For a batch-specific product recall, the key question is not simply which customers bought the product: it is which customers bought the affected batch. RecallRadar, a one-day prototype by Adarsh Kurumali, explores how Java, Kafka on Confluent Cloud, Apache Flink SQL, and Spring Boot can connect simulated purchase and recall events to answer that question. Its central engineering lesson is that matching the two records is easy; making sure the right records are available when events arrive in either order is the harder part.
Why a recall detector has to match the batch
A recall may apply to a particular manufacturing batch rather than every unit sold under a product identifier. If a system matches only on productId, it can flag customers whose purchases are outside the recall. RecallRadar instead correlates on both productId and batchId, then associates a match with the customer who made the purchase.
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The project’s simplified examples show a purchase carrying customer, product, and batch identifiers, and a recall carrying product and batch identifiers. They illustrate the matching idea; they are not a complete event schema. The practical question is: which customers purchased that exact batch?
How RecallRadar moves events through the system
The project account describes four Kafka topics. Simulated purchase and recall events enter separately; a Java detection service correlates them and emits customer-specific alerts. The alert stream then supports a dashboard and an analytics pipeline.
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| Topic | Role in the prototype |
|---|---|
rr-purchases |
Simulated retail purchase events |
rr-recalls |
Product recall announcements |
rr-alerts |
Customer-specific alert events produced by the Java detector |
rr-recall-impact |
Recall-impact analytics generated by Flink SQL |
In the described design, Apache Flink SQL continuously aggregates alert data into recall-impact analytics. The Spring Boot dashboard calculates its displayed metrics independently; it does not read the Flink output topic. That distinction matters when interpreting the demo: the dashboard and analytics pipeline are separate consumers, not one shared calculation.
Why event arrival order changes the design
There are two basic cases. A purchase can arrive before a recall, leaving the detector to find that purchase when the matching recall appears. Or a recall can arrive first, requiring a later purchase to be