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Oracle GlassFish Server Message Queue 4.5 Administration Guide
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Document Information


Part I Introduction to Message Queue Administration

1.  Administrative Tasks and Tools

2.  Quick-Start Tutorial

Part II Administrative Tasks

3.  Starting Brokers and Clients

4.  Configuring a Broker

5.  Managing a Broker

6.  Configuring and Managing Connection Services

7.  Managing Message Delivery

8.  Configuring Persistence Services

9.  Configuring and Managing Security Services

10.  Configuring and Managing Broker Clusters

11.  Managing Administered Objects

12.  Configuring and Managing Bridge Services

13.  Monitoring Broker Operations

14.  Analyzing and Tuning a Message Service

About Performance

The Performance Tuning Process

Aspects of Performance


Baseline Use Patterns

Factors Affecting Performance

Message Delivery Steps

Application Design Factors Affecting Performance

Delivery Mode (Persistent/Nonpersistent Messages)

Use of Transactions

Acknowledgment Mode

Durable and Nondurable Subscriptions

Use of Selectors (Message Filtering)

Message Size

Message Body Type

Message Service Factors Affecting Performance


Operating System

Java Virtual Machine (JVM)


Message Service Architecture

Broker Limits and Behaviors

Data Store Performance

Client Runtime Configuration

Adjusting Configuration To Improve Performance

System Adjustments

Solaris Tuning: CPU Utilization, Paging/Swapping/Disk I/O

Java Virtual Machine Adjustments

Tuning Transport Protocols

Tuning the File-based Persistent Store

Broker Memory Management Adjustments

Using Physical Destination Limits

Using System-Wide Limits

Client Runtime Message Flow Adjustments

Message Flow Metering

Message Flow Limits

Adjusting Multiple-Consumer Queue Delivery

15.  Troubleshooting

Part III Reference

16.  Command Line Reference

17.  Broker Properties Reference

18.  Physical Destination Property Reference

19.  Administered Object Attribute Reference

20.  JMS Resource Adapter Property Reference

21.  Metrics Information Reference

22.  JES Monitoring Framework Reference

Part IV Appendixes

A.  Distribution-Specific Locations of Message Queue Data

B.  Stability of Message Queue Interfaces

C.  HTTP/HTTPS Support

D.  JMX Support

E.  Frequently Used Command Utility Commands


Adjusting Configuration To Improve Performance

The following sections explain how configuration adjustments can affect performance.

System Adjustments

The following sections describe adjustments you can make to the operating system, JVM, communication protocols, and persistent data store.

Solaris Tuning: CPU Utilization, Paging/Swapping/Disk I/O

See your system documentation for tuning your operating system.

Java Virtual Machine Adjustments

By default, the broker uses a JVM heap size of 192MB. This is often too small for significant message loads and should be increased.

When the broker gets close to exhausting the JVM heap space used by Java objects, it uses various techniques such as flow control and message swapping to free memory. Under extreme circumstances it even closes client connections in order to free the memory and reduce the message inflow. Hence it is desirable to set the maximum JVM heap space high enough to avoid such circumstances.

However, if the maximum Java heap space is set too high, in relation to system physical memory, the broker can continue to grow the Java heap space until the entire system runs out of memory. This can result in diminished performance, unpredictable broker crashes, and/or affect the behavior of other applications and services running on the system. In general, you need to allow enough physical memory for the operating system and other applications to run on the machine.

In general it is a good idea to evaluate the normal and peak system memory footprints, and configure the Java heap size so that it is large enough to provide good performance, but not so large as to risk system memory problems.

To change the minimum and maximum heap size for the broker, use the -vmargs command line option when starting the broker. For example:

/usr/bin/imqbrokerd -vmargs "-Xms256m -Xmx1024m"

This command will set the starting Java heap size to 256MB and the maximum Java heap size to 1GB.

In any case, verify settings by checking the broker’s log file or using the imqcmd metrics bkr -m cxn command.

Tuning Transport Protocols

Once a protocol that meets application needs has been chosen, additional tuning (based on the selected protocol) might improve performance.

A protocol’s performance can be modified using the following three broker properties:

For TCP and SSL protocols, these properties affect the speed of message delivery between client and broker. For HTTP and HTTPS protocols, these properties affect the speed of message delivery between the Message Queue tunnel servlet (running on a Web server) and the broker. For HTTP/HTTPS protocols there are additional properties that can affect performance (see HTTP/HTTPS Tuning).

The protocol tuning properties are described in the following sections.


The nodelay property affects Nagle’s algorithm (the value of the TCP_NODELAY socket-level option on TCP/IP) for the given protocol. Nagle’s algorithm is used to improve TCP performance on systems using slow connections such as wide-area networks (WANs).

