PostgreSQL Shared Buffer Metrics Guide - Optimize Cache Performance

Buffered_size

• What it measures: The total memory consumed by a database object (table, index) in the shared buffer, displayed in a human-readable format (e.g., "128 MB").
• Why it matters: This metric helps you quickly identify which objects are consuming the most memory in your shared buffer cache. Large values on infrequently accessed objects indicate memory waste, while small values on frequently queried tables may suggest insufficient caching.
• Ideal value & Best Practice: Focus on ensuring frequently accessed tables have adequate buffered_size. For large tables, aim to cache at least the most frequently accessed portions. Use this metric to identify memory-hungry objects that might need partitioning or optimization.

Buffer_blocks

• What it measures: The number of 8KB blocks allocated to an object in the shared buffer cache.
• Why it matters: Higher values indicate larger portions of an object are cached, which generally improves performance. However, excessive blocks allocated to rarely used data signals inefficient memory utilization that could be better allocated to hotter data.
• Ideal value & Best Practice: Monitor the ratio of buffer_blocks to total object size. Ideally, frequently accessed objects should have higher block counts. If large objects with low access patterns have high buffer_blocks, consider query optimization or implementing table partitioning.

Shared_buffers_usage_percent

• What it measures: The percentage of the total shared_buffers pool allocated to a specific database object.
• Why it matters: This metric highlights memory-heavy candidates that dominate your shared buffer space. Objects with high usage percentages but low access frequency may be hogging memory that could be better used for more frequently accessed data.
• Ideal value & Best Practice: Distribute memory proportionally to access patterns. Critical tables should have higher usage percentages. If less important objects show high usage percentages, investigate whether they truly need that level of caching.

Relation_memory_coverage_percent

• What it measures: The ratio of an object's cached size to its on-disk size (e.g., 80% means 80% of the object is memory-resident).
• Why it matters: Low coverage indicates frequent disk access, which significantly slows query performance. High coverage reduces physical I/O and accelerates data retrieval. This metric helps identify tables that would benefit from more aggressive caching.
• Ideal value & Best Practice: Aim for high coverage (>80%) on frequently accessed tables. For very large tables, focus on caching hot partitions or indexes rather than the entire table. Low coverage on important tables may indicate need for increased shared_buffers or query optimization.

Avg_usagecount

• What it measures: How often cached blocks are accessed, indicating the "temperature" of your data (higher values = "hotter" data).
• Why it matters: PostgreSQL uses this value to determine which blocks to evict from cache. Low usagecount values indicate underutilized cached data that's wasting valuable memory space, while high values indicate critical data that should remain cached.
• Ideal value & Best Practice: Higher values are better for frequently accessed data. Monitor for blocks with very low usagecount (≤2) as candidates for eviction. If important tables show low avg_usagecount, investigate whether queries are properly leveraging indexes.

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Shared Buffer Content Summary

The Shared Buffer Content Summary table provides a comprehensive overview of how your PostgreSQL shared memory is being utilized across all database objects. This visualization helps you:

• Identify memory hogs (objects consuming disproportionate buffer space)
• Spot under-cached critical tables (important tables with low memory coverage)
• Detect inefficient memory allocation (high buffer blocks on rarely used objects)
• Optimize your overall caching strategy based on actual usage patterns

Use this summary to prioritize which objects deserve more memory allocation and which might need optimization to reduce their memory footprint.

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