We use analytics cookies.We use cookies to analyze site traffic and improve your experience.
Loading...
System Design Masterclass · Stream Batch Processing
Free preview: the first part of this 6-part lesson.
Already bought the course? Sign in to read the whole lesson.
this lesson is part of
System Design Masterclass
Unlock this lesson and the 751 other paid lessons
$25 once, by PayPal
See the System Design courseread the rest of this lesson in
System Design Masterclass
Unlock this lesson and the 751 other paid lessons
$25 once, by PayPal
See the System Design coursein Stream Batch Processing
This lesson is part of the full System Design Masterclass. Unlock it along with 769 other lessons for a one-time payment.
One-time payment by card or PayPal
Get Lifetime AccessOne-time payment: UPI, card or netbanking
Get Lifetime Access18 Foundation lessons are free, no payment needed. Try them now
It is easy and good
“It is really nice and good Please join the course”
Imagine a driver ends a trip at 18:42:11 and the payment receipt event stamps 18:42:13. The network is slow, the receipt reaches first, and the trip end reaches Kafka second. Your fraud detector now sees a payment before the corresponding trip exists. A naive implementation flags it as orphaned and pages the on call engineer. The fact is the events are just out of order by two seconds.
This is the daily reality of stream processing. Network paths have different delays. Producer retries resend records after newer ones already arrived. Kafka partitioning scrambles global order the moment you have more than one partition. Multiple producers with slightly different clocks interleave at the broker. By the time an event lands in a Flink operator, any assumption about arrival order is wrong.
The Google Dataflow paper called this out explicitly as the reason watermarks and event time were needed in the first place. Before Dataflow, most streaming systems pretended arrival order and event order were the same and broke the moment production traffic hit them. The answer is to separate ordering assumptions from processing time and to give the developer explicit tools for handling out of order input.
LinkedIn Samza was the first widely used system to commit to per key ordering as the practical guarantee. Kafka Streams adopted the same model. KSQL builds on Kafka Streams and inherits it. Flink keyBy does the same thing with richer state APIs. Netflix Mantis scaled the model to millions of keys per second for real time stream ingestion. The shared insight is that global ordering is impossible and per key ordering is free if you get the partitioning right.
By the end of this lesson you will know how a reorder buffer turns an out of order stream into an in order one, when per key ordering is enough, and how KSQL uses both techniques internally for its join guarantees.