Eclipse GlassFish Performance Tuning Guide, Release 7

Application users must be authenticated. The Eclipse GlassFish provides a number of choices for user authentication, including file-based, administration, LDAP, certificate, JDBC, digest, PAM, Solaris, and custom realms.

The default file based security realm is suitable for developer environments, where new applications are developed and tested. At deployment time, the server administrator can choose between the Lighweight Directory Access Protocol (LDAP) or Solaris security realms. Many large enterprises use LDAP-based directory servers to maintain employee and customer profiles. Small to medium enterprises that do not already use a directory server may find it advantageous to leverage investment in Solaris security infrastructure.

For more information on security realms, see "Administering Authentication Realms" in Eclipse GlassFish Security Guide.

The type of authentication mechanism chosen may require additional hardware for the deployment. Typically a directory server executes on a separate server, and may also require a backup for replication and high availability. Refer to the Oracle Java System Directory Server (https://www.oracle.com/us/products/middleware/identity-management/oracle-directory-services/index.html) documentation for more information on deployment, sizing, and availability guidelines.

An authenticated user’s access to application functions may also need authorization checks. If the application uses the role-based Jakarta EE authorization checks, the application server performs some additional checking, which incurs additional overheads. When you perform capacity planning, you must take this additional overhead into account.

For security reasons, sensitive user inputs and application output must be encrypted. Most business-oriented web applications encrypt all or some of the communication flow between the browser and Eclipse GlassFish. Online shopping applications encrypt traffic when the user is completing a purchase or supplying private data. Portal applications such as news and media typically do not employ encryption. Secure Sockets Layer (SSL) is the most common security framework, and is supported by many browsers and application servers.

The Eclipse GlassFish supports SSL 2.0 and 3.0 and contains software support for various cipher suites. It also supports integration of hardware encryption cards for even higher performance. Security considerations, particularly when using the integrated software encryption, will impact hardware sizing and capacity planning.

Consider the following when assessing the encryption needs for a deployment:

For information on how to encrypt the communication between web servers and Eclipse GlassFish, see "Administering Message Security" in Eclipse GlassFish Security Guide.

Determine how much additional performance you gain when you add processors. That is, you are indirectly measuring the amount of shared resource contention that occurs on the server for a specific workload. Either obtain this information based on additional load testing of the application on a multiprocessor system, or leverage existing information from a similar application that has already been load tested.

Running a series of performance tests on one to eight CPUs, in incremental steps, generally provides a sense of the vertical scalability characteristics of the system. Be sure to properly tune the application, Eclipse GlassFish, backend database resources, and operating system so that they do not skew the results. 3. Determine horizontal scalability.

+ If sufficiently powerful hardware resources are available, a single hardware node may meet the performance requirements. However for better availability, you can cluster two or more systems. Employing external load balancers and workload simulation, determine the performance benefits of replicating one well-tuned application server node, as determined in step 2.

Follow these general guidelines to increase performance of the presentation tier:

In the servlet multithread model (the default), a single instance of a servlet is created for each application server instance. All requests for a servlet on that application instance share the same servlet instance. This can lead to thread contention if there are synchronization blocks in the servlet code. Therefore, avoid using shared modified class variables because they create the need for synchronization.

Follow these guidelines when using HTTP sessions:

Follow these configuration tips to improve performance. These tips are intended for production environments, not development environments.

Optimize SSL

Optimize SSL by using routines in the appropriate operating system library for concurrent access to heap space. The library to use depends on the version of the Solaris Operating System (SolarisOS) that you are using. To ensure that you use the correct library, set the LD_PRELOAD environment variable to specify the correct library file. For more information, refer to the following table.

To set the LD_PRELOAD environment variable, edit the entry for this environment variable in the startserv script. The startserv script is located is located in the bin/startserv directory of your domain.

The exact syntax to define an environment variable depends on the shell that you are using.

Disable Security Manager

The security manager is expensive because calls to required resources must call the doPrivileged() method and must also check the resource with the server.policy file. If you are sure that no malicious code will be run on the server and you do not use authentication within your application, then you can disable the security manager.

See "Enabling and Disabling the Security Manager" in Eclipse GlassFish Application Development Guide for instructions on enabling or disabling the security manager. If using the Eclipse GlassFish Administration Console, navigate to the Configurations>configuration-name>Security node and check or uncheck the Security Manager option as desired. Refer to the Administration Console online help for more information.

To gather method invocation statistics for all methods in a bean, use the following command:

where monitorableObject is a fully-qualified identifier from the hierarchy of objects that can be monitored, shown below.

where moduleName is x_jar for module x.jar.

For standalone beans, use this pattern:

The possible identifiers are the same as for ejb-module.

For example, to get statistics for a method in an entity bean, use this command:

For more information about administering the monitoring service in general, see "Administering the Monitoring Service" in Eclipse GlassFish Administration Guide. For information about viewing comprehensive EJB monitoring statistics, see "EJB Statistics" in Eclipse GlassFish Administration Guide.

To configure EJB monitoring using the Eclipse GlassFish Administration Console, navigate to the Configurations>configuration-name>Monitoring node. After configuring monitoring, you can view monitoring statistics by navigating to the server (Admin Server) node and then selecting the Monitor tab. Refer to the Administration Console online help for instructions on each of these procedures.

Alternatively, to list EJB statistics, use the asadmin list subcommand. For more information, see list(1).

For statistics on stateful session bean passivations, use this command:

From the attribute values that are returned, use this command:

num-passivationsnum-passivation-errorsnum-passivation-success

Use high-performance beans as much as possible to improve the overall performance of your application. For more information, see Tuning Tips for Specific Types of EJB Components.

