What It Is
Apache Flink continuously processes unbounded streams of events such as clicks, sensor readings, and transactions. It can also run batch jobs on the same engine.
Key Points
- Event-time processing: handles late and out-of-order data correctly.
- Exactly-once state: periodic checkpoints prevent data loss and duplicates after failures.
- Windowing and joins: aggregate and combine streams in real time.
- Works with Kafka: Kafka supplies the stream and Flink processes it.
Why It Matters
Because Flink keeps state locally and checkpoints it to durable storage, applications recover from failures without losing data. This makes it a strong choice where both correctness and speed matter.
How ClearLeaff Applies It
We engineer Flink pipelines inside platforms that process more than one million events per second. We tune parallelism, state size, and checkpointing, and add monitoring so backpressure and lag are visible early.