How would you optimize MongoDB queries?

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✅ How to Optimize MongoDB Queries

🔹 1. Use Indexing

• Create indexes on fields used in filters, sorting, and joins ($lookup).

• Types of indexes:

  • Single field index

  • Compound index (for multiple fields)

  • Text index (for search)

  • TTL index (auto-expiring docs)

• Example:

  db.users.createIndex({ email: 1 })   // single field
  db.orders.createIndex({ userId: 1, createdAt: -1 }) // compound index
  

🔹 2. Use .explain() to Analyze Queries

• Always check if your query is using an index.

• Example:

  db.orders.find({ userId: 123 }).explain("executionStats")
  

• Look at IXSCAN (index scan) instead of COLLSCAN (collection scan).

🔹 3. Project Only Required Fields

• Avoid returning unnecessary fields.

• Example:

  // ❌ Returns whole doc
  db.users.find({ age: { $gt: 25 } })  
  
  // ✅ Returns only needed fields
  db.users.find({ age: { $gt: 25 } }, { name: 1, email: 1 })
  

🔹 4. Optimize Query Operators

• Use range queries ($gte, $lte) instead of $nin or $not.

• Prefer $in over multiple $or.

• Avoid regex without prefix search (since it can’t use indexes well).

🔹 5. Limit and Paginate Results

• Always use .limit() with .skip() or cursor-based pagination to avoid scanning millions of docs.

• Example:

  db.products.find().sort({ _id: 1 }).limit(20)
  

🔹 6. Schema Design Optimization

• Follow data access patterns → design schema based on queries, not just normalization.

• Use embedding for frequently accessed related data (denormalization).

• Use referencing for large/rarely accessed relations.

🔹 7. Use Aggregation Pipeline Efficiently

• Place $match and $project stages early in the pipeline to reduce scanned docs.

• Avoid $lookup when possible (can be expensive).

🔹 8. Sharding for Very Large Datasets

• Distribute data across multiple servers with sharding.

• Choose a good shard key (frequently used in queries and with high cardinality).

📌 Short Interview Answer (2–3 sentences):

“To optimize MongoDB queries, I’d first create indexes on frequently queried fields and use .explain() to ensure queries use index scans. I’d also limit returned fields, paginate results, and optimize schema design (embedding vs referencing). For large datasets, I’d apply sharding and caching strategies with Redis to reduce DB load.”

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