationsMongoDB
In this session, you will learn how to translate one-to-one, one-to-many and many-to-many relationships, and learn how MongoDB's JSON structures, atomic updates and rich indexes can influence your design. We will also explore implications of storage engines, indexing and query patterns, available tools and related new features in MongoDB 3.2.
MongoDB World 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are looking for a more complete or agile process than what you are following currently? In this talk we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Jane Uyvova
Senior Solutions Architect, MongoDB
March 21, 2017
MongoDB Evenings San Francisco
Learn how easy it is to set up, operate, and scale your MongoDB deployments in the cloud with MongoDB Atlas.
MongoDB World 2019: Fast Machine Learning Development with MongoDBMongoDB
Today an increasingly large number of products use machine learning to deliver a great personalized user experience, and workplace software is no exception. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and automatically train and serve thousands of models, with multiple customized models per client.
MongoDB .local London 2019: A Complete Methodology to Data Modeling for MongoDBLisa Roth, PMP
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local London 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Whether you're a MongoDB professional or totally new to document databases, our MongoDB performance success factors & evaluation framework has something for you,
Curious about MongoDB performance?
Mydbops CTO, Manosh Malai illustrates the secret sauce for MongoDB performance best practices & analysis tool.
Speaker: Alex Komyagin
MongoDB replica sets allow you to make the database highly available so that you can keep your applications running even when some of the database nodes are down. In a distributed system, local durability of writes with journaling is no longer enough to guarantee system-wide durability, as the node might go down just before any other node replicates new write operations from it. As such, we need a new concept of cluster-wide durability.
How do you make sure that your write operations are durable within a replica set? How do you make sure that your read operations do not see those writes that are not yet durable? This talk will cover the mechanics of ensuring durability of writes via write concern and how to prevent reading of stale data in MongoDB using read concern. We will discuss the decision flow for selecting an appropriate level of write concern, as well as associated tradeoffs and several practical use cases and examples."
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: http://bit.ly/2gFPTi8
MongoDB for Coder Training (Coding Serbia 2013)Uwe Printz
Slides of my MongoDB Training given at Coding Serbia Conference on 18.10.2013
Agenda:
1. Introduction to NoSQL & MongoDB
2. Data manipulation: Learn how to CRUD with MongoDB
3. Indexing: Speed up your queries with MongoDB
4. MapReduce: Data aggregation with MongoDB
5. Aggregation Framework: Data aggregation done the MongoDB way
6. Replication: High Availability with MongoDB
7. Sharding: Scaling with MongoDB
Webinar: MongoDB Schema Design and Performance ImplicationsMongoDB
In this session, you will learn how to translate one-to-one, one-to-many and many-to-many relationships, and learn how MongoDB's JSON structures, atomic updates and rich indexes can influence your design. We will also explore implications of storage engines, indexing and query patterns, available tools and related new features in MongoDB 3.2.
MongoDB World 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are looking for a more complete or agile process than what you are following currently? In this talk we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Jane Uyvova
Senior Solutions Architect, MongoDB
March 21, 2017
MongoDB Evenings San Francisco
Learn how easy it is to set up, operate, and scale your MongoDB deployments in the cloud with MongoDB Atlas.
MongoDB World 2019: Fast Machine Learning Development with MongoDBMongoDB
Today an increasingly large number of products use machine learning to deliver a great personalized user experience, and workplace software is no exception. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and automatically train and serve thousands of models, with multiple customized models per client.
MongoDB .local London 2019: A Complete Methodology to Data Modeling for MongoDBLisa Roth, PMP
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local London 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local Chicago 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local Toronto 2019: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local Bengaluru 2019: A Complete Methodology to Data Modeling for Mo...MongoDB
Are you new to schema design for MongoDB, or are looking for a more complete or agile process than what you are following currently? In this talk we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Data Modelling for MongoDB - MongoDB.local Tel AvivNorberto Leite
At this point, you may be familiar with MongoDB and its Document Model.
However, what are the methods you can use to create an efficient database schema quickly and effectively?
This presentation will explore the different phases of a methodology to create a database schema. This methodology covers the description of your workload, the identification of the relationships between the elements (one-to-one, one-to-many and many-to-many) and an introduction to design patterns. Those patterns present practical solutions to different problems observed while helping our customers over the last 10 years.
In this session, you will learn about:
The differences between modeling for MongoDB versus a relational database.
A flexible methodology to model for MongoDB, which can be applied to simple projects, agile ones or more complex ones.
Overview of some common design patterns that help improve the performance of systems.
MongoDB .local Munich 2019: A Complete Methodology to Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
Daniel Coupal "At this point, you may be familiar with the design of MongoDB databases and collections, however what are the frequent patterns you may have to model?
This presentation will build on the knowledge of how to represent common relationships (1-1, 1-N, N-N) into MongoDB. Going further than relationships, this presentation aims at identifying a set of common patterns in a similar way the Gang of Four did for Object Oriented Design. Finally, this presentation will guide you through the steps of modeling those patterns into MongoDB collections.
