Building your First MEAN App
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The document summarizes an agenda for a presentation on building a MEAN stack application with MongoDB. It discusses designing schemas in MongoDB, using the MEAN stack, visualizing MongoDB data with tools like D3.js and NVD3.js, scalability and availability with MongoDB, and deployment architectures and operations.
![7
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, … }
}
}](https://image.slidesharecdn.com/eveningwithmongodb-buildingyourfirstmeanapp20140408-140409105844-phpapp02/85/Building-your-First-MEAN-App-7-320.jpg)
![8
Documents are Rich Data Structures
{
first_name: „Paul‟,
surname: „Miller‟,
cell: „+447557505611‟
city: „London‟,
location: [45.123,47.232],
Profession: [banking, finance, trader],
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}
Fields can contain an array of
sub-documents
Fields
Typed field values
Fields can
contain arrays](https://image.slidesharecdn.com/eveningwithmongodb-buildingyourfirstmeanapp20140408-140409105844-phpapp02/85/Building-your-First-MEAN-App-8-320.jpg)
![14
Do More With Your Data
MongoDB
Rich Queries
• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980
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, … }
}
}](https://image.slidesharecdn.com/eveningwithmongodb-buildingyourfirstmeanapp20140408-140409105844-phpapp02/85/Building-your-First-MEAN-App-13-320.jpg)
![{
“_id”: ,
“name”: ,
“title”: ,
“company”: ,
“address”: [
{
“street”: ,
“city”: ,
“state”: ,
“zip_code”: ,
“country”:
},
{
“street”: ,
“city”: ,
“state”:
}
],
“phone”:
}
Schema Flexibility
{
_id : ,
name : ,
title : ,
company : ,
address : {
street : ,
city : ,
state : ,
zip_code : ,
country :
},
phone :
}](https://image.slidesharecdn.com/eveningwithmongodb-buildingyourfirstmeanapp20140408-140409105844-phpapp02/85/Building-your-First-MEAN-App-20-320.jpg)
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Building your First MEAN App
- 1. Evening with MongoDB Building your first MEAN app Solution Architect, MongoDB, Inc. [email protected] Dmitry Baev
- 2. Agenda • Designing your schema in MongoDB • How to be MEAN • Visualizing your MongoDB data in a MEANingful way! • Scalability &Availability • DeploymentArchitectures & Operations
- 4. Terminology MongoDB RDBMS Collection Table Document Row Index Index Embedded Document Join Reference Foreign Key
- 5. Focus on data storage Focus on data use
- 6. What answers do I have? What questions do I have?
- 7. 7 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, … } } }
- 8. 8 Documents are Rich Data Structures { first_name: „Paul‟, surname: „Miller‟, cell: „+447557505611‟ city: „London‟, location: [45.123,47.232], Profession: [banking, finance, trader], cars: [ { model: „Bentley‟, year: 1973, value: 100000, … }, { model: „Rolls Royce‟, year: 1965, value: 330000, … } } } Fields can contain an array of sub-documents Fields Typed field values Fields can contain arrays
- 9. 9 Document Model Benefits • Agility and flexibility – Data model supports business change – Rapidly iterate to meet new requirements • Intuitive, natural data representation – Eliminates ORM layer – Developers are more productive • Reduces the need for joins, disk seeks – Programming is more simple – Performance delivered at scale
- 10. 10 Developers are more productive
- 11. 11 Developers are more productive
- 13. 14 Do More With Your Data MongoDB Rich Queries • Find Paul’s cars • Find everybody in London with a car built between 1970 and 1980 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, … } } }
- 14. 15 Drivers & Ecosystem Drivers Support for the most popular languages and frameworks Frameworks Morphia MEAN Stack Java Python Perl Ruby
- 15. Modeling Data with MongoDB
- 17. Referencing Addresses { _id : , street : , city : , state : ”, zip_code : , country : } Contacts { _id : , name : , title : , company : ”, phone : , address_id : }
- 18. Embedding Contacts { _id : , name : , title : , company : , address : { street : , city : , state : , zip_code : , country : }, phone : }
- 19. Schema Flexibility { name : , title : , company : , address : { street : , city : , state : , zip_code : }, phone : } { name : , url : , title : , company : , email : , address : { street : , city : , state : , zip_code : } phone : , fax }
