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
Data-Driven Digitalization with Apache Kafka in the Food Industry at BAADER
Chaos Engineering with Apache Kafka and Gremlin
Boosting Security for Apache Kafka with Confluent Cloud Private Link ft. Dan LaMotte
Confluent Platform 6.2 | What’s New in This Release + Updates
Adopting OpenTelemetry in Confluent and Beyond ft. Xavier Léauté
Running Apache Kafka Efficiently on the Cloud ft. Adithya Chandra
Engaging Database Partials with Apache Kafka for Distributed System Consistency ft. Pat Helland
The Truth About ZooKeeper Removal and the KIP-500 Release in Apache Kafka ft. Jason Gustafson and Colin McCabe
Resilient Edge Infrastructure for IoT Using Apache Kafka ft. Kai Waehner
Data Management and Digital Transformation with Apache Kafka at Van Oord
Powering Microservices Using Apache Kafka on Node.js with KafkaJS at Klarna ft. Tommy Brunn
Apache Kafka 2.8 - ZooKeeper Removal Update (KIP-500) and Overview of Latest Features
Connecting Azure Cosmos DB with Apache Kafka - Better Together ft. Ryan CrawCour
Resurrecting In-Sync Replicas with Automatic Observer Promotion ft. Anna McDonald
Building Real-Time Data Pipelines with Microsoft Azure, Databricks, and Confluent
Smooth Scaling and Uninterrupted Processing with Apache Kafka ft. Sophie Blee-Goldman
Event-Driven Architecture - Common Mistakes and Valuable Lessons ft. Simon Aubury
The Human Side of Apache Kafka and Microservices ft. SPOUD
Gamified Fitness at Synthesis Software Technologies Using Apache Kafka and IoT
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