If you’ve heard the term “clusters,” then you might know it refers to Confluent components and features that we run in all three major cloud providers today, including an event streaming platform based on Apache Kafka®, ksqlDB, Kafka Connect, the Kafka API, databalancers, and Kafka API services. Rashmi Prabhu, a software engineer on the Control Plane team at Confluent, has the opportunity to help govern the data plane that comprises all these clusters and enables API-driven operations on these clusters.
But running operations on the cloud in a scaling organization can be time consuming, error prone, and tedious. This episode addresses manual upgrades and rolling restarts of Confluent Cloud clusters during releases, fixes, experiments, and the like, and more importantly, the progress that’s been made to switch from manual operations to an almost fully automated process. You’ll get a sneak peek into what upcoming plans to make cluster operations a fully automated process using the Cluster Upgrader, a new microservice in Java built with Vertx. This service runs as part of the control plane and exposes an API to the user to submit their workflows and target a set of clusters. It performs statement management on the workflow in the backend using Postgres.
So what’s next? Looking forward, there will be the selection phase will be improved to support policy-based deployment strategies that enable you to plan ahead and choose how you want to phase your deployments (e.g., first Azure followed by part of Amazon Web Services and then Google Cloud, or maybe Confluent internal clusters on all cloud providers followed by customer clusters on Google Cloud, Azure, and finally AWS)—the possibilities are endless!
The process will become more flexible, more configurable, and more error tolerant so that you can take measured risks and experience a standardized way of operating Cloud. In addition, expanding operation automations to internal application deployments and other kinds of fleet management operations that fit the “Select/Apply/Monitor” paradigm are in the works.
EPISODE LINKS
From Monolith to Microservices with Sam Newman
Exploring Event Streaming Use Cases with µKanren ft. Tim Baldridge
Introducing JSON and Protobuf Support ft. David Araujo and Tushar Thole
Scaling Apache Kafka in Retail with Microservices ft. Matt Simpson from Boden
Connecting Snowflake and Apache Kafka ft. Isaac Kunen
AMA with Tim Berglund | Streaming Audio Special
Kubernetes Meets Apache Kafka ft. Kelsey Hightower
Apache Kafka Fundamentals: The Concept of Streams and Tables ft. Michael Noll
IoT Integration and Real-Time Data Correlation with Kafka Connect and Kafka Streams ft. Kai Waehner
Confluent Platform 5.5 | What's New in This Release + Updates
Making Abstract Algebra Count in the World of Event Streaming ft. Sam Ritchie
Apache Kafka 2.5 – Overview of Latest Features, Updates, and KIPs
Streaming Data Integration – Where Development Meets Deployment ft. James Urquhart
How to Run Kafka Streams on Kubernetes ft. Viktor Gamov
Cloud Marketplace Considerations with Dan Rosanova
Explore, Expand, and Extract with 3X Thinking ft. Kent Beck
Ask Confluent #17: The “What is Apache Kafka?” Episode ft. Tim Berglund
Domain-Driven Design and Apache Kafka with Paul Rayner
Machine Learning with TensorFlow and Apache Kafka ft. Chris Mattmann
Distributed Systems Engineering with Apache Kafka ft. Gwen Shapira
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