mongodb

MongoDB schema design balances flexibility with query performance. Start by mapping your data relationships, then denormalize or reference based on access patterns. Use document-oriented thinking—nest frequently accessed data together, separate large arrays, and apply indexing strategies to optimize query speed across your collections.

Aggregation pipelines chain stages like $match, $group, and $project to transform and analyze data efficiently. Build pipelines incrementally: filter early with $match, reshape with $group and $project, and use $lookup for joins. Index your match fields and test stages independently to optimize performance.

MongoDB scaling combines replication for high availability and sharding for horizontal scaling. Configure replica sets to replicate data across nodes, then shard collections by a key that distributes load evenly. Atlas handles cluster setup; self-managed and Kubernetes deployments require manual configuration of shard keys and replication.

Yes. MongoDB provides official drivers for Node.js, Python, Java, Go, and other languages. Install the driver for your stack, establish a connection string to Atlas or self-managed MongoDB, and use CRUD methods and aggregation APIs native to your framework. Driver versions align with MongoDB server versions.

Implement authentication with username/password or LDAP, authorization using role-based access control (RBAC) to restrict permissions, and encryption both in transit (TLS) and at rest (CSFLE). On Atlas, enable network access controls and IP whitelisting. Self-managed deployments require manual configuration of these layers.

Slow queries often lack indexes or scan unnecessary documents. Analyze query plans with explain(), create indexes on filter and sort fields, and verify selectivity. Rewrite queries to filter early, avoid expensive operations like $regex, and use covered indexes when possible. Monitor performance in Atlas or self-managed environments.