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Let's examine the following five factors:

1. Workload: It is the activity the DBMS is requested to perform, which drives the processing demands placed on the system. The workload can include a combination of transactions, batch jobs, ad hoc queries, reporting and analysis applications, database utilities and system commands. Workload can fluctuate drastically by day, hour or even minute. Sometimes it's steady and predictable, but at other times it can spike, such as a heavy transaction workload on Black Friday for a database supporting a retailer's e-commerce site.
2. Throughput: It is a measurement of a system's ability to process data. It's a composite of I/O and CPU speed, and it can be affected by the parallel capabilities of the database server and the efficiency of the operating system and system software.
3. Resources: These are the hardware and software tools at the disposal of the system -- such as database kernel, disk space, memory, cache controllers and microcode. The performance of accessing and modifying data in the database can be improved by the proper allocation and application of resources.
4. Optimization: It speeds up query performance. All types of systems can be optimized, but relational databases are unique in that query optimization is primarily accomplished internal to the DBMS. Additional factors do need to be considered as well, including SQL formulation and database parameters, to enable the database optimizer to create the most efficient access paths.
5. Contention: It results when the demand for a particular system resource is high, for example, if two or more components of the workload are attempting to use a single resource in a conflicting way, such as dual updates to the same piece of data. As contention increases, throughput decreases.
So, database performance can be more accurately defined as the optimization of resource usage to increase throughput and minimize contention, enabling the largest possible workload to be processed.

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