I remember the first time I laid eyes on the beast: Summer, 2012. The air conditioning at Yelp HQ hummed imperceptibly as I reviewed code for a colleague. This wasn’t my first rodeo, but I was new to Yelp and to working in a large Python codebase. I painstakingly scanned the code for lurking bugs, but couldn’t find any. “Ship it!” I declared electronically, freeing my colleague to deploy his changes. It is chilling to think that on that day, I looked the beast squarely in the eyes and never realized it. Cunning camouflage allowed it to slip past me...

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The Northeast may be covered in snow, but here in sunny San Francisco, we’re operating at full steam! We got the year off to a great start with a few meetups and the launch of our new tech talk series (more on that later). This month, on top of the meetups we’re hosting, we’re also getting involved with GirlDevWeek. We’ll be hosting a panel here at Yelp HQ, as well as throwing the official after party in conjunction with Pandora. So if you’re going to be at GirlDevWeek, we hope to see you at both events! Meetups at Yelp HQ:...

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One of the most engaging features of Yelp is our photos and videos gallery. When you visit a Yelp Business Page inside of the mobile app, there is a photo at the top of the page to provide visual context. It also serves as a compelling entry point to our photo viewer if you pull it down. We wanted to have this same effect on our mobile site, so we set out to develop a nice, smooth animation to pull down this photo and delight mobile web users with the same experience they’re used to on our mobile applications. The...

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At Yelp, we’re very big fans of Amazon’s RedShift data warehouse. We have multiple deployments of RedShift with different data sets in use by product management, sales analytics, ads, SeatMe and many other teams. While it minimizes a lot of the work the RedShift team has done to call RedShift a simple fork of Postgres 8.4, RedShift does share a common code ancestry with PG 8.4. This means that much of the advanced query functionality of Postgres is available, which, when combined with the petabyte scale of RedShift, offers some amazingly powerful analytics tools. Because most of the PG 8.4...

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2014 was a busy year for us at Yelp Engineering and with 2015 having just kicked off, we wanted to highlight some of our most exciting events from last year. Open Source Yelp loves open source. We love contributing back to the developer and open source communities and last year was no exception to that. We saw a large number of our engineers contributing to many different projects. In July, to help organize all of the projects we worked on, we launched a new open source site along with the revamped developer site. Over the course of the year, we...

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Introducing OSXCollector We use Macs a lot at Yelp, which means that we see our fair share of Mac-specific security alerts. Host based detectors will tell us about known malware infestations or weird new startup items. Network based detectors see potential C2 callouts or DNS requests to resolve suspicious domains. Sometimes our awesome employees just let us know, “I think I have like Stuxnet or conficker or something on my laptop.” When alerts fire, our incident response team’s first goal is to “stop the bleeding” - to contain and then eradicate the threat. Next, we move to “root cause the...

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This past November, Yelp Engineering hosted a Bay Area Girl Geek Dinner and I had the pleasure of presenting a brief overview of how Yelp uses MySQL. I received a lot of great questions and wanted to take a few minutes to share the information with our wider blog audience. Overview: MySQL @ Yelp Yelp has used MySQL since the beginning, and in more recent years has adopted the Percona fork. We like MySQL because it fits our data needs, is configurable, fast, and scalable. The Percona fork includes performance enhancements and additional features on top of stock MySQL, we...

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At Yelp, we solve a lot of different information retrieval problems ranging from highlighting reviews for businesses to recommending nearby restaurants. We recently improved one system called business matching, moving from a naive ranking function to a machine learned approach using Learn to Rank. The Business Matching Problem Business matching is a system that accepts a description of a business (e.g. name, location, phone number) and returns the Yelp businesses that description best fits. We can use this in several different contexts. For example, in 2012 Yelp announced a partnership spearheaded by the City of San Francisco to allow municipalities...

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