MongoDB Highload Checklist

Hardware Selection and Configuration

• Choose hardware that meets your performance requirements, including CPU, RAM, and storage.
• Use SSDs for storage to improve read/write performance.
• Set up hardware redundancy to avoid single points of failure.

MongoDB Configuration:

• Choose the appropriate storage engine (e.g., WiredTiger) for your workload.
• Configure the MongoDB instance to use the recommended settings for your hardware and workload.
• Set up sharding if your data size exceeds the capacity of a single server.

Indexes and Query Optimization:

• Design efficient indexes based on your query patterns.
• Use the explain() method to analyze and optimize queries.
• Avoid unnecessary or inefficient queries.

Replication and High Availability:

• Implement replica sets for data redundancy and high availability.
• Monitor the health of replica set members.
• Configure automatic failover and recovery.

Backup and Disaster Recovery:

• Establish a robust backup and recovery strategy.
• Regularly back up data using tools like mongodump or use online backup solutions.
• Test and document the restore process.

Monitoring and Alerting:

• Set up monitoring tools like MongoDB's own monitoring and alerting tools, or third-party solutions.
• Create alerts for key performance metrics, such as CPU usage, memory usage, disk space, and query performance.

Scaling:

• Plan for horizontal scaling using sharding when data volume grows beyond the capacity of a single server.
• Add new shards to the cluster as needed to distribute the data load.

Security:

• Implement authentication and authorization for users and applications.
• Enable encryption for data in transit and at rest.
• Regularly audit and review security configurations.

Optimize Data Modeling:

• Normalize or denormalize data structures based on query patterns.
• Use best practices for schema design to reduce contention and improve performance.

Connection Pooling:

• Implement connection pooling to efficiently manage client connections.
• Adjust connection pool settings based on traffic and resource utilization.

Caching:

• Implement caching mechanisms like Redis or Memcached to reduce database load for frequently accessed data.

Load Testing:

• Conduct load testing to simulate high-load scenarios and identify bottlenecks.
• Use tools like AB or custom scripts to perform load testing.

Auto-Scaling (Cloud Deployments):

• If running in a cloud environment, set up auto-scaling based on predefined triggers and metrics.

Logs and Monitoring for Slow Queries:

• Monitor the MongoDB logs for slow queries and optimize them as needed.
• Use profiling to identify and optimize slow operations.

Documentation and Disaster Recovery Plan:

• Maintain thorough documentation of your MongoDB setup, configurations, and procedures.
• Have a well-documented disaster recovery plan in place.

Regular Maintenance:

• Schedule regular maintenance tasks such as index rebuilds and hardware upgrades as needed.

Testing and Staging Environment:

• Maintain a separate testing and staging environment to test changes and updates before applying them to the production environment.

Scalability Planning:

• Continuously monitor and plan for future scalability requirements to accommodate growing workloads.