The types of EJB components are listed below, from the highest performance to the lowest:

For more information about configuring high availability session persistence, see "Configuring High Availability Session Persistence and Failover" in Eclipse GlassFish High Availability Administration Guide. To configure EJB beans using the Eclipse GlassFish Administration Console, navigate to the Configurations>configuration-name>EJB Container node and then refer to the Administration Console online help for detailed instructions.

Caching can greatly improve performance when used wisely. For example:

The stub classes needed by EJB applications are generated dynamically at runtime when an EJB client needs them. This means that it is not necessary to generate the stubs or retrieve the client JAR file when deploying an application with remote EJB components. When deploying an application, it is no longer necessary to specify the --retrieve option, which can speed up deployment.

If you have a legacy rich-client application that directly uses the CosNaming service (not a recommended configuration), then you must generate the stubs for your application explicitly using RMIC. For more information, see the Eclipse GlassFish Troubleshooting Guide for more details.

Removing unneeded stateful session beans avoids passivating them, which requires disk operations.

Follow these tips when using the EJB cache and pools to improve performance:

An EJB component can have remote and local interfaces. Clients not located in the same application server instance as the bean (remote clients) use the remote interface to access the bean. Calls to the remote interface require marshalling arguments, transportation of the marshalled data over the network, un-marshaling the arguments, and dispatch at the receiving end. Thus, using the remote interface entails significant overhead.

If an EJB component has a local interface, then local clients in the same application server instance can use it instead of the remote interface. Using the local interface is more efficient, since it does not require argument marshalling, transportation, and un-marshalling.

If a bean is to be used only by local clients then it makes sense to provide only the local interface. If, on the other hand, the bean is to be location-independent, then you should provide both the remote and local interfaces so that remote clients use the remote interface and local clients can use the local interface for efficiency.

By default, the Eclipse GlassFish uses pass-by-value semantics for calling the remote interface of a bean, even if it is co-located. This can be expensive, since clients using pass-by-value semantics must copy arguments before passing them to the EJB component.

However, local clients can use pass-by-reference semantics and thus the local and remote interfaces can share the passed objects. But this means that the argument objects must be implemented properly, so that they are shareable. In general, it is more efficient to use pass-by-reference semantics when possible.

Using the remote and local interfaces appropriately means that clients can access EJB components efficiently. That is, local clients use the local interface with pass-by-reference semantics, while remote clients use the remote interface with pass-by-value semantics.

However, in some instances it might not be possible to use the local interface, for example when:

For these cases, the Eclipse GlassFish provides a pass-by-reference option that clients can use to pass arguments by reference to the remote interface of a co-located EJB component.

You can specify the pass-by-reference option for an entire application or a single EJB component. When specified at the application level, all beans in the application use pass-by-reference semantics when passing arguments to their remote interfaces. When specified at the bean level, all calls to the remote interface of the bean use pass-by-reference semantics. See "Value Added Features" in Eclipse GlassFish Application Development Guide for more details about the pass-by-reference flag.

To specify that an EJB component will use pass by reference semantics, use the following tag in the sun-ejb-jar.xml deployment descriptor:

This avoids copying arguments when the EJB component’s methods are invoked and avoids copying results when methods return. However, problems will arise if the data is modified by another source during the invocation.

Container-managed transactions are preferred for consistency, and provide better performance.

To avoid resources being held unnecessarily for long periods, a transaction should not encompass user input or user think time.

Declare non-transactional methods of session EJB components with NotSupported or Never transaction attributes. These attributes can be found in the ejb-jar.xml deployment descriptor file. Transactions should span the minimum time possible since they lock database rows.

For very large transaction chains, use the transaction attribute TX_REQUIRED. To ensure EJB methods in a call chain, use the same transaction.

Use the lowest cost locking available from the database that is consistent with any transaction. Commit the data after the transaction completes rather than after each method call.

When multiple database resources, connector resources or JMS resources are involved in one transaction, a distributed or global transaction needs to be performed. This requires XA capable resource managers and data sources. Use XA capable data sources, only when two or more data source are going to be involved in a transaction. If a database participates in some distributed transactions, but mostly in local or single database transactions, it is advisable to register two separate JDBC resources and use the appropriate resource in the application.

To improve performance of transactions involving multiple resources, the Eclipse GlassFish uses last agent optimization (LAO), which allows the configuration of one of the resources in a distributed transaction as a one-phase commit (1PC) resource. Since the overhead of multiple-resource transactions is much higher for a JDBC resource than a message queue, LAO substantially improves performance of distributed transactions involving one JDBC resource and one or more message queues. To take advantage of LAO, configure a JDBC resource as a 1PC resource. Nothing special needs to be done to configure JMS resources.

In global transactions involving multiple JDBC resources, LAO will still improve performance, however, not as much as for one JDBC resource. In this situation, one of the JDBC resources should be configured as 1PC, and all others should be configured as XA.

Set the following transaction attributes in the EJB deployment descriptor file (ejb-jar.xml). Options are listed from best performance to worst. To improve performance, choose the least expensive attribute that will provide the functionality your application needs:

Use version consistency to improve performance while protecting the integrity of data in the database. Since the application server can use multiple copies of an EJB component simultaneously, an EJB component’s state can potentially become corrupted through simultaneous access.

The standard way of preventing corruption is to lock the database row associated with a particular bean. This prevents the bean from being accessed by two simultaneous transactions and thus protects data. However, it also decreases performance, since it effectively serializes all EJB access.

Version consistency is another approach to protecting EJB data integrity. To use version consistency, you specify a column in the database to use as a version number. The EJB lifecycle then proceeds like this:

Subsequent uses of the bean behave similarly, except that the ejbLoad() method loads its initial data (including the version number) from an internal cache. This saves a trip to the database. When the ejbStore() method is called, the version number is checked to ensure that the correct data was used in the transaction.

