Why CQRS pattern in Kafka? - Purpose & Use Cases

The Big Idea

What if your app could handle thousands of users without slowing down or mixing up data?

The Scenario

Imagine you have a busy online store where customers place orders and check their order status. You try to handle all requests--both placing orders and checking statuses--using the same simple system.

As more customers come, the system slows down, and sometimes order updates get mixed up with status checks.

The Problem

Using one system for both reading and writing data makes things messy and slow.

Every time you update an order, the system must also handle many read requests, causing delays and errors.

This mix makes it hard to keep data accurate and the system responsive.

The Solution

The CQRS pattern splits the system into two parts: one just for writing (commands) and one just for reading (queries).

This separation lets each part work faster and more reliably, without getting in each other's way.

Using Kafka, these parts communicate smoothly by sending messages, keeping data in sync and the system efficient.

Before vs After

✗ Before

function handleRequest(request) {
  if (request.type === 'read') {
    return database.query(request);
  } else {
    return database.update(request);
  }
}

✓ After

function sendCommand(command) {
  kafka.produce('commands', command);
}

const readModel = kafka.consume('events');
return readModel.get(request.id);

What It Enables

CQRS enables building systems that handle many users smoothly by separating reading and writing, making everything faster and more reliable.

Real Life Example

In a ticket booking app, CQRS lets users quickly see available seats (reads) while others book tickets (writes) without slowing each other down.

Key Takeaways

Manual handling mixes reads and writes, causing slowdowns and errors.

CQRS splits commands and queries for better speed and accuracy.

Kafka helps these parts communicate efficiently with messages.