When the algorithm is used, TCP tries to prevent several small chunks of data from being sent to the remote system (by bundling the data in larger packets). If the data written to the socket does not fill the required buffer size, the protocol delays sending the packet until either the buffer is filled or a specific delay time has elapsed. Once the buffer is full or the timeout has occurred, the packet is sent.

For most messaging applications, performance is best if there is no delay in the sending of packets (Nagle’s algorithm is not enabled). This is because most interactions between client and broker are request/response interactions: the client sends a packet of data to the broker and waits for a response. For example, typical interactions include:

For these interactions, most packets are smaller than the buffer size. This means that if Nagle’s algorithm is used, the broker delays several milliseconds before sending a response to the consumer.

However, Nagle’s algorithm may improve performance in situations where connections are slow and broker responses are not required. This would be the case where a client sends a nonpersistent message or where a client acknowledgment is not confirmed by the broker (DUPS_OK_ACKNOWLEDGE session).


The inbufsz property sets the size of the buffer on the input stream reading data coming in from a socket. Similarly, outbufsz sets the buffer size of the output stream used by the broker to write data to the socket.

In general, both parameters should be set to values that are slightly larger than the average packet being received or sent. A good rule of thumb is to set these property values to the size of the average packet plus 1 kilobyte (rounded to the nearest kilobyte). For example, if the broker is receiving packets with a body size of 1 kilobyte, the overall size of the packet (message body plus header plus properties) is about 1200 bytes; an inbufsz of 2 kilobytes (2048 bytes) gives reasonable performance. Increasing inbufsz or outbufsz greater than that size may improve performance slightly, but increases the memory needed for each connection.


In addition to the general properties discussed in the previous two sections, HTTP/HTTPS performance is limited by how fast a client can make HTTP requests to the Web server hosting the Message Queue tunnel servlet.

A Web server might need to be optimized to handle multiple requests on a single socket. With JDK version 1.4 and later, HTTP connections to a Web server are kept alive (the socket to the Web server remains open) to minimize resources used by the Web server when it processes multiple HTTP requests. If the performance of a client application using JDK version 1.4 is slower than the same application running with an earlier JDK release, you might need to tune the Web server keep-alive configuration parameters to improve performance.

In addition to such Web server tuning, you can also adjust how often a client polls the Web server. HTTP is a request-based protocol. This means that clients using an HTTP-based protocol periodically need to check the Web server to see if messages are waiting. The imq.httpjms.http.pullPeriod broker property (and the corresponding imq.httpsjms.https.pullPeriod property) specifies how often the Message Queue client runtime polls the Web server.

If the pullPeriod value is -1 (the default value), the client runtime polls the server as soon as the previous request returns, maximizing the performance of the individual client. As a result, each client connection monopolizes a request thread in the Web server, possibly straining Web server resources.

If the pullPeriod value is a positive number, the client runtime periodically sends requests to the Web server to see if there is pending data. In this case, the client does not monopolize a request thread in the Web server. Hence, if large numbers of clients are using the Web server, you might conserve Web server resources by setting the pullPeriod to a positive value.

Tuning the File-based Persistent Store

For information on tuning the file-based persistent store, see Configuring a File-Based Data Store.

Broker Memory Management Adjustments

You can improve performance and increase broker stability under load by properly managing broker memory. Memory management can be configured on a destination-by-destination basis or on a system-wide level (for all destinations, collectively).

Using Physical Destination Limits

To configure physical destination limits, see the properties described in Physical Destination Properties.

Using System-Wide 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 under 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:


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

When an exception is thrown in sending a message, the client should process the exception by pausing for a moment and retrying the send again. (Note that the exception will never be due to the broker’s failure to receive a message; the exception is thrown by the client runtime before the message is sent to the broker.)

Client Runtime Message Flow Adjustments

This section discusses client runtimeflow control behaviors that affect performance. These behaviors are configured as attributes of connection factory administered objects. For information on setting connection factory attributes, see Chapter 11, Managing Administered Objects

Message Flow Metering

Messages sent and received by clients (payload messages), as well as Message Queue control messages, pass over the same client-broker connection. Delays in the delivery of control messages, such as broker acknowledgments, can result if control messages are held up by the delivery of payload messages. To prevent this type of congestion, Message Queue meters the flow of payload messages across a connection.

Payload messages are batched (as specified with the connection factory attribute imqConnectionFlowCount) so that only a set number are delivered. After the batch has been delivered, delivery of payload messages is suspended and only pending control messages are delivered. This cycle repeats, as additional batches of payload messages are delivered followed by pending control messages.

