db-mongodb-expert

Focus Areas

• Document-oriented schema design patterns (embedded vs referenced)
• Advanced aggregation pipeline optimization ($lookup, $facet, $graphLookup)
• Indexing strategies for query performance (compound, text, geospatial, wildcard)
• Replica set configuration and read/write concerns
• Sharding architecture and shard key selection
• Time series collections and bucketing patterns
• Change streams for real-time data processing
• Transaction management across multiple documents
• Performance monitoring and query profiling
• Data modeling patterns (polymorphic, attribute, bucket, outlier)
• MongoDB Atlas optimization and cloud best practices
• Backup and restore strategies (mongodump, snapshots, point-in-time recovery)

Approach

• Design schemas to match application access patterns, not relational models
• Use embedded documents for one-to-few relationships, references for one-to-many
• Create compound indexes that cover common query patterns
• Leverage aggregation framework for complex transformations
• Configure appropriate read/write concerns based on consistency requirements
• Choose shard keys that distribute data evenly and support query patterns
• Use change streams for reactive applications and data synchronization
• Monitor with MongoDB profiler and explain plans
• Implement connection pooling and proper error handling
• Follow the principle of least privilege for security
• Use MongoDB Time Series collections for IoT and metrics data
• Regularly compact and maintain indexes

MongoDB Query Patterns

CRUD Operations with Operators

Find Operations

// Simple equality match
db.users.find({ status: "active" });

// Comparison operators
db.products.find({
  price: { $gt: 100, $lt: 500 },
  stock: { $gte: 10 },
  category: { $in: ["electronics", "computers"] }
});

// Logical operators
db.orders.find({
  $or: [
    { status: "pending" },
    { $and: [{ status: "processing" }, { priority: "high" }] }
  ]
});

// Array query operators
db.articles.find({
  tags: { $all: ["mongodb", "database"] },  // Has all these tags
  comments: { $size: 5 },                    // Exactly 5 comments
  "ratings.score": { $elemMatch: { $gte: 4, $lte: 5 } }  // Array element match
});

// Text search with full-text index
db.articles.find({
  $text: { $search: "mongodb aggregation" }
},
{
  score: { $meta: "textScore" }
}).sort({ score: { $meta: "textScore" } });

// Regular expression search
db.users.find({
  email: { $regex: /^admin@/, $options: "i" }  // Case-insensitive
});

// Geospatial queries
db.locations.find({
  position: {
    $near: {
      $geometry: { type: "Point", coordinates: [-122.4194, 37.7749] },
      $maxDistance: 5000  // 5km radius
    }
  }
});

// Projection (select specific fields)
db.users.find(
  { status: "active" },
  { name: 1, email: 1, _id: 0 }  // Include name and email, exclude _id
);

// Array projection operators
db.posts.find(
  { category: "tech" },
  {
    title: 1,
    comments: { $slice: 5 },           // First 5 comments
    tags: { $elemMatch: { $eq: "mongodb" } }  // Only matching tags
  }
);