The document discusses the challenges of developing applications using a relational database and how MongoDB provides a more flexible solution. It provides examples of companies that struggled with projects on a relational database due to issues like unstructured data, multiple data sources, and scalability. Their projects were more successful after switching to MongoDB due to its flexible document model. The document also compares how data would be modeled in a relational database versus MongoDB's document model using examples of product catalog data. MongoDB's embedded documents, arrays and dynamic schema allows for easier modeling of complex and varied data structures.
This is a quick overview of the challenges that BigData and Flexible Schema Databases like MongoDB offer regarding Data Treatment and strategies to overcome them.
Operationalizing the Value of MongoDB: The MetLife ExperienceMongoDB
It was a lot of fun bringing exciting emerging technology into the rigid enterprise infrastructure eco-system. And then the real work began. How do you make the new technology operational? Learn from MetLife’s journey of operationalizing MongoDB to the level compliant with large enterprise requirements in High Availability, Recoverability, Security, Monitoring, Alerting, Workload management and Automation.
Best Practices for MongoDB in Today's Telecommunications MarketMongoDB
It is a challenging time for telecommunications providers: landline voice is in decline, mobile voice margins falling, and the High Speed Internet market is saturated. In order to win in this increasingly competitive landscape, operators must dramatically increase their pace of innovation, focus on the customer experience and, most importantly, bring to market new applications.
In this webinar find out how MongoDB is enabling Telecommunications operators worldwide to:
Improve their current offerings with faster time to market
Roll out new services such as M2M, unified messaging, cloud, and OTT video
Increase customer satisfaction
Operators have tremendous assets in their networks, billing relationships, and knowledge of subscriber behavior across devices and applications. Those that leverage these strengths with greater agility will succeed in the market. The use cases are specific to Telecommunications, but the patterns of usage - agility, scale, global distribution - will be applicable across many industries.
Drop your table ! MongoDB Schema DesignTugdual Grall
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. In this talk, we’ll discuss the tradeoff of various data modeling strategies in MongoDB using various example. You will learn how to work with documents, evolve your schema, and common schema design patterns.
Delivered at Soft Shake '14 and Jug Summer Camp '14
This is a quick overview of the challenges that BigData and Flexible Schema Databases like MongoDB offer regarding Data Treatment and strategies to overcome them.
Operationalizing the Value of MongoDB: The MetLife ExperienceMongoDB
It was a lot of fun bringing exciting emerging technology into the rigid enterprise infrastructure eco-system. And then the real work began. How do you make the new technology operational? Learn from MetLife’s journey of operationalizing MongoDB to the level compliant with large enterprise requirements in High Availability, Recoverability, Security, Monitoring, Alerting, Workload management and Automation.
Best Practices for MongoDB in Today's Telecommunications MarketMongoDB
It is a challenging time for telecommunications providers: landline voice is in decline, mobile voice margins falling, and the High Speed Internet market is saturated. In order to win in this increasingly competitive landscape, operators must dramatically increase their pace of innovation, focus on the customer experience and, most importantly, bring to market new applications.
In this webinar find out how MongoDB is enabling Telecommunications operators worldwide to:
Improve their current offerings with faster time to market
Roll out new services such as M2M, unified messaging, cloud, and OTT video
Increase customer satisfaction
Operators have tremendous assets in their networks, billing relationships, and knowledge of subscriber behavior across devices and applications. Those that leverage these strengths with greater agility will succeed in the market. The use cases are specific to Telecommunications, but the patterns of usage - agility, scale, global distribution - will be applicable across many industries.
Drop your table ! MongoDB Schema DesignTugdual Grall
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. In this talk, we’ll discuss the tradeoff of various data modeling strategies in MongoDB using various example. You will learn how to work with documents, evolve your schema, and common schema design patterns.
Delivered at Soft Shake '14 and Jug Summer Camp '14
The Details That Matter: Kafka in Production, at Scale with Or Arnon and Elad...HostedbyConfluent
Are you running at scale? Did you experience “voodoo problems” in your infrastructure? We have a 5M messages/sec cluster that taught us some valuable lessons. Seeing our Kafka clusters become sluggish or crash, taking our production services with them, we have some insights that we hope help you steer your next production incident and make sure your data pipelines run smoothly. We’ll tell the story of skews and anomalies in CPU and disk metrics - drawing graphs and conclusions. Understand how compacted topics, partitions distribution, and RAM can affect your cluster’s performance. Finally, look at how a small configuration drift can rattle your cluster. Our goal is to provide you with the tools and knowledge to navigate this uncharted territory.
