In the early stages, the payment infrastructure operated smoothly. User numbers were climbing, new features launched frequently, and from the outside, everything appeared stable. However, as the user base expanded, cracks began to show. Customers started reporting issues: payments processed without confirmations, duplicate charges, and missing receipts. The support team was inundated with complaints about unacknowledged transactions and unresolved payments.

Initially, these incidents were dismissed as temporary glitches—perhaps a slow server or a third-party API hiccup. But as reports multiplied, it became clear the problem was rooted deeper in the system’s architecture.

The Core Issue: A Monolithic Payment Flow

A thorough review of the payment workflow revealed a critical flaw. Every payment triggered a sequence of actions executed synchronously:

• Transaction verification with the payment gateway
• Database updates reflecting payment status
• Sending confirmation emails
• Dispatching mobile notifications
• Logging data for fraud detection
• Updating internal dashboards and reports

Each step depended on the previous one, all running on a single thread. If any service—such as the email provider—slowed down, the entire process stalled. A single failure could cause the whole transaction to collapse, resulting in users being charged without confirmation and, in some cases, multiple charges due to frontend retries.

This tightly coupled design created a domino effect, where one delay cascaded into multiple failures. The issue was not a bug, but a fundamental design limitation that left no room for system resilience.

The Solution: Decoupling with Kafka

Recognizing the need for change, the team concluded that the payment core must be isolated from auxiliary processes. The essential payment verification and confirmation needed to be fast, reliable, and independent of other services.

Kafka emerged as the ideal solution. Initially met with skepticism for its perceived complexity, Kafka’s value became evident upon closer inspection. As a message broker, Kafka enabled asynchronous communication between services, allowing events to be passed without forcing synchronous execution.

Kafka in Action: Event-Driven Architecture

With Kafka, the process changed fundamentally. After a successful payment, the system would simply publish a message to Kafka, such as:

“Payment successful for user A, amount ₹500”

All other services—email, notifications, fraud detection, analytics—became listeners to this event. Each service acted independently upon receiving the message. If any were slow or temporarily unavailable, the core payment flow remained unaffected. The user’s transaction was already complete, ensuring a seamless experience.

First Implementation: Email Confirmation

The transition began with decoupling the email confirmation. Instead of directly calling the email service post-payment, the system published a payment_success event to Kafka. The email service, now a Kafka consumer, picked up the event and sent the confirmation independently.

This change meant that even if the email service experienced downtime, payments continued uninterrupted. There were no more cascading failures or repeated attempts.

Gradually, other processes—notifications, fraud checks, logging, analytics—were migrated to this event-driven model, each as an independent Kafka consumer.

Immediate Impact: Speed, Reliability, and Flexibility

The improvements were immediate and dramatic:

• The payment API responded almost instantly.
• User wait times dropped significantly.
• Support tickets related to payment issues declined sharply.
• Adding new post-payment services, such as SMS alerts or cashback triggers, required only the addition of new Kafka consumers—no changes to the core payment logic.

This modular approach allowed each service to operate independently, eliminating bottlenecks and reducing the risk of system-wide failures.

Scaling with Kafka: From Millions to Tens of Millions

As the user base soared from millions to tens of millions, Kafka seamlessly handled the increased load. The system was able to publish thousands of payment events per second without performance degradation.

Each consumer service could be scaled independently. For example, if notification volume increased, more consumers could be added without affecting other services. Kafka’s durability ensured that messages were stored until consumers caught up, and replaying old events for audits or backups was straightforward.

The architecture shifted from a fragile, monolithic setup to a robust, event-driven system capable of supporting rapid growth.

The True Value of Kafka

Kafka delivered speed, reliability, and scalability. More importantly, it provided peace of mind. The team could deploy changes without fear of cascading failures. Each service operated within its own boundaries, reducing interdependencies and risk.

This newfound confidence allowed for rapid innovation and growth, supporting a user base of over 10 million with ease.

Key Takeaways for Modern Payment Systems

For any organization managing payment systems or complex workflows triggered by user actions, the lesson is clear: avoid monolithic, tightly coupled designs. Instead, leverage event-streaming platforms like Kafka to decouple services.

• Publish a single event after the core transaction.
• Allow independent services to consume and act on these events.
• Build for scalability and resilience from the start.

Kafka didn’t just stabilize the payment system—it enabled sustainable growth and operational freedom.

Read more such articles from our Newsletter here.