Having recently returned from Hadoop Summit 2014 in San Jose, I wanted to take some time to jot down my thoughts on the sessions. I primarily focused on the sessions that revolved around operational management of Hadoop to see how other companies are tackling the same problems I am facing. It is comforting to know that I am not alone in my quest to deliver a reliable Hadoop platform across the development lifecycle for my internal customers to consume. However, one of the frustrating things to witness was the inherent lack of large-scale organizations operating within their own private cloud environments. Many of the demonstrations involved utilizing resources from AWS or made the assumption you would never run out of bare-metal hardware to deploy on. My experience is wholly different.

The challenging part of offering a true Hadoop-as-a-Service platform is the expectation that additional resources will always be available for an Engineering team or Operations team to consume at a moments notice. For that to be the case, in my experience, AWS becomes too expensive too quickly and bare-metal hardware is difficult to procure at a moments notice within a large, publicly traded organization. For that, a private cloud environment is perfect — but no one wants to openly talk about running Hadoop on a virtual platform. Which, when you start thinking about it is quite humorous because most demonstrations showed Hadoop running in AWS — what do they think an EC2 instance is exactly?

My talk with Andrew Nelson on running a production Hadoop-as-a-Service platform using VMware vCenter Big Data Extensions went well. The audience was well-educated and we received some rather good questions at the end. Virtualizing Hadoop for my organization has been a great way to solve many of the lifecycle management issues faced in today’s rapidly changing environment.

All that being said, here are the key takeaways/questions I gained from Hadoop Summit 2014:

  • Failure handling (Docker) — What happens when a container is lost and you are waiting for a new container to be created?
  • Docker can easily be virtualized within existing private cloud environments.
  • Data locality and container affinity can be accomplished with existing private cloud environments.
  • Writing an Application Master is hard and error prone.
  • Performance evaluation != Workload
  • Hbase regionserver splits are expensive & it is suggested that you pre-split the region — elasticity is really lacking here.
  • Best session by far was from Alex Moundalexis @Cloudera: http://tiny.cloudera.com/7steps
  • Performance tuning the Linux OS is key and oftentimes overlooked by DevOps. There are several low lift, high yield changes that can be made.
  • Ambari Apache Project is another manager to evaluate.
  • Many projects trying to solve the same problems that VMware vCloud Automation Center already offers at a larger-scale and more feature rich solution.
  • Read and re-read Hadoop Operations.