Lessons learned running Hadoop and Spark in Docker
Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale environments poses new challenges, especially for big data applications like Hadoop. Thomas Phelan shares lessons learned and some tips and tricks on how to Dockerize your big data applications in a reliable, scalable, and high-performance environment.
|Talk Title||Lessons learned running Hadoop and Spark in Docker|
|Conference||Strata + Hadoop World|
|Conf Tag||Make Data Work|
|Location||New York, New York|
|Date||September 27-29, 2016|
Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed big data applications like Apache Hadoop and Apache Spark. Some of these challenges include container life-cycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. BlueData is “all in” on Docker containers—with a specific focus on big data applications. BlueData has learned firsthand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy big data workloads using Docker. BlueData’s Thomas Phelan demonstrates how to securely network Docker containers across multiple hosts and discusses ways to achieve high availability across distributed big data applications and hosts in your data center. Since we’re talking about very large volumes of data, performance is a key factor, so Thomas shares some of the storage options implemented at BlueData to achieve near bare-metal I/O performance for Hadoop and Spark using Docker as well as lessons learned and some tips and tricks on how to Dockerize your big data applications in a reliable, scalable, and high-performance environment.