© 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc.

Building a Cloud Culture at Yelp

Jim Blomo – Engineering Manager, Yelp

November 15, 2013

Why Cloud?

Yelp!

Yelp Data

Why Cloud?

specificity & optimization

generality & abstraction

Make the Trade-Off!

How Cloud?

Back to the Past

Logging – an aside

{user_id: 5,

request_id: "b1946ac92492d2347c6235b4d2611184",

search_query: "farm to table",

city: "Portland, OR",

results: [17289, 8230452, 825429, 184312,...],

timestamp: 1384469660

}

Hadoop Trade-Offs

Riddle: Success? • How do you know

when your

infrastructure is a

success?

• Starts failing under

heavy load

• "Too many" users

overloading the system

Hadoop Issues

EMR Solutions • Clusters up for limited

amount of time

• Upgrades handled by

Amazon

• Multiple clusters

means no capacity

coordination S3

Trade-Offs

Trade-Offs

Trade-Offs

Standard configs

Resource consumption tracking

testing

No Cargo Cults

Standard Configs

mrjob Configs

# standard

# memory intensive

# cpu intensive

Resource Tracking

python -m mrjob.tools.emr.terminate_idle_job_flows -c mrjob.conf

Testing

--runner emr

mr_canary.py

mrjob is Open Source

Adoption

Cloud Calculations • 5 days with 10

machines = 1 day

with 50 machines

• (On demand pricing

simplification)

Cost

Cost

Cost Control

Data Availability

• s3mysqldump

Overview

S3 in: s3mysqldump

out: LOAD DATA or rsync in: JSON logs

in: logs & DB dumps out: CSV, JSON, MyISAM

Pitfalls

Leaky Abstractions

Closed Source

bootstrap-actions/configure-hadoop

mapred.reduce.tasks.speculative.execution=false

Data Explosion

Tron

Wanted

Next Up: Services

Cloud Strategy

Target

generality & abstraction

easiest way

Tandem

We’re Hiring

yelp.com/careers

Please give us your feedback on this

presentation

As a thank you, we will select prize

winners daily for completed surveys!

BDT305 – Cloud @Yelp