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.
Operational Logging for MongoDB Clusters
Track collection activity, oplog events, shard synchronization, and query performance signals.
Challenges
Monitor MongoDB logs across standalone instances, replica sets, and sharded clusters
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.
Query Operations and Collection Activity
- Track log generation across CRUD operations, aggregation pipelines, and collection-level activity to understand workload behavior in MongoDB environments.
- Correlate query logs with application requests and data access patterns to trace read and write execution flows.
- Identify failed operations, query timeouts, and write conflicts affecting database responsiveness.
- Detect disruptions in collection activity impacting data availability and application performance.
Replication and Sharding Diagnostics
- Capture replication logs, oplog activity, and shard communication events across distributed MongoDB clusters.
- Correlate replication lag and shard balancing behavior with workload spikes and infrastructure conditions.
- Identify recurring synchronization failures and shard inconsistencies affecting cluster reliability.
- Detect operational risks impacting failover readiness and distributed data consistency.
Performance and Index Utilization Signals
- Analyze slow query logs, index usage messages, and execution timing signals affecting MongoDB performance.
- Correlate query behavior with indexing strategy, document size, and workload distribution patterns.
- Identify inefficient queries, missing indexes, and resource contention increasing latency.
- Detect performance degradation through abnormal execution timing and irregular query patterns.
Security and Access Monitoring
- Track authentication attempts, authorization failures, and suspicious database activity captured in MongoDB logs.
- Identify abnormal access patterns, misuse attempts, and unauthorized operations affecting data integrity.
- Correlate database logs with application and infrastructure activity for incident investigation.
- Detect operational and security risks affecting MongoDB deployments.
Why Atatus
Why teams choose Atatus for MongoDB logs monitoring
01
MongoDB-native log insights
Atatus recognizes MongoDB log formats and extracts structured fields like operations, namespaces, timing, and error codes.
02
Unified cluster visibility
Atatus centralizes logs from standalone nodes, replica sets, and sharded clusters into one searchable timeline.
03
High-cardinality search
Atatus filters MongoDB logs by database, collection, command type, and error codes for faster troubleshooting.
04
Real-time alerting
Atatus triggers alerts on slow operations, replication warnings, authentication failures, and critical log events.
05
Faster root cause analysis
Atatus correlates MongoDB log anomalies with application and infrastructure signals.
06
Production-scale ingestion
Atatus reliably ingests high-volume MongoDB logs during peak load and maintenance operations.
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.
MongoDB Logs Monitoring refers to the process of tracking and analyzing logs generated by MongoDB databases. It is crucial for identifying performance issues, debugging queries, and ensuring the security of your database. Monitoring MongoDB logs helps in optimizing database performance and maintaining data integrity.
Monitoring MongoDB 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 MongoDB logs in Atatus, you need to install the Atatus Agent, configure MongoDB 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 MongoDB 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.
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 MongoDB 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 MongoDB 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.