MongoDB Logs Monitoring & Observability
Effortlessly track MongoDB logs, gaining instant insights into errors and refining logging for a more efficient and reliable application.
Monitor MongoDB logs across standalone instances, replica sets, and sharded clusters
Analyze slow MongoDB operations
Inspect MongoDB slow query log entries to identify long-running commands, inefficient aggregation pipelines, and collection scans impacting database response time.
Track query execution behavior
Extract execution details such as command type, namespace, execution duration, keys examined, and documents scanned directly from MongoDB logs.
Monitor replication and elections
Follow replica set log events to detect primary elections, replication lag warnings, rollback activity, and heartbeat failures affecting availability.
Detect authentication failures
Capture MongoDB authentication and authorization log events to identify failed login attempts, role misconfigurations, and TLS handshake errors.
Observe storage engine activity
Analyze WiredTiger log messages to understand cache pressure, eviction behavior, checkpoint delays, and disk I/O contention.
Track index and schema changes
Monitor index creation, index removal, and schema-related log entries to understand changes that influence query execution patterns.
Identify runtime errors and warnings
Group MongoDB error codes, warning levels, and fatal events from logs to surface recurring database stability issues.
Correlate database logs with apps
Link MongoDB log events with application logs to trace database failures back to specific services, deployments, or request paths.
Make MongoDB Logs Work Harder for You in Atatus
Ingest and analyze MongoDB logs in real time so you can structure raw entries, explore patterns quickly, and understand system behavior without chasing files across hosts.
Logs Scattered Across Hosts
MongoDB writes logs locally on database hosts, and without centralized ingestion it becomes difficult to explore behavior across instances.
Raw Log Text Mixes Signals Together
Unstructured MongoDB log lines hide attributes such as severity or operation type, and structured parsing turns them into meaningful fields you can query.
Critical Events Get Lost in Volume
High volumes of MongoDB logs can bury important messages, and custom pipelines help extract and focus on the events that matter most.
Recreating Context Is Time-Consuming
Manually reapplying filters slows investigations, and saved views let you preserve tailored log contexts for repeated troubleshooting.
Manually Searching Files Is Slow
Grepping across MongoDB log files delays insight, and centralized fast search across all ingested logs helps identify patterns quickly.
Why teams choose Atatus for MongoDB logs monitoring
Native MongoDB log understanding
Atatus automatically recognizes MongoDB log formats and extracts structured fields such as operation type, namespace, execution time, and error codes.
Unified cluster-level visibility
Centralize logs from standalone MongoDB nodes, replica sets, and sharded clusters into a single searchable timeline.
High-cardinality log search
Filter MongoDB logs using indexed attributes like database name, collection, command type, or error code for faster troubleshooting.
Real-time issue alerting
Trigger alerts on spikes in slow operations, replication warnings, authentication failures, or fatal MongoDB log events.
Faster root cause isolation
Correlate MongoDB log anomalies with application errors and infrastructure signals to reduce investigation time.
Production-scale log ingestion
Reliably ingest high-volume MongoDB logs during peak traffic, replica synchronization, and index rebuild operations.
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
Frequently Asked Questions
Find answers to common questions about our platform