Design and development of polymer column shutterVignesh Cmvr
: The aim of the project is to Convert metal column shuttering plate to plastics using Unigraphics/NX-8. The static structure Analysis done using Ansys. Moldflow analysis is done for flow analysis. Then the Component is Manufactured using rapid prototyping.
Objectives:
To study about current formworks with literature survey & recyclability
To select material and prepare a product design as per the design procedure.
To done Design analysis and Process analysis.
To manufacture the component using Rapid prototyping .
Esteller - Distribuidor de Buck Knives en España y Portugal - CatálogoEsteller
Fundada por Hoyt Buck, la compañía fue fundadaen San Diego, California, y está basada ahora en Post Falls, Idaho. Fabrica cuchillos y navajas de alto rendimiento y calidad desde 1902.
Buck Knives se especializa de cuchillos tácticos, cuchillos de caza y navajas
My name is Elena Long. I am associated with matlabassignmentexperts.com as an Matlab Assignment help expert for the past 4 years and have been helping students with their assignments. I have a Ph.D. in Mechanical Engineering, from McGill University, Canada.
At the PASS Summit in Seattle, one of the outstanding keynote presentations was by Dr. Dave DeWitt, Microsoft Fellow, and leader of the Microsoft Jim Gray Systems Lab, in Madison, WI.
Dr. DeWitt is working on releases 1 and 2 of SQL Server Parallel Database Warehouse. In his keynote he reviewed the 30 year history of CPU, memory, and disk performance. Variations in performance gains across these subsystems, with disk performance lagging badly, have major impacts on database system performance.
Disk performance gains have been made in three areas, Capacity, Transfer Rate, and Average Seek Time. However, the gains over the last 30 years have not been uniform.
Capacity of high performance disk drives has increased by a factor of 10,000. Transfer rates have increased by a factor of 65. The average seek time has only increased by a factor of 10. Dr. DeWitt talked about the impact of these discrepancies on OLTP and Data Warehouse applications.
One of his conclusions is that some problems can be fixed through smarter software, but that “SSDs provide the only real help.”
For more classes visit
www.snaptutorial.com
Write a 700- to 1,050-word informal paper using Microsoft® Word in which you address the following:
Accurately definethe terms "database system" and "relational database architecture".
In your Learning Team next week, you will have a defined application for a database that you will construct together, but this week, think about what you personally could use a database for in your personal life. Choose a subject from your work/home/hobby that requires the keeping of multiple records; e.g., music collections,
For more classes visit
www.snaptutorial.com
Write a 700- to 1,050-word informal paper using Microsoft® Word in which you address the following:
Accurately definethe terms "database system" and "relational database architecture".
In your Learning Team next week, you will have a defined
For more classes visit
www.snaptutorial.com
Write a 700- to 1,050-word informal paper using Microsoft® Word in which you address the following:
Accurately definethe terms "database system" and "relational database architecture".
In your Learning Team next week, you will have a defined application for a database that
Real-world data is naturally connected. Learn how to create graph database applications on Azure Cosmos DB and explore the different solutions that it provides to common data scenarios in the enterprise. We will also cover customer cases that currently leverage graph databases in their day-to-day workloads.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
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.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
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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The Details That Matter: Kafka in Production, at Scale with Or Arnon and Elad...HostedbyConfluent
Are you running at scale? Did you experience “voodoo problems” in your infrastructure? We have a 5M messages/sec cluster that taught us some valuable lessons. Seeing our Kafka clusters become sluggish or crash, taking our production services with them, we have some insights that we hope help you steer your next production incident and make sure your data pipelines run smoothly. We’ll tell the story of skews and anomalies in CPU and disk metrics - drawing graphs and conclusions. Understand how compacted topics, partitions distribution, and RAM can affect your cluster’s performance. Finally, look at how a small configuration drift can rattle your cluster. Our goal is to provide you with the tools and knowledge to navigate this uncharted territory.
