How MongoDB Powers Real-Time Applications: A Deep Dive into Streaming Data

MongoDB, known for its flexibility and scalability, has evolved into a powerful database for real-time applications. Whether it's live data feeds, streaming analytics, or event-driven architectures, MongoDB provides the necessary tools to ingest, store, and analyze data in real time. This article delves into the core components of MongoDB that enable real-time data streaming, exploring change streams, aggregation pipelines, and integrations with tools like Kafka and Redis.

Real-Time Data and Streaming in MongoDB

Real-time data refers to the continuous flow of information that is immediately processed and delivered as it's generated. For real-time applications—such as financial systems, IoT devices, gaming, and chat applications—it’s crucial to ingest and act upon data in milliseconds.

MongoDB enables real-time data streaming through several key features:

• Change Streams: A powerful tool to listen to changes in MongoDB collections.
• Aggregation Pipelines: For real-time analytics and data transformation.
• Integration with Kafka and Redis: Enabling seamless data flow between MongoDB and external services.

Let's explore these concepts in depth.

Change Streams in MongoDB

Change streams are one of MongoDB's most powerful features for building real-time applications. They allow you to subscribe to changes happening in a MongoDB collection or database. You can capture document inserts, updates, deletes, and more, making it ideal for event-driven architectures or streaming pipelines.

How Change Streams Work : MongoDB’s change streams use the underlying replication mechanism to monitor data changes. By leveraging the oplog (operations log) in a replica set, MongoDB streams changes to subscribers in near real-time.

• Key Operations Tracked by Change Streams
  • Insert: When a document is inserted into a collection.
  • Update: When a document is updated.
  • Delete: When a document is deleted.
  • Replace: When a document is replaced by another.
  • Invalidate: Occurs when a collection or database is dropped.

Code Example: Setting Up a Change Stream

Here’s an example of how to set up a change stream to listen to changes in a users collection:

// Connect to MongoDB
const { MongoClient } = require("mongodb");

async function monitorChanges() {
  const client = new MongoClient("mongodb://localhost:27017");

  try {
    await client.connect();
    const db = client.db("myDatabase");
    const collection = db.collection("users");

    // Set up the change stream
    const changeStream = collection.watch();

    // Listen to the changes
    changeStream.on("change", (next) => {
      console.log("Change detected: ", next);
    });
  } finally {
    // Keep the connection open
  }
}

monitorChanges();

In this example:

• We connect to a MongoDB instance and set up a change stream on the users collection.
• The change stream listens for any insertions, updates, or deletions, and outputs the changes in real time.

Use Case: Real-Time Notifications in a Chat Application

A common use case of MongoDB Change Streams is building real-time notification systems. For example, in a chat app, change streams can instantly trigger notifications for new messages, online status updates, or reactions, ensuring users receive real-time alerts.

• Problem Scenario : In a chat application, the system needs to notify users about various events in real time:
  • New messages sent to the user.
  • Read receipts when the other party views the message.
  • Online status changes of friends or users in a chat group.

Using MongoDB change streams, you can monitor collections (e.g., messages, users, reactions) for document changes and trigger real-time notifications without constant database polling.

Solution Using MongoDB Change Streams

Change streams enable developers to listen to changes in MongoDB collections and react immediately. Whenever a new message is inserted into the messages collection, a change stream can pick up this event and notify the recipient instantly.

Step-by-Step Implementation

Let’s break down how to implement a real-time messaging notification system using MongoDB change streams.

1. Data Model

Here’s a basic schema for a messages collection:

{
  "_id": ObjectId("messageId"),
  "senderId": ObjectId("userId1"),
  "recipientId": ObjectId("userId2"),
  "content": "Hello, how are you?",
  "timestamp": ISODate("2023-09-10T12:00:00Z"),
  "status": "sent" // or "delivered", "read"
}

2. Setting Up the Change Stream

In the chat application, we want to notify the recipient when a new message is sent. We will use change streams to monitor the messages collection for insert events.

const { MongoClient } = require("mongodb");

async function watchMessages() {
  const client = new MongoClient("mongodb://localhost:27017");

  try {
    await client.connect();
    const db = client.db("chatApp");
    const messagesCollection = db.collection("messages");

    // Set up the change stream to monitor inserts (new messages)
    const changeStream = messagesCollection.watch([
      { $match: { operationType: "insert" } },
    ]);