- 20. { “_id”: , “name”: , “title”: , “company”: , “address”: [ { “street”: , “city”: , “state”: , “zip_code”: , “country”: }, { “street”: , “city”: , “state”: } ], “phone”: } Schema Flexibility { _id : , name : , title : , company : , address : { street : , city : , state : , zip_code : , country : }, phone : }
- 21. Let’s get MEAN
- 23. How to be MEAN! ServerClient ------------------------------------------------------ View Controller Service Route Controller Model REST API call
- 24. Node.js MongoDB Driver • Install using npm • Basic CRUD • Mongos • Aggregation • Replica Set • Sharding • Read preferences
- 25. Mongoose • Provides “elegant MongoDB object modeling for Node.js” using Schemas and Models • Each schema maps to a MongoDB collection • Models can be instantiated based on a schema • Instances of models represent documents in MongoDB • Support for CRUD, aggregation, validation, pseudo-joins, rich query builder + lots more! • Install using npm
- 26. Mongoose (getting started) • Require Node module • Connect to MongoDB • Define your schema in JSON • Create the model object
- 27. Mongoose (example) • Create/ Update – #model.save() • Read – #model.findOne() – #model.find() • Update – #model.update() • Delete – #model.remove() • Aggregate – #model.aggregate()
- 28. Visualizing data • D3.js – JavaScript library for manipulating documents based on data – bring your data to life • NVD3.js – reusable charts for d3.js • Angularjs-nvd3-directives – Angular‟s way of extending HTML to create new elements and functionality
- 30. Real-time streaming charts • Socket.IO (Node.js module) – “aims to make realtime apps possible in every browser and mobile device” • MongoDB Tailable Cursors – requires a capped collection • NVD3.js Event Listeners – charts can be refreshed continuouslyas data is inserted/update in MongoDB collection
- 31. Demo: Visualizing your MongoDB data in a MEANingful way!
- 32. City of Toronto – Open Data • Data source: REST ServiceAPI – To retrieve a list of service requestbetween Feb 1, 2012 to Feb 7, 2012 https://secure.toronto.ca/webwizard/ws/requests.json?start_date=2012-02- 01T00:00:00Z&end_date=2012-02-07T00:00:00Z&jurisdiction_id=toronto.ca • Output format: JSON • Refresh rate: continuously - real time refresh
- 33. Getting started with MEAN • MEAN.io – ready to use MEAN stack boilerplate • Yeoman – scaffolding tool for building modern web apps – CommunityMEAN stack generators: • https://github.com/wlepinski/generator-meanstack • https://github.com/jrcryer/generator-mean • https://github.com/chrisenytc/generator-meanis
- 35. 36 Scalability Auto-Sharding • Increase capacity as you go • Commodity and cloud architectures • Improved operational simplicity and cost visibility
- 36. 37 High Availability • Automated replication and failover • Multi-data center support • Improved operational simplicity (e.g., HW swaps) • Data durability and consistency
- 39. 40 Single Data Center • Automated failover • Tolerates server failures • Tolerates rack failures • Number of replicas defines failure tolerance Primary – A Primary – B Primary – C Secondary – A Secondary – ASecondary – B Secondary – BSecondary – CSecondary – C
- 40. 41 Active/Active Data Center • Tolerates server, rack, data center failures, network partitions Data Center - West Primary – A Primary – B Primary – C Secondary – A Secondary – BSecondary – C Data Center - East Secondary – A Secondary – B Secondary – C Secondary – B Secondary – C Secondary – A Data Center - Central Arbiter – A Arbiter – B Arbiter – C
- 42. References • MEAN.io - http://www.mean.io • MongoDB - http://mongodb.com • Express - http://expressjs.com • Angular - http://angularjs.org • Node - http://nodejs.org • Socket.IO- http://http://socket.io • Angularjs-nvd3-directives - http://cmaurer.github.io/angularjs-nvd3- directives
- 44. Online Training at MongoDB University
- 45. For More Information Resource Location MongoDB Downloads mongodb.org/downloads Free Online Training education.mongodb.com Webinars and Events mongodb.com/events White Papers mongodb.com/white-papers Case Studies mongodb.com/customers Presentations mongodb.com/presentations Documentation docs.mongodb.org Additional Info [email protected] Resource Location
- 46. Thank You Solution Architect, MongoDB, Inc. [email protected] Dmitry Baev
Editor's Notes
- #8: 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.
- #14: Rich queries, text search, geospatial, aggregation, mapreduce are types of things you can build based on the richness of the query model.