RabbitMQ specialists for your dedicated team.

When a product needs RabbitMQ expertise.

RabbitMQ usually enters a product quietly, as the queue behind background jobs. It needs a specialist once messages carry money or promises: orders, payments, bookings, a notification a customer is waiting for. From then on, a lost or doubled message is a support ticket.

Version pressure is another trigger. RabbitMQ 4.0 removed classic mirrored queues. A cluster that relied on them for high availability has to move to quorum queues before it can upgrade.

Peak traffic exposes the rest. Queues grow to millions of messages during a peak, a memory alarm blocks the publishers, and the whole product waits behind one slow consumer.

RabbitMQ tasks our engineers handle in a sprint.

• Topology design Laying out exchanges, queues and routing keys per business event, so a new consumer can subscribe with no change to the publishing service.
• Safe publishing and consuming Turning on publisher confirms and manual acknowledgements, and setting a prefetch that fits the work. Consumers are made idempotent, because delivery is at least once.
• Failure paths Adding dead-letter exchanges, delayed retries and a delivery limit, plus an alert and a small tool to inspect and replay what lands there.
• Quorum queue migration Moving mirrored classic queues to quorum queues one at a time, as the step before a 4.x upgrade.
• Monitoring Enabling the Prometheus plugin and Grafana dashboards for queue depth, consumer count, unacknowledged messages and the memory and disk alarms.
• Cluster operations Running three-node clusters on virtual machines or with the Kubernetes Cluster Operator, including rolling upgrades of RabbitMQ and Erlang.

Messaging belongs to the developers who write the services on both sides of the queue. Companies that hire offshore developers through us get backend and integration engineers who own RabbitMQ together with that code. Those engineers work on nothing but your product.

The RabbitMQ faults a good engineer designs out.

Automatic acknowledgement is the usual cause of lost messages. The broker treats a message as done the moment it is sent, so a consumer that crashes halfway takes the message with it. Acknowledge after the work is finished, and design for the redelivery that follows.

Using a queue as a database causes trouble more slowly. RabbitMQ is fastest when queues are short. A queue holding days of backlog uses memory, slows down and takes a long time to recover after a restart. If you need to keep and replay history, a stream or Kafka fits better.

Opening a new connection for every message is a quieter fault. Connections are expensive to set up, and thousands of short ones can bring a broker down. The client libraries expect long-lived connections with a channel per thread.

RabbitMQ questions buyers ask.

• RabbitMQ or Kafka: which fits our product? They do different jobs. RabbitMQ routes each message to the workers that should handle it and forgets it once it is acknowledged. Kafka keeps an ordered log that many readers can replay. Task queues and request routing suit RabbitMQ, and event history and analytics pipelines suit Kafka.
• Should we run RabbitMQ ourselves or use a hosted service? A hosted service such as Amazon MQ or CloudAMQP takes patching and node failures off your team. It's a sensible default for a small team. Running it yourself gives you newer versions, more plugins and lower cost at volume. It also needs an engineer who has done cluster upgrades before.
• Is RabbitMQ consulting enough, or do we need someone on the team? A consultant can review a topology in a week. The risk sits in the daily changes: a new consumer without acknowledgements, a queue with no dead-letter route. A dedicated engineer who reviews those changes every sprint keeps the design intact.