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Case Study

Groupon Logo
Languages: Ruby Frameworks: Rails

Scaling seamlessly during traffic bursts.

Groupon has enjoyed tremendous success with a simple idea: folks love coupons and they love to share them with their friends, so why not offer group discounts in the form of a daily deal? And how does the web app team at Groupon measure success? One daily deal at a time. For engineering director Shinji Kuwayama and his team of developers, Groupon's growth translates into traffic bursts on the site that on any given day can double or triple the normal traffic. In August of this year for example, the Gap partnered with Groupon on a Daily Deal, which resulted in a burst of 70,000 concurrent users. Similar deals with companies like eBay have significantly increased site traffic. To help the team keep an eye on the health and availability of Groupon.com, Shinji has implemented several tools including Nagios for infrastructure monitoring, Pingdom for availability monitoring, and New Relic RPM for 24×7 application monitoring.

"The key to monitoring the app and then rapidly determining the cause of a potential issue is seeing the transaction in context. That's the power of having this tool in place." – Shinji Kuwayama Director of Engineering – Groupon

Challenges

  • Maintain superior application performance through weekly and daily deploys
  • Manage traffic bursts that can at any time double or triple normal activity
  • Detect potential problems in the infrastructure or web app before they affect customers

Solution

  • Infrastructure monitoring with Nagios, availability monitoring with Pingdom, 24x7 app monitoring with New Relic RPM
  • Application response time, throughput and customer experience dashboards displayed at Groupon HQ
  • Troubleshooting and deep-dive diagnostics for the application and connected MySQL database

Results

  • Increased operational efficiency for Groupon’s agile web app team
  • Optimized site performance to scale the app seamlessly during expected and unexpected traffic bursts
  • Shorter mean time to identify and repair performance issues

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