# Stream Processing: Kafka Streams, Flink and ksqlDB — Apache Kafka: Event Streaming from Basics to Production

Source: https://www.geekswithgeeks.com/en/kafka/eco-streams

> Choose a stream-processing approach for filtering, joining and aggregating events.

## Compute on the stream

Stream processors read topics, transform events and write results to other topics. **Kafka Streams** is a Java library that runs inside your application, giving stateful operations (aggregations, joins, windows) with local state backed by changelog topics. **ksqlDB** offers a SQL-like layer. **Apache Flink** is a separate, powerful engine for large, complex streaming jobs. Core ideas: **event time vs processing time**, **windows** (tumbling, hopping, session), **state** and **late data**. Start with the simplest tool that fits: a plain consumer for simple transforms, Streams or Flink when you need joins, windows and state.

## A windowed count, run

I ran a plain-Python model of a tumbling one-minute window counting events per window start. Real engines add state stores, late-data handling and fault tolerance.

```python
from collections import Counter
events = [5, 20, 59, 61, 75, 130]            # event times in seconds
window = 60
counts = Counter((t // window) * window for t in events)
print(dict(sorted(counts.items())))
```

Output:

```
{0: 3, 60: 2, 120: 1}
```

## Plan for late and out-of-order events

Networks and retries mean events arrive late. Decide how long you will wait (a grace period) before closing a window, and what to do with later arrivals.

**Quiz:** What does a tumbling window do?

- [ ] Drops all events
- [x] Groups events into fixed, non-overlapping time intervals
- [ ] Overlaps every window by 99%
- [ ] Sorts topics alphabetically

*Answer:* Groups events into fixed, non-overlapping time intervals. Tumbling windows split time into back-to-back fixed buckets.