    // Listen for new message events
    changeStream.on("change", (next) => {
      const newMessage = next.fullDocument;

      // Notify the recipient in real-time
      notifyUser(newMessage.recipientId, newMessage);
    });
  } catch (err) {
    console.error(err);
  }
}

function notifyUser(userId, message) {
  // Here you would typically send a notification through websockets, push notifications, etc.
  console.log(`New message for user ${userId}: ${message.content}`);
}

watchMessages();

Explanation of Code

• Change Stream Setup: We use the watch() method to monitor changes in the messages collection.
• $match Operation: Filters the change events to focus only on insert operations, as we're only interested in new messages.
• Real-Time Notifications: When a new message is inserted, the change stream triggers an event, capturing the full document of the new message. The notifyUser() function is then called, which could send a notification to the user (via WebSocket or another mechanism).

3. Real-Time Notification Delivery

In a real-world scenario, you would likely send this notification to the user via:

• WebSockets: To instantly push notifications to a connected user.
• Push Notifications: For mobile apps or web apps where the user might be offline.
• Emails or SMS: For systems that deliver notifications via more traditional means.

For example, if the app uses WebSockets, you might send the message like this:

function notifyUser(userId, message) {
  // Assuming you have a WebSocket connection for each user
  const userSocket = getUserWebSocket(userId);

  if (userSocket) {
    userSocket.send(
      JSON.stringify({
        type: "newMessage",
        message: message.content,
        timestamp: message.timestamp,
      }),
    );
  }
}

This would instantly deliver the notification to the recipient, letting them know they have received a new message.

Benefits

• No Polling Overhead : Change streams provide a real-time feed of changes without the need for continuously polling the database, reducing server load and improving performance.

• Scalability : MongoDB’s distributed architecture allows change streams to scale across large collections, making it ideal for applications with many concurrent users and high traffic.

• Low Latency : Change streams operate in near real-time, ensuring that events such as new messages or updates are captured and processed with minimal delay.

• Fine-Grained Control : With change streams, you can filter the changes based on specific operations (like inserts, updates, deletes) and take action based on the event type.

• Event-Driven Architecture : Change streams fit naturally into event-driven architectures, enabling systems to react to changes as they occur. This is ideal for building microservices, notification systems, and real-time data pipelines.

Additional Use Cases

While real-time notifications are a prominent use case, MongoDB change streams can be used in various other real-time applications:

• Real-Time Analytics Dashboards : Display live metrics, transactions, or user activity by listening to changes in collections and updating dashboards instantly.

• Inventory Management : In an e-commerce platform, track stock levels in real time by monitoring changes in product inventories and triggering alerts when stock runs low.

• Data Synchronization : Keep multiple databases or services in sync by using change streams to capture data changes and propagate them to other systems, such as Elasticsearch or Redis.

• Audit Logging : Create detailed audit logs for document changes (inserts, updates, deletes) in the system, ensuring a comprehensive record of all operations in real-time.

Aggregation Pipelines for Streaming Analytics

MongoDB’s aggregation framework enables real-time data processing and transformation through a pipeline of stages. It allows for complex operations like filtering, grouping, sorting, and calculating metrics on streaming data.

How Aggregation Pipelines Work : The aggregation framework processes data in stages, where the output of one stage becomes the input for the next. This makes it ideal for real-time data transformation and analytics.

Common Aggregation Stages:

• $match: Filters documents based on a condition.
• $group: Groups documents by a specified field.
• $sort: Orders documents by a specific field.
• $limit: Restricts the number of documents in the result.
• $project: Reshapes documents by adding or removing fields.

Code Example: Real-Time Aggregation Pipeline

In this example, we’ll use an aggregation pipeline to monitor and analyze incoming transactions in real time:

async function realTimeAggregation() {
  const client = new MongoClient("mongodb://localhost:27017");

  try {
    await client.connect();
    const db = client.db("finance");
    const transactions = db.collection("transactions");

    const changeStream = transactions.watch();

    changeStream.on("change", async (next) => {
      const pipeline = [
        { $match: { operationType: "insert" } }, // Only process new transactions
        { $group: { _id: "$accountId", totalAmount: { $sum: "$amount" } } },
        { $sort: { totalAmount: -1 } },
        { $limit: 5 },
      ];

      const results = await transactions.aggregate(pipeline).toArray();
      console.log("Top 5 accounts by total transaction amount:", results);
    });
  } finally {
    // Keep the connection open
  }
}

realTimeAggregation();

Explanation:

• A change stream listens for new insert operations in the transactions collection.
• The aggregation pipeline calculates the total transaction amount for each account and outputs the top 5 accounts with the highest transaction amounts.

