For information about tuning performance of your database, see Tuning Oracle Database Performance on Database as a Service in Using Oracle Database Cloud - Database as a Service.
For general information about good database tuning practices, see General Suggestions.
This section provides general database tuning suggestions:
Good database design — Distribute the database workload across multiple disks to avoid or reduce disk overloading. Good design also includes proper sizing and organization of tables, indexes, and logs.
Disk I/O optimization — Disk I/O optimization is related directly to throughput and scalability. Access to even the fastest disk is orders of magnitude slower than memory access. Whenever possible, optimize the number of disk accesses. In general, selecting a larger block/buffer size for I/O reduces the number of disk accesses and might substantially increase throughput in a heavily loaded production environment.
Checkpointing — This mechanism periodically flushes all dirty cache data to disk, which increases the I/O activity and system resource usage for the duration of the checkpoint. Although frequent checkpointing can increase the consistency of on-disk data, it can also slow database performance. Most database systems have checkpointing capability, but not all database systems provide user-level controls. Oracle, for example, allows administrators to set the frequency of checkpoints while users have no control over SQLServer 7.x checkpoints. For recommended settings, see the product documentation for the database you are using.
Disk and database overhead can sometimes be dramatically reduced by batching multiple operations together and/or increasing the number of operations that run in parallel (increasing concurrency). Examples:
Increasing the value of the Message bridge BatchSize or the Store-and-Forward WindowSize can improve performance as larger batch sizes produce fewer but larger I/Os.
Programmatically leveraging JDBC's batch APIs.
Use the MDB transaction batching feature. See Tuning Message-Driven Beans.
Increasing concurrency by increasing max-beans-in-free-pool and thread pool size for MDBs (or decreasing it if batching can be leveraged).