PostgreSQL Database Information Metrics Guide - Database Health & Performance
Comprehensive guide to PostgreSQL database metrics. Monitor rollback rates, deadlocks, cache efficiency, tuple statistics, and temp file usage for optimal database health and performance.
Rollback Percentage
What it measures: The percentage of transactions that are rolled back versus committed in the database.
Why it matters: A high rollback percentage indicates application bugs causing unintended rollbacks, data integrity issues, locking conflicts (deadlocks), or insufficient resources leading to transaction aborts. This can significantly impact application performance and user experience.
Ideal value & Best Practice:
Maintain rollback percentage below 1%
Investigate immediately if rollback rates exceed 2-3%
Monitor for patterns indicating specific problematic transactions
Review application error handling and transaction management
Ensure adequate resources for transaction processing
Deadlocks Count
What it measures: The number of deadlock occurrences where two or more transactions block each other indefinitely.
Why it matters: Deadlocks cause performance degradation, transaction failures, and may require manual intervention to resolve. They indicate concurrency issues in application logic or database design.
Ideal value & Best Practice:
Aim for zero deadlocks in production environments
Implement retry logic in applications for deadlock scenarios
Ensure consistent transaction ordering across applications
Use appropriate transaction isolation levels
Monitor and optimize query patterns that frequently cause locking
Fetched/Returned Tuple Percentage
What it measures: The ratio of tuples fetched from disk to tuples returned to queries, indicating index efficiency.
Why it matters: Measures how effectively PostgreSQL uses indexes to minimize disk accesses. High percentages indicate inefficient index usage or missing indexes.
Ideal value & Best Practice:
OLTP systems: target less than 1.00%
OLAP systems: keep as low as possible despite higher expected values
Ensure proper indexing strategy for frequently queried columns
Regularly analyze and update statistics for query optimization
Monitor for query patterns that bypass indexes
Deleted/Inserted Tuple Percentage
What it measures: The ratio of deleted tuples to inserted tuples, indicating data modification patterns.
Why it matters: High deletion rates may indicate inefficient data modeling, excessive data retention policies, or unoptimized cleanup operations leading to table fragmentation and increased I/O load.
Ideal value & Best Practice:
Target 0.1-0.2% for healthy, growing datasets
Investigate rates exceeding 1%
Implement efficient data archiving strategies
Consider partitioning for tables with high deletion rates
Regular VACUUM operations to manage table bloat
Cache/Hit Percentage
What it measures: The efficiency of PostgreSQL serving data from memory (shared_buffers) instead of disk.
Why it matters: Low cache hit rates indicate excessive disk I/O, which slows down queries and indicates insufficient memory allocation or inefficient queries.
Optimize shared_buffers configuration based on workload
Implement query optimization to reduce large sequential scans
Consider increasing memory allocation for database cache
Temp File Usage
What it measures: The number and size of temporary files created when queries exceed work_mem allocations.
Why it matters: Excessive temp file usage indicates queries relying on slow disk operations instead of memory, suggesting inefficient queries or inadequate memory settings.
Ideal value & Best Practice:
Minimize temp file creation through proper work_mem configuration
Monitor for queries generating large temp files
Optimize queries with large sorts, joins, or aggregations
Set appropriate work_mem per connection based on workload
Use EXPLAIN ANALYZE to identify temp file-generating operations
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