Mastering MongoDB: Key Concepts and Operations Explained

Here are the answers to the MongoDB-related questions:

1. What is the purpose of transactions in MongoDB?

The purpose of transactions in MongoDB is to ensure data consistency, integrity, and isolation across multiple operations or documents. Transactions allow you to execute multiple write operations (across different documents or collections) as a single atomic unit. This means that either all operations in a transaction will succeed, or none will (rollback on failure). Transactions in MongoDB support ACID properties, ensuring reliability and consistency in complex operations.

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2. How do you start and commit a transaction?

To start a transaction, use the startSession() method to create a session, and then use session.startTransaction() to begin the transaction. When you are ready to commit the transaction, you call session.commitTransaction(). If you want to abort the transaction, you use session.abortTransaction().

Example:

const session = client.startSession();

try {
  session.startTransaction();

  // Perform database operations
  db.collection('users').updateOne({ _id: 1 }, { $set: { name: 'Alice' } }, { session });
  db.collection('orders').updateOne({ _id: 123 }, { $set: { status: 'completed' } }, { session });

  // Commit transaction
  session.commitTransaction();
} catch (error) {
  session.abortTransaction();
  throw error;
} finally {
  session.endSession();
}

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3. What are MongoDB's ACID properties?

MongoDB provides support for ACID (Atomicity, Consistency, Isolation, Durability) properties for multi-document transactions:

• Atomicity: Ensures that all operations within a transaction are executed as a single unit. Either all operations succeed, or none of them take effect (rollback).
• Consistency: Ensures that the database transitions from one valid state to another, maintaining the integrity of the database.
• Isolation: Ensures that concurrent transactions do not interfere with each other. In MongoDB, this is handled using the default isolation level of "snapshot isolation."
• Durability: Guarantees that once a transaction is committed, its changes are permanent, even in the event of a crash.

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4. How do you implement multi-document transactions?

To implement multi-document transactions, you use the startSession() method to begin a session and perform operations on multiple collections. The session ensures that all operations within the transaction are treated atomically.

Example:

const session = client.startSession();

try {
  session.startTransaction();

  // Multiple operations across different documents/collections
  db.collection('orders').updateOne({ orderId: 1 }, { $set: { status: 'shipped' } }, { session });
  db.collection('inventory').updateOne({ productId: 123 }, { $inc: { quantity: -1 } }, { session });

  // Commit transaction
  session.commitTransaction();
} catch (error) {
  session.abortTransaction();
  throw error;
} finally {
  session.endSession();
}

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5. Explain the watch method for change streams.

The watch method in MongoDB is used to monitor changes in a collection or database. It provides a real-time stream of changes (insertions, updates, deletions) using change streams. You can use it to listen for data modifications, allowing applications to react to changes as they happen.

Example:

const changeStream = db.collection('orders').watch();

changeStream.on('change', (change) => {
  console.log(change);
});

This method allows for asynchronous listening of database changes without constantly polling the database.

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6. What is a database cursor in MongoDB?

A cursor in MongoDB is an object that allows you to iterate over the results of a query. When you execute a query, MongoDB returns a cursor that points to the first document of the result set. You can use the cursor to retrieve documents one by one or in batches, and it provides mechanisms for pagination and iteration.

Example:

const cursor = db.collection('users').find({});

cursor.forEach((doc) => {
  console.log(doc);
});

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7. How do you handle concurrency in MongoDB?

MongoDB handles concurrency by using a mechanism called document-level locking. In a single document, only one operation can be performed at a time (e.g., updates, deletes). For operations across multiple documents or collections, MongoDB uses read/write locks.

For higher concurrency:

• Sharding: Distributes data across multiple servers to improve parallel processing.
• Optimistic Concurrency Control (OCC): Used in some applications where conflict resolution is handled by the application, such as comparing version numbers of documents before updates.
• Transactions: MongoDB’s transactions ensure that operations across multiple documents/collections are executed atomically.

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8. What is a write concern?

Write concern in MongoDB defines the level of acknowledgment requested from the database for write operations. It controls the durability of the write operation by specifying the number of replica set members (or shards) that must acknowledge the write before it is considered successful.

Write concern levels:

• w: 1: The write must be acknowledged by the primary.
• w: "majority": The write must be acknowledged by a majority of replica set members.
• w: 0: No acknowledgment is required.
• wtimeout: Specifies the maximum time the system waits for the acknowledgment.

Example:

db.collection('users').insertOne({ name: 'Alice' }, { writeConcern: { w: "majority", wtimeout: 5000 } });

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9. Explain read preferences in MongoDB.

Read preferences in MongoDB determine how read operations are directed in a replica set. MongoDB allows clients to specify different read preferences to control whether they read from the primary, secondaries, or a mix of both.

Read preference modes:

• primary: Only reads from the primary.
• primaryPreferred: Reads from the primary, but if it is unavailable, reads from secondaries.
• secondary: Only reads from secondaries.
• secondaryPreferred: Reads from secondaries, but if none are available, reads from the primary.
• nearest: Reads from the nearest replica (can be either primary or secondary).

Example:

const result = db.collection('users').find().readPref('secondary');

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10. What is database partitioning?

Database partitioning is the process of splitting a large database into smaller, more manageable pieces, called partitions. These partitions can be based on specific criteria like ranges of values or hashes. Partitioning improves query performance, scalability, and simplifies database management.

In MongoDB, partitioning is achieved using sharding, where data is distributed across multiple servers. Each shard contains a subset of the data, and the sharded key determines how data is distributed.

Example:

sh.shardCollection("db.collection", { "shardKey": 1 });

Sharding allows horizontal scaling, as it distributes data across multiple nodes, ensuring the system can handle large amounts of data efficiently.

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