"
Speaker: Daniel Coupal
At this point, you may be familiar with the design of MongoDB databases and collections – but what are the frequent patterns you may have to model?
This presentation will add knowledge of how to represent common relationships (1-1, 1-N, N-N) in MongoDB. Going further than relationships, this presentation identifies a set of common patterns, in a similar way to what the Gang of Four did for Object Oriented Design. Finally, this presentation will guide you through the steps of modeling those patterns in MongoDB collections.
In this session, you will learn about:
How to create the appropriate MongoDB collections for some of the patterns discussed.
Differences in relationships vs. the relational database world, and how those differences translate to MongoDB collections.
Common patterns in developing applications with MongoDB, plus a specific vocabulary with which to refer to them.
Greenfield projects are awesome – you can develop highest quality application using best practices on the market. But what if your bread actually is Legacy projects? Does it mean that you need to descend into darkness of QA absence? Does it mean that you can’t use Agile or modern communication practices like BDD?
[MongoDB.local Bengaluru 2018] Jumpstart: Introduction to Schema DesignMongoDB
Presented by: Saurabh Kashikar
Abstract: MongoDB’s basic unit of storage is a document. Documents can represent rich, schema-free data structures, meaning that we have several viable alternatives to the normalized, relational model. If you are new to MongoDB, learn the schema design basics in this introductory session. This session will help you model basic relationships in MongoDB.
What You Will Learn:
- The fundamentals of the MongoDB document model.
- How to model 1-1 and one-to-many (1-N) relationships in MongoDB.
- How to model many-to-many (N-N) relationships in MongoDB.
CQRS recipes or how to cook your architectureThomas Jaskula
The principles of CQRS is very simple. Separate Reads from Writes. Although when you try to implement it in you can face many technical and functional problems. This presentation starts from very simple architecture and while business requirements are added we consider other architecture ending with a CQRS + DDD + ES one.
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During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
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These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
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Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
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Introduction
Document vs
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Recognize the
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4. Goals of the Presentation
Introduction
Document vs
Tabular
Recognize the
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Methodology
Summarize the
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MongoDB
5. Goals of the Presentation
Introduction
Document vs
Tabular
Recognize the
differences
Methodology
Summarize the
steps when
modeling for
MongoDB
Use Case
Franchise of
coffee shops
6. Goals of the Presentation
Introduction
Document vs
Tabular
Recognize the
differences
Methodology
Summarize the
steps when
modeling for
MongoDB
Patterns
Recognize
when to apply
them
Use Case
Franchise of
coffee shops
7. Goals of the Presentation
Introduction
Document vs
Tabular
Recognize the
differences
Methodology
Summarize the
steps when
modeling for
MongoDB
Patterns
Recognize
when to apply
them
Use Case
Franchise of
coffee shops
Conclusion
and
Questions
11. #MDBLocal
A. Fields/Attributes in the Document Model
{
007,
"Daniel",
"Ferrari",
"GTS",
1982
}
Explicit column names for defined values
12. #MDBLocal
A. Fields/Attributes in the Document Model
{
"_id": 007
"owner": "Daniel",
"make": "Ferrari",
"model": "GTS",
"year": 1982
}
Explicit column names for defined values
16. #MDBLocal
C. Sub-documents in the Document Model
{
owner: "Daniel",
make: "Ferrari",
power: 660hp,
consumption: 10mpg
…
}
Use to represents One-to-One relationships
17. #MDBLocal
C. Sub-documents in the Document Model
{
owner: "Daniel",
make: "Ferrari",
engine: {
power: 660hp,
consumption: 10mpg
}
…
}
Use to represents One-to-One relationships
18. #MDBLocal
C. Sub-documents in the Document Model
{
owner: "Daniel",
make: "Ferrari",
engine: {
power: 660hp,
consumption: 10mpg
}
…
}
Use to represents One-to-One relationships
db.cars.find(
{"owner":"Daniel"},
{"engine":1}
)
Projection
19. #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
...
