MongoDB Monitoring
Make data-driven decisions, optimize MongoDB performance, and ensure the security and resilience of your MongoDB environment.
MongoDB Database Monitoring
Query Planner Insights
Analyzes MongoDB query execution stages, index usage, and collection scans to identify inefficient document access patterns.
Connection Pool Load
Monitors client connections, cursor usage, and socket limits to detect connection saturation across MongoDB nodes.
Replica Set Stability
Tracks primary elections, replication lag, and oplog window size to identify replica set health risks.
Lock Contention Metrics
Observes global, database, and collection-level locks that impact concurrent read and write operations.
Memory Working Set
Measures working set size and cache residency to detect memory pressure affecting read performance.
Disk and Storage IO
Monitors WiredTiger disk usage, checkpoint frequency, and compaction impact on write latency.
Error and Warning Logs
Captures MongoDB logs related to replication failures, rollbacks, and storage engine errors.
Operational Throughput
Tracks read and write operation rates to understand workload distribution and peak usage periods.
Operation Throughput
Measures the number of operations (reads, writes, updates, and deletes) processed per second. Sudden drops or spikes may indicate workload imbalances or performance bottlenecks within your MongoDB cluster.
Query Execution Time
Tracks the time taken to execute database queries, focusing on slow queries. High execution times often point to inefficient query structures or missing indexes that impact application performance.
Replication Lag
Monitors the delay between the primary and secondary nodes in a MongoDB replica set. Significant lag can compromise data consistency and affect failover mechanisms in distributed systems.
Memory Usage (Resident Memory)
Measures the amount of memory actively used by MongoDB processes. High memory consumption may lead to increased disk I/O and degraded performance if not optimized properly.
Connections
Tracks the number of active client connections to the MongoDB instance. Exceeding connection limits can cause service interruptions and affect database accessibility during peak loads.
Log Volume
Monitors the size and frequency of logs generated by a container. Sudden spikes in log volume can signal errors or abnormal behavior in the application.
Understand What's Slowing Down Your MongoDB Workload
Measure operation timing, throughput, slow ops, replication lag, and resource utilization with correlated performance metrics so you can pinpoint inefficiencies.
Slow Operations Hide in Aggregate Metrics
Without detailed timing for read and write operations, performance issues remain buried, while operation duration metrics show which commands are most costly.
Inefficient Queries Inflate Response Time
Unoptimized queries or missing indexes can pad execution time, and slow operation detection highlights these patterns so you know where to focus.
Replication Lag Affects Read Freshness
Secondary replication lag can delay data propagation, and replication insight reveals when replicas fall behind and impact read performance.
Throughput Spikes Mask Rising Latency
High operation volume can make latency increases look normal, and viewing throughput alongside latency trends exposes when load patterns degrade performance.
Resource Saturation Obscures True Bottlenecks
CPU or memory pressure on database hosts can slow operations, and resource utilization metrics correlated with performance timing uncover when system limits contribute.
Why choose Atatus for MongoDB database monitoring?
Document-store specific insights
Atatus captures MongoDB metrics including collection scans, index usage, cursor behavior, and query execution stages.
Replica set observability
Primary elections, oplog replication delay, and node state transitions are tracked to assess replica set stability.
Lock and concurrency awareness
Global, database, and collection-level locks are monitored to identify write contention.
Memory working set analysis
Cache residency and working set size are tracked to detect memory pressure affecting read latency.
Storage engine monitoring
WiredTiger checkpointing, compaction activity, and disk utilization are continuously observed.
Operational workload visibility
Read and write operation rates are tracked to understand workload distribution and peak usage.
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.
Ensure Continuous Database Monitoring Across all SQL and No-SQL Databases
Frequently Asked Questions
Find answers to common questions about our platform