13 posts tagged with "Streams"

RabbitMQ Go Stream client 1.5.8 is a newbug fix release that includes a critical fix.

The fix reverts the pull request 393 that introduced a dangerous bug where the library skipped chunk delivery when the channel's maximum capacity was reached. In practical terms, message dispatch to the application would stop.

The bug was triggered when the consumer was experiencing a near peak delivery pressure for some time, or when the consumer was consistently slow to process the deliveries.

Affected versions​

The bug affects the following versions: 1.5.5, 1.5.6 and 1.5.7.

We strongly recommend updating the client to 1.5.8 as soon as possible.

It's time to retire the myth that RabbitMQ stores messages in memory. While it was true in the early days of RabbitMQ, and an option for the last 10 years, modern RabbitMQ versions almost always write messages to disk right away. In this blog post we review how different queue types store messages but the short answer is: not in memory!

RabbitMQ 4.1 introduces an exciting new feature: AMQP filter expressions for streams.

This feature enables RabbitMQ to support multiple concurrent clients, each consuming only a specific subset of messages while preserving message order. Additionally, it minimizes network traffic between RabbitMQ and its clients by dispatching only the messages that match the clients' interests.

In this blog post, we’ll explore what AMQP filter expressions are and walk through a simple Java example of how to use them.

A previous post gave an introduction to stream filtering, a new and exciting feature in RabbitMQ 3.13. In this post we cover the internals of stream filtering. Knowing the design and implementation will help you to configure and use stream filtering in the most optimal way for your use cases.

Stream filtering is a new feature in RabbitMQ 3.13. It allows to save bandwidth between the broker and consuming applications when those applications need only a subset of the messages of a stream.

Keep reading to find out how stream filtering works and see it in action.

RabbitMQ 3.11 will bring a feature with one of the coolest names in its history: super streams. Super streams are a way to scale out by partitioning a large stream into smaller streams. They integrate with single active consumer to preserve message order within a partition.

This blog post gives an overview of super streams and the use cases they unlock. Read on to learn more, we value your feedback to make this feature the best it can be.

RabbitMQ 3.11 will bring a noteworthy feature to streams: single active consumer. Single active consumer provides exclusive consumption and consumption continuity on a stream. It is also critical to get the most out of super streams, our solution for partitioning, that provide scalability for streams.

Read on to find out more about single active consumer for streams and don't hesitate to experiment with what is already available: try it, break it, tell us what you like and don't like, what's missing. Your feedback is essential to make this feature the best it can be.

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.