Design and development of polymer column shutterVignesh Cmvr
: The aim of the project is to Convert metal column shuttering plate to plastics using Unigraphics/NX-8. The static structure Analysis done using Ansys. Moldflow analysis is done for flow analysis. Then the Component is Manufactured using rapid prototyping.
Objectives:
To study about current formworks with literature survey & recyclability
To select material and prepare a product design as per the design procedure.
To done Design analysis and Process analysis.
To manufacture the component using Rapid prototyping .
Esteller - Distribuidor de Buck Knives en España y Portugal - CatálogoEsteller
Fundada por Hoyt Buck, la compañía fue fundadaen San Diego, California, y está basada ahora en Post Falls, Idaho. Fabrica cuchillos y navajas de alto rendimiento y calidad desde 1902.
Buck Knives se especializa de cuchillos tácticos, cuchillos de caza y navajas
My name is Elena Long. I am associated with matlabassignmentexperts.com as an Matlab Assignment help expert for the past 4 years and have been helping students with their assignments. I have a Ph.D. in Mechanical Engineering, from McGill University, Canada.
At the PASS Summit in Seattle, one of the outstanding keynote presentations was by Dr. Dave DeWitt, Microsoft Fellow, and leader of the Microsoft Jim Gray Systems Lab, in Madison, WI.
Dr. DeWitt is working on releases 1 and 2 of SQL Server Parallel Database Warehouse. In his keynote he reviewed the 30 year history of CPU, memory, and disk performance. Variations in performance gains across these subsystems, with disk performance lagging badly, have major impacts on database system performance.
Disk performance gains have been made in three areas, Capacity, Transfer Rate, and Average Seek Time. However, the gains over the last 30 years have not been uniform.
Capacity of high performance disk drives has increased by a factor of 10,000. Transfer rates have increased by a factor of 65. The average seek time has only increased by a factor of 10. Dr. DeWitt talked about the impact of these discrepancies on OLTP and Data Warehouse applications.
One of his conclusions is that some problems can be fixed through smarter software, but that “SSDs provide the only real help.”
For more classes visit
www.snaptutorial.com
Write a 700- to 1,050-word informal paper using Microsoft® Word in which you address the following:
Accurately definethe terms "database system" and "relational database architecture".
In your Learning Team next week, you will have a defined application for a database that you will construct together, but this week, think about what you personally could use a database for in your personal life. Choose a subject from your work/home/hobby that requires the keeping of multiple records; e.g., music collections,
For more classes visit
www.snaptutorial.com
Write a 700- to 1,050-word informal paper using Microsoft® Word in which you address the following:
Accurately definethe terms "database system" and "relational database architecture".
In your Learning Team next week, you will have a defined
For more classes visit
www.snaptutorial.com
Write a 700- to 1,050-word informal paper using Microsoft® Word in which you address the following:
Accurately definethe terms "database system" and "relational database architecture".
In your Learning Team next week, you will have a defined application for a database that
Real-world data is naturally connected. Learn how to create graph database applications on Azure Cosmos DB and explore the different solutions that it provides to common data scenarios in the enterprise. We will also cover customer cases that currently leverage graph databases in their day-to-day workloads.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
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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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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Bob Boule
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Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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- Visualization tools to display your network;
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
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Defect reporting
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
2. Agenda
• What can go wrong?
– Plus, some success stories
• Why are relational projects difficult?
• Why more successful with MongoDB?
3. Part 1 – What can go wrong?
(and how MongoDB customers
got it right)
4. Projects Fail
1. Never get to initial version?
2. Initial success, but then can’t scale?
3. Initial success, but new requirements derail them?
4. Application development is slow
5. Common Reasons
1. Working with unstructured or semi-structure data
2. Polymorphic data
3. Disparate data from multiple sources
4. Platform not architected for scale
5. Development environment does meet developers
needs
6. The problem is in the foundation
Your Application
RDBMS
Lets look at a few organizations that
chose a different foundation
7. Example 1 – Managing Diverse
Products
Delivers agile automated supply chain service to
retailers powered by MongoDB
Problem
• RDBMS poorlyequipped to handle
varying data types
(e.g., SKUs, images)
• Inefficient use of
storage in RDBMS
(i.e., 90% empty
columns)
• Complex joins
degraded performance
Why MongoDB
• Document-oriented model
less complex, easier to
code
Results
• Decreased supplier
onboard time by 12x
• Grew from 400K records to
40M in 12 months
• Single data store for
structured, semi-structured
• Significant cost reductions
and unstructured data
on schema design
• Scalability and availability
time, ongoing developer
effort, and storage usage
• Analytics with MapReduce
8. Example 2 – Multiple Data
Sources
The Wall
–
–
–
–
–
360 real-time view of 100M customers
70 siloed systems
Tried for 2 years on RDBMS
Massive TCO savings
$300M commitment to Big Data
• Why MongoDB?
