Final answer:
Specific metrics for database anomaly detection include read and write latencies, CPU and memory usage, query execution time, and network throughput. These metrics are essential in identifying potential issues that could lead to database degradation, ensuring that they can be addressed promptly.
Step-by-step explanation:
Anomaly detection in database systems is a critical aspect of database maintenance and optimization. It involves identifying unusual patterns or activities that may indicate a problem with database performance or security. Some specific anomaly detection metrics for database degradation include:
- Read and write latencies: These are measures of the time it takes for a database to read data from or write data to storage. If latencies increase significantly, it could indicate hardware issues, performance bottlenecks, or inefficient indexes.
- CPU and memory usage: High CPU usage or memory consumption can be signs of inefficient queries, inadequate resources, or malfunctioning hardware that may lead to database degradation.
- Query execution time: Long execution times for queries may point towards suboptimal query design, insufficient indexing, or hardware issues affecting database performance.
- Network throughput: The amount of data that can be transferred over the network in a given period. While not a direct metric for database degradation, poor network performance can impact the overall perception of database performance and can be indicative of related issues.