When to use MongoDB

Part 1 of a series

Real-Time

Analytics with

Mongodb

April 12th

Content

Management with

MongoDB

May 17th

@forjared

TodayLast 10 years

Emerging NoSQL Space

RDBMS

Data

Warehou

se

NoSQL

RDBMS

Data

Warehou

se

The beginning

RDBMS

Qualities of NoSQL

Workloads

Flexible data models

• Lists, Nested Objects

• Sparse schemas

• Semi-structured data

• Agile Development

High Throughput

• Lots of reads

• Lots of writes

Large Data Sizes

• Aggregate data size

• Number of objects

Low Latency

• Both reads and writes

• Millisecond latency

Cloud Computing

• Run anywhere

• No assumptions about hardware

• No / Few Knobs

Commodity Hardware

• Ethernet

• Local disks

MongoDB was designed for

this

Flexible data models

• Lists, Nested Objects

• Sparse schemas

• Semi-structured data

• Agile Development

High Throughput

• Lots of reads

• Lots of writes

Large Data Sizes

• Aggregate data size

• Number of objects

Low Latency

• Both reads and writes

• Millisecond latency

Cloud Computing

• Run anywhere

• No assumptions about hardware

• No / Few Knobs

Commodity Hardware

• Ethernet

• Local disks

• JSON based

object model

• Dynamic

schemas

• Replica Sets to

scale reads

• Sharding to

scale writes

• 1000’s of shards

in a single DB

• Partitioning of

data

• In-memory

cache

• Scale-out

working set

• Scale-out to

overcome

hardware

limitations

• Designed for

“typical” OS and

local file system

Example customers

User Data Management High Volume Data Feeds

Content Management Operational Intelligence Product Data Management

USE CASES THAT

LEVERAGE NOSQL

High Volume Data Feeds

• More machines, more sensors, more data

• Variably structured

Machine Generated

Data

• High frequency tradingStock Market

Data

• Multiple sources of data

• Each changes their format constantly

Social Media Firehose

High Volume Data Feed

Data

Sources

Asynchronous writes

Flexible document

model can adapt to

changes in sensor

format

Write to memory with

periodic disk flush

Data

SourcesData

SourcesData

Sources

Scale writes over

multiple shards

Operational Intelligence

• Large volume of state about users

• Very strict latency requirementsAd Targeting

• Expose report data to millions of customers

• Report on large volumes of data

• Reports that update in real time

Customer Facing

Dashboards

• Need to join the conversation _now_Social Media Monitoring

Operational Intelligence

Dashboards

API

Low latency readsParallelize queries

across replicas and

shards

In database

aggregation

Flexible schema

adapts to changing

input dataCan use same cluster

to collect, store, and

report on data

Behavioral Profiles

1

2

3

See Ad

See Ad

4

Click

Convert

{ cookie_id: ‚1234512413243‛,advertiser:{

apple: {actions: [

{ impression: ‘ad1’, time: 123 },{ impression: ‘ad2’, time: 232 },{ click: ‘ad2’, time: 235 },{ add_to_cart: ‘laptop’,

sku: ‘asdf23f’, time: 254 },

{ purchase: ‘laptop’, time: 354 } ]

}}

}

Rich profiles

collecting multiple

complex actions

Scale out to support

high throughput of

activities tracked

Indexing and

querying to support

matching, frequency

capping

Dynamic schemas

make it easy to track

vendor specific

attributes

Product Data

• Diverse product portfolio

• Complex querying and filtering

E-Commerce Product Catalog

• Scale for short bursts of high volume traffic

• Scalable, but consistent view of inventoryFlash Sales

Product Data

{ sku: ‚00e8da9b‛,type: ‚MP3‛,details: {

artist: ‚John Coltrane‛,title: ‚A love supreme‛, length: 123

}}

{ sku: ‚00a9f3a‛,type: ‚Book‛,details: {

author: ‚David Eggers‛,title: ‚You shall know our velocity‛, isbn: ‚0-9703355-5-5‛

}}

Flexible data model

for similar, but

different objects

Indexing and rich

query API for easy

searching and sorting

db.products.find({ ‚details.author”: ‚David Eggers‛ }).sort({ ‚title‛ : -1 });

Content Management

• Comments and user generated content

• Personalization of content, layoutNews Site

• Generate layout on the fly for each device that connects

• No need to cache static pages

Multi-Device rendering

• Store large objects

• Simple modeling of metadataSharing

Content Management

{ camera: ‚Nikon d4‛,location: [ -122.418333, 37.775 ]

}

{ camera: ‚Canon 5d mkII‛,people: [ ‚Jim‛, ‚Carol‛ ], taken_on: ISODate("2012-03-07T18:32:35.002Z")

}

{ origin: ‚facebook.com/photos/xwdf23fsdf‛,license: ‚Creative Commons CC0‛, size: {

dimensions: [ 124, 52 ],units: ‚pixels‛

}}

Flexible data model

for similar, but

different objects

Horizontal scalability

for large data sets

Geo spatial indexing

for location based

searchesGridFS for large

object storage

User Data Management

• User state and session management

Video Games

• Scale out to large graphs

• Easy to search and processSocial Graphs

• Authentication, Authorization and Accounting

Identity Management

User Game State

Flexible documents

supports new game

features without

schema migration

Sharding enables

whole data set to be

in memory, ensuring

low latency

JSON data model

maps well to

HTML5/JS & Flash

based clients

Easy to store entire

player state in a

single document.

Social Graph

Social Graphs

Documents enable

disk locality of all

profile data for a user

Sharding partitions

user profiles across

available servers

Native support for

Arrays makes it easy

to store connections

inside user profile

IS MY USE CASE A GOOD

FIT FOR MONGODB?

Good fits for MongoDB

Application Characteristic Why MongoDB might be a good fit

Large number of objects to

store

Sharding lets you split objects across multiple

servers

High write or read throughput Sharding + Replication lets you scale read and

write traffic across multiple servers

Low Latency Access Memory Mapped storage engine caches

documents in RAM, enabling in-memory

performance. Data locality of documents can

significantly improve latency over join based

approaches

Variable data in objects Dynamic schema and JSON data model enable

flexible data storage without sparse tables or

complex joins

Cloud based deployment Sharding and replication let you work around

hardware limitations in clouds.

Thanks!

Real-Time Analytics

April 12th

MongoDB and AWS CloudFormation

April 25th

New Aggregation Framework

May 10th

Content Management

May 17th