What is Apache Flink? Understanding Stateful Stream Processing

The Core Problem: Imagine you're building a fraud detection system. You need to analyze every credit card transaction as it happens, comparing it against the user's past behavior (location, amount, merchant patterns) stored somewhere. Processing 2 million events per second means you can't afford round trips to an external database for every lookup. That would add 50 to 100 milliseconds per database query, destroying your latency budget. Traditional approaches force a painful choice. You can batch events every few minutes for processing, which lets you use standard databases but adds unacceptable delay for fraud detection. Or you can build a stateless stream processor that calls external stores, which is slow. Or you embed state in individual application instances, which creates complex coordination problems. Flink's Solution: Flink keeps state inside the stream processing engine itself, distributed across the cluster. When processing a transaction for user ID 12345, the state for that user (recent purchases, average amounts, known locations) lives in memory on the same machine executing the fraud detection logic. Access times drop to microseconds instead of milliseconds. The engine manages this through key based partitioning. Events are routed by key (like user_id or account_id) to specific machines, and each machine maintains state only for its assigned keys. With 100 million users and 50 machines, each machine handles roughly 2 million user states. The Critical Feature: What makes this production ready is Flink's checkpoint mechanism. Every 30 to 300 seconds, the system takes a consistent snapshot of all state across all machines and writes it to durable storage like HDFS or S3. If a machine fails, Flink restarts the affected tasks on healthy machines and restores their state from the latest checkpoint. You get both speed and reliability. This architecture enables applications that need both history and freshness: real time recommendation features, session analytics, per user rate limiting, or anomaly detection where sub second latency matters.