Collect PostgreSQL server logs
Ingest PostgreSQL server log output including errors, warnings, checkpoints, and background writer activity from production databases.
Real-time PostgreSQL operational behavior and durability signals
Track WAL activity, locking behavior, query execution signals, and replication events across PostgreSQL workloads in real time.
Challenges
Monitor PostgreSQL logs and database activity in production environments
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.
Transaction and WAL Activity Visibility
- Track log generation across transaction commits, rollbacks, and WAL activity to understand durability and data consistency behavior in PostgreSQL systems.
- Correlate transaction logs with application requests and session context to trace write-heavy workload patterns.
- Identify transaction aborts, checkpoint delays, and WAL write issues affecting database reliability.
- Detect disruptions in durability workflows impacting recovery and replication readiness.
Locking, Deadlock, and Concurrency Diagnostics
- Capture lock wait logs, deadlock reports, and concurrency-related messages generated during multi-session workloads.
- Correlate blocking queries with affected sessions, transactions, and application operations to identify contention sources.
- Identify recurring deadlocks triggered by schema design, long-running transactions, or workload spikes.
- Detect hidden concurrency failures affecting throughput and query execution stability.
Query Planner and Execution Behavior
- Analyze execution plan logs, slow query activity, and optimization-related messages affecting PostgreSQL performance.
- Correlate planner decisions with indexing strategy, table statistics, and workload patterns across queries.
- Identify inefficient execution paths, sequential scans, and misconfigured planner parameters increasing latency.
- Detect performance degradation through abnormal execution timing and query planning anomalies.
Replication, Failover, and Operational Signals
- Track replication logs, standby synchronization activity, and failover-related events across PostgreSQL clusters.
- Correlate replication lag, streaming interruptions, and node failures with infrastructure and network conditions.
- Identify recurring replication breakdowns caused by configuration issues or workload pressure.
- Detect operational risks affecting high availability, failover readiness, and data synchronization.
Why Atatus
Why choose Atatus for PostgreSQL logs monitoring?
01
PostgreSQL-native insights
Atatus ingests and interprets PostgreSQL logs including query duration, autovacuum activity, and replication messages.
02
Query performance visibility
Atatus helps teams detect slow queries and execution inefficiencies using log-driven analysis.
03
Maintenance health signals
Atatus surfaces vacuum, checkpoint, and recovery events to support stable database operations.
04
Faster incident diagnosis
Atatus centralizes PostgreSQL logs across environments to reduce troubleshooting time.
05
High-volume ready
Atatus handles heavy PostgreSQL log output without impacting database performance.
06
Modern deployment coverage
Atatus aggregates PostgreSQL logs from VMs, bare-metal, and cloud-hosted environments.
Teams
Unified Observability for Every Engineering Team
Atatus adapts to how engineering teams work across development, operations, and reliability.
Developers
Debug errors and slow paths with the full context of what happened.
Atatus for developersDevOps
Track deployments and see how each release affects stability.
Atatus for DevOpsRelease engineers
Watch latency and error rates to ship with less production risk.
Atatus for release engineers
FAQ
Frequently Asked Questions
Pricing, deployment, data and the agent. If something is missing, a person will answer it on a demo call.
PostgreSQL Logs Monitoring refers to the process of tracking and analyzing logs generated by PostgreSQL databases. It is crucial for identifying performance issues, debugging queries, and ensuring the security of your database. Monitoring PostgreSQL logs helps in optimizing database performance and maintaining data integrity.
Monitoring PostgreSQL logs in Atatus enables you to gain visibility into your database's performance, detect issues early, and ensure optimal operation. Atatus provides a centralized platform for log aggregation, analysis, and visualization, making it easier to troubleshoot problems and optimize database performance.
To start monitoring PostgreSQL logs in Atatus, you need to install the Atatus Agent, configure PostgreSQL logs to be written to a file, and then use the Agent's log collection feature to send these logs to Atatus.
Yes, Atatus allows you to set up custom alerts based on specific PostgreSQL log events. You can define alert conditions, thresholds, and notification preferences to receive timely notifications when predefined events occur.
Log data parsing prioritizes customer security by meticulously anonymizing Personally Identifiable Information (PII) data. Sensitive fields can be completely excluded during the parsing process, ensuring the privacy and compliance of customer information.
In PostgreSQL Logs Monitoring, log pipelines are customizable workflows that allow users to filter, parse, and fine-tune log data based on specific criteria.
Log pipelines can be customized by using diverse filters and defining custom parsing rules. Users have the flexibility to configure pipelines to meet their specific monitoring requirements, ensuring that log data is processed and organized according to their preferences.
Yes, log pipelines can be configured to filter log data based on various attributes such as log source, severity, timestamp, or any custom field. This customization allows users to focus on specific aspects of log data that are most relevant to their monitoring goals.
Saved Views in PostgreSQL Logs Monitoring enable you to create and save custom configurations for different troubleshooting scenarios. This feature simplifies collaboration, allows for quick context switching, and automates repetitive tasks, significantly improving overall efficiency.
If you exceed your log ingestion limits, we would contact you to discuss on stopping further processing new log data or upgrade your subscription.
You can choose to store logs in Atatus for a limited time (e.g., 7 days) or export them to external storage solutions like Amazon S3 for long-term retention.
To access historical log data beyond the retention period, you can rely on log data exports from Amazon S3, where you can push the logs into Atatus for further analysis.
Yes, Atatus provides users with the flexibility to customize log retention settings. Users can adjust retention periods based on their specific needs, aligning with compliance standards or internal data management policies.