Problem Statement
How do you monitor and troubleshoot MongoDB performance issues in production?
Explanation
Monitoring and troubleshooting MongoDB performance requires a systematic approach using built-in tools, metrics analysis, and query optimization techniques. Proactive monitoring helps identify issues before they impact users.
First, use MongoDB's built-in monitoring tools. The db.serverStatus command provides comprehensive metrics including connections, memory usage, operations, network traffic, and storage statistics. The db.currentOp command shows currently running operations, helping identify long-running queries. The mongostat utility displays real-time statistics on operations, connections, and resource usage.
Second, enable database profiling to capture slow queries. The profiler records queries exceeding a specified threshold to the system.profile collection. Set profiling level 1 to log slow queries or level 2 to log all queries. Analyze profiled queries to identify optimization opportunities. Use explain method on slow queries to understand execution plans and index usage.
Third, monitor key performance metrics. Watch for high query execution times, low cache hit ratios indicating insufficient memory, high page faults showing working set exceeds RAM, connection saturation, replication lag in replica sets, and uneven shard distribution in sharded clusters. Set up alerts for abnormal values.
Fourth, use monitoring platforms like MongoDB Cloud Manager, Ops Manager, or third-party tools like Prometheus and Grafana. These provide historical trending, alerting, and dashboards. They help identify patterns and correlate metrics across multiple servers.
Fifth, when troubleshooting specific issues, follow a structured approach. For slow queries, use explain to check if indexes are used, verify index selectivity, and ensure query patterns match shard keys in sharded clusters. For memory issues, check working set size versus available RAM and consider adding memory or sharding. For write performance, verify write concern settings, check for lock contention, and ensure indexes are not over-indexed causing write overhead.
Regularly review and optimize indexes, remove unused indexes, monitor index size and fragmentation, and rebuild indexes if needed. Test changes in staging environments before applying to production.