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MongoDB Reference

Complete MongoDB reference with query operators, aggregation, and best practices

5 min read
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Official Docs

CRUD Operations

Create, Read, Update, and Delete documents in MongoDB

Insert Documents

Add new documents to a collection

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🟢 Essential - Every MongoDB app needs insert operations
💡 MongoDB automatically adds _id if not provided
⚡ insertMany() is faster than multiple insertOne()
📌 Returns insertedId(s) for tracking
⚠️ Check writeConcern for production apps
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Find Documents

Query and retrieve documents from collections

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🟢 Essential - Most common MongoDB operation
💡 findOne() returns first match only
📌 Use projection to reduce network transfer
⚡ Create indexes for frequently queried fields
🔗 Related: explain() to analyze query performance
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Update Documents

Modify existing documents in collections

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🟢 Essential - Keep data up-to-date
💡 Use $set to update specific fields only
⚠️ updateMany() can affect many documents - be careful
📌 upsert: true creates doc if not found
⚡ findOneAndUpdate() returns the document
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Delete Documents

Remove documents from collections

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⚠️ Delete operations are permanent - no undo
💡 Consider soft delete for important data
📌 deleteMany({}) removes ALL documents
🟢 Essential - Clean up old/unused data
⚡ findOneAndDelete() returns deleted document
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Query Operators

Powerful operators for filtering and matching documents

Comparison Operators

Compare field values in queries

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🟢 Essential - Foundation of MongoDB queries
💡 $eq is implicit when using key: value
📌 $in is like SQL IN operator
⚡ Combine operators for complex queries
🔗 Related: $exists, $type for field checks
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Logical Operators

Combine multiple query conditions

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💡 Implicit AND when listing multiple fields
📌 $or requires array of conditions
⚡ $nor useful for exclusion logic
⚠️ Complex logic can impact performance
🔗 Related: $where for JavaScript expressions
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Array Operators

Query and match array fields

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🟢 Essential for working with arrays
💡 Simple value matches any array element
📌 $elemMatch for complex array element conditions
⚡ Index array queries for better performance
⚠️ $size doesn't accept ranges (use $where)
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Text Search

Full-text search capabilities

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💡 Text index required for $text search
📌 $text searches all indexed fields
⚡ Text indexes can be large - use wisely
⚠️ Only one text index per collection
🔗 Consider Atlas Search for advanced needs
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Update Operators

Operators for modifying document fields and arrays

Field Update Operators

Modify document field values

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🟢 Essential - Most common update operations
💡 $set creates field if it doesn't exist
📌 Use dot notation for nested fields
⚡ $inc is atomic - safe for concurrent updates
⚠️ $unset removes field entirely
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Array Update Operators

Modify array fields in documents

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💡 $addToSet prevents duplicate values
📌 $ positional operator updates matched element
⚡ $push with $each for bulk array updates
⚠️ $pull removes ALL matching elements
🔗 Related: $pullAll, $pushAll (deprecated)
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Aggregation Pipeline

Transform and analyze data with pipeline stages

Basic Pipeline Stages

Common stages for data transformation

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🟢 Essential - Powerful data processing
💡 Pipeline stages process sequentially
⚡ $match early to reduce documents
📌 $group _id can be complex expression
🔗 Related: $facet for multiple pipelines
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Aggregation Operators

Operators for calculations and transformations

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💡 $sum: 1 counts documents
📌 Use $$ for variables in expressions
⚡ $cond for if-then-else logic
🟢 Essential for data analysis
🔗 Related: $map, $reduce for array processing
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Indexes & Performance

Optimize query performance with proper indexing

Index Types

Different index types for various use cases

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🟢 Essential - Indexes make queries fast
💡 Compound indexes support multiple query patterns
⚠️ Too many indexes slow down writes
📌 Index order matters in compound indexes
⚡ Use explain() to verify index usage
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Query Optimization

Analyze and optimize query performance

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💡 explain() shows query execution plan
📌 "COLLSCAN" means no index used (slow)
⚡ Profiler helps find slow queries
⚠️ hint() forces index but use carefully
🔗 Related: MongoDB Compass for visual analysis
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Schema Design & Validation

Design patterns and data validation strategies

Schema Validation

Enforce document structure and data types

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🟢 Essential - Ensure data integrity
💡 JSON Schema provides rich validation
📌 validationLevel controls when to validate
⚠️ Validation adds small performance overhead
🔗 Related: Mongoose for application-level validation
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Design Patterns

Common MongoDB schema design patterns

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💡 Embed for 1:1 or 1:few relationships
📌 Reference for 1:many or many:many
⚡ Denormalization improves read performance
⚠️ Avoid unbounded arrays (use bucketing)
🟢 Essential - Choose pattern based on access patterns
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