This page describes how to scale out endpoints using NServiceBus. There are several reasons to scale out, such as to achieve higher message throughput, or to provide high availability.
It is important to distinguish between a logical endpoint and an endpoint instance. Review the documentation on logical endpoints for more details.
One message type might take considerably longer to process than other message types. The faster processing messages might suffer in throughput because of the slower processing messages. A good way to monitor and detect this is by using ServicePulse's monitoring capabilities.
Separating slower messages from faster messages leads to higher throughput for the faster messages. For this reason it can be beneficial to include messages and/or handlers in separate assemblies, making it easier to separate them from others.
An endpoint may reach maximum message throughput when resources such as CPU or disk are fully utilized. In such cases it may be beneficial to scale out an endpoint to multiple nodes.
However, a centralized resource, such as a database, may also be a bottleneck. Scaling out an endpoint to another node that uses the same database may not improve message throughput, or could even reduce it.
The easiest way to scale out is with brokered transports, as those can make use of the competing consumer pattern. This is done by deploying multiple instances of an endpoint that will all start processing messages from the same queue. When a message is delivered, any of the endpoint instances could potentially process it. The NServiceBus transport will try to ensure that only one instance will actually process the message.
The image below shows the component
ClientUI sending a command message to the logical endpoint
Sales. But with messaging, the message is actually sent to the
Sales queue. With two consumers competing for the
Sales endpoint, both could potentially process the incoming message.
Because of the federated nature of queues with MSMQ, with scaled out endpoints across different nodes, each node has its own physical queue. This makes it impossible to apply the competing consumer pattern. See Sender Side Distribution for more information.
Though not discussed here, there are many ways to achieve high availability for endpoints using infrastructure with either on-premise or cloud-based solutions. However, a different reason to try to achieve high availability is to make sure an endpoint continues to process messages while upgrading it to a newer version of either the endpoint itself or its messages. For more information on how to do message versioning, see this sample.
Upgrading an endpoint without stopping message processing, can be accomplished by also using the competing consumer pattern, without necessarily deploying multiple endpoint instances to different nodes. In order words, this can even be achieved by deploying two endpoint instances on the same node.
The following image explains the process.
Execute the following steps to upgrade an endpoint without downtime:
Salesendpoint has a reference to version 1 of the message assembly
- Take down one endpoint instance of
Financeand upgrade it to version 2 of the message assembly
Finance.. During this time,
Salescan continue sending messages and the running endpoint instance for
Financecan continue processing them.
- Bring the upgraded version of
Financeback up so it can start processing version 1 messages.
- Take down the still-running version 1 of
Financeand upgrade it as well to version 2 of
Salesto also have message assembly