Performance tuning WebLogic Server and your WebLogic Server application is a complex and iterative process. To get you started, we have created a short list of recommendations to help you optimize your application's performance. These tuning techniques are applicable to nearly all WebLogic applications.
Provide pool sizes (such as pools for JDBC connections, Stateless Session EJBs, and MDBs) that maximize concurrency for the expected thread utilization.
For WebLogic Server releases 9.0 and higher—A server instance uses a self-tuned thread-pool. The best way to determine the appropriate pool size is to monitor the pool's current size, shrink counts, grow counts, and wait counts. See Thread Management. Tuning MDBs are a special case, please see Chapter 11, "Tuning Message-Driven Beans".
For releases prior to WebLogic Server 9.0—In general, the number of connections should equal the number of threads that are expected to be required to process the requests handled by the pool. The most effective way to ensure the right pool size is to monitor it and make sure it does not shrink and grow. See How to Enable the WebLogic 8.1 Thread Pool Model.
The prepared statement cache keeps compiled SQL statements in memory, thus avoiding a round-trip to the database when the same statement is used later. See Chapter 12, "Tuning Data Sources".
When using transactional database applications, consider using the JDBC data source Logging Last Resource (LLR) transaction policy instead of XA. The LLR optimization can significantly improve transaction performance by safely eliminating some of the 2PC XA overhead for database processing, especially for two-phase commit database insert, update, and delete operations. For more information, see Chapter 12, "Tuning Data Sources".
You can tune the number of connection requests that a WebLogic Server instance accepts before refusing additional requests. This tunable applies primarily for Web applications. See Tuning Connection Backlog Buffering.
A chunk is a unit of memory that the WebLogic Server network layer, both on the client and server side, uses to read data from and write data to sockets. A server instance maintains a pool of these chunks. For applications that handle large amounts of data per request, increasing the value on both the client and server sides can boost performance. See Tune the Chunk Parameters.
Use optimistic concurrency with cache-between-transactions or read-only concurrency with query-caching for CMP EJBs wherever possible. Both of these two options leverage the Entity Bean cache provided by the EJB container.
Optimistic-concurrency with cache-between-transactions work best with read-mostly beans. Using verify-reads in combination with these provides high data consistency guarantees with the performance gain of caching. See Chapter 10, "Tuning WebLogic Server EJBs".
Query-caching is a WebLogic Server 9.0 feature that allows the EJB container to cache results for arbitrary non-primary-key finders defined on read-only EJBs. All of these parameters can be set in the application/module deployment descriptors. See Concurrency Strategy.
Use local-interfaces or use call-by-reference semantics to avoid the overhead of serialization when one EJB calls another or an EJB is called by a servlet/JSP in the same application. Note the following:
In release prior to WebLogic Server 8.1, call-by-reference is turned on by default. For releases of WebLogic Server 8.1 and higher, call-by-reference is turned off by default. Older applications migrating to WebLogic Server 8.1 and higher that do not explicitly turn on call-by-reference may experience a drop in performance.
This optimization does not apply to calls across different applications.
Use eager-relationship-caching wherever possible. This feature allows the EJB container to load related beans using a single SQL statement. It improves performance by reducing the number of database calls to load related beans in transactions when a bean and it's related beans are expected to be used in that transaction. See Chapter 10, "Tuning WebLogic Server EJBs".
Optimize your application so that it does as little work as possible when handling session persistence and sessions. You should also design a session management strategy that suits your environment and application. See Session Management.
Oracle provides messaging users a rich set of performance tunables. In general, you should always configure quotas and paging. See: