Yelp Case Study

Yelp was founded in 2004 with the main goal of helping people connect with great local businesses. The Yelp community is best known for sharing in-depth reviews and insights on local businesses of every sort. In their ten years of operation Yelp went from a one-city wonder (San Francisco) to an international phenomenon spanning 29 countries and more than 120 markets. As of June 2014, Yelp had an average of 138 million monthly unique visitors and more than 61 million local reviews have been written by yelpers.

Yelp has established a loyal consumer following, due in large part to the fact that they are vigilant in protecting the user from shill or suspect content. Yelp uses an automated review filter to identify suspicious content and minimize exposure to the consumer. The site also features a wide range of other features that help people discover new businesses (lists, special offers, and events), and communicate with each other. Additionally, business owners and managers are able to set up free accounts to post special offers, upload photos, and message customers.

The company has also been focused on developing mobile apps and was recently voted into the iTunes Apps Hall of Fame. Yelp apps are also available for Android, Blackberry, Windows 7, Palm Pre and WAP.

Local search advertising makes up the majority of Yelp’s revenue stream. The search ads are colored light orange and clearly labeled “Sponsored Results.” Paying advertisers are not allowed to change or re-order their reviews.

Yelp originally depended upon giant RAIDs to store their logs, along with a single local instance of Hadoop. When Yelp made the move to Amazon Elastic MapReduce (Amazon EMR), they replaced the RAIDs with Amazon Simple Storage Service (Amazon S3) and immediately transferred all Hadoop jobs to Amazon Elastic MapReduce.

“We were running out of hard drive space and capacity on our Hadoop cluster,” says Yelp search and data-mining engineer Dave Marin.

Yelp uses Amazon S3 to store daily logs and photos, generating around 1.2TB of logs per day. The company also uses Amazon EMR to power approximately 20 separate batch scripts, most of those processing the logs. Features powered by Amazon Elastic MapReduce include:

• People Who Viewed this Also Viewed
• Review highlights
• Auto complete as you type on search
• Search spelling suggestions
• Top searches
• Ads
  
  

Using Amazon Elastic MapReduce Yelp was able to save $55,000 in upfront hardware costs and get up and running in a matter of days not months. However, most important to Yelp is the opportunity cost. “With AWS, our developers can now do things they couldn’t before,” says Marin. “Our systems team can focus their energies on other challenges.”