Use Case: Aggregation Pipelines for Streaming Analytics

• Problem : Consider a real-time fraud detection system for an e-commerce platform. The platform processes thousands of transactions per second and needs to identify suspicious activity, such as unusually large purchases, rapidly. The system must:
  • Continuously monitor incoming transactions.
  • Flag transactions that exceed a predefined threshold or exhibit abnormal patterns.
  • Provide real-time alerts or recommendations for further investigation.

Steps for Real-Time Fraud Detection

• Ingest real-time transaction data into a MongoDB collection (e.g., transactions).
• Use the change streams feature to monitor new transactions as they are inserted.
• Apply an aggregation pipeline to filter high-value transactions and detect anomalies.

Example

Let’s implement a real-time fraud detection mechanism that monitors new transactions and flags those exceeding a set limit (e.g., $10,000).

1. Ingesting Real-Time Transactions

New transactions are continuously being inserted into the transactions collection. Each document represents a purchase and contains information like the amount, account ID, location, and timestamp.

{
   "_id": ObjectId("txn123"),
   "accountId": "user1",
   "amount": 15000,
   "location": "New York",
   "timestamp": ISODate("2024-09-10T12:34:56Z")
}

2. Setting Up a Change Stream

We can monitor new transactions in real-time using MongoDB’s change streams.

const { MongoClient } = require("mongodb");

async function monitorTransactions() {
  const client = new MongoClient("mongodb://localhost:27017");

  try {
    await client.connect();
    const db = client.db("ecommerce");
    const transactions = db.collection("transactions");

    // Start watching changes to the transactions collection
    const changeStream = transactions.watch();

    // For each new transaction, apply the aggregation pipeline
    changeStream.on("change", async (next) => {
      const pipeline = [
        { $match: { operationType: "insert" } }, // Only process new inserts
        { $match: { "fullDocument.amount": { $gt: 10000 } } }, // Filter for high-value transactions
        {
          $project: {
            accountId: "$fullDocument.accountId",
            amount: "$fullDocument.amount",
            location: "$fullDocument.location",
            timestamp: "$fullDocument.timestamp",
          },
        },
      ];

      // Run the aggregation pipeline
      const results = await transactions.aggregate(pipeline).toArray();
      console.log("Suspicious transactions detected:", results);
    });
  } finally {
    // Keep the connection open
  }
}

monitorTransactions();

Explanation of the Aggregation Pipeline

1. $match: The pipeline first filters for newly inserted transactions with operationType: 'insert' and selects transactions where the amount field is greater than $10,000. This ensures we only process high-value transactions.
2. $project: The next stage reshapes the result to include only relevant fields like accountId, amount, location, and timestamp for flagged transactions. This helps focus on the data necessary for the fraud detection system.

3. Actionable Insights

Whenever a suspicious transaction is detected, the system logs the details. The data can then be used for triggering real-time alerts (e.g., via an email or notification service), or the flagged transactions can be forwarded to a dedicated team for further investigation.

Additional Enhancements

• Anomaly Detection : Extend the pipeline to detect anomalies based on more complex rules, such as frequent purchases from geographically distant locations within a short time.
• Historical Analysis : Use $group stages in the aggregation pipeline to compare current transactions with historical data, identifying patterns like unusually high spending by an account in a short period.
• Machine Learning Integration : Embed machine learning predictions by combining MongoDB with external tools like Python’s Scikit-learn or TensorFlow, which can evaluate each transaction’s risk score.

Benefits

• Real-Time Monitoring : MongoDB's change streams and aggregation pipelines allow continuous, real-time processing of incoming transactions, making it possible to detect and act on suspicious activities immediately.
• Customizable Filtering : The flexible aggregation stages like $match and $project enable fine-grained filtering of transactions, ensuring that only suspicious events are flagged for review.
• Low Latency : By processing data within MongoDB without needing to export it to external systems, the pipeline can operate with minimal latency, providing fast and actionable results.