Version consistency is advantageous when you have EJB components that are rarely modified, because it allows two transactions to use the same EJB component at the same time. Because neither transaction modifies the data, the version number is unchanged at the end of both transactions, and both succeed. But now the transactions can run in parallel. If two transactions occasionally modify the same EJB component, one will succeed and one will fail and can be retried using the new values—which can still be faster than serializing all access to the EJB component if the retries are infrequent enough (though now your application logic has to be prepared to perform the retry operation).

To use version consistency, the database schema for a particular table must include a column where the version can be stored. You then specify that table in the sun-cmp-mapping.xml deployment descriptor for a particular bean:

In addition, you must establish a trigger on the database to automatically update the version column when data in the specified table is modified. The Eclipse GlassFish requires such a trigger to use version consistency. Having such a trigger also ensures that external applications that modify the EJB data will not conflict with EJB transactions in progress.

For example, the following DDL illustrates how to create a trigger for the Order table:

Request partitioning enables you to assign a request priority to an EJB component. This gives you the flexibility to make certain EJB components execute with higher priorities than others.

An EJB component which has a request priority assigned to it will have its requests (services) executed within an assigned threadpool. By assigning a threadpool to its execution, the EJB component can execute independently of other pending requests. In short, request partitioning enables you to meet service-level agreements that have differing levels of priority assigned to different services.

Request partitioning applies only to remote EJB components (those that implement a remote interface). Local EJB components are executed in their calling thread (for example, when a servlet calls a local bean, the local bean invocation occurs on the servlet’s thread).

To Enable Request Partitioning

Follow this procedure.

Depending on the usage of a particular entity bean, one should tune max-cache-size so that the beans that are used less frequently (for example, an order that is created and never used after the transaction is over) are cached less, and beans that are used frequently (for example, an item in the inventory that gets referenced very often), are cached more.

When a stateful bean represents a user, a reasonable max-cache-size of beans is the expected number of concurrent users on the application server process. If this value is too low (in relation to the steady load of users), beans would be frequently passivated and activated, causing a negative impact on the response times, due to CPU intensive serialization and deserialization as well as disk I/O.

Another important variable for tuning is cache-idle-timeout-in-seconds where at periodic intervals of cache-idle-timeout-in-seconds, all the beans in the cache that have not been accessed for more than cache-idle-timeout-in-seconds time, are passivated. Similar to an HTTP session timeout, the bean is removed after it has not been accessed for removal-timeout-in-seconds. Passivated beans are stored on disk in serialized form. A large number of passivated beans could not only mean many files on the disk system, but also slower response time as the session state has to be de-serialized before the invocation.

Checkpoint only when needed

In high availability mode, when using stateful session beans, consider checkpointing only those methods that alter the state of the bean significantly. This reduces the number of times the bean state has to be checkpointed into the persistent store.

Stateless session beans are more readily pooled than entity or the stateful session beans. Valid values for steady-pool-size, pool-resize-quantity and max-pool-size are the best tunables for these type of beans. Set the steady-pool-size to greater than zero if you want to pre-populate the pool. This way, when the container comes up, it creates a pool with steady-pool-size number of beans. By pre-populating the pool it is possible to avoid the object creation time during method invocations.

Setting the steady-pool size to a very large value can cause unwanted memory growth and can result in large garbage collection times. pool-resize-quantity determines the rate of growth as well as the rate of decay of the pool. Setting it to a small value is better as the decay behaves like an exponential decay. Setting a small max-pool-size can cause excessive object destruction (and as a result excessive object creation) as instances are destroyed from the pool if the current pool size exceeds max-pool-size.

Read-only entity beans cache data from the database. Eclipse GlassFish supports read-only beans that use both bean-managed persistence (BMP) and container-managed persistence (CMP). Of the two types, CMP read-only beans provide significantly better performance. In the EJB lifecycle, the EJB container calls the ejbLoad() method of a read-only bean once. The container makes multiple copies of the EJB component from that data, and since the beans do not update the database, the container never calls the ejbStore() method. This greatly reduces database traffic for these beans.

If there is a bean that never updates the database, use a read-only bean in its place to improve performance. A read-only bean is appropriate if either:

For example, an application might use a read-only bean to represent a list of best-seller books. Although the list might change occasionally in the database (say, from another bean entirely), the change need not be reflected immediately in an application.

The ejbLoad() method of a read-only bean is handled differently for CMP and BMP beans. For CMP beans, the EJB container calls ejbLoad() only once to load the data from the database; subsequent uses of the bean just copy that data. For BMP beans, the EJB container calls ejbLoad() the first time a bean is used in a transaction. Subsequent uses of that bean within the transaction use the same values. The container calls ejbLoad() for a BMP bean that doesn’t run within a transaction every time the bean is used. Therefore, read-only BMP beans still make a number of calls to the database.

To create a read-only bean, add the following to the EJB deployment descriptor sun-ejb-jar.xml:

Refresh Period

An important parameter for tuning read-only beans is the refresh period, represented by the deployment descriptor entity refresh-period-in-seconds. For CMP beans, the first access to a bean loads the bean’s state. The first access after the refresh period reloads the data from the database. All subsequent uses of the bean uses the newly refreshed data (until another refresh period elapses). For BMP beans, an ejbLoad() method within an existing transaction uses the cached data unless the refresh period has expired (in which case, the container calls ejbLoad() again).

This parameter enables the EJB component to periodically refresh its "snapshot" of the database values it represents. If the refresh period is less than or equal to 0, the bean is never refreshed from the database (the default behavior if no refresh period is given).

