RabbitMQ streams allow applications to convey detailled information thanks to the powerful message format they use. Streams are a feature of their own, but they also fully integrate with the existing resources and protocols that RabbitMQ supports. This blog post covers the interoperability of streams in RabbitMQ and explores the scenarios it unlocks.
RabbitMQ Streams provides server-side offset tracking for consumers. This features allows a consuming application to restart consuming where it left off in a previous run. This post covers the semantics of offset tracking and how it is implemented in the stream Java client.
RabbitMQ Streams Overview introduced streams, a new feature in RabbitMQ 3.9 and RabbitMQ Streams First Application provided an overview of the programming model with the stream Java client. This post covers how to deduplicate published messages in RabbitMQ Streams.
As deduplication is a critical and intricate concept, the post will walk you through this mechanism step by step, from a naive and somewhat broken publishing application to an optimized and reliable implementation.
RabbitMQ Streams Overview introduced streams, a new feature in RabbitMQ 3.9. This post covers how client applications should connect to RabbitMQ nodes to get the most benefit from streams when the stream protocol is in use.
Streams are optimized for high throughput scenarios, that's why technical details like data locality are critical to get the best out of your RabbitMQ cluster. Client libraries can handle most of the details, but a basic understanding of how things work under the hood is essential when a setup involves extra layers like containers and load balancers. Keep reading if you want to learn more about streams and avoid some headaches when deploying your first stream applications!
RabbitMQ Streams Overview introduced streams, a new feature in RabbitMQ 3.9. This post continues by showing how to use streams with the Java client. We will write our first application that publishes messages to a stream, and then consumes them.
RabbitMQ 3.9 introduces a new type of data structure: streams. Streams unlock a set of use cases that could have been tedious to implement with "traditional" queues. Let's discover in this post how streams expand the capabilities of RabbitMQ.
If you want to be notified when your RabbitMQ deployments have a problem, now you can set up the RabbitMQ monitoring and alerting that we have made available in the RabbitMQ Cluster Operator repository. Rather than asking you to follow a series of steps for setting up RabbitMQ monitoring & alerting, we have combined this in a single command. While this is a Kubernetes-specific quick-start, and you can use these Prometheus alerts outside of Kubernetes, the setup will require more consideration and effort on your part. We share the quick & easy approach, open source and free for all.
We have been constantly improving the monitoring capabilities that are built into RabbitMQ since shipping native Prometheus support in 3.8.0. Monitoring the broker and its clients is critically important for detecting issues before they affect the rest of the environment and, eventually, the end users.
RabbitMQ 3.8.10 exposes client authentication attempts metrics via both the Prometheus endpoint and the HTTP API.
We are pleased to announce that the RabbitMQ Operator for Kubernetes is now generally available. The RabbitMQ Operator makes it easy to provision and manage RabbitMQ clusters consistently on any certified Kubernetes distribution. Operators inform the Kubernetes container orchestration system how to provision and control specific applications. The Kubernetes (hereafter K8s) Operator pattern is a way to extend the K8s API and state management to include the provisioning and management of custom resources -- resources not provided in a default K8s deployment. In this post, we’ll discuss how the Operator enables the K8s system to control a RabbitMQ cluster.
This is the first part of a series on quorum queues, our new replicated queue type. We'll be covering everything from what quorum queues are, to hardware requirements, migration from mirrored queues and best practices.
Mirrored queues, also known as HA queues have been the de facto option for years when requiring extra data safety guarantees for your messages. Quorum queues are the next generation of replicated queue that aim to replace most use cases for mirrored queues and are available from the 3.8 release and onward.
In this blog series we’re going to cover the following: