using data to determine where to build a new data center at shutterstock from thousandeyes connect
TRANSCRIPT
Using Data to Determine Where to Build a New
Data CenterEugene Yaacobi, Shutterstock
Who Am I?• Nerd
• Manager, Infrastructure @ Shutterstock
• I also like cars
A Little About Shutterstock• We are a stock photo,
footage, and music company
• 12 years old - founded in 2003
• Made in NY• Offices in Amsterdam,
Berlin, Chicago, Dallas, Denver, London, Los Angeles, Montreal, Paris, San Francisco, and Silicon Valley
• Over 600 employees• 4 images sold every
second• Focus on volume, unbiased
search experience.• Translated into 20
languages• Accepting 9 currencies • Customers in 150+
countries
A Little About Shutterstock (cont.)• Two-sided marketplace
• Over 1.3 million active customers from 150 countries
• 80,000 contributors in 100 countries
• 60 mm images; over 50,000 images added every day (look at the counter on the site and round down to the nearest million)
• 3mm+ video clips; over 90% HD, 160,000 4K clips, and 3,000 videos added every day
• 500 mm paid downloads to date, selling 4 images per second
• $350 mm paid to contributors since 2003
Infrastructure at Shutterstock• The service that every other
service is built on:
• 3 Datacenters
• 4,000 servers running CentOS
• Servers are mostly virtualized
• ~500 network devices
• Brocade load balancers
• Juniper routers and switches, running JunOS
The Infrastructure Group at Shutterstock• First responders to site issues
• 1OC for immediate alerts
• Made up of five teams:
• SRE - training
• Tools - internal tooling
• Storage - Purchasable asset storage
• Assets - Hardware acquisition
• Traffic - Network engineering
Hey Infrastructure Where do we put our next deployment?
Great question! Our assets are downloaded all over the world
Every second counts!
40% of people abandon a website that takes more than 3 seconds to load.
If an e-commerce site is making $100,000 per day, a 1 second
page delay could potentially cost you $2.5 million in lost sales every
year
This makes executives sad pandas
Step 1 Decide what regions are important to you
Step 2 Create some Tests
Step 3 Let Simmer
Step 4 Review and Analyze
Look at response times Pay attention to where the client is spending
more time
Look at your page loads Good starting spot is your worst
performer
Drill down into your data You can learn some cool
things
Waterfalls are super useful Use them
What else can the data be used for? Things
Like what?• Ad-Hoc Troubleshooting
• Monitor and Alert on Overall Site Performance
• Does the site function properly and efficiently in all locations?
• If not:
• Which locations are performing under expectations?
• Why are these locations seeing degraded performance?
Bananas are YummySo are Grapes
Now you know where to deploy your next site