20. #MDBLocal
Car Stored in a Document Database
db.cars.find( {"owner":"Daniel"} )
What goes together is stored together
22. #MDBLocal
CRDs: A few Collection-Relationship-Diagrams Solutions
Solution A
Queries by
users
Simple
23. #MDBLocal
CRDs: A few Collection-Relationship-Diagrams Solutions
Solution A
Queries by
articles
Queries by
users
Duplication
of users
information
Simple
Solution B
24. #MDBLocal
CRDs: A few Collection-Relationship-Diagrams Solutions
Solution A Solution C
Queries by
articles
Queries by
users
Duplication
of users
information
Simple
Solution B
29. #MDBLocal
Example 2: Modeling a Social Network
Solution C
writes reads
ü Slower writes
ü More storage space
ü Duplication
ü Faster reads
Pre-aggregated
Data
Follower Profiles
30. #MDBLocal
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
31. #MDBLocal
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
Initial schema • 3rd normal form
• one possible solution
• many possible solutions
32. #MDBLocal
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
Initial schema • 3rd normal form
• one possible solution
• many possible solutions
Final schema • likely denormalized • few changes
33. #MDBLocal
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
Initial schema • 3rd normal form
• one possible solution
• many possible solutions
Final schema • likely denormalized • few changes
Schema evolution • difficult and not optimal
• likely downtime
• easy
• no downtime
34. #MDBLocal
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
Initial schema • 3rd normal form
• one possible solution
• many possible solutions
Final schema • likely denormalized • few changes
Schema evolution • difficult and not optimal
• likely downtime
• easy
• no downtime
Performance • mediocre • optimized
47. #MDBLocal
Case Study: Coffee Shop Franchises
Name: Beyond the Stars Coffee
Objective:
• 10 000 stores in North America
48. #MDBLocal
Case Study: Coffee Shop Franchises
Name: Beyond the Stars Coffee
Objective:
• 10 000 stores in North America
• … then we expend to the rest of the World
49. #MDBLocal
Case Study: Coffee Shop Franchises
Name: Beyond the Stars Coffee
Objective:
• 10 000 stores in North America
• … then we expand to the rest of the World
Keys to success:
1. Best coffee in the world
50. #MDBLocal
Case Study: Coffee Shop Franchises
Name: Beyond the Stars Coffee
Objective:
• 10 000 stores in North America
• … then we expand to the rest of the World
Keys to success:
1. Best coffee in the world
2. Best Technology
51. #MDBLocal
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.
52. #MDBLocal
Second Key to Success: Use the Best Technology
a) Intelligent Coffee Machines
• Weightings, temperature, time to produce, …
• Coffee perfection
53. #MDBLocal
Key to Success 2: Best Technology
a) Intelligent Coffee Machines
• Weightings, temperature, time to produce, …
• Coffee perfection
b) Intelligent Shelves
• Measure inventory in real time
54. #MDBLocal
Key to Success 2: Best Technology
a) Intelligent Coffee Machines
• Weightings, temperature, time to produce, …
• Coffee perfection
b) Intelligent Shelves
• Measure inventory in real time
c) Intelligent Data Storage
• MongoDB
56. #MDBLocal
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
57. #MDBLocal
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
2. Coffee to deliver to stores read How much coffee do we have to ship to the store in
the next days
58. #MDBLocal
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
2. Coffee to deliver to stores read How much coffee do we have to ship to the store in
the next days
3. Anomalies in the inventory read Analytics
59. #MDBLocal
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
2. Coffee to deliver to stores read How much coffee do we have to ship to the store in
the next days
3. Anomalies in the inventory read Analytics
4. Making a cup of coffee write A coffee machine reporting on the production of a
coffee cup
60. #MDBLocal
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
2. Coffee to deliver to stores read How much coffee do we have to ship to the store in
the next days
3. Anomalies in the inventory read Analytics
4. Making a cup of coffee write A coffee machine reporting on the production of a
coffee cup
5. Analysis of cups of coffee read Analytics
61. #MDBLocal
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
2. Coffee to deliver to stores read How much coffee do we have to ship to the store in
the next days
3. Anomalies in the inventory read Analytics
4. Making a cup of coffee write A coffee machine reporting on the production of a
coffee cup
5. Analysis of cups of coffee read Analytics
6. Technical Support read Helping our franchisees
62. #MDBLocal
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
63. #MDBLocal
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
64. #MDBLocal
1 – Workload: details of the most important queries
Attribute Value
Description Making a cup of coffee at rush hour
Type Write
Frequency 3 000 000 writes/hr
833 writes/sec
Size 100 bytes
Consistency/Integrity weak
Latency < 10 sec
Durability weak
Life/Duration 1 year
Security None
65. #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)
67. #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
71. #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
83. Takeaways from the Presentation
Document vs Tabular
Recognize the
differences
Methodology
Summarize the steps
when modeling for
MongoDB
Patterns
Recognize when to apply
84. Takeaways from the Presentation
Document vs Tabular
Recognize the
differences
Methodology
Summarize the steps
when modeling for
MongoDB
Patterns
Recognize when to apply
85. Takeaways from the Presentation
Document vs Tabular
Recognize the
differences
Methodology
Summarize the steps
when modeling for
MongoDB
Patterns
Recognize when to apply
86. Thank you for taking our FREE
MongoDB classes at
university.mongodb.com
88. #MDBlocal
Every session you rate enters you into a drawing for a gift card and
TWO passes to MongoDB World 2020!
A Complete Methodology
of Data Modeling
with MongoDB
https://www.surveymonkey.com/r/W8N6DLY
96. #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.
97. #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
99. #MDBLocal
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
105. #MDBLocal
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