Building your first app with MongoDB

#mongodbdays @mongodb @nleite #developers
Building your first app
An introduction to MongoDB
Norberto Leite
SA, MongoDB

Let’s not talk about Fußball!

Quick Introduction
• Norberto Leite
– SA
– Madrid, Spain
– norberto@mongodb.com
– @nleite

Welcome to MongoDB Days
Munich!
This is YOUR conference!

Thanks for being part of the Family!

Grab our staff for anything you need!

MongoDB is a ___________ database
• Document
• Open source
• High performance
• Horizontally scalable
• Full featured

Document Database
• Not for .PDF & .DOC files
• A document is essentially an associative array
• Document = JSON object
• Document = PHP Array
• Document = Python Dict
• Document = Ruby Hash
• etc

Open Source
• MongoDB is an open source project
• On GitHub
• Licensed under the AGPL
• Started & sponsored by MongoDB Inc (formerly
10gen)
• Commercial licenses available
• Contributions welcome

High Performance
• Written in C++
• Extensive use of memory-mapped files
i.e. read-through write-through memory caching.
• Runs nearly everywhere
• Data serialized as BSON (fast parsing)
• Full support for primary & secondary indexes
• Document model = less work

Shard 1 Shard 2 Shard 3 Shard N
Horizontally Scalable

Full Featured
• Ad Hoc queries
• Real time aggregation
• Rich query capabilities
• Strongly consistent
• Geospatial features
• Support for most programming languages
• Flexible schema

Setting Expectations
• What is MongoDB
• How to develop with MongoDB
• Scale with MongoDB
• Analytics
• MMS
• Sharding
• Setting the correct environment

Running MongoDB
$ tar –z xvf mongodb-osx-x86_64-2.6.5.tgz
$ cd mongodb-osx-i386-2.4.4/bin
$ mkdir –p /data/db
$ ./mongod

Mongo Shell
MacBook-Air-:~ $ mongo
MongoDB shell version: 2.6.5
connecting to: test
> db.test.insert({text: 'Welcome to MongoDB'})
> db.test.find().pretty()
{
"_id" : ObjectId("51c34130fbd5d7261b4cdb55"),
"text" : "Welcome to MongoDB"
}

Terminology
RDBMS MongoDB
Table, View ➜ Collection
Row ➜ Document
Index ➜ Index
Join ➜ Embedded Document
Foreign Key ➜ Reference
Partition ➜ Shard

Let’s Build a Personal Data Hub!

First step in any application is
Determine your entities

Entities in our Data Hub
• Accounts
• Messages
– emails
– tweets
– comments
– streams
• Notifications

In a relational base app
We would start by doing schema
design

Typical (relational) ERD
Messages
Email
Tweets
Facebook
messages
Alerts
Accounts

In a MongoDB based app
We start building our app
and let the schema evolve

MongoDB ERD
Accounts
- account
- user
- password
- refresh_rate
- uri
Alerts
Messages
- id
- time
- account_id
- from
- to
- body
- attachments
- text
- user
- time
- retweets
- subscribers
- channel
- rate
- period
- metrics:[]
…

Switch to Your DB
>db
test
> use datahub
switching to db datahub

Create our first Document
>var account = {
"name": "gmail",
"credentials": {
"user": "norberto@mongodb.com",
"password": "YOU WISH!"
},
"smtp": "smpt.gmail.com",
"tls": true
}

Switch to Your DB
>db
test
> use datahub
switching to db datahub
> db.accounts.insert( account )

Insert the Record
> db.accounts.insert(account)
No collection creation necessary

Find One Record
> db.accounts.findOne()
{
"_id": ObjectId("54490561150027cc775b1019"),
"name": "gmail",
"credentials": {
},
"tls": true
}

_id
• _id is the primary key in MongoDB
• Automatically indexed
• Automatically created as an ObjectId if not provided
• Any unique immutable value could be used

ObjectId
• ObjectId is a special 12 byte value
• Guaranteed to be unique across your cluster
• ObjectId("50804d0bd94ccab2da652599")
|----ts-----||---mac---||-pid-||----inc-----|
4 3 2 3

