Imagine you are building a fraud detection system that needs to analyze millions of credit card transactions per second, correlate them with user behavior patterns, and flag suspicious activity before the transaction completes.

Or perhaps you are designing a real-time analytics dashboard that must aggregate click streams across thousands of dimensions while handling data that arrives out of order due to network delays.

These problems share a common challenge: processing unbounded data streams with strict latency requirements while maintaining accuracy even when things go wrong.

This is where Apache Flink excels.

Flink is more than just another stream processor. It is a distributed stateful computation engine designed from the ground up for continuous data processing. Unlike systems that bolt streaming capabilities onto batch frameworks, Flink treats streams as the fundamental abstraction, with batch processing as simply a special case of bounded streams.

What makes Flink particularly relevant for system design interviews is its elegant solutions to hard distributed systems problems. Its checkpoint-based fault tolerance mechanism achieves exactly-once semantics without sacrificing performance. Its watermark system handles the reality that events arrive out of order in distributed systems. Its state management allows applications to maintain terabytes of state while recovering seamlessly from failures.

This chapter covers the practical knowledge you need to confidently propose and discuss Flink in interviews. We will explore not just how Flink works, but why it works that way, and when its design trade-offs make it the right choice for your system.

1. When to Choose Flink