Memory usage higher than total memory usage postgreSQL

[jenishjain]

I observed that one of our PostgreSQL instances' memory usage is higher than the total memory usage, 
attaching screenshots for reference. Understood from one of the older threads, this could be because of the way these metrics are measured
https://groups.google.com/g/google-cloud-sql-discuss/c/bnShRIM820k
But how can we debug the exact cause of the same, as the occurrence was significantly higher

[ms4446]

It’s a known behavior in Cloud SQL for PostgreSQL that memory usage can appear to exceed the instance’s allocated memory. This is due to how Cloud Monitoring aggregates metrics, especially when including OS-level memory such as file system caches and kernel buffers, in addition to PostgreSQL processes.

The older discussion you referenced correctly notes that metrics like cloudsql.googleapis.com/database/memory/total_usage reflect total host memory, not just PostgreSQL memory. This includes memory used by the VM’s OS for caching, which PostgreSQL relies on heavily.

How to Debug a Memory Spike

1. Understand the Metrics

• Allocated Memory: Defined by your instance type (e.g., 16 GB).
• Total Usage: Likely reflects OS-level memory including PostgreSQL, system cache, and kernel usage.
• PostgreSQL-specific Usage: Look at metrics like postgres_memory_bytes for memory used by PostgreSQL processes (e.g., shared buffers, work_mem, maintenance_work_mem).

2. Correlate with System Activity

• Disk I/O: Spikes in disk/read_bytes_count or write_bytes_count often indicate increased caching demand.
• Query Logs: Look for large or slow queries, VACUUMs, ANALYZEs, or temp file creation (log_temp_files).
• Autovacuum: May cause short-term memory and I/O spikes.
• Backups: Scheduled or on-demand backups are I/O intensive and can increase memory use.

3. PostgreSQL Tools

• pg_stat_statements: Identify high-I/O or memory-consuming queries.
• pg_stat_activity: Monitor active queries and their wait events.
• Connection spikes (num_backends) and high CPU (cpu/utilization) can also contribute.

4. Assess Severity

• Brief spikes that exceed allocated memory are often due to OS caching and not a concern unless they coincide with performance degradation.

• If spikes are frequent or sustained, further analysis is needed.

Here are some additional steps;

• Optimize queries (via EXPLAIN ANALYZE, indexing, and reducing temp file usage).

• Tune config: Adjust work_mem, and monitor temp_buffers and connection count.

• Scale: If spikes are workload-driven and persistent, consider a larger instance.

• Alerting: Use Cloud Monitoring alerts to track memory, disk, and CPU anomalies.

In your case, the short-lived spike likely resulted from a burst of I/O (e.g., a large query or background task) triggering expanded file system caching by the OS.