💻 Common MongoDB Commands 🌱

Here’s a comprehensive example that covers many common MongoDB features (Quick Setup). I'll showcase operations like document creation, querying, indexing, aggregation, and more, based on a hypothetical e-commerce database structure.

Use Case: E-Commerce Database

We will use a database called ecommerce, which contains collections like users, products, orders, and reviews.

1. Database Setup

use ecommerce;

This switches to (or creates) the ecommerce database.

2. Inserting Documents (CRUD)

Insert Single Document

db.users.insertOne({ username: "johndoe", email: "johndoe@example.com", password: "hashed_password", createdAt: new Date()
});

Insert Multiple Documents

db.products.insertMany([ { name: "Laptop", description: "A high-end gaming laptop.", price: 1500, stock: 30, categories: ["electronics", "gaming"], ratings: [] }, { name: "Smartphone", description: "A latest model smartphone.", price: 800, stock: 50, categories: ["electronics", "mobile"], ratings: [] }
]);

3. Querying Documents

Find All Documents

db.products.find({});

Find with Conditions

db.products.find({ price: { $gt: 1000 }, categories: "electronics"
});

Find Specific Fields (Projections)

db.products.find( { price: { $gt: 1000 } }, { name: 1, price: 1, _id: 0 }
);

Sorting and Limiting Results

db.products.find().sort({ price: -1 }).limit(5);

4. Updating Documents

Update a Single Document

db.products.updateOne( { name: "Laptop" }, { $set: { price: 1400 } }
);

Update Multiple Documents

db.products.updateMany( { categories: "electronics" }, { $inc: { stock: 10 } }
);

Upsert (Update or Insert)

db.products.updateOne( { name: "Tablet" }, { $set: { name: "Tablet", description: "A lightweight tablet.", price: 500, stock: 20, categories: ["electronics", "tablet"] } }, { upsert: true }
);

5. Deleting Documents

Delete a Single Document

db.products.deleteOne({ name: "Smartphone" });

Delete Multiple Documents

db.products.deleteMany({ categories: "gaming" });

6. Indexes

Indexes are critical for performance optimization.

Create Indexes

db.products.createIndex({ name: 1 }); // Ascending index on the `name` field
db.products.createIndex({ price: -1 }); // Descending index on the `price` field

Compound Index

db.products.createIndex({ name: 1, price: -1 });

Text Index (For Full-Text Search)

db.products.createIndex({ description: "text", name: "text" });

7. Full-Text Search

Using the text index created earlier, you can perform full-text searches.

db.products.find({ $text: { $search: "gaming laptop" } });

8. Aggregation Framework

Aggregation is powerful for transforming and analyzing data.

Example: Calculate the Average Price of Products

db.products.aggregate([ { $group: { _id: null, averagePrice: { $avg: "$price" } } }
]);

Example: Products by Category

db.products.aggregate([ { $unwind: "$categories" }, { $group: { _id: "$categories", totalProducts: { $sum: 1 }, avgPrice: { $avg: "$price" } } }, { $sort: { totalProducts: -1 } }
]);

Example: Join Collections (Using $lookup)

db.orders.aggregate([ { $lookup: { from: "users", localField: "user_id", foreignField: "_id", as: "user_details" } }
]);

9. Transactions

Starting with MongoDB 4.0, multi-document transactions are supported for replica sets.

const session = db.getMongo().startSession();
session.startTransaction(); try { db.products.updateOne( { name: "Laptop" }, { $inc: { stock: -1 } }, { session } ); db.orders.insertOne( { product_id: ObjectId("..."), user_id: ObjectId("..."), quantity: 1, totalPrice: 1500, createdAt: new Date() }, { session } ); session.commitTransaction();
} catch (e) { session.abortTransaction();
} finally { session.endSession();
}

10. Geospatial Queries

If your data includes geographical coordinates, you can perform spatial queries.

Create a Geospatial Index

db.stores.createIndex({ location: "2dsphere" });

Find Stores Near a Coordinate

db.stores.find({ location: { $near: { $geometry: { type: "Point", coordinates: [-73.97, 40.77] }, $maxDistance: 5000 // meters } }
});

11. Handling Large Documents (GridFS)

For storing large files like images or videos, MongoDB provides GridFS. Here's how you can interact with it:

Storing a File in GridFS

var bucket = new mongoose.mongo.GridFSBucket(db, { bucketName: 'myFiles'
});
bucket.openUploadStream("myImage.jpg").end("image_binary_data");

Reading a File from GridFS

bucket.openDownloadStreamByName("myImage.jpg").pipe(fs.createWriteStream("./myImage.jpg"));

12. Schema Validation

You can enforce some schema validation rules on your collections.

Example: Enforce Price to Be a Positive Number

db.createCollection("products", { validator: { $jsonSchema: { bsonType: "object", required: ["name", "price", "stock"], properties: { price: { bsonType: "double", minimum: 0, description: "Price must be a positive number" }, stock: { bsonType: "int", minimum: 0, description: "Stock must be a non-negative integer" } } } }
});

13. Role-Based Access Control (RBAC)

MongoDB supports fine-grained control over database user permissions.

