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AI-Assisted Post-Mortem Analysis and Optimizations for PostgreSQL Performance

Abstract For database administrators (DBAs), resolving PostgreSQL performance issues is a time-sensitive task that traditionally relies on manual log reviews and system analysis. This paper presents a concise approach to using AI for post-mortem analysis, enabling faster identification of failure patterns and root causes based on historical data. AI algorithms can detect inefficiencies in queries, resource usage, and configurations that might be missed in manual assessments. Additionally, AI can predict potential bottlenecks by analyzing past trends, allowing DBAs to implement preventive measures proactively. This AI-driven solution streamlines database management, reduces downtime, and optimizes PostgreSQL performance, providing DBAs with a powerful tool for efficient database operations.