Haohai Ma - Databricks

Haohai Ma

Software Architect, IBM

Haohai Ma is a Software Architect at Spectrum Computing, IBM. In the past 10 years, he is the expertise on HPC, SOA, MapReduce, Yarn, Spark , Docker and Kubernetes. He is one of the major contributors of IBM BigData offerings. Haohai holds PhD in Computer Science from Peking University



Running Spark Inside Docker Containers: From Workload to ClusterSummit Europe 2017

This presentation describes the journey we went through in containerizing Spark workload into multiple elastic Spark clusters in a multi-tenant kubernetes environment. Initially we deployed Spark binaries onto a host-level filesystem, and then the Spark drivers, executors and master can transparently migrate to run inside a Docker container by automatically mounting host-level volumes. In this environment, we do not need to prepare a specific Spark image in order to run Spark workload in containers. We then utilized Kubernetes helm charts to deploy a Spark cluster. The administrator could further create a Spark instance group for each tenant. A Spark instance group, which is akin to the Spark notion of a tenant, is logically an independent kingdom for a tenant's Spark applications in which they own dedicated Spark masters, history server, shuffle service and notebooks. Once a Spark instance group is created, it automatically generates its image and commits to a specified repository. Meanwhile, from Kubernetes' perspective, each Spark instance group is a first-class deployment and thus the administrator can scale up/down its size according to the tenant's SLA and demand. In a cloud-based data center, each Spark cluster can provide a Spark as a service while sharing the Kubernetes cluster. Each tenant that is registered into the service gets a fully isolated Spark instance group. In an on-prem Kubernetes cluster, each Spark cluster can map to a Business Unit, and thus each user in the BU can get a dedicated Spark instance group. The next step on this journey will address the resource sharing across Spark instance groups by leveraging new Kubernetes' features (Kubernetes31068/9), as well as the Elastic workload containers depending on job demands (Spark18278). Demo: https://www.youtube.com/watch?v=eFYu6o3-Ea4&t=5s Session hashtag: #EUeco5

Learn more:
  • Lessons Learned From Running Spark On Docker
  • Apache Spark on Kubernetes