Create a User with Read-Only Access to the ecommerce Database

db.createUser({ user: "readonly_user", pwd: "password", roles: [{ role: "read", db: "ecommerce" }]
});

Create a User with Full Access

db.createUser({ user: "admin_user", pwd: "securepassword", roles: [{ role: "dbOwner", db: "ecommerce" }]
});

14. Sharding (For Scalability)

MongoDB allows horizontal scaling through sharding. This example assumes a sharded cluster is already set up.

Enable Sharding on a Database

sh.enableSharding("ecommerce");

Shard a Collection

sh.shardCollection("ecommerce.products", { product_id: "hashed" });

15. Explain Command

The explain command is used to understand how MongoDB processes queries. It provides insights into the query plan, index usage, and performance metrics

Example: Using explain for Query Optimization

db.products.find({ price: { $gt: 1000 } }).explain("executionStats");

This will return detailed statistics on how the query is executed, including whether an index was used, the number of documents examined, and the time taken.

16. Change Streams

MongoDB supports Change Streams, which allow applications to listen for real-time changes to a collection, database, or even across an entire cluster.

Example: Watching Changes in a Collection

const changeStream = db.products.watch();
changeStream.on("change", (next) => { console.log("Change detected:", next);
});

This is particularly useful for real-time applications where you need to monitor data updates.

17. TTL Index (Time to Live)

TTL indexes allow you to automatically remove documents after a certain period.

Example: Set a TTL Index for Expiring Documents

db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 });

In this case, any document in the sessions collection will be automatically removed 1 hour (3600 seconds) after its createdAt timestamp.

18. Partial Indexes

Partial indexes are useful when you want to index a subset of documents in a collection, improving performance and reducing storage costs.

Example: Create a Partial Index

db.products.createIndex({ price: 1 }, { partialFilterExpression: { price: { $gt: 1000 } } });

This index will only be created for products with a price greater than 1000.

19. WildCard Indexes

WildCard indexes can be used when you need to create an index on all fields of a document, useful for semi-structured data.

Example: Create a WildCard Index

db.products.createIndex({ "$**": 1 });

This will index all fields and subfields of documents in the products collection.

20. Hidden Indexes

Hidden indexes allow you to create indexes that MongoDB does not automatically consider during query execution, useful for testing purposes.

Example: Create and Hide an Index

db.products.createIndex({ name: 1 }, { hidden: true });

This creates an index on name, but MongoDB will not use it for queries unless explicitly told.

21. Collations (for String Comparisons)

Collations allow you to specify language-specific rules for string comparisons, such as case-sensitivity and accent marks.

Example: Create a Collection with Case-Insensitive Collation

db.products.createIndex({ name: 1 }, { collation: { locale: "en", strength: 2 } });

This makes the name field in the index case-insensitive for English.

22. Field-Level Encryption (FLE)

MongoDB supports Field-Level Encryption (FLE), allowing sensitive data to be encrypted at the field level.

Example: Encrypting a Field

const encryptedField = clientEncryption.encrypt("sensitive_data", { keyId: encryptionKeyId, algorithm: "AEAD_AES_256_CBC_HMAC_SHA_512-Deterministic"
}); db.users.insertOne({ name: "John Doe", email: "johndoe@example.com", password: encryptedField
});

23. Replication

While sharding focuses on horizontal scaling, replication is designed for fault tolerance and high availability. MongoDB supports automatic failover with replica sets.

Example: Initiating a Replica Set

rs.initiate({ _id: "rs0", members: [ { _id: 0, host: "localhost:27017" }, { _id: 1, host: "localhost:27018" }, { _id: 2, host: "localhost:27019" } ]
});

24. Concurrency Control (Optimistic Locking)

MongoDB doesn’t lock records like relational databases but allows for optimistic concurrency control via the use of a version field (for example).

Example: Optimistic Locking with version Field

db.orders.updateOne( { _id: ObjectId("..."), version: 1 }, { $set: { status: "shipped" }, $inc: { version: 1 } }
);

The update will only succeed if the document's version is 1, preventing race conditions in concurrent updates.

25. MongoDB Atlas (Cloud Features)

MongoDB Atlas is the fully managed cloud version of MongoDB. It includes features like:

• Performance Advisor: Suggests indexes based on query patterns.
• Backup & Restore: Automated backups and point-in-time recovery.
• Global Clusters: Distribute data across multiple geographic regions for faster access.

Example: Setting Up Global Clusters in Atlas

In Atlas, you can define zones for data distribution across different regions. For example, store European users' data in the EU, and American users' data in the US.

Conclusion

This example provides a thorough introduction to many common MongoDB features:

• CRUD operations (insert, find, update, delete)
• Indexing (single-field, compound, text)
• Aggregation (grouping, joining)
• Transactions (multi-document)
• Full-text search
• Schema validation
• Geospatial queries
• GridFS for large files
• Role-based access control
• Sharding
• Explain command for query optimization.
• Real-time change streams.
• TTL indexes for expiring data.
• Partial and wildcard indexes.
• Hidden indexes for testing.
• Collations for string comparisons.
• Field-level encryption for sensitive data.
• Replication for fault tolerance.
• Optimistic locking for concurrency control.
• Atlas-specific cloud features like backups and global clusters.

Each of these operations is essential for building efficient, scalable applications with MongoDB.

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