Broker Adjustments

The following sections describe adjustments you can make to broker properties to improve performance.

Memory Management: Increasing Broker Stability Under Load

Memory management can be configured on a destination-by-destination basis or on a systemwide level (for all destinations, collectively).

Using Physical Destination Limits

For information on physical destination limits, see Chapter 6, Physical Destinations

Using Systemwide Limits

If message producers tend to overrun message consumers, messages can accumulate in the broker. The broker contains a mechanism for throttling back producers and swapping messages out of active memory in low memory conditions, but it is wise to set a hard limit on the total number of messages (and message bytes) that the broker can hold.

Control these limits by setting the imq.system.max_count and the imq.system.max_size broker properties.

For example:

imq.system.max_count=5000

The defined value above means that the broker will only hold up to 5000 undelivered/unacknowledged messages. If additional messages are sent, they are rejected by the broker. If a message is persistent then the producer will get an exception when it tries to send the message. If the message is nonpersistent, the broker silently drops the message.

When an exception is returned in sending a message, the client should pause for a moment and retry the send again. (Note that the exception will never be due to the broker’s failure to receive a message; the only exceptions raised are those detected by the client on the sending side.)

Multiple Consumer Queue Performance

The efficiency with which multiple queue consumers process messages in a queue destination depends on the following configurable queue destination attributes:

• The number of active consumers (maxNumActiveConsumers)

• The maximum number of messages that can be delivered to a consumer in a single batch (consumerFlowLimit)

To achieve optimal message throughput there must be a sufficient number of active consumers to keep up with the rate of message production for the queue, and the messages in the queue must be routed and then delivered to the active consumers in such a way as to maximize their rate of consumption. The general mechanism for balancing message delivery among multiple consumers is described in the Message Queue Technical Overview.

If messages are accumulating in the queue, it is possible that there is an insufficient number of active consumers to handle the message load. It is also possible that messages are being delivered to consumers in batch sizes that cause messages to be backing up on the consumers. For example, if the batch size (consumerFlowLimit) is too large, one consumer might receive all the messages in a queue while other active consumers receive none. If consumers are very fast, this might not be a problem. However, if consumers are relatively slow, you want messages to be distributed to them evenly, and therefore you want the batch size to be small. Although smaller batch sizes require more overhead to deliver messages to consumers, for slow consumers there is generally a net performance gain in using small batch sizes. In the extreme case, setting consumerFlowLiimit to 1 configures the queue for “round-robin” delivery, in which single messages are delivered, one to each consumer in rotation.