If a container-managed relationship (CMR) exists in your application, loading one bean will load all its related beans. The canonical example of CMR is an order-orderline relationship where you have one Order EJB component that has related OrderLine EJB components. In previous releases of the application server, to use all those beans would require multiple database queries: one for the Order bean and one for each of the OrderLine beans in the relationship.

In general, if a bean has n relationships, using all the data of the bean would require n+1 database accesses. Use CMR pre-fetching to retrieve all the data for the bean and all its related beans in one database access.

For example, you have this relationship defined in the ejb-jar.xml file:

When a particular Order is loaded, you can load its related OrderLines by adding this to the sun-cmp-mapping.xml file for the application:

Now when an Order is retrieved, the CMP engine issues SQL to retrieve all related OrderLines with a SELECT statement that has the following WHERE clause:

This clause indicates an outer join. These OrderLines are pre-fetched.

Pre-fetching generally improves performance because it reduces the number of database accesses. However, if the business logic often uses Orders without referencing their OrderLines, then this can have a performance penalty, that is, the system has spent the effort to pre-fetch the OrderLines that are not actually needed.

Avoid pre-fetching for specific finder methods; this can often avoid that penalty. For example, consider an order bean has two finder methods: a findByPrimaryKey method that uses the Orderlines, and a findByCustomerId method that returns only order information and therefore does not use the Orderlines. If you have enabled CMR pre-fetching for the Orderlines, both finder methods will pre-fetch the Orderlines. However, you can prevent pre-fetching for the findByCustomerId method by including this information in the sun-ejb-jar.xml descriptor:

When dealing with large amounts of data, such as searching a large database, use JDBC directly rather than using Entity EJB components.

Combine business logic with the Entity EJB component that holds the data needed for that logic to process.

To ensure that connections are returned to the pool, always close the connections after use.

Use the default isolation level provided by the JDBC driver rather than calling setTransactionIsolationLevel(), unless you are certain that your application behaves correctly and performs better at a different isolation level.

Reduce the database transaction isolation level when appropriate. Reduced isolation levels reduce work in the database tier, and could lead to better application performance. However, this must be done after carefully analyzing the database table usage patterns.

To set the database transaction isolation level using the Eclipse GlassFish Administration Console, navigate to the Resources>JDBC>JDBC Connection Pools>pool-name node. Refer to the Administration Console online help for complete instructions. Alternatively, follow the instructions in "Administering Database Connectivity" in Eclipse GlassFish Administration Guide. For more information on tuning JDBC connection pools, see JDBC Connection Pool Settings.

JMS connections are served from a connection pool. This means that calling getConnection() on a Queue connection factory is fast.

The container for message-driven beans (MDB) is different than the containers for entity and session beans. In the MDB container, sessions and threads are attached to the beans in the MDB pool. This design makes it possible to pool the threads for executing message-driven requests in the container.

Tune the Message-Driven bean’s pool size to optimize the concurrent processing of messages. Set the size of the MDB pool to, based on all the parameters of the server (taking other applications into account). For example, a value greater than 500 is generally too large.

To configure MDB pool settings in the Eclipse GlassFish Administration Console, navigate to the Configurations>configuration-name>EJB Container node and then select the MDB Settings tab. Refer to the Administration Console online help for more information. Alternatively, you can set the MDB pool size by using the following asadmin set subcommand:

Use the setMessageDrivenContext() or ejbCreate() method to cache bean specific resources, and release those resources from the ejbRemove() method.

When designing an application that uses JMS connections make sure you use a methodology that sparingly uses connections, by either pooling them or using the same connection for multiple sessions.

The JMS connection uses two threads and the sessions use one thread each. Since these threads are not taken from a pool and the resultant objects aren’t pooled, you could run out of memory during periods of heavy usage.

One workaround is to move createTopicConnection into the init of the servlet.

Make sure to specifically close the session, or it will stay open, which ties up resources.

Both stateless session beans and entity beans can be pooled to improve server performance. In addition, both stateful session beans and entity beans can be cached to improve performance.

Table 3-1 Bean Type Pooling or Caching

The difference between a pooled bean and a cached bean is that pooled beans are all equivalent and indistinguishable from one another. Cached beans, on the contrary, contain conversational state in the case of stateful session beans, and are associated with a primary key in the case of entity beans. Entity beans are removed from the pool and added to the cache on ejbActivate() and removed from the cache and added to the pool on ejbPassivate(). ejbActivate() is called by the container when a needed entity bean is not in the cache. ejbPassivate() is called by the container when the cache grows beyond its configured limits.

A bean in the pool represents the pooled state in the EJB lifecycle. This means that the bean does not have an identity. The advantage of having beans in the pool is that the time to create a bean can be saved for a request. The container has mechanisms that create pool objects in the background, to save the time of bean creation on the request path.

Stateless session beans and entity beans use the EJB pool. Keeping in mind how you use stateless session beans and the amount of traffic your server handles, tune the pool size to prevent excessive creation and deletion of beans.

EJB Pool Settings

An individual EJB component can specify cache settings that override those of the EJB container in the <bean-pool> element of the EJB component’s sun-ejb-jar.xml deployment descriptor.

The EJB pool settings are:

A bean in the cache represents the ready state in the EJB lifecycle. This means that the bean has an identity (for example, a primary key or session ID) associated with it.

Beans moving out of the cache have to be passivated or destroyed according to the EJB lifecycle. Once passivated, a bean has to be activated to come back into the cache. Entity beans are generally stored in databases and use some form of query language semantics to load and store data. Session beans have to be serialized when storing them upon passivation onto the disk or a database; and similarly have to be deserialized upon activation.

Any incoming request using these "ready" beans from the cache avoids the overhead of creation, setting identity, and potentially activation. So, theoretically, it is good to cache as many beans as possible. However, there are drawbacks to caching:

Keeping in mind how your application uses stateful session beans and entity beans, and the amount of traffic your server handles, tune the EJB cache size and timeout settings to minimize the number of activations and passivations.

