building and improving products with hadoop

2013

Building and

Improving Products

with Hadoop

Matthew Rathbone

2013

What is Foursquare

Foursquare helps you explore the world around you.

Meet up with friends, discover new places, and save money using your phone.

4bn check-ins

35mm users

50mm POI

150 employees

1tb+ a day of data

2013

FIRST, A STORY

http://www.flickr.com/photos/shannonpatrick17

2013

The Right Tool for the Job

• Nginx – Serving static files

• Perl – Regular expressions

• XML – Frustrating people

• Hadoop (Map Reduce) – Counting

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COUNTING – WHAT IS IT GOOD FOR

http://www.flickr.com/photos/blaahhi/

2013

Statistically Improbable Phrases

Statistically Improbable Phrases

2013

SIPS use cases

• menu extraction

• sentiment analysis

• venue ratings

• specific recommendations

• search indexing

• pricing data

• facility information

2013

How is SIPS built?

Basically lots of counting.

2013

SIPS

• Tokenize data with a language model (into N-Grams)

• built using tips, shouts, menu items, likes, etc

• Apply a TF-IDF algorithm (Term frequency, inverse document frequency)

• Global phrase count

• Local phrase count ( in a venue )

• Some Filtering and ranking

• Re-compute & deploy nightly

2013

WHY USE HADOOP?

http://www.flickr.com/photos/dbrekke/

2013

SIPS – Without Hadoop

Potential Problems

• Database Query Throttling

• Venues are out of sync

• Altering the algorithm could take forever to populate for all venues

• Where would you store the results?

• What about debug data?

• Does it scale to 10x, 100x?

• What about other, similar workflows?

2013

SIPS – Hadoop Benefits

• Quick Deployment

• Modular & Reusable

• Arbitrarily complex combination of many datasets

• Every step of the workflow creates value

2013

Apple Store - Downtown San Francisco

1 tip mentions "haircuts"

Search for "haircuts" in "san francisco" Apple store???

Fixed by looking at % of tips and overall frequency

“Hey Apple, how bout less shiny pizzazz and fancy haircuts and more fix-

my-f!@#$-imac”

2013

Data & Modularity

2013

ACTUALLY, IT’S A BIT MORE

COMPLICATED http://www.flickr.com/photos/bfishadow

2013

These benefits require infrastructure

2013

Dependency Management

Many options

• Oozie (Apache)

• Azkaban (LinkedIn)

• Luigi ( Spotify, we <3 this )

• Hamake ( Codeminders )

• Chronos ( AirBNB)

2013

Database / Log Ingestion

• Sqoop

• Mongo-Hadoop

• Kafka

• Flume

• Scribe

• etc

2013

MapReduce Friendly Datastore

A few obvious ones:

• Hbase

• Cassandra

• Voldemort

we built our own, it’s very similar to

Voldemort and uses the Hfile API

2013

Getting started without all that stuff

2013

Components you likely don’t have

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The best way to start

Don’t use Hadoop.

*but pretend you do

2013

Other reasons to not use Hadoop

• Your idea might not be very good

• Hadoop will slow you down to start with

• You don’t have enough infrastructure yet

• build it when you need it

• V1 might not be that complex

• V1 could be a spreadsheet

2013

SIPS

Version 1

• Off the shelf language model

• A subset of Venues & Tips

• Did not use Map Reduce

• Did not push to production at all

2013

SIPS

Version 2

• Started building our own language model

• Rewritten as a Map Reduce

• Manually loaded data to production

• Filters for English data only.

Tweak, improve, etc

2013

SIPS

Version 3

• Incorporated more data sources into our language model

• Deployment to KV store (auto)

• Incorporated lots of debug output

• Language pipeline also feeds sentiment analysis

Now we’re in the perfect place to iterate & improve

2013

…to explore data

2013

In Summary

• Hadoop is good for counting, so use it for counting

• Move quickly whenever possible and don’t worry about automation

• Bring in new production services as you need them

• Freedom!

20132013

Thanks!

[email protected]

@rathboma

Bonus:

http://hadoopweekly.com

from my colleague, Joe Crobak (presenting later!)

http://hadoopweekly.com

building and improving products with hadoop

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