– Flexible data model
– Fast app development
9. Example 3 - Scalability
Stores billions of posts in myriad formats with
MongoDB
Problem
Why MongoDB
Results
• 1.5M posts per
day, different structures
• Flexible documentbased model
• Inflexible
MySQL, lengthy delays
for making changes
• Horizontal scalability
built in
• Data piling up in
production database
• Easy to use
• Automated failover
provides high
availability
• Interface in familiar
language
• Schema changes are
quick and easy
• Poor performance
• Initial deployment held
over 5B documents and
10TB of data
11. Relational: All Data is Column/Row
Customer ID
0
1
2
3
4
First Name
John
Mark
Jay
Meagan
Edward
Last Name
Doe
Smith
Black
White
Daniels
City
New York
San Francisco
Newark
London
Boston
Account Number
10
11
12
13
14
15
Branch ID
100
101
101
200
200
201
Account Type
Checking
Savings
IRA
Checking
Savings
IRA
Customer ID
0
0
0
1
1
2
12. But, some things are hard to model
Let’s look at a product catalog:
13. Baseball Bat
-3 length to weight ratio
2-5/8" barrel diameter
Two-piece construction
R2 alloy barrel provides outstanding durability, performance and "pop"
R2 composite handle shifts weight into the bat's knob for ultra-fast swing speeds
Rifle Barrel design removes weight from the barrel for thinner wall thickness
Acoustic barrel offers that sweet-sounding "ping"
Contact grip helps eliminate sting and vibration
AIR Elite is RIP-IT's® fastest BBCOR bat and the one with the most performance
BBCOR certified - approved for high school and collegiate play
Includes RIP-IT's® "Love It Or Return It" 30 Day Refund Policy with free return shipping
Manufacturer's warranty: 400 days
Made in the USA
Model: B1403E
14. Bat Product Table
Categor
Model
y
Name
Length
to
Barrel
Brand
weight Dia
ratio
Type
Barrel Handle
Bat
Bat
B1403E Air Elite RIP-IT
B1403 Prototype RIP-IT
-3
-3
Bat
Marucc
i
-3
Composit
2 5/8
e
R2 Alloy
2 5/8 One-piece R1 Alloy
AZ3000
aluminu
2 5/8 One-piece
m
-3
Composit
2 5/8
e
Bat
MCB1B
BB14S1
One
S1
Easton
IMX
R2
composi
te
R1 Alloy
AZ3000
aluminu
m
SIC
Black
Carbon
Cert.