Integrating MongoDB with Apache Kafka for Streaming

For real-time data processing in distributed systems, Apache Kafka integrates seamlessly with MongoDB. Kafka is a distributed event streaming platform that handles large-scale data feeds, enabling real-time streaming of data into and out of MongoDB collections.

• How Kafka and MongoDB Work Together :

  • MongoDB Source Connector: Streams data from MongoDB to Kafka topics.
  • MongoDB Sink Connector: Streams data from Kafka topics into MongoDB.

Kafka is particularly useful for handling large volumes of event-driven data. With Kafka, you can build robust, scalable real-time applications that process and analyze data from multiple sources.

Code Example: MongoDB Sink Connector

Here's an example of how you might configure a Kafka connector to stream data into MongoDB:

{
  "name": "mongodb-sink-connector",
  "config": {
    "connector.class": "com.mongodb.kafka.connect.MongoSinkConnector",
    "tasks.max": "1",
    "topics": "real-time-transactions",
    "connection.uri": "mongodb://localhost:27017",
    "database": "finance",
    "collection": "transactions",
    "key.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter"
  }
}

In this configuration:

• The MongoDB Sink Connector streams data from the Kafka topic real-time-transactions into the transactions collection in the finance database.

Use Case

Problem: A financial services company needs a real-time fraud detection system to process millions of transactions per second from various microservices, detecting fraud and taking action immediately.

Solution: By integrating Apache Kafka for real-time data ingestion and event-driven processing with MongoDB for scalable storage and querying, the system can efficiently handle large data volumes for real-time and historical fraud analysis.

Data Flow Architecture

• Transactions Generated by Microservices : Microservices across the company's infrastructure generate transaction events (e.g., purchase orders, payments, withdrawals). Each transaction event is published to an Apache Kafka topic.

• Kafka as the Event Broker : Kafka acts as the intermediary, receiving the transaction events and distributing them to various consumers, such as analytics engines and MongoDB.

• MongoDB as the Persistent Data Store : A Kafka MongoDB Sink Connector is used to stream transaction events from Kafka into MongoDB. As transactions are processed, they are stored in MongoDB in real time, where they can be queried for further analysis.

• Fraud Detection Algorithm : As transactions are streamed into MongoDB, an analytics engine listens to these real-time transactions using MongoDB change streams. The engine processes the data, applying machine learning models to flag suspicious transactions.

• Action on Fraud Detection : Once a fraudulent transaction is detected, the system can take immediate action (e.g., alert the customer, block the transaction) by sending alerts through Kafka to relevant systems like notification services.

Kafka and MongoDB Setup

1. Kafka Producer for Transactions

Each microservice in the system generates transactions and sends them to a Kafka topic, for example, transaction-events. Below is a simple Kafka producer in Python:

from kafka import KafkaProducer
import json

producer = KafkaProducer(
    bootstrap_servers='localhost:9092',
    value_serializer=lambda v: json.dumps(v).encode('utf-8')
)

# Simulate a transaction event
transaction_event = {
    "accountId": "12345",
    "amount": 2000,
    "timestamp": "2023-09-10T12:34:56Z",
    "location": "New York"
}

# Send the event to the Kafka topic
producer.send('transaction-events', transaction_event)
producer.flush()

2. Kafka MongoDB Sink Connector Configuration

To stream data from Kafka into MongoDB, we use the MongoDB Sink Connector. This connector reads data from Kafka topics and writes it into a MongoDB collection.

Here’s an example configuration file for the sink connector:

{
  "name": "mongodb-sink-connector",
  "config": {
    "connector.class": "com.mongodb.kafka.connect.MongoSinkConnector",
    "tasks.max": "1",
    "topics": "transaction-events",
    "connection.uri": "mongodb://localhost:27017",
    "database": "finance",
    "collection": "transactions",
    "key.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter"
  }
}

Explanation:

• topics: The Kafka topic (transaction-events) from which to consume data.
• connection.uri: The URI to connect to the MongoDB instance.
• database: The target MongoDB database (finance).
• collection: The MongoDB collection where the transactions will be stored (transactions).

3. Real-Time Querying and Fraud Detection Using Change Streams

MongoDB change streams enable the system to monitor new transactions as they arrive and process them for fraud detection.

