Yelp uses Elasticsearch to rapidly prototype and launch new search applications, and moving quickly at our scale raises challenges. In particular, we often encounter difficulty making changes to query logic without impacting users, as well as finding client library bugs, problems with multi-tenancy, and general reliability issues. As the number of engineers at Yelp writing new Elasticsearch queries grew, our Search Infrastructure team was having difficulty supporting the multitude of ways engineers were finding to send queries to our Elasticsearch clusters. The infrastructure we designed for a single team to communicate with a single cluster did not scale to tens...

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This month we have a handful of exciting events and a few new ones! We kicked off the month with Hackathon 15.0 where Yelpers created and shared some amazing projects (more on that in a few weeks!). Now that the hackathon dust has settled, we’re starting off by hosting the Bay Area Girl Geek Dinner to help encourage networking between girl geeks. On top of that, we’ll be at AnDevCon November 18 - 21 so make sure to find us there too. Events happening at Yelp HQ: Tuesday, November 11, 2014 - 5:30PM - Yelp Girl Geek Dinner ( Bay...

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Yelp Dataset Challenge Round 3 Winners We recently opened the fourth round of the Yelp Dataset Challenge. This announcement included an update to the dataset, adding four new international cities and bringing the total number of reviews in the dataset to over one million. You can download it and participate in the challenge here. Submissions for this round are open until December 31, 2014. See the full terms for more details. With the opening of our fourth iteration of the challenge, we closed the third round, which ran from February 1, 2014 to July 31, 2014. We are proud to announce...

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Welcome back! Today we have Wei, Rachel, Jen, Virginia and Anusha sharing their experiences. Wei is an engineer on the consumer team and she brings amazing user experiences to our customers. Rachel and Jen are both engineers on our international team, bringing the power of Yelp to all of our international communities. Virginia works as an engineer on our partnerships team and Anusha is an engineer on our infrastructure team. Overall, we all had a blast getting to know each other, meeting other amazing women in the industry, hearing some great stories from inspiring women role models, and sourcing some...

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As you probably heard, Yelp attended Grace Hopper this year. Nine software engineers from different teams attended and, for many of us, it was our first time. It was a unique experience to see so many talented women in one place. In addition to the talks and panel discussions, we also had the opportunity and pleasure to represent Yelp at the career fair. It was amazing to see a consistent flow of students and industry talent, all happy customers, stop by our booth to speak with us and tell us their stories of using Yelp. We had such a great...

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At LAUNCH Scale last week, I gave a talk to over 75 co-founders (CEOs and CTOs) on how we’ve scaled traffic here at Yelp. It brought back memories of Darwin biting through our ethernet cable and reminded me of the run up to our IPO, making sure we had enough capacity to handle the expected surge in traffic from the world’s press (and more recently, the launch of Yelp in Hong Kong!). For close to 8 years, I’ve had the privilege to work alongside some of the best engineers in the world and have seen the meticulous work and thought...

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Yelp loves Python, and we use it at scale to power our websites and process the huge amount of data we produce. Pyleus is a new open-source framework that aims to do for Storm what mrjob, another open-source Yelp project, does for Hadoop: let developers process large amounts of data in pure Python and iterate quickly, spending more time solving business-related problems and less time concerned with the underlying platform. First, a brief introduction to Storm. From the project’s website, “Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded...

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A/B Testing Experiment Frameworks and MOE We recently open sourced MOE, the Metric Optimization Engine, a machine learning tool for solving global, black box optimization problems. An example application for such a system is optimally running online A/B experiments. A/B testing segments the users that come to a site into buckets, or cohorts, and show different versions of the site to different cohorts of users. One can show 50% of users one version of a site (version A) and 50% of users another version of a site (version B). After some amount of time we can see which version of...

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