In today's data-driven landscape, MongoDB Atlas has emerged as the leading multi-cloud developer data platform, enabling organizations to work seamlessly with document-based data models while ensuring flexible schema design and easy scalability. However, as your Atlas deployments grow in complexity and criticality, comprehensive observability becomes essential for maintaining optimal performance, security, and reliability.
The Elastic MongoDB Atlas integration transforms how you monitor and troubleshoot your Atlas infrastructure by providing deep insights into every aspect of your deployment—from real-time alerts and audit trails to detailed performance metrics and organizational activities. This integration empowers teams to minimize Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) while gaining actionable insights for capacity planning and performance optimization.
Why MongoDB Atlas Observability Matters
MongoDB Atlas abstracts much of the operational complexity of running MongoDB, but this doesn't eliminate the need for monitoring. Modern applications demand:
- Proactive Issue Detection: Identify performance bottlenecks, resource constraints, and security threats before they impact users
- Comprehensive Audit Trails: Track database operations, user activities, and configuration changes for compliance and security
- Performance Optimization: Monitor query performance, resource utilization, and capacity trends to optimize costs and user experience
- Operational Insights: Understand organizational activities, project changes, and infrastructure events across your multi-cloud deployments
The Elastic MongoDB Atlas integration addresses these needs by collecting comprehensive telemetry data and presenting it through powerful visualizations and alerting capabilities.
Integration Architecture and Data Streams
The MongoDB Atlas integration leverages the Atlas Administration API to collect eight distinct data streams, each providing specific insights into different aspects of your Atlas deployment:
Log Data Streams
Alert Logs: Capture real-time alerts generated by your Atlas instances, covering resource utilization thresholds (CPU, memory, disk space), database operations, security issues, and configuration changes. These alerts provide immediate visibility into critical events that require attention.
Database Logs: Collect comprehensive operational logs from MongoDB instances, including incoming connections, executed commands, performance diagnostics, and issues encountered. These logs are invaluable for troubleshooting performance problems and understanding database behavior.
MongoDB Audit Logs: Enable administrators to track system activity across deployments with multiple users and applications. These logs capture detailed events related to database operations including insertions, updates, deletions, user authentication, and access patterns—essential for security compliance and forensic analysis.
Organization Logs: Provide enterprise-level visibility into organizational activities, enabling tracking of significant actions involving database operations, billing changes, security modifications, host management, encryption settings, and user access management across teams.
Project Logs: Offer project-specific event tracking, capturing detailed records of configuration modifications, user access changes, and general project activities. These logs are crucial for project-level auditing and change management.
Metrics Data Streams
Hardware Metrics: Collect comprehensive hardware performance data including CPU usage, memory consumption, JVM memory utilization, and overall system resource metrics for each process in your Atlas groups.
Disk Metrics: Monitor storage performance with detailed insights into I/O operations, read/write latency, and space utilization across all disk partitions used by MongoDB Atlas. These metrics help identify storage bottlenecks and plan capacity expansion.
Process Metrics: Gather host-level metrics per MongoDB process, including detailed CPU usage patterns, I/O operation counts, memory utilization, and database-specific performance indicators like connection counts, operation rates, and cache utilization.
Implementation Guide
Setting Up the Integration
Getting started with MongoDB Atlas observability requires establishing API access and configuring the integration in Kibana:
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Generate Atlas API Keys: Create programmatic API keys with Organization Owner permissions in the Atlas console, then invite these keys to your target projects with appropriate roles (Project Read Only for alerts/metrics, Project Data Access Read Only for audit logs).
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Enable Prerequisites: Enable database auditing in Atlas for projects where you want to collect audit and database logs. Gather your Project ID and Organization ID from the Atlas UI.
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Configure in Kibana: Navigate to Management > Integrations, search for "MongoDB Atlas," and add the integration using your API credentials.
The integration supports different permission levels for each data stream, ensuring you can collect operational metrics with minimal privileges while protecting sensitive audit data with elevated permissions.
Considerations and Limitations
- Cluster Support: Log collection doesn't support M0 free clusters, M2/M5 shared clusters, or serverless instances
- Historical Data: Most log streams collect the previous 30 minutes of historical data
- Performance Impact: Large time spans may cause request timeouts; adjust HTTP Client Timeout accordingly
Real-World Use Cases and Benefits
Security and Compliance Monitoring
Audit Trail Management: Organizations in regulated industries leverage the audit logs to maintain comprehensive records of database access and modifications. The integration automatically parses and indexes audit events, making it easy to search for specific user activities, failed authentication attempts, or unauthorized access patterns.
Security Incident Response: When security events occur, teams can quickly correlate alert logs with audit trails to understand the scope and timeline of incidents.
Performance Optimization and Capacity Planning
Proactive Resource Management: By monitoring disk, hardware, and process metrics, teams can identify resource constraints before they impact application performance. For example, tracking disk I/O latency trends helps predict when storage upgrades are needed.
Query Performance Analysis: Database logs combined with process metrics provide insights into slow queries, connection patterns, and resource utilization that enable database performance tuning.
Operational Excellence
Multi-Environment Monitoring: Organizations running Atlas across development, staging, and production environments can standardize monitoring across all environments while maintaining environment-specific alerting thresholds.
Change Management: Project and organization logs provide complete audit trails for infrastructure changes, enabling teams to correlate application issues with recent configuration modifications.
Let's Try It!
The MongoDB Atlas integration delivers comprehensive database observability that enables proactive management and optimization of your Atlas deployments. With pre-built dashboards and alerting capabilities, teams can gain immediate value while leveraging rich data streams for advanced analytics and custom monitoring solutions.
Deploy a cluster on Elastic Cloud or Elastic Serverless, or download the Elasticsearch stack, then spin up the MongoDB Atlas Integration, open the curated dashboards in Kibana and start monitoring your service!