A high memory usage ratio in MongoDB monitoring typically indicates that MongoDB is consuming a significant portion of available system memory. This can be due to several reasons, such as large datasets being cached in RAM for faster query performance, excessive indexing, or memory leaks.
Possible Causes:
- Working Set Size: MongoDB caches frequently accessed data in RAM (the "working set") to improve query speed. If the working set exceeds available memory, performance may degrade.
- Excessive Indexing: Too many indexes can consume significant memory, especially if they are frequently updated.
- Memory Leaks: Rare but possible, especially in older MongoDB versions or misconfigured deployments.
- System Pressure: Other processes on the same server may compete for memory, forcing MongoDB to use swap space (which severely impacts performance).
Example Scenario:
If a MongoDB instance is monitoring shows 90% memory usage, but the system has 32GB RAM and only 8GB is allocated to MongoDB, the high ratio might be due to:
- A large working set (e.g., 10GB of active data) being cached.
- Multiple indexes on frequently queried collections.
Recommended Actions:
- Check Working Set Size: Use
db.serverStatus().wiredTiger.cache (for WiredTiger storage engine) to see cached data size.
- Optimize Indexes: Remove unused indexes with
db.collection.dropIndex().
- Scale Resources: Increase RAM or scale out to a sharded cluster if the working set is too large.
- Use Tencent Cloud Services:
- TencentDB for MongoDB provides auto-scaling and optimized memory management.
- Cloud Monitor helps track memory usage trends and set alerts.
- Tencent Cloud Server (CVM) allows vertical scaling if on-premises resources are insufficient.
If memory usage is consistently high and affects performance, consider upgrading hardware or migrating to a managed service like TencentDB for MongoDB.