Spark JobServer: from Spark Standalone to Mesos, Marathon and Docker. Part I

Series overview After several years of running Spark JobServer workloads, the need for better availability and multi-tenancy emerged across several projects author was involved in. This series cover design decisions made to provide higher availability and fault tolerance of JobServer installations, multi-tenancy for Spark workloads, scalability and failure recovery automation, and software choices made in »

Resource Allocation in Mesos: Dominant Resource Fairness

Apache Mesos provides a unique approach to cluster resource management called two-level scheduling: instead of storing information about available cluster resources in centralized manner it operates with a notion of resource offers which slave nodes advertise to running frameworks via Mesos master, thus keeping the whole system architecture concise and scalable. Master's allocation module is »

Apache Spark: core concepts, architecture and internals

This post covers core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. There's a github.com/datastrophic/spark-workshop project created alongside with this post which contains Spark Applications examples and dockerized Hadoop environment to play with. »

Data processing platforms architectures with SMACK: Spark, Mesos, Akka, Cassandra and Kafka

This post is a follow-up of the talk given at Big Data AW meetup in Stockholm and focused on different use cases and design approaches for building scalable data processing platforms with SMACK(Spark, Mesos, Akka, Cassandra, Kafka) stack. While stack is really concise and consists of only several components it is possible to implement »