BBCOR
BBCOR
Country Price
USA
USA
$399.99
$199.99
BBCOR Imported $199.99
BBCOR China $399.99
15. Lets Add Gloves
Size: 12"
Infield/Outfield/Pitcher model
2-Piece Web pattern
Most popular MLB® pattern among pitchers
Pro Stock® American steerhide leather offers rugged durability and a superior feel
Dual-Welting™ on "exposed edges" of the fingers helps maintain pocket shape and durability
Pro Stock™ hand-designed pattern for unbeatable craftsmanship
Dri-Lex® ultra-breathable wrist lining repels moisture from your hand
Black leather with rich brown embellishments
Pattern: B212
Model: WTA2000BBB212
Wilson
16. Bat and Glove Product Table
Categor
Model
y
Bat
Name
Length
to
Barrel
Brand
weight Dia
ratio
Type
Barrel
Handle
Cert. Country Price
R2
Composit
composit
e
R2 Alloy
e
BBCOR
USA
$399.99
USA
$199.99
-3
2 5/8
Bat
B1403E Air Elite RIP-IT
Prototyp
B1403
e
RIP-IT
-3
2 5/8 One-piece R1 Alloy R1 Alloy BBCOR
Bat
MCB1B
-3
2 5/8 One-piece
Easton
-3
Composit
e
Brand
Size
Wilson
12"
One
Bat BB14S1
S1
Categor
Model Name
y
WTA200
0BBB21
Glove
2
A2000
PRO112
Glove
PT
HOH
Pro
Marucci
2 5/8
AL
AL
BBCOR Imported $199.99
SIC
Black
Carbon BBCOR China $399.99
IMX
Web
Position Pattern
Material Color Country Price
Pattern
Infield
Rawling
s
11.25" Outfield
B212
Pro
taper
2-piece Leather
Modified Horwee
Trapn
Eze
Leather
black
Vietnam $299.99
black
China $229.99
17. Add some baseballs
Cover: Full grain leather for excellent durability
Core: Cushioned cork core
Additions/Technologies: Made to the exact specifications of MLB
Stitching/Seams: 108 classic red stitches/Rawlings® Major League seaming
League/Certification(s): MLB
Balls included per purchase: individual
Recommended Age: All ages
Model : ROMLB
Rawlings
18. Bat and Glove and Ball Product
Table
Categor
Model
y
Name
Length
to
Barrel
Brand
weight Dia
ratio
Type
Bat
B1403E Air Elite RIP-IT
-3
2 5/8 Composite
Bat
B1403 Prototype RIP-IT
Marucc
MCB1B
One
i
-3
2 5/8 One-piece
-3
2 5/8 One-piece
Easton
Brand
-3
Size
2 5/8 Composite
Position Pattern
Wilson
12"
Bat
Bat
S1
Categor BB14S1
Model Name
y
WTA200
0BBB21
Glove
2
A2000
Infield
B212
PRO112 HOH Rawling
Pro
Categor
Glove Model Name Brand 11.25" Outfield taper
PT
Pro
s
Color Cover
Core
y
DICRLL Little Rawling
Cork
Baseball B1PBG League
s
white Leather rubber
Rawling
Baseball ROML
MLB
s
white Leather cork
Barrel
Handle
Cert. Country Price
R2
BBCO
R2 Alloy composite R
USA $399.99
BBCO
R1 Alloy R1 Alloy
R
USA $199.99
BBCO
AL
AL
R Imported $199.99
SIC Black BBCO
IMX
Carbon
R
China $399.99
Web
Material Color Country Price
Pattern
2-piece Leather
Modified Horwee
Trapn
Eze
Leather
black
Vietnam $299.99
black
Cert.
China
Country $229.99
Price
Little
League
China
$4.99
China
$6.99
19. Sparse Table
Length
Catego
to
Barrel
Model Name Brand
ry
weight Dia
ratio
Type
Barrel Handle
Certific Countr
Price
ation
y
Size
Bat
B1403E
Air Elite
RIP-IT
-3
2 5/8 Composite
R2 Alloy
R2 composite BBCOR
USA
B1403
Prototype
RIP-IT
-3
2 5/8 One-piece
R1 Alloy
R1 Alloy
BBCOR
USA
$199.99
2 5/8 One-piece
AZ3000
aluminum
AZ3000
aluminum
BBCOR
Imported
$199.99
IMX
SIC Black
Carbon
BBCOR
China
$399.99
Vietnam
$299.99 12"
Core
$399.99
Bat
Positio
Web Materia
Pattern
Color Cover
n
Pattern
l
Bat
MCB1B
One
Bat
BB14S1
Glove
WTA2000BBB
212
A2000
S1
HOH Pro
Marucci
Easton
Wilson
-3
-3
2 5/8 Composite
2-piece
Leather
black
Pro taper
Modified
Trap-Eze
Horween
Leather
black
PRO112PT
Rawlings
China
Baseball
DICRLLB1PBG Little League Rawlings
Little League China
$4.99
white
Leather
cork and
rubber
Baseball
ROML
China
$6.99
white
Leather
cork
Rawlings
Outfield
B212
Glove
MLB
$229.99 11.25"
