PostgreSQL Monitoring & Query Performance
Detect and resolve sluggish PostgreSQL queries in your requests, along with transaction traces, to gather actionable insights for optimized functionality.
PostgreSQL Database Monitoring
Query Plan Analysis
Analyzes execution plans, index scans, and sequential scans to detect inefficient SQL execution paths.
Lock and Wait Events
Tracks row, table, and advisory locks that cause transaction blocking and latency spikes.
Connection Saturation
Monitors active sessions, idle transactions, and max connection limits.
Autovacuum Activity
Observes vacuum frequency, table bloat growth, and dead tuple accumulation.
WAL and Replication Lag
Measures WAL generation rate and replica replay delay affecting data consistency.
Cache Hit Efficiency
Tracks shared buffer cache effectiveness and disk read dependency.
Error Log Monitoring
Captures PostgreSQL errors related to crashes, checkpoints, and replication.
Resource Usage Trends
Analyzes CPU, memory, and disk IO consumption under transactional workloads.
Monitor Query Performance in PostgreSQL
Track query execution time, slow queries, active connections, and throughput using real-time database performance metrics.
Queries Taking Longer Than Expected
Some PostgreSQL queries can consume more time than usual, and query execution metrics show which statements consistently run slow.
Slow Queries Hidden in Overall Load
A small number of slow queries can impact overall performance, and slow query visibility helps isolate these statements quickly.
Connection Count Increasing Over Time
Rising active connections can affect database responsiveness, and connection metrics reveal when usage grows abnormally.
Throughput Changes Affecting Latency
Increased query volume can lead to higher response times, and throughput trends help correlate load with performance shifts.
Latency Spikes Missed in Averages
Average latency may appear stable while short spikes occur, and latency trend views expose these brief performance drops.
Why choose Atatus for PostgreSQL database monitoring?
PostgreSQL-specific execution metrics
Query plans, index scans, and sequential scans are analyzed to detect inefficient execution paths.
Lock and wait diagnostics
Row-level, table-level, and advisory locks are monitored to identify blocking transactions.
Autovacuum behavior tracking
Vacuum frequency, dead tuple growth, and table bloat are tracked to maintain query performance.
WAL and replication insight
WAL generation rate and replica replay lag are monitored to assess data synchronization health.
Connection state visibility
Idle sessions, long-running transactions, and connection limits are monitored.
Resource utilization correlation
Database activity is correlated with system CPU, memory, and disk usage.
Unified Observability for Every Engineering Team
Atatus adapts to how engineering teams work across development, operations, and reliability.
Developers
Trace requests, debug errors, and identify performance issues at the code level with clear context.
DevOps
Track deployments, monitor infrastructure impact, and understand how releases affect application stability.
Release Engineer
Measure service health, latency, and error rates to maintain reliability and reduce production risk.
Ensure Continuous Database Monitoring Across all SQL and No-SQL Databases
Frequently Asked Questions
Find answers to common questions about our platform