EJB Cache Settings

An individual EJB component can specify cache settings that override those of the EJB container in the <bean-cache> element of the EJB component’s sun-ejb-jar.xml deployment descriptor.

The EJB cache settings are:

Individual EJB pool and cache settings in the sun-ejb-jar.xml deployment descriptor override those of the EJB container. The following table lists the cache and pool settings for each type of EJB component.

The commit option controls the action taken by the EJB container when an EJB component completes a transaction. The commit option has a significant impact on performance.

The following are the possible values for the commit option:

Option B avoids ejbAcivate() and ejbPassivate() calls. So, in most cases it performs better than option C since it avoids some overhead in acquiring and releasing objects back to pool.

However, there are some cases where option C can provide better performance. If the beans in the cache are rarely reused and if beans are constantly added to the cache, then it makes no sense to cache beans. With option C is used, the container puts beans back into the pool (instead of caching them) after method invocation or on transaction completion. This option reuses instances better and reduces the number of live objects in the JVM, speeding garbage collection.

Determining the Best Commit Option

To determine whether to use commit option B or commit option C, first take a look at the cache-hits value using the monitoring command for the bean. If the cache hits are much higher than cache misses, then option B is an appropriate choice. You might still have to change the max-cache-size and cache-resize-quantity to get the best result.

If the cache hits are too low and cache misses are very high, then the application is not reusing the bean instances and hence increasing the cache size (using max-cache-size) will not help (assuming that the access pattern remains the same). In this case you might use commit option C. If there is no great difference between cache-hits and cache-misses then tune max-cache-size, and probably cache-idle-timeout-in-seconds.

To view monitoring information for the transaction service in the Eclipse GlassFish Administration Console, navigate to the server (Admin Server) node and then select the Monitor tab.

The following statistics are gathered on the transaction service:

You can disable transaction logging through the Administration Console or by using the following asadmin set subcommand:

Disabling transaction logging can improve performance. Setting it to false (the default), makes the transaction service write transactional activity to transaction logs so that transactions can be recovered. If Recover on Restart is checked, this property is ignored.

Set this property to true only if performance is more important than transaction recovery.

You can set the Recover on Restart attribute through the Administration Console or by entering the following asadmin set subcommand:

When Recover on Restart is true, the server will always perform transaction logging, regardless of the Disable Distributed Transaction Logging attribute.

If Recover on Restart is false, then:

The keypoint interval determines how often entries for completed transactions are removed from the log file. Keypointing prevents a process log from growing indefinitely.

Frequent keypointing is detrimental to performance. The default value of the Keypoint Interval is 2048, which is sufficient in most cases.

The DNS cache caches IP addresses and DNS names. The DNS cache is disabled by default. In the DNS Statistics for Process ID All page under Monitor in the web-based Administration interface the following statistics are displayed:

Enabled

If the DNS cache is disabled, the rest of this section is not displayed.

By default, the DNS cache is off. Enable DNS caching in the Administration Console by clicking the Configurations>configuration-name>Network Config>http-listener-name node. Click the HTTP tab and enable the DNS Lookup option.

CacheEntries (CurrentCacheEntries / MaxCacheEntries)

The number of current cache entries and the maximum number of cache entries. A single cache entry represents a single IP address or DNS name lookup. Make the cache as large as the maximum number of clients that access your web site concurrently. Note that setting the cache size too high is a waste of memory and degrades performance.

Set the maximum size of the DNS cache by entering or changing the value in the in the Administration Console by clicking the Configurations>configuration-name>Network Config>http-listener-name node. Click the File tab and set the desired options.

HitRatio

The hit ratio is the number of cache hits divided by the number of cache lookups.

This setting is not tunable.

Caching DNS Entries

It is possible to also specify whether to cache the DNS entries. If you enable the DNS cache, the server can store hostname information after receiving it. If the server needs information about the client in the future, the information is cached and available without further querying. specify the size of the DNS cache and an expiration time for DNS cache entries. The DNS cache can contain 32 to 32768 entries; the default value is 1024. Values for the time it takes for a cache entry to expire can range from 1 second to 1 year specified in seconds; the default value is 1200 seconds (20 minutes).

Limit DNS Lookups to Asynchronous

Do not use DNS lookups in server processes because they are resource-intensive. If you must include DNS lookups, make them asynchronous.

Enabled

If asynchronous DNS is disabled, the rest of this section will not be displayed.

NameLookups

The number of name lookups (DNS name to IP address) that have been done since the server was started. This setting is not tunable.

AddrLookups

The number of address loops (IP address to DNS name) that have been done since the server was started. This setting is not tunable.

LookupsInProgress

The current number of lookups in progress.

The file cache caches static content so that the server handles requests for static content quickly. The file-cache section provides statistics on how your file cache is being used.

For information about tuning the file cache, see File Cache Settings.

The Monitoring page lists the following file cache statistics:

The following are statistics related to the Keep Alive system. The most important settings you can tune here relate to HTTP Timeout. See Timeout for more information.

Max Connections controls the number of requests that a particular client can make over a keep-alive connection. The range is any positive integer, and the default is 256.

Adjust this value based on the number of requests a typical client makes in your application. For best performance specify quite a large number, allowing clients to make many requests.

The number of connections specified by Max Connections is divided equally among the keep alive threads. If Max Connections is not equally divisible by Thread Count, the server can allow slightly more than Max Connections simultaneous keep alive connections.