The value of imqConnectionFlowCount should be kept low if the client is doing operations that require many responses from the broker: for example, if the client is using CLIENT_ACKNOWLEDGE or AUTO_ACKNOWLEDGE mode, persistent messages, transactions, or queue browsers, or is adding or removing consumers. If, on the other hand, the client has only simple consumers on a connection using DUPS_OK_ACKNOWLEDGE mode, you can increase imqConnectionFlowCount without compromising performance.

Message Flow Limits

There is a limit to the number of payload messages that the Message Queue client runtime can handle before encountering local resource limitations, such as memory. When this limit is approached, performance suffers. Hence, Message Queue lets you limit the number of messages per consumer (or messages per connection) that can be delivered over a connection and buffered in the client runtime, waiting to be consumed.

Consumer Flow Limits

When the number of payload messages delivered to the client runtime exceeds the value of imqConsumerFlowLimit for any consumer, message delivery for that consumer stops. It is resumed only when the number of unconsumed messages for that consumer drops below the value set with imqConsumerFlowThreshold.

The following example illustrates the use of these limits: consider the default settings for topic consumers:


When the consumer is created, the broker delivers an initial batch of 1000 messages (providing they exist) to this consumer without pausing. After sending 1000 messages, the broker stops delivery until the client runtime asks for more messages. The client runtime holds these messages until the application processes them. The client runtime then allows the application to consume at least 50% (imqConsumerFlowThreshold ) of the message buffer capacity (i.e. 500 messages) before asking the broker to send the next batch.

In the same situation, if the threshold were 10%, the client runtime would wait for the application to consume at least 900 messages before asking for the next batch.

The next batch size is calculated as follows:

imqConsumerFlowLimit - (current number of pending msgs in buffer)

So if imqConsumerFlowThreshold is 50%, the next batch size can fluctuate between 500 and 1000, depending on how fast the application can process the messages.

If the imqConsumerFlowThreshold is set too high (close to 100%), the broker will tend to send smaller batches, which can lower message throughput. If the value is set too low (close to 0%), the client may be able to finish processing the remaining buffered messages before the broker delivers the next set, again degrading message throughput. Generally speaking, unless you have specific performance or reliability concerns, you will not need to change the default value of imqConsumerFlowThreshold attribute.

The consumer-based flow controls (in particular, imqConsumerFlowLimit ) are the best way to manage memory in the client runtime. Generally, depending on the client application, you know the number of consumers you need to support on any connection, the size of the messages, and the total amount of memory that is available to the client runtime.

Note - Setting the imqConsumerFlowLimitPrefetch property to false disables the prefetching and buffering specified by imqConsumerFlowLimit and imqConsumerFlowThreshold, in which case messages are delivered to consumers one at a time and a new message is not sent to a consumer until it consumes the message it has. This delivery constraint, which can degrade message throughput, is for use when business logic demands that each consumer have only one message at a time.

When the JMS resource adapter, jmsra, is used to consume messages in a GlassFish Server cluster, this behavior is defined using different properties, as described in About Shared Topic Subscriptions for Clustered Containers.

Connection Flow Limits

In the case of some client applications, however, the number of consumers may be indeterminate, depending on choices made by end users. In those cases, you can still manage memory using connection-level flow limits.

Connection-level flow controls limit the total number of messages buffered for all consumers on a connection. If this number exceeds the value of imqConnectionFlowLimit, delivery of messages through the connection stops until that total drops below the connection limit. (The imqConnectionFlowLimit attribute is enabled only if you set imqConnectionFlowLimitEnabled to true.)

The number of messages queued up in a session is a function of the number of message consumers using the session and the message load for each consumer. If a client is exhibiting delays in producing or consuming messages, you can normally improve performance by redesigning the application to distribute message producers and consumers among a larger number of sessions or to distribute sessions among a larger number of connections.

Adjusting Multiple-Consumer Queue Delivery

The efficiency with which multiple queue consumers process messages in a queue destination depends on a number of factors. To achieve optimal message throughput there must be a sufficient number of 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 message delivery mechanism for multiple-consumer queues is that messages are delivered to consumers in batches as each consumer is ready to receive a new batch. The readiness of a consumer to receive a batch of messages depends upon configurable client runtime properties, such as imqConsumerFlowLimit and imqConsumerFlowThreshold, as described in Message Flow Limits. As new consumers are added to a queue, they are sent a batch of messages to consume, and receive subsequent batches as they become ready.

Note - The message delivery mechanism for multiple-consumer queues described above can result in messages being consumed in an order different from the order in which they are produced.

If messages are accumulating in the queue, it is possible that there is an insufficient number of 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 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. The value of consumerFlowLimit can be set on a destination as well as on the client runtime: the smaller value overrides the larger one.