Cluster Metrics July 1, 2025
The Metrics dashboard provides comprehensive monitoring and observability for your PostgreSQL database cluster. This centralized view helps you track performance, identify bottlenecks, and ensure optimal database health.
The Metrics dashboard displays real-time and historical data about your database cluster's performance across multiple dimensions. You can filter metrics by:
Server Filter : Monitor all servers or focus on specific instancesBranch : Select which database branch to monitorTime Range : View data from the past 15 minutes up to custom time rangesLive update : Toggle on/off the auto-refresh of data every ~30 secondsThe primary cluster utilization panel shows your primary database server's resource consumption:
Metric Unit Purpose Key Insights CPU Percent Real-time CPU utilization Monitor for consistent performance and identify when optimization may be needed Memory Percent Current memory consumption Track memory usage patterns and plan for scaling when approaching limits
Each replica displays individual performance metrics in dedicated panels:
Metric Unit Purpose Key Insights CPU Percent Individual CPU tracking per replica Compare replica performance against primary and identify load distribution Memory Percent Individual memory tracking per replica Monitor replica resource consumption and ensure balanced utilization
Metric Unit Purpose Key Insights IOPS Operations/second Tracks database read/write operations per second Monitor I/O patterns and identify peak usage periods for performance optimization
Metric Unit Purpose Key Insights Storage Usage MB/GB Current storage consumption Track storage growth trends for capacity planning and ensure adequate free space
Metric Unit Purpose Key Insights Total Connections Count Active database connections Monitor connection patterns and trends for capacity planning cluster size
Metric Unit Purpose Key Insights CPU Percent PSBouncer process CPU usage Monitor connection pooler performance and resource consumption Memory Percent PSBouncer process memory usage Track memory usage of the connection pooling layer
Metric Unit Purpose Key Insights Active Count Active server connections Monitor backend database connections from the pool Active Cancel Count Connections being cancelled Track connection cleanup and cancellation events Being Cancelled Count Connections in cancellation process Monitor connection state transitions Idle Count Idle server connections Track connection pool efficiency and unused connections Login Count Connections in login state Monitor authentication and connection establishment Testing Count Connections being tested Track connection health check activities Tested Count Recently tested connections Monitor connection validation processes Used Count Total used connections Overall connection utilization from the pool
Metric Unit Purpose Key Insights Active Count Active client connections Monitor incoming client connection load Active Cancel Count Client connections being cancelled Track client-side connection cleanup Waiting Count Client connections waiting for server Identify connection queue buildup and potential bottlenecks
Metric Unit Purpose Key Insights Success Count Successfully archived WAL files Monitor backup and replication health Failed Count Failed WAL archival attempts Track archival failures that could impact recovery capabilities
Metric Unit Purpose Key Insights Seconds Time Age of oldest unarchived WAL Monitor WAL archival latency and ensure timely backup operations
Metric Unit Purpose Key Insights Storage Usage MB Write-ahead log storage consumption Track WAL disk usage for capacity planning and cleanup monitoring
Metric Unit Purpose Key Insights Lag Seconds Time delay between primary and replica Monitor replication health and ensure acceptable lag for read replica consistency
CPU : 0-30% for typical workloadsMemory : 20-80% depending on dataset sizeIOPS : Varies by workload type (OLTP vs. analytics)Disk Usage : Keep below 80% for optimal performanceConsistent Low CPU/Memory : Indicates healthy, optimized queriesSpiky IOPS : May indicate batch processing or analytical workloadsLow Connection Pool Utilization : Suggests efficient connection managementHigh CPU : Check for inefficient queries or missing indexesHigh Memory : Monitor for high memory usage from large queries or buffer cache pressureHigh IOPS : Analyze query patterns and consider query optimizationHigh Disk Usage : Plan for storage scaling or data archivingArchive age : Should typically be under 60 seconds for healthy systemsArchival success rate : Aim for 100% success rate with zero failuresWAL storage : Monitor for steady-state usage with periodic cleanup cyclesReplication lag : High lag may indicate WAL transmission issuesBaseline Establishment : Understand your normal operating rangesAlert Thresholds : Set up monitoring alerts for critical thresholdsTrend Analysis : Use historical data to predict scaling needsPerformance Correlation : Cross-reference metrics with application performanceThe Metrics dashboard serves as your primary tool for maintaining optimal database performance and ensuring reliable service delivery.
Get help from the PlanetScale Support team , or join our GitHub discussion board to see how others are using PlanetScale.
