Kafka Streaming for Live Currency Rates

Problem Statement

In the fintech world, live currency rate streaming is a critical feature that ensures users receive real-time updates on exchange rates. Our challenge was to build a continuous live rate streaming system for Real-Time Currency Exchange Rates that updates every second for multiple currency pairs and serves multiple users concurrently. Traditional database-driven approaches struggled with performance bottlenecks, latency, and scalability issues, prompting us to explore a more robust solution.

Why Not a Normal PostgreSQL Database?

While PostgreSQL is a powerful relational database, it was not well-suited for real-time, high-frequency data streaming due to the following reasons:

1. High Write and Read Latency: Storing every second’s rate updates would lead to frequent write operations, causing significant latency when querying real-time data.
2. Scalability Issues: A large number of concurrent users fetching live rates would overload the database, leading to slow response times.
3. Polling Inefficiency: Fetching the latest rates via polling would increase database load and lead to unnecessary resource consumption.
4. Event-Driven Processing Challenges: A traditional database does not natively support event-driven architectures, making real-time processing complex.

Why We Used Kafka?

To address these challenges, we chose Apache Kafka as the backbone of our live rate streaming system. Here’s why:

1. Real-Time Data Streaming: Kafka’s publish-subscribe model allows us to continuously push live rates without querying a database.
2. High Throughput and Low Latency: Kafka can handle millions of messages per second with minimal latency, ensuring seamless rate updates.
3. Scalability: It supports partitioning, allowing horizontal scaling as the number of users grows.
4. Fault Tolerance: Kafka’s distributed architecture ensures data durability and prevents single points of failure.
5. Decoupling Producers and Consumers: Currency rate providers (producers) can push data independently of users (consumers), reducing bottlenecks.
6. Efficient Data Processing: Kafka integrates well with stream processing frameworks like Apache Flink or Kafka Streams to process and analyze data in real time.

Implementation Overview

1. Data Source Integration: We fetch live currency rates from multiple financial market APIs.
2. Kafka Producer: A producer service ingests real-time currency rates into Kafka topics.
3. Kafka Topics & Partitioning: All currency pair details are received from a single subscribed topic, rather than separate topics for each pair, ensuring centralized data access.
4. Kafka Consumer: User applications subscribe to relevant topics to receive instant rate updates.
5. WebSockets for UI Updates: A WebSocket layer pushes live rate updates to the front end, ensuring a smooth real-time experience.
6. Monitoring & Alerts: Kafka’s built-in metrics and third-party tools (Prometheus, Grafana) monitor system performance.

Additional Considerations

• Message Retention: We configured a short retention period since users only need the latest rate.
• Backpressure Handling: Implemented a rate limiter to prevent overwhelming consumers.
• Security & Authentication: Used Kafka ACLs and SSL encryption to secure data transmission.

Conclusion

Using Kafka for real-time currency rate streaming enabled us to build a scalable, low-latency, and fault-tolerant system that efficiently handles high-frequency updates for multiple users. This approach significantly outperformed traditional database-driven methods, making Kafka the perfect choice for our use case.