This setting specifies whether the server performs DNS (domain name service) lookups on clients that access the server. When DNS lookup is not enabled, when a client connects, the server knows the client’s IP address but not its host name (for example, it knows the client as 198.95.251.30, rather than www.xyz.com). When DNS lookup is enabled, the server will resolve the client’s IP address into a host name for operations like access control, common gateway interface (CGI) programs, error reporting, and access logging.

If the server responds to many requests per day, reduce the load on the DNS or NIS (Network Information System) server by disabling DNS lookup. Enabling DNS lookup will increase the latency and load on the system, so modify this setting with caution.

Timeout determines the maximum time (in seconds) that the server holds open an HTTP keep alive connection. A client can keep a connection to the server open so that multiple requests to one server can be serviced by a single network connection. Since the number of open connections that the server can handle is limited, a high number of open connections will prevent new clients from connecting.

The default time out value is 30 seconds. Thus, by default, the server will close the connection if idle for more than 30 seconds. The maximum value for this parameter is 300 seconds (5 minutes).

The proper value for this parameter depends upon how much time is expected to elapse between requests from a given client. For example, if clients are expected to make requests frequently then, set the parameter to a high value; likewise, if clients are expected to make requests rarely, then set it to a low value.

Both HTTP 1.0 and HTTP 1.1 support the ability to send multiple requests across a single HTTP session. A server can receive hundreds of new HTTP requests per second. If every request was allowed to keep the connection open indefinitely, the server could become overloaded with connections. On Unix/Linux systems, this could easily lead to a file table overflow.

The Eclipse GlassFish’s Keep Alive system, which is affected by the Timeout setting, addresses this problem. A waiting keep alive connection has completed processing the previous request, and is waiting for a new request to arrive on the same connection. The server maintains a counter for the maximum number of waiting keep-alive connections. If the server has more than the maximum waiting connections open when a new connection waits for a keep-alive request, the server closes the oldest connection. This algorithm limits the number of open waiting keep-alive connections.

If your system has extra CPU cycles, incrementally increase the keep alive settings and monitor performance after each increase. When performance saturates (stops improving), then stop increasing the settings.

The size (in bytes) of the buffer used by each of the request processing threads for reading the request data from the client.

Adjust the value based on the actual request size and observe the impact on performance. In most cases the default should suffice. If the request size is large, increase this parameter.

Max File Count determines how many files are in the cache. If the value is too big, the server caches little-needed files, which wastes memory. If the value is too small, the benefit of caching is lost. Try different values of this attribute to find the optimal solution for specific applications—generally, the effects will not be great.

This parameter controls how long cached information is used after a file has been cached. An entry older than the maximum age is replaced by a new entry for the same file.

If your Web site’s content changes infrequently, increase this value for improved performance. Set the maximum age by entering or changing the value in the Maximum Age field of the File Cache Configuration page in the web-based Admin Console for the HTTP server node and selecting the File Caching Tab.

Set the maximum age based on whether the content is updated (existing files are modified) on a regular schedule or not. For example, if content is updated four times a day at regular intervals, you could set the maximum age to 21600 seconds (6 hours). Otherwise, consider setting the maximum age to the longest time you are willing to serve the previous version of a content file after the file has been modified.

The following statistics are gathered on ORB connections:

Use the following command to get ORB connection statistics:

The following statistics are gathered on ORB thread pools:

Use the following command to display ORB thread pool statistics:

You can tune ORB parameters using the instructions in "Administering the Object Request Broker (ORB)" in Eclipse GlassFish Administration Guide. If using the Administration Console, navigate to the Configurations>configuration-name>ORB node and refer to the online help.

The following table summarizes the tunable ORB parameters.

Table 3-2 Tunable ORB Parameters

The ORB thread pool contains a task queue and a pool of threads. Tasks or jobs are inserted into the task queue and free threads pick tasks from this queue for execution. Do not set a thread pool size such that the task queue is always empty. It is normal for a large application’s Max Pool Size to be ten times the size of the current task queue.

The Eclipse GlassFish uses the ORB thread pool to:

Thus, even when one is not using ORB for remote-calls (via RMI/ IIOP), set the size of the threadpool to facilitate cleaning up the EJB pools and caches.

You can set ORB thread pool attributes using the instructions in "Administering Thread Pools" in Eclipse GlassFish Administration Guide. If using the Administration Console, navigate to Configurations>configuration-name> Thread Pools>thread-pool-name, where thread-pool-name is the thread pool ID selected for the ORB. Thread pools have the following attributes that affect performance.

In particular, the maximum pool size is important to performance. For more information, see Thread Pool Sizing.

Specify the following properties as command-line arguments when launching the client program. You do this by using the following syntax when starting the Java VM:

The following topics are addressed here:

Controlling Connections Between Client and Server ORB

When using the default JDK ORB on the client, a connection is established from the client ORB to the application server ORB every time an initial context is created. To pool or share these connections when they are opened from the same process by adding to the configuration on the client ORB.

Limiting the Maximum Number of Client Connections

You can specify the total maximum number of client connections on all ORB listener ports (TCP, SSL and SSL with mutual authentication). When open client connections exceed the maximum value you specify, the ORB rejects any new incoming client connections.

Set this value to support the expected number of simultaneous client connections, but not to exceed the VM or system file descriptor limits. If the value is set too high, the ORB will continue accepting new client connections, resulting in a "too many open files" error if the VM runs out of file descriptors.

To specify the maximum number of client connections, set the configs.config.config-name.iiop-service.orb.max-connections attribute to the number that you require:

For updates to this value to take effect, restart Eclipse GlassFish.

The following example shows how to set the maximum number of client connections for the ORB in the DAS to 512:

Load Balancing

For information on how to configure HTTP load balancing, see "Configuring HTTP Load Balancing" in Eclipse GlassFish High Availability Administration Guide.

