How do you optimize queries in a document database?

To optimize queries in a document database, focus on indexing strategies, query design, and schema structure. Start by analyzing your most frequent query patterns and ensure the database is indexed to support them. Avoid unnecessary data processing by writing precise queries and structuring documents to minimize joins or complex aggregations. Regularly monitor query performance and adjust your approach as usage patterns evolve.

Indexing is the primary tool for optimization. Create indexes on fields commonly used in filters, sorts, or joins. For example, if queries often filter by userId and sort by createdAt, a compound index on { userId: 1, createdAt: -1 } improves performance. Avoid over-indexing, as too many indexes slow down writes. Use database-specific tools like MongoDB’s explain() to check if queries use indexes effectively. For time-series data, consider Time-To-Live (TTL) indexes to automatically expire old documents, reducing dataset size. Partial indexes can also help by indexing only a subset of documents (e.g., active users), saving storage and improving speed.

Optimize query logic to reduce overhead. Use projection to return only necessary fields, minimizing data transfer. For example, in MongoDB, db.collection.find({ ... }, { name: 1, date: 1 }) fetches only name and date. Avoid expensive operations like unanchored regular expressions or $where clauses. When using aggregation pipelines, place $match stages early to filter data before processing it. If querying nested arrays, leverage $elemMatch to target specific elements without scanning the entire array. For read-heavy workloads, use secondary replicas to distribute load, but ensure eventual consistency is acceptable.

Schema design impacts performance significantly. Denormalize data to reduce joins—for example, embed user profiles directly in order documents if they’re frequently accessed together. However, balance this with update efficiency; over-embedding can make writes slower. Use document references when data is updated independently. Shard large collections horizontally if scaling beyond a single node. For example, shard by region if queries are geographically scoped. Monitor slow queries using built-in profilers and adjust indexes or schemas iteratively. Tools like MongoDB Atlas Performance Advisor automate this process by suggesting index optimizations based on real-world usage.