Ensure applications properly close database connections when they are no longer needed.
Adjust the maximum number of connections in the application's database connection pool to an appropriate level.
Configure background processes to use a limited amount of system resources to avoid impacting live operations.
Reschedule maintenance, backups, and batch jobs to off-peak hours.
Implement application-level caching or load balancing to distribute requests and reduce direct database pressure.
Identify and refactor inefficient queries or code patterns that cause excessive database load.
Increase the CPU, RAM, or storage capacity of the database server or consider a more powerful instance type.
Optimize slow queries, add appropriate indexes, and tune database configuration parameters for better resource utilization.
Explore different index types (e.g., B-tree, hash, full-text) based on the nature of the queries and data.
Create indexes that include all columns needed by a query to avoid table lookups.
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Human error during manual data operations, script execution, or application updates can lead to unintended data loss. This is often the most common cause of unexpected data disappearance.
Flaws in application code, particularly in data processing or deletion routines, can cause data to be removed incorrectly or prematurely. This can be subtle and hard to detect without thorough code review.
Scheduled jobs or automated processes designed to clean up old or irrelevant data might be misconfigured or malfunctioning, leading to premature data removal.
Underlying storage issues, hardware failures, or database corruption can lead to data loss. This is less common but can be catastrophic when it occurs.
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