This guides provides an overview features of RabbitMQ, AMQP 0-9-1 and other supported protocols related to data safety. They help application developers and operators achieve reliable delivery, that is, to ensure that messages are always delivered, even encountering failures of various kinds.
The following guides discuss data safety and resilience topics in more detail:
Messaging-based systems are distributed by definition and can fail in different, and sometimes subtle, ways.
Network connection problems and congestion are probably the most common class of failure. Not only can networks fail, firewalls can interrupt connections they consider to be idle, and network failures take time to detect.
In addition to connectivity failures, the server and client applications can experience hardware failure (or software can crash) at any time. Additionally, even if client applications keep running, logic errors can cause channel or connection errors which force the client to establish a new channel or connection and recover from the problem.
This list of failures, of course, is not at all exhaustive. It does not cover more subtle failures such as omission failures (failure to respond in a predictable amount of time), performance degradations, malicious or buggy applications that exhaust the system of resources and so on. Those failures can be detected with monitoring, metrics and health checks.
In the event of a network connection failure between a client and RabbitMQ node, the client will need to establish a new connection to the broker. Any channels opened on the previous connection will have been automatically closed and these will need re-opening too.
In general when connections fail, the client will be informed by the connection throwing an exception (or similar language construct).
Most client libraries provide a feature that automatically recovers from connection failures. For cases where this opinionated recovery is not suitable, application developers can implement their own recovery by defining connection failure event handlers. See client documentation, such as the Java and .NET client guides, to learn more.
When a connection fails, messages may be in transit between client and server - they may be in the middle of being decoded or encoded on either side, sit in TCP stack buffers, or be in flight on the wire. In such events messages in transit will not be delivered — they will need to be retransmitted. Acknowledgements let the server and clients know when to do this.
Acknowledgements can be used in both directions - to allow a consumer to indicate to the server that it has received and/or processed a delivery and to allow the server to indicate the same thing to the publisher. They are known as consumer acknowledgements and publisher confirms.
While TCP ensures that packets have been delivered to connection peer, and will retransmit until they are, that only handles failures at the network layer. Acknowledgements and confirms indicate that messages have been received and acted upon by the peer application. An acknowledgement signals both the receipt of a message, and a transfer of ownership where the receiver assumes full responsibility for it.
Acknowledgements therefore have semantics. A consuming application should not acknowledge messages until it has done whatever it needs to do with them: recorded them in a data store, forwarded them on, or performed any other operation. Once it does so, the broker is free to mark the delivery for deletion.
Similarly, the broker will confirm messages once it has taken responsibility for them. The details are covered in the Acknowledgements and Confirms guide.
Use of acknowledgements guarantees at least once delivery. Without acknowledgements, message loss is possible during publish and consume operations and only at most once delivery is guaranteed.
In some types of network failure, packet loss can mean that disrupted TCP connections take a moderately long time (about 11 minutes with default configuration on Linux, for example) to be detected by the operating system. AMQP 0-9-1 offers a heartbeat feature to ensure that the application layer promptly finds out about disrupted connections (and also completely unresponsive peers). Heartbeats also defend against certain network equipment which may terminate "idle" TCP connections. See the guide on heartbeats for details.
In order to avoid losing messages on the RabbitMQ (as opposed to application) side, queues and messages must be able to cope with RabbitMQ node restarts, node and hardware failures.
With some messaging protocols supported by RabbitMQ, applications control durability of queues and messages. It's therefore critically important that durable queues (or replicated queue types covered below) are used for important data, and messages are published as persistent by publishers.
Clusters of nodes offer redundancy and can tolerate failure of a single node. In a RabbitMQ cluster, all definitions (of exchanges, bindings, users, etc) are replicated across the entire cluster.
Quorum queues and streams are replicated to multiple cluster nodes. One of the nodes hosts a leader replica, others host followers. In case of leader failure one of the followers is elected to be a new leader. Queue state changes (enqueueing, keeping track of deliveries and acknowledgements) happen on the leader replica, although some operations can be performed on followers, too.
