Kubeflow Training Operators and Istio: solving the proxy sidecar lifecycle problem for AI/ML workloads

With Kubeflow gaining traction in the community and its early adoption in enterprises, security and observability concerns become more and more important. Many organizations that are running AI/ML workloads, operate with sensitive personal or financial data and have stricter requirements for data encryption, traceability, and access control. Quite often, we can see the use of the Istio service mesh for solving these problems and gaining other benefits of the rich functionality it provides.

Spark JobServer: from Spark Standalone to Mesos, Marathon and Docker

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 blog post covers 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 order to reach these goals. Spark JobServer Spark JobServer is widely used across a variety of reporting and aggregating systems.

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 a 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 responsible for making the decisions about which application should receive the next resource offer and it relies on Dominant Resource Fairness(DRF) algorithm for making these decisions.

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 the architecture and main components of Spark Driver. There’s a github.com/datastrophic/spark-workshop project created alongside this post which contains Spark Applications examples and dockerized Hadoop environment to play with. Slides are also available at slideshare. Intro Spark is a generalized framework for distributed data processing providing functional API for manipulating data at scale, in-memory data caching, and reuse across computations.

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 different system designs which list not only purely batch or stream processing, but more complex Lambda and Kappa architectures as well.