Here’s how to set up a change stream to listen for new transactions:

const { MongoClient } = require("mongodb");

async function monitorTransactions() {
  const client = new MongoClient("mongodb://localhost:27017");

  try {
    await client.connect();
    const db = client.db("finance");
    const transactions = db.collection("transactions");

    const changeStream = transactions.watch();

    // Listen for changes in the transactions collection
    changeStream.on("change", (change) => {
      const transaction = change.fullDocument;
      console.log("New transaction detected:", transaction);

      // Apply fraud detection logic here
      if (isFraudulent(transaction)) {
        console.log("Fraud detected:", transaction);
        // Take action on fraud (e.g., send an alert)
      }
    });
  } finally {
    // Keep the connection open
  }
}

function isFraudulent(transaction) {
  // Simple fraud detection logic (e.g., large transactions or suspicious locations)
  return (
    transaction.amount > 10000 || transaction.location === "SuspiciousLocation"
  );
}

monitorTransactions();

Explanation:

• A change stream is opened on the transactions collection to capture new documents (inserted in real-time from Kafka).
• The isFraudulent function checks each transaction for suspicious behavior (e.g., large amounts or suspicious locations).
• When fraud is detected, the system can trigger further actions (e.g., send alerts to the user or the fraud detection team).

Why This Architecture Works

• Real-Time Data Ingestion : Kafka handles real-time event streaming, ensuring that transaction events are processed as soon as they are generated by the microservices.

• Scalable Storage with MongoDB : MongoDB offers a scalable, flexible schema, allowing the system to handle millions of transactions in real time while being easy to query for analytics.

• Fraud Detection in Real Time : MongoDB change streams provide the ability to act on each transaction as it is inserted, enabling real-time fraud detection and immediate action.

• Fault Tolerance : Kafka ensures data durability by storing transaction events, allowing the system to recover from failures without losing any transactions.

• Seamless Integration : The combination of Kafka and MongoDB allows for seamless integration of real-time data ingestion, processing, and storage, making it ideal for applications that require immediate insights and actions.

Integration with Redis for Caching Real-Time Data

While MongoDB excels at storing and querying data, integrating with Redis allows you to build efficient caching layers for frequently accessed real-time data. Redis is an in-memory data store known for its speed, and it's ideal for caching high-velocity data.

• How Redis and MongoDB Work Together :

  • Use Redis as a cache to store frequently accessed data (e.g., session data, user profiles).
  • MongoDB remains the primary database for persistence, while Redis handles quick lookups and fast reads.

Code Example: MongoDB with Redis Caching

In this example, we use Redis to cache frequently queried user data:

const redis = require("redis");
const client = redis.createClient();
const { MongoClient } = require("mongodb");

async function getUserProfile(userId) {
  // Check if the user data is cached in Redis
  client.get(`user:${userId}`, async (err, userData) => {
    if (userData) {
      console.log("User data retrieved from Redis:", JSON.parse(userData));
    } else {
      // If not found in Redis, fetch from MongoDB and cache it
      const mongoClient = new MongoClient("mongodb://localhost:27017");
      await mongoClient.connect();
      const db = mongoClient.db("myDatabase");
      const users = db.collection("users");

      const user = await users.findOne({ _id: ObjectId(userId) });
      console.log("User data retrieved from MongoDB:", user);

      // Cache the user data in Redis
      client.set(`user:${userId}`, JSON.stringify(user));
    }
  });
}

getUserProfile("someUserId");

In this code:

• We first check Redis for the cached user data. If found, it returns the data from Redis.
• If not, it fetches the user profile from MongoDB, then caches the result in Redis for future queries.

Use Case

In real-time analytics platforms, especially those handling a high volume of rapidly changing data (such as stock trading systems, IoT sensor data, or gaming leaderboards), accessing frequently used data with low latency is critical. While MongoDB efficiently handles large-scale data storage and complex queries, the overhead of frequent reads for the same data can cause performance bottlenecks. This is where Redis excels, acting as an in-memory cache for fast data retrieval and reducing the load on MongoDB.

• Problem : Consider a real-time stock trading platform where millions of stock price updates are processed every second. Traders and users frequently query stock prices to make instant trading decisions. While MongoDB can efficiently store and update stock price data, constantly querying it for real-time updates can increase database load and latency.

