mongodb-usage

Query MongoDB collections using find() with filter conditions to retrieve matching documents. Apply projection to select specific fields, use sort() and limit() to control results, and leverage indexes on frequently queried fields to improve performance.

MongoDB supports embedding (storing related data within a document) and referencing (storing document IDs to link collections). Choose embedding for one-to-few relationships with infrequent updates; use referencing for one-to-many or many-to-many relationships to avoid data duplication and reduce document size.

Create single-field indexes on frequently queried columns and compound indexes on fields used together in filters. Use explain() to analyze query execution plans, monitor index usage, and create TTL indexes for automatic document expiration on time-based fields.

Use aggregation pipelines for complex multi-stage transformations: filtering, grouping, joining collections, computing statistics, and reshaping documents. Pipelines process data server-side more efficiently than client-side iteration for large datasets and support stages like $match, $group, $lookup, and $project.

Retrieve database and collection metadata using read-only operations: list all databases, show collections within a database, inspect collection indexes with getIndexes(), and examine sample documents to infer schema structure and field types.

Use connection strings with proper authentication, enable connection pooling to reuse connections efficiently, set appropriate timeouts, and follow principle of least privilege by restricting database user permissions to read-only access when queries only require data retrieval.