Sun Java System Message Queue 3.7 UR1 Developer's Guide for Java Clients

Factors Affecting Performance

Application design decisions can have a significant effect on overall messaging performance. The most important factors affecting performance are those that impact the reliability of message delivery; among these are the following:

Other application design factors impacting performance include the following:

The sections that follow describe the impact of each of these factors on messaging performance. As a general rule, there is a trade-off between performance and reliability: factors that increase reliability tend to decrease performance.

Table 3–4 shows how application design factors affect messaging performance. The table shows two scenarios—a high-reliability, low-performance scenario and a high-performance, low-reliability scenario—and the choice of application design factors that characterizes each. Between these extremes, there are many choices and trade-offs that affect both reliability and performance.

Table 3–4 Comparison of High Reliability and High Performance Scenarios

Application DesignFactor 

High Reliability, Low Performance 

High Performance, Low Reliability 

Delivery mode 

Persistent messages 

Nonpersistent messages 

Use of transactions 

Transacted sessions 

No transactions 

Acknowledgment mode 

AUTO_ACKNOWLEDGE

CLIENT_ACKNOWLEDGE

DUPS_OK_ACKNOWLEDGE

NO_ACKNOWLEDGE

Durable/nondurable subscriptions 

Durable subscriptions 

Nondurable subscriptions 

Use of selectors 

Message filtering 

No message filtering 

Message size 

Small messages 

Large messages 

Message body type 

Complex body types 

Simple body types 

Delivery Mode (Persistent/Nonpersistent)

Persistent messages guarantee message delivery in case of broker failure. The broker stores these message in a persistent store until all intended consumers acknowledge that they have consumed the message.

Broker processing of persistent messages is slower than for nonpersistent messages for the following reasons:

For both queues and topics with durable subscribers, performance was approximately 40% faster for non-persistent messages. We obtained these results using 10K-size messages and AUTO_ACKNOWLEDGE mode.

Use of Transactions

A transaction guarantees that all messages produced in a transacted session and all messages consumed in a transacted session will be either processed or not processed (rolled back) as a unit. Message Queue supports both local and distributed transactions.

A message produced or acknowledged in a transacted session is slower than in a non-transacted session for the following reasons:

Acknowledgment Mode

Other than using transactions, you can ensure reliable delivery by having the client acknowledge receiving a message. If a session is closed without the client acknowledging the message or if the message broker fails before the acknowledgment is processed, the broker redelivers that message, setting a JMSRedelivered flag.

For a non-transacted session, the client can choose one of three acknowledgment modes, each of which has its own performance characteristics:

Performance is impacted by acknowledgment mode for the following reasons:

Durable vs. Nondurable Subscriptions

Subscribers to a topic destination have either durable and nondurable subscriptions. Durable subscriptions provide increased reliability at the cost of slower throughput for the following reasons:

We compared performance for durable and non-durable subscribers in two cases: persistent and nonpersistent 10k-sized messages. Both cases use AUTO_ACKNOWLEDGE acknowledgment mode. We found a performance impact only in the case of persistent messages, which slowed messages conveyed to durable subscribers by about 30%.

Use of Selectors (Message Filtering)

Application developers can have the messaging provider sort messages according to criteria specified in the message selector associated with a consumer and deliver to that consumer only those messages whose property value matches the message selector. For example, if an application creates a subscriber to the topic WidgetOrders and specifies the expression NumberOfOrders >1000 for the message selector, messages with a NumberOfOrders property value of 1001 or more are delivered to that subscriber.

Creating consumers with selectors lowers performance (as compared to using multiple destinations) because additional processing is required to handle each message. When a selector is used, it must be parsed so that it can be matched against future messages. Additionally, the message properties of each message must be retrieved and compared against the selector as each message is routed. However, using selectors provides more flexibility in a messaging application and may lower resource requirements at the expense of speed.

Message Size

Message size affects performance because more data must be passed from producing client to broker and from broker to consuming client, and because for persistent messages a larger message must be stored.

However, by batching smaller messages into a single message, the routing and processing of individual messages can be minimized, providing an overall performance gain. In this case, information about the state of individual messages is lost.

In our tests, which compared throughput in kilobytes per second for 1K, 10K, and 100K-sized messages to a queue destination using AUTO_ACKNOWLEDGE mode, we found that non-persistent messaging was about 50% faster for 1K messages, about 20% faster for 10K messages, and about 5% faster for 100K messages. The size of the message affected performance significantly for both persistent and non-persistent messages. 100k messages are about 10 times faster than 10K, and 10K messages are about 5 times faster than 1K.

Message Body Type

JMS supports five message body types, shown below roughly in the order of complexity:

While, in general, the message type is dictated by the needs of an application, the more complicated types (map and object) carry a performance cost — the expense of serializing and deserializing the data. The performance cost depends on how simple or how complicated the data is.