• Solution : By integrating Redis with MongoDB, you can cache frequently accessed stock prices in Redis, dramatically reducing the response time for these queries and lowering the load on MongoDB.

  • MongoDB: Acts as the primary persistent data store, storing all stock price updates with a full history.
  • Redis: Caches the most recent stock prices for fast, low-latency access.

Architecture

• Stock Price Updates: Stock price updates flow into MongoDB as they occur, typically through a real-time data stream or API.
• Cache Updates in Redis: Every time a stock price is updated, the new price is also written to Redis, allowing instant access to the latest prices.
• Low-Latency Reads from Redis: When users query the platform for the latest stock price, the application checks Redis first. If the price is in Redis, it's returned immediately. If not, the application retrieves the price from MongoDB, caches it in Redis, and returns it to the user.

Code Example: Caching Stock Prices in Redis

Below is a simplified example of how to integrate Redis with MongoDB to cache stock prices for low-latency access:

const redis = require("redis");
const { MongoClient, ObjectId } = require("mongodb");

const redisClient = redis.createClient();
const mongoClient = new MongoClient("mongodb://localhost:27017");

async function getStockPrice(stockSymbol) {
  // Check Redis cache for the stock price
  redisClient.get(`stock:${stockSymbol}`, async (err, priceData) => {
    if (priceData) {
      console.log(
        `Stock price for ${stockSymbol} retrieved from Redis:`,
        priceData,
      );
    } else {
      // If not found in Redis, query MongoDB
      await mongoClient.connect();
      const db = mongoClient.db("stockMarket");
      const stocks = db.collection("prices");

      const stock = await stocks.findOne({ symbol: stockSymbol });
      if (stock) {
        console.log(
          `Stock price for ${stockSymbol} retrieved from MongoDB:`,
          stock.price,
        );

        // Cache the stock price in Redis with an expiration time of 10 seconds
        redisClient.setex(
          `stock:${stockSymbol}`,
          10,
          JSON.stringify(stock.price),
        );
      } else {
        console.log(`Stock symbol ${stockSymbol} not found.`);
      }
    }
  });
}

// Simulate a stock price query
getStockPrice("AAPL");

In this example:

• Stock Symbol Lookup: The function first checks Redis for the cached stock price (stock:AAPL). If found, the price is returned immediately.
• Fallback to MongoDB: If the price is not found in Redis, MongoDB is queried. The result is then cached in Redis for future queries, with a short expiration time to ensure fresh data (in this case, 10 seconds).
• Expiration Time: Caching with a short expiration time ensures that prices remain relatively up-to-date, but also minimizes the load on MongoDB.

Advantages of Redis Caching in Real-Time Applications

• Low Latency : Redis is an in-memory store, so it provides sub-millisecond response times for reads. This is especially beneficial for applications like stock trading, where speed is crucial.

• Reduced MongoDB Load : By caching frequent queries, Redis reduces the number of read operations MongoDB must handle, freeing up resources for write-heavy workloads.

• Scalability : Redis can handle large volumes of data with very low overhead, ensuring that real-time applications can scale to handle more users and queries without significant performance degradation.

• Efficient Use of Resources : Using Redis for fast, in-memory lookups complements MongoDB’s efficient data storage and complex querying abilities, resulting in an architecture that leverages the strengths of both systems.

--

Next.Js FAQ

const changeStream = db.collection.watch();
changeStream.on("change", (change) => {
  console.log("Document changed:", change);
});

# Kafka connector configuration
"connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
"topics": "customer-updates"

db.sales.aggregate([
  { $match: { timestamp: { $gte: ISODate("2023-09-10T00:00:00Z") } } },
  { $group: { _id: "$category", totalSales: { $sum: "$amount" } } },
]);

redisClient.setex("user:123:score", 60, 1000); // Cache score for 60 seconds

db.drivers.find({
  location: {
    $near: {
      $geometry: { type: "Point", coordinates: [-73.97, 40.77] },
      $maxDistance: 1000, // 1km
    },
  },
});

Conclusion

MongoDB's features like change streams, aggregation pipelines, and integration with Kafka and Redis make it ideal for real-time applications. Whether building a notification system or a real-time analytics platform, MongoDB offers the flexibility and performance needed to process and react to streaming data instantly, enabling developers to build scalable, real-time systems efficiently.