Handling Large Datasets in MongoDB

1. Introduction

MongoDB is a NoSQL database that can handle large datasets efficiently with its flexible schema and powerful indexing capabilities. This lesson will guide you through the best practices for managing large datasets in MongoDB.

2. Key Concepts

2.1 Sharding

Sharding is a method for distributing data across multiple servers. It allows MongoDB to scale horizontally by partitioning data across different shards.

Key Terms:

Shard: A single instance of MongoDB that holds a subset of the data.
Shard Key: A field (or fields) that determines how data is distributed across shards.

2.2 Indexing

Indexes improve query performance by allowing MongoDB to quickly locate data without scanning the entire collection.

Types of Indexes:

Single Field Index
Compound Index
Text Index
Geospatial Index

3. Best Practices

Use Sharding for Horizontal Scalability.
Implement Indexing to Optimize Query Performance.
Regularly Monitor Database Performance using Tools like MongoDB Atlas.
Use Bulk Operations for Inserting/Updating Large Datasets.

Note: Always back up your data before making significant changes to your database.

4. Code Examples

4.1 Sharding Setup

use admin
sh.addShard("shard1/example-shard1:27017")
sh.addShard("shard2/example-shard2:27017")

4.2 Creating an Index

db.collection.createIndex({ field: 1 })

5. FAQ

Q: What is the maximum size of a MongoDB document?

A: The maximum BSON document size is 16MB.

Q: How can I optimize my queries in MongoDB?

A: Use indexes, analyze query plans, and reduce the number of fields returned in queries.