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System Design Masterclass · Stream Batch Processing
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Here is the question that separates streaming pipelines that produce correct results from ones that only look correct during testing: is your window assignment using event time or processing time? Tyler Akidau devoted the majority of Streaming 101 and 102 to this distinction because getting it wrong is invisible until an audit, a billing dispute, or a regulatory review surfaces the discrepancy.
The distinction sounds academic until you have mobile clients. A phone takes a photo at 10:00:15 while the user is underground. The phone buffers the event and uploads it at 10:07:00. A processing time window has already fired the 10:00 bucket. The event lands in the 10:07 bucket. Your per minute photo count is now wrong in both directions: the 10:00 bucket is missing a photo and the 10:07 bucket has a phantom one. Event time windows place the event in the 10:00 bucket where it belongs, at the cost of delayed firing until the watermark lets it.
Every modern streaming engine (Flink, Streams, Beam, Spark Structured Streaming) supports both modes explicitly. The default in Kafka Streams since version 2.1 is event time. The Beam model does not even expose processing time windows as a first class primitive. The industry consensus is clear: use event time for anything that matters and processing time only for server side internal metrics where the distinction is irrelevant.
By the end of this lesson you will understand how both modes assign events to buckets, how watermarks drive firing in event time, and why one idle Kafka partition can stall your entire pipeline.
The properties of the two time models differ in every dimension that matters for production correctness. This comparison captures the trade offs you need to evaluate before picking one.