Key Performance Indicators (KPIs) in database performance management provide a quantifiable measure to track, analyze and assess the effectiveness of database operations over time. Here are some commonly used KPIs:
Query Response Time: This is the period from when a database receives a query until it begins to return the requested data. It's crucial because prolonged query response times can slow down applications relying on the database.
Throughput: Throughput measures the number of transactions processed per unit of time. Higher throughput often indicates better database performance.
CPU Utilization: This is the percentage of CPU's processing power being used by the database. If it's too high, it may indicate that your database is struggling to process requests, possibly due to inefficient queries or inadequate hardware.
Disk I/O: The rate at which data is read from or written to disk. High disk I/O rates may indicate bottlenecks in data access.
Cache Hit Ratio: This is the ratio of the number of hits in the cache to the total number of cache accesses. A higher ratio is preferable as it means more requests are being served from cache, reducing disk I/O.
Memory Usage: This measures how much RAM is used by the database. If memory usage is consistently high, it might indicate a need for additional resources.
Connection Usage: The number of active connections to the database. Too many can strain the database and degrade performance.
Index Usage: The frequency of index usage for query optimization. Poor index usage can lead to full table scans, resulting in slower performance.
It's noteworthy that these KPIs should not be considered in isolation as they can often impact each other. Moreover, the importance of individual KPIs can vary depending on the specific use case and workload of the database. By regularly monitoring these KPIs using profiling tools or built-in features of the database management system, you can ensure optimal performance of your database.