March 2014 Summaries

Major League Soccer (MLS) has been using Datadog as their exclusive performance monitoring and graphing tool for nearly a year. They love the fact that Datadog's agent is open-source, which allows them to create custom checks and contribute back to the community. MLS Digital developed a new check for Couchbase, a distributed NoSQL database, six months ago based on an existing CouchDB version. Monitoring Couchbase metrics alongside application metrics has been crucial in identifying and resolving performance and availability issues in their products. Key Couchbase metrics to monitor include operations per second, view operations per second, current connections, total objects, resident item ratio, memory headroom, cache miss ratio, disk reads per second, ejections, disk write queue, and out of memory errors.

Justin Slattery, Sr. Director of Software Development at MLS Digital, shares the company's experience with using Datadog for performance monitoring and graphing of its distributed NoSQL database Couchbase. The team has found that Datadog strikes a good balance between ease of use, flexibility, and extensibility, providing them with leverage to monitor key Couchbase metrics such as operations per second, view operations per second, current connections, total objects, resident item ratio, memory headroom, cache miss ratio, disk reads per second, ejections, and disk write queue. These metrics help the team identify performance and availability issues in their products built on top of Couchbase, including a recent issue with runaway object additions that was caught thanks to the total objects metric. The Datadog integration exposes all Couchbase metrics, making it easy for the team to monitor and gain visibility into their database's performance.

The author, a biologist with limited programming experience, transitioned into Big Data by enrolling in online courses, reading computer science books, attending bootcamps, and participating in certification programs. They landed a job at Datadog where they continue to learn through practical projects and support-side issues while receiving help from the company's supportive team members. The author encourages others with or without tech backgrounds to consider working at Datadog and provides contact information for further inquiries.

Celene Chang, a biologist with limited programming experience, embarked on a self-study journey to transition into the field of Big Data. With the help of online resources such as MOOCs, YouTube tutorials, and coursework at Columbia's Institute for Data Science, she established skills in Python, SQL, and Git. She recently started working at Datadog, where she is gaining practical knowledge about Cloud computing, developer tools, and programming languages like Ruby and JavaScript. The company's supportive team has been instrumental in her growth, providing valuable feedback and guidance. Celene now encourages others to explore a career in tech, offering resources and support through the Datadog Careers page.