Software Engineer, MongoDB

Hannes Magnusson

#MongoDB

Common MongoDB Use Cases

NoSQL and MongoDB

NoSQL Features

Flexible Data Models• Lists, embedded

objects• Sparse data• Semi-structured

data• Agile development

High Data Throughput• Reads• Writes

Big Data• Aggregate Data

Size• Number of Objects

Low Latency • For reads and

writes• Millisecond Latency

Cloud Computing• Runs everywhere• No special

hardware

Commodity Hardware• Ethernet• Local data storage

• JSON Based • Dynamic

Schemas

• Replica Sets to scale reads

• Sharding to scale writes

• 1000s of shards in a single DB

• Data partitioning

• Designed for “typical” OS and local file system

• Scale-out to overcome hardware limitations

• In-memory cache

• Scale-out working set

Use Cases

High Volume Data Feeds

• More machine forms, sensors & data

• Variably structured

Machine Generated

Data

• High frequency trading• Daily closing price

Securities Data

• Multiple data sources• Each changes their format

consistently• Student Scores, ISP logs

Social Media /General Public

High Volume Data Feeds

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 users• Very strict latency requirements• Sentiment Analysis

Ad Targeting

• Expose data to millions of customers

• Reports on large volumes of data• Reports that update in real time

Real time dashboards

• Join the conversation• Catered Games • Customized Surveys

Social Media Monitoring

Operational Intelligence

Dashboards

API

Low latency reads

Parallelize queries across replicas and

shards

In database aggregation

Flexible schema adapts to

changing input data

Can use same cluster to

collect, store and report on

data

{ 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 } ] …

Behavioural Profiles

1

2

3

See Ad

See Ad

4

Click

Convert

Rich profiles collecting multiple complex actions

Scale out to support high throughput of

activities tracked

Dynamic schemas make it easy to

Metadata

• Diverse product portfolio• Complex querying and filtering• Multi-faceted product attributes

Product Catalogue

• Data mining• Call records• Insurance Claims

Data analysis

• Retina Scans• FingerprintsBiometric

Metadata

{ ISBN: “00e8da9b”, type: “Book”, country: “Egypt”, title: “Ancient Egypt”}

{ type: “Artifact”, medium: “Ceramic”, country: “Egypt”, year: “3000 BC”}

Flexible data model for similar

but different objects

Indexing and rich query API for easy

searching and sorting

db.archives. find({ “country”: “Egypt” });

db.archives. find({key:“type”, value:“Artifact”});

Indexing techniques that

fit your data modeling

Content Management

• Comments and user generated content

• Personalization of content and layout

News Site

• Generate layout on the fly• No need to cache static pages

Multi-device rendering

• Store large objects• Simpler 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 searches

GridFS for large object storage

Is MongoDB a good fit for my use case?

Is there an Ideal use case?

Application Why MongoDB Might be a good fit

Large number of objects to store

Sharding lets you split objects across multiple servers

High write / read throughput and data distribution

Sharding + Replication lets you scale read and write traffic across multiple servers, multiple tenants, or data centers

Low latency access Memory mapped storage engine caches documents in RAM, enabling in-memory operations. Data locality of documents significantly improves 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, and provide for an intuitive query language

Cloud based deployment Sharding and replication let you work around hardware limitations in the cloud.

Hannes.magnusson@mongodb.comHannes Magnusson

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Thank You!

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