Infield
Continue adding columns as you add new products
20. Maybe this design will work better
prodI
D
Catego
Model
Name
Brand Country Price
ry
prodID
1
Bat B1403E Air Elite RIP-IT
USA $399.99
2
Bat
B1403 Prototype RIP-IT
USA $199.99
Importe
3
Bat
MCB1B
One
Marucci
d
$199.99
4
Bat BB14S1
S1
Easton China $399.99
WTA200
0BBB21
5
Glove
2
A2000 Wilson Vietnam $299.99
PRO112
Rawling
6
Glove
PT
HOH Pro
s
China $229.99
Basebal DICRLL
Little
Rawling
7
l
B1PBG League
s
China
$4.99
Basebal
Rawling
8
l
ROML
MLB
s
China
$6.99
1
1
1
1
property value
length/wei
ght
-3
barrel dia 2 5/8
composi
type
te
certificatio
n
BBCOR
…
5
5
5
5
5
size
position
pattern
material
color
12
infield
B212
leather
black
…
8
8
8
color
cover
core
white
leather
cork
21. What about this product?
One product = a set of products
Lists are hard
Need more tables
Properties
Product
Sub
Products
22. Add this product?
More complexity
There are solutions
Designing/redesigning takes
time
More tables?
23. This is hard…
Long time to develop…
Difficult to change…
Queries are complex…
31. How would we model this
in MongoDB?
Size: 12"
Infield/Outfield/Pitcher model
2-Piece Web pattern
Most popular MLB® pattern among pitchers
Pro Stock® American steerhide leather offers rugged durability and a superior feel
Dual-Welting™ on "exposed edges" of the fingers helps maintain pocket shape and durability
Pro Stock™ hand-designed pattern for unbeatable craftsmanship
Dri-Lex® ultra-breathable wrist lining repels moisture from your hand
Black leather with rich brown embellishments
Pattern: B212
Model: WTA2000BBB212
Wilson
32. We use a document
{
category: “glove”,
model: “PRO112PT”,
name: “Air Elite”,
brand: “Rawlings”,
price: 229.99,
available: Date(“2013-03-31”)
Fields
}
Values
Field values
are typed
37. Variation is easy with document
model
{
category: glove,
model: PRO112PT,
name: Air Elite,
brand: “Rawlings”,
price: “229.99”
category: bat,
model: B1403E,
name: Air Elite,
brand: “Rip-IT”,
price: 399.99
size: 11.25,
position: outfield,
pattern: “Pro taper”,
material: leather,
color: black
diameter: “2 5/8”,
barrel: R2 Alloy,
handle: R2 Composite,
type: composite,
}
{
}
{
category: ball,
model: ROML,
name: MLB,
brand: “Rawlings”,
price: “6.99”
cover: leather,
core: cork,
color: white
}
38. MongoDB: Adapting to new
requirements
meta
meta
meta
meta
…
meta
meta
meta
meta
Legacy
data
New
data
39. Life is much easier for developers
Code
XML Config
DB Schema
Application
Object Relational
Mapping
Relational
Database
Code
Text Search
Rich
Queries
Application
Geospatial
Aggregatio
n
Map Reduce
40. Write queries using familiar language
Drivers
Drivers for most popular
programming languages and
frameworks
Java
Ruby
JavaScript
Perl
Python
Shell
Command-line shell for interacting
directly with database
> db.collection.insert({company:“10gen”, product:“MongoDB”})
>
> db.collection.findOne()
{
“_id”: ObjectId(“5106c1c2fc629bfe52792e86”),
“company”: “10gen”
“product”: “MongoDB”
}
Haskell
49. Relational DBs don’t work for modern
applications
•
Application foundation doesn’t support data
requirements
•
•
Product catalog
Integrating data from multiple systems
•
Developers manage lots of complexity
•
Change is onerous
•
Scaling is expensive
Your Application
RDBMS
50. Life is simpler with MongoDB
•
Flexible Document Model
•
Easily supports
•
•
New data
Changing requirements
•
Developer friendly
•
Scales horizontially
•
Low cost
Your Application
RDBMS
From mainframes, to RAC Oracle servers... People solved problems by adding more resources to a single machine.
secondary indexes, compound indexes, multikey indexes.why is it important to have all of document together? data locality
Large scale operation can be combined with high performance on commodity hardware through horizontal scalingBuild - Document oriented database maps perfectly to object oriented languagesScale - MongoDB presents clear path to scalability that isn't ops intensive - Provides same interface for sharded cluster as single instance