Queues and streams remain visible and reachable from all nodes regardless of what node their leader replica is located.
Exclusive queues are tied to the lifecycle of their connection and thus are never mirrored and by definition will not survive a node restart.
Consumers connected to the failed node will have to recover as usual. Consumers that were connected to a different node will be automatically re-registered by RabbitMQ when a new leader replica for the queue is elected. Those consumers do not need to perform recovery (e.g. reconnect or resubscribe).
When using confirms, producers recovering from a channel or connection failure should retransmit any messages for which an acknowledgement has not been received from the broker. There is a possibility of message duplication here, because the broker might have sent a confirmation that never reached the producer (due to network failures, etc). Therefore consumer applications will need to perform deduplication or handle incoming messages in an idempotent manner.
In some circumstances it can be important for producers to ensure that their messages are being routed to queues (although not always - in the case of a pub-sub system producers will just publish and if no consumers are interested it is correct for messages to be dropped).
To ensure messages are routed to a single known queue, the producer can just declare a destination queue and publish directly to it. If messages may be routed in more complex ways but the producer still needs to know if they reached at least one queue, it can set the
mandatory flag on a
basic.publish, ensuring that a
basic.return (containing a reply code and some textual explanation) will be sent back to the client if no queues were appropriately bound. See the Publishers guide for details.
Producers should also be aware that when publishing to a clustered node, if one or more destination queues that are bound to the exchange have mirrors in the cluster, it's possible to incur delays in the face of network failures between nodes, due to flow control between replicas and the queue leader replica. See inter-node heartbeat guide for more details.
In the event of network failure (or a node failure), messages can be redelivered, and consumers must be prepared to handle deliveries they have seen in the past. It is recommended that consumer implementation is designed to be idempotent rather than to explicitly perform deduplication.
If a message is delivered to a consumer and then requeued, either automatically by RabbitMQ or by the same or different consumer, RabbitMQ will set the
redelivered flag on it when it is delivered again. This is a hint that a consumer may have seen this message before. This is not guaranteed as the original delivery might have not made it to any consumers due to a network or consumer application failure.
redelivered flag is not set then it is guaranteed that the message has not been seen before. Therefore if a consumer finds it more expensive to deduplicate messages or process them in an idempotent manner, it can do this only for messages with the
redelivered flag set.
If a consumer determines that it cannot handle a message then it can reject it using the
basic.nack method, either asking the server to requeue it, or not (in which case the server might be configured to dead-letter it instead).
When the queue a consumer was consuming from has been deleted, RabbitMQ will notify the consumer. Such consumer must take action to recover, whether it is consuming from a different queue or redeclaring the one it was originally consuming from when this is safe and appropriate.
RabbitMQ provides two plugins to assist with distributing nodes over unreliable networks (such as wide-area networks): Federation and the Shovel. Both will recover from network failures and retransmit messages when necessary. Both use confirms and acknowledgements by default.
When connecting clusters with Federation or the Shovel, it is desirable to ensure that the federation links and Shovels can recover from node failures, including permanent (fail-stop) scenarios.
Federation will automatically distribute links across the downstream cluster and migrate them on failure of a downstream node. In order to connect to a new upstream when an upstream node fails, multiple upstream URIs must be specified for an upstream, or connection has to happen over a load balancer with sufficient availability characteristics.
Shovels can use multiple source and destination endpoints; first reachable endpoint will be used. A failed Shovel will be restarted after a configurable delay and retry.
Some failure scenarios are subtle and hard to observe or detect. For example, a slow connection leak can build up over time and like a chronic disease, go unnoticed for a period of time. Monitoring and metrics is the way to detect many types of failures. Longer-term metric data collected using tools such as Prometheus can help spot irregularities and problematic patterns in system behaviour.
In addition to monitoring, health checks is another tool that can be used to detect point-in-time problems, that is, problems observable at the moment. Extensive health check coverage can suffer from false positives, so more checks isn't necessarily better.
Both monitoring and health checks are covered in a dedicated guide.