PostgreSQL Logs Monitoring & Observability
Effortlessly track PostgreSQL logs, gaining instant insights into errors and refining logging for a more efficient and reliable application.
Monitor PostgreSQL logs and database activity in production environments
Collect PostgreSQL server logs
Ingest PostgreSQL server log output including errors, warnings, checkpoints, and background writer activity from production databases.
Analyze slow and expensive queries
Inspect PostgreSQL log entries generated by duration thresholds to identify long-running queries and inefficient execution paths.
Track database startup and shutdown
Monitor PostgreSQL startup, recovery, and shutdown logs to diagnose configuration issues and unexpected restarts.
Observe vacuum and autovacuum activity
Capture PostgreSQL vacuum and autovacuum log messages to understand table bloat, cleanup behavior, and maintenance impact.
Detect replication and WAL issues
Analyze PostgreSQL replication and write-ahead logging related messages to identify replication lag and streaming issues.
Monitor connection and authentication failures
Surface PostgreSQL connection attempts, authentication errors, and role permission issues recorded in server logs.
Correlate logs with performance metrics
Link PostgreSQL log events with query timing, connection counts, and resource usage metrics for deeper performance analysis.
Support clustered PostgreSQL setups
Aggregate logs from primary servers, replicas, and high-availability PostgreSQL deployments into a unified view.
Centralize PostgreSQL Logs for Real-Time Visibility
Collect, parse, and explore PostgreSQL logs in Atatus so you can understand query behavior, spot patterns, and troubleshoot quickly without switching between servers.
Logs Scattered Across Instances
PostgreSQL logs live on individual database hosts and files, and centralizing them in Atatus makes it easier to explore activity across servers in one place.
Unstructured Messages Hide Meaning
Raw PostgreSQL log lines mix text and fields, and structuring them at ingestion makes attributes like timestamps, query text, and severity searchable.
High Volume Obscures Critical Events
Large volumes of PostgreSQL log output can bury important entries, and custom pipelines with filters help surface the logs that matter most.
Without Saved Views, Context Is Lost
Troubleshooting often requires narrowing logs to a specific timeframe or attribute set, and saved views let you restore that context instantly.
Finding Entries by Pattern Is Slow
Manually grepping through PostgreSQL log files is inefficient, and fast centralized search allows you to locate patterns or timing issues quickly.
Why choose Atatus for PostgreSQL logs monitoring?
Production-grade visibility into PostgreSQL query behavior, maintenance activity, and database health
PostgreSQL-native log understanding
Built to ingest and interpret PostgreSQL server logs, including query duration, autovacuum output, and replication messages.
Query performance investigation
Helps database teams identify slow queries and execution inefficiencies using log-driven insights.
Maintenance and reliability visibility
Surfaces log signals related to vacuum activity, checkpoints, and recovery to support stable database operations.
Faster incident diagnosis
Centralizes PostgreSQL logs across environments to reduce time spent troubleshooting outages and performance regressions.
Scales with database load
Handles high-volume PostgreSQL log output generated by busy production databases without affecting performance.
Works across modern deployments
Aggregates PostgreSQL logs from virtual machines, bare-metal servers, and cloud-hosted database environments.
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.
Unified Logs Monitoring & Observability Across Different Platforms
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