Rich Data Types
> db.accounts.findOne()
{
"_id": ObjectId("54490561150027cc775b1019"),
"name": "gmail",
"credentials": {
Strings
},
”last_access": ISODate("2014-10-30T13:09:36.724Z"),
"tls": true
}
Date
Boolean

Inserting Messages (emails, tweets …)
> db.messages.insert({
"_id" : ObjectId("54527e08257844421e64623f"),
"favorited" : false,
"contributors" : null,
"truncated" : false,
"text" : "converting to #java 8",
"in_reply_to_status_id" : null,
”hashtags”: [ "#java", ]
…
}

Inserting Messages (emails, tweets …)
> db.messages.insert({
"_id" : ObjectId("54523d2d25784427c6fabce1"),
"From" : "norberto@mongodb.com",
"To" : "mongodb-user@googlegroups.com",
"Date" : ISODate("2012-08-15T22:32:34Z"),
"body" : {
"text/plain" : ”Hello Munich, nice to see yalll!"
},
"Subject" : ”Live From MongoDB World"
})

Finding a Message
> db.message.find().pretty()
{
"Date" : ISODate("2012-08-15T22:32:34Z"),
"body" : {
},
"Subject" : ”Live From MongoDB World"
}
{
"_id" : ObjectId("54527e08257844421e64623f"),
"text" : "converting to #java 8",
”hashtags”: [ "#java", ]
…
}

Querying An Array
> db.article.find({"hashtags":"#java"}).pretty()
{
"_id" : ObjectId("54527e08257844421e64623f"),
"text" : "converting to #java 8, #programing ",
”hashtags”: [ "#java", "#programing"]
…
}
query in JSON

Using Update to Add a Comment
> db.messages.update({
"_id" : ObjectId("54523d2d25784427c6fabce1") },
{$set: { opened:
{date: ISODate("2012-08-15T22:32:34Z"), user: ’Norberto'}
}
})
>
which is a subdocument set new field on the document

Post with Comment Attached
> db.message.findOne({"_id" : ObjectId("54523d2d25784427c6fabce1")})
{
"Date" : ISODate("2012-08-15T22:32:34Z"),
"body" : {
},
"Subject" : ”Live From MongoDB World”
"opened" : {"date": ISODate("2012-08-15T22:32:34Z"), "user": ’Norberto'}
}
Find document by primary key

Real applications are not
built in the shell

MongoDB has native
bindings for over 12
languages

Morphia
MEAN Stack
Java
Python
Perl
Ruby
Support for the
most popular
languages and
frameworks

Great, I’m excited! What’s next?

Schema Design, Schema
Design, Schema Design,
Schema Design!

Legacy Migration
1. Copy existing schema & some data to MongoDB
2. Iterate schema design development
Measure performance, find bottlenecks, and embed
1. one to one associations first
2. one to many associations next
3. many to many associations
3. Migrate full dataset to new schema
New Software Application? Embed by default

Embedding over Referencing
• Embedding is a bit like pre-joined data
– BSON (Binary JSON) document ops are easy for the server
• Embed (90/10 following rule of thumb)
– When the “one” or “many” objects are viewed in the context
of their parent
– For performance
– For atomicity
• Reference
– When you need more scaling
– For easy consistency with “many to many” associations
without duplicated data

It’s All About Your Application
• Programs+Databases = (Big) Data Applications
• Your schema is the impedance matcher
– Design choices: normalize/denormalize, reference/embed
– Melds programming with MongoDB for best of both
– Flexible for development and change
• Programs×MongoDB = Great Big Data Applications

We've introduced a lot of
concepts here

MMS @
Scale Easily
Best Practices,
Automated
Cut
Management
Overhead

DevOps @
Provision
Upgrade
Scale
Continuous Backup
Point-in-Time Recovery
Performance Alerts

Wrapping up …
• MongoDB is a great Developers Tool
• Designed for :
• Scalability
• Flexibility
• Performance
• Multipurpose
• Great Ecosystem

#mongodbdays
Obrigado!
Norberto Leite
norberto@mongodb.com

Building your first app with MongoDB

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