Summary
The mongodb-atlas-local Docker container becomes unresponsive after approximately 15-20 minutes of normal operation, with the mongod process freezing and stopping logging, responding to connections, and WiredTiger checkpoints. This issue occurs on both local machines and Linux-based self-hosted runners, with or without connections to the container.
Root Cause
The root cause of this issue is likely due to the accumulation of TCP connections in the CLOSE_WAIT state on port 27017, which can cause the mongod process to freeze. Possible causes include:
- Healthcheck connections not being properly closed
- Connection timeouts not being properly handled
- Resource leaks causing the container to become unresponsive
Why This Happens in Real Systems
This issue can occur in real systems due to:
- Insufficient resource allocation, leading to resource constraints and container unresponsiveness
- Inadequate connection management, resulting in accumulated connections and process freezing
- Incompatible or outdated dependencies, causing compatibility issues and container crashes
Real-World Impact
The real-world impact of this issue includes:
- Downtime and unavailability of the MongoDB service
- Data loss or corruption due to the container becoming unresponsive
- Increased latency and decreased performance caused by the accumulation of connections and process freezing
Example or Code
docker exec xi-mongodb-atlas-1 mongosh --eval "db.runCommand({ping: 1})"This command can be used to test the connection to the MongoDB container and verify if it is responsive.
How Senior Engineers Fix It
Senior engineers can fix this issue by:
- Increasing resource allocation to the container to prevent resource constraints
- Implementing proper connection management, including connection timeouts and closure of healthcheck connections
- Monitoring container performance and adjusting settings as needed to prevent downtime and unavailability
- Updating dependencies to ensure compatibility and prevent crashes
Why Juniors Miss It
Junior engineers may miss this issue due to:
- Lack of experience with containerization and Docker
- Insufficient understanding of connection management and resource allocation
- Inadequate testing and verification of container responsiveness and performance
- Failure to monitor container logs and performance metrics, leading to delayed detection of issues