For information on how to configure RMI/IIOP for multiple application server instances in a cluster, "RMI-IIOP Load Balancing and Failover" in Eclipse GlassFish High Availability Administration Guide.

When tuning the client ORB for load-balancing and connections, consider the number of connections opened on the server ORB. Start from a low number of connections and then increase it to observe any performance benefits. A connection to the server translates to an ORB thread reading actively from the connection (these threads are not pooled, but exist currently for the lifetime of the connection).

After examining the number of inbound and outbound connections as explained above, tune the size of the thread pool appropriately. This can affect performance and response times significantly.

The size computation takes into account the number of client requests to be processed concurrently, the resource (number of CPUs and amount of memory) available on the machine and the response times required for processing the client requests.

Setting the size to a very small value can affect the ability of the server to process requests concurrently, thus affecting the response times since requests will sit longer in the task queue. On the other hand, having a large number of worker threads to service requests can also be detrimental because they consume system resources, which increases concurrency. This can mean that threads take longer to acquire shared structures in the EJB container, thus affecting response times.

The worker thread pool is also used for the EJB container’s housekeeping activity such as trimming the pools and caches. This activity needs to be accounted for also when determining the size. Having too many ORB worker threads is detrimental for performance since the server has to maintain all these threads. The idle threads are destroyed after the idle thread timeout period.

It is sometimes useful to examine the IIOP messages passed by the Eclipse GlassFish. To make the server save IIOP messages to the server.log file, set the JVM option -Dcom.sun.CORBA.ORBDebug=giop. Use the same option on the client ORB.

The following is an example of IIOP messages saved to the server log. Note: in the actual output, each line is preceded by the timestamp, such as [29/Aug/2002:22:41:43] INFO (27179): CORE3282: stdout.

In this sample output above, the createFromStream type is shown as 4. This implies that the message is a fragment of a bigger message. To avoid fragmented messages, increase the fragment size. Larger fragments mean that messages are sent as one unit and not as fragments, saving the overhead of multiple messages and corresponding processing at the receiving end to piece the messages together.

If most messages being sent in the application are fragmented, increasing the fragment size is likely to improve efficiency. On the other hand, if only a few messages are fragmented, it might be more efficient to have a lower fragment size that requires smaller buffers for writing messages.

Statistics-gathering is disabled by default for JDBC Connection Pools. Refer to for instructions on enabling JDBC monitoring in "Administering the Monitoring Service" in Eclipse GlassFish Administration Guide. If using the Administration Console, JDBC monitoring can be enabled on the Configurations>configuration-name>Monitoring page.

The following attributes are monitored:

To get JDBC monitoring statistics, use the following commands:

To view JDBC monitoring statistics through the Administration Console, navigate to the server (Admin Server) page and click the Monitor tab. Refer to the Administration Console online help for complete instructions.

Refer to "Administering Database Connectivity" in Eclipse GlassFish Administration Guide for instructions on configuring JDBC connection pools. If using the Eclipse GlassFish Administration Console by navigating to the Resources>JDBC>JDBC Connection Pools>jdbc-pool-name page and then clicking the desired tab.

The following JDBC properites affect Eclipse GlassFish performance:

Pool Size Settings

Pool Size settings can be configured in the Pool Settings section on the General tab in the Edit JDBC Connection Pool page.

The following settings control the size of the connection pool:

The following table summarizes advantages and disadvantages to consider when sizing connection pools.

Table 3-3 Connection Pool Sizing

Timeout Settings

The following JDBC timeout settings can be configured on the in the Pool Settings section on the General tab in the Edit JDBC Connection Pool page.

Isolation Level Settings

The following JDBC Isolation Level settings can be configured in the Transaction section on the General tab in the Edit JDBC Connection Pool page.

Avoid specifying the Transaction Isolation level. If that is not possible, consider disabling the Isolation Level Guaranteed option and then make sure applications do not programmatically alter the connections; isolation level.

If you must specify a Transaction Isolation level, specify the best-performing level possible. The isolation levels listed from best performance to worst are:

Choose the isolation level that provides the best performance, yet still meets the concurrency and consistency needs of the application.

Connection Validation Settings

JDBC Connection Validation settings can be configured in the Connection Validation section on the Advanced tab in the Edit JDBC Connection Pool page.

You may be able to improve performance by overriding the default transaction support specified for each connector connection pool.

For example, consider a case where an Enterprise Information System (EIS) has a connection factory that supports local transactions with better performance than global transactions. If a resource from this EIS needs to be mixed with a resource coming from another resource manager, the default behavior forces the use of XA transactions, leading to lower performance. However, by changing the EIS’s connector connection pool to use LocalTransaction transaction support and leveraging the Last Agent Optimization feature previously described, you could leverage the better-performing EIS LocalTransaction implementation. For more information on LAO, see Configure JDBC Resources as One-Phase Commit Resources.

You can specify transaction support when you create or edit a connector connection pool.

Also set transaction support using asadmin. For example, the following asadmin command could be used to create a connector connection pool TESTPOOL with transaction-support set to LOCAL.

The default collector for Java server class machines will optimize for throughput but be tolerant of somewhat long pause times. If you would prefer to have minimal pause times at the expense of some throughput and increased CPU usage, consider using the CMS collector.

To use the CMS collector

Follow this procedure.

Use the jstat utility to monitor Java Virtual Machine statistics. (See Further Information.)

For detailed information on tuning the garbage collector, see Java Garbage Collection Tuning (` https://docs.oracle.com/en/java/javase/17/gctuning/introduction-garbage-collection-tuning.html`).

For applications that do not dynamically generate and load classes, the size of the permanent generation does not affect GC performance. For applications that dynamically generate and load classes (for example, JSP applications), the size of the permanent generation does affect GC performance, since filling the permanent generation can trigger a Full GC. Tune the maximum permanent generation with the -XX:MaxPermSize option.

Although applications can explicitly invoke GC with the System.gc() method, doing so is a bad idea since this forces major collections, and inhibits scalability on large systems. It is best to disable explicit GC by using the flag -XX:+DisableExplicitGC.

Eclipse GlassFish uses RMI in the Administration module for monitoring. Garbage cannot be collected in RMI-based distributed applications without occasional local collections, so RMI forces a periodic full collection. Control the frequency of these collections with the property -sun.rmi.dgc.client.gcInterval. For example, - java -Dsun.rmi.dgc.client.gcInterval=3600000 specifies explicit collection once per hour instead of the default rate of once per minute.

Maximum heap size depends on maximum address space per process. The following table shows the maximum per-process address values for various platforms:

Table 4-1 Maximum Address Space Per Process

Maximum heap space is always smaller than maximum address space per process, because the process also needs space for stack, libraries, and so on. To determine the maximum heap space that can be allocated, use a profiling tool to examine the way memory is used. Gauge the maximum stack space the process uses and the amount of memory taken up libraries and other memory structures. The difference between the maximum address space and the total of those values is the amount of memory that can be allocated to the heap.

You can improve performance by increasing your heap size or using a different garbage collector. In general, for long-running server applications, use the Java SE throughput collector on machines with multiple processors (-XX:+AggressiveHeap) and as large a heap as you can fit in the free memory of your machine.

You can control the heap size with the following JVM parameters:

The -Xms and -Xmx parameters define the minimum and maximum heap sizes, respectively. Since GC occurs when the generations fill up, throughput is inversely proportional to the amount of the memory available. By default, the JVM grows or shrinks the heap at each GC to try to keep the proportion of free space to the living objects at each collection within a specific range. This range is set as a percentage by the parameters -XX:MinHeapFreeRatio=`minimum and `-XX:MaxHeapFreeRatio=`maximum; and the total size bounded by `-Xms and -Xmx.

Set the values of -Xms and -Xmx equal to each other for a fixed heap size. When the heap grows or shrinks, the JVM must recalculate the old and new generation sizes to maintain a predefined NewRatio.

The NewSize and MaxNewSize parameters control the new generation’s minimum and maximum size. Regulate the new generation size by setting these parameters equal. The bigger the younger generation, the less often minor collections occur. The size of the young generation relative to the old generation is controlled by NewRatio. For example, setting -XX:NewRatio=3 means that the ratio between the old and young generation is 1:3, the combined size of eden and the survivor spaces will be fourth of the heap.

By default, the Eclipse GlassFish is invoked with the Java HotSpot Server JVM. The default NewRatio for the Server JVM is 2: the old generation occupies 2/3 of the heap while the new generation occupies 1/3. The larger new generation can accommodate many more short-lived objects, decreasing the need for slow major collections. The old generation is still sufficiently large enough to hold many long-lived objects.

To size the Java heap:

For up-to-date defaults, see Java HotSpot VM Options (https://www.oracle.com/java/technologies/javase/vmoptions-jsp.html).

Example 4-1 Heap Configuration on Solaris

This is an exmple heap configuration used by Eclipse GlassFish on Solaris for large applications:

Survivor Ratio Sizing

The SurvivorRatio parameter controls the size of the two survivor spaces. For example, -XX:SurvivorRatio=6 sets the ratio between each survivor space and eden to be 1:6, each survivor space will be one eighth of the young generation. The default for Solaris is 32. If survivor spaces are too small, copying collection overflows directly into the old generation. If survivor spaces are too large, they will be empty. At each GC, the JVM determines the number of times an object can be copied before it is tenured, called the tenure threshold. This threshold is chosen to keep the survivor space half full.

Use the option -XX:+PrintTenuringDistribution to show the threshold and ages of the objects in the new generation. It is useful for observing the lifetime distribution of an application.

Before You Begin

To perform rebasing, you need:

Example 4-2 Heap Configuration on Windows

This is an example heap configuration used by Eclipse GlassFish for heavy server-centric applications, on Windows, as set in the domain.xml file.

See Also

For more information on rebasing, see MSDN documentation for rebase utility (http://msdn.microsoft.com/library/default.asp?url=/library/en-us/tools/tools/rebase.asp).

The size of UDP buffer that is required to prevent excessive UDP datagram loss depends on many factors, such as:

If only one instance is running on each host in your cluster, the default UDP buffer size should suffice. If several instances are running on each host, determine whether the UDP buffer is large enough by testing for the loss of UDP packets.

On Linux systems, a default UDP buffer size is set for the client, but not for the server. Therefore, on Linux systems, the UDP buffer size might have to be increased. Setting the UDP buffer size involves setting the following kernel parameters:

Set the kernel parameters in the /etc/sysctl.conf file or at runtime.

If you set the parameters in the /etc/sysctl.conf file, the settings are preserved when the system is rebooted. If you set the parameters at runtime, the settings are not preserved when the system is rebooted.

Example 5-1 Setting the UDP Buffer Size in the /etc/sysctl.conf File

This example shows the lines in the /etc/sysctl.conf file for setting the kernel parameters for controlling the UDP buffer size to 524288.

Example 5-2 Setting the UDP Buffer Size at Runtime

This example sets the kernel parameters for controlling the UDP buffer size to 524288 at runtime.

The connection hash table keeps all the information for active TCP connections. Use the following command to get the size of the connection hash table:

This value does not limit the number of connections, but it can cause connection hashing to take longer. The default size is 512.

To make lookups more efficient, set the value to half of the number of concurrent TCP connections that are expected on the server. You can set this value only in /etc/system, and it becomes effective at boot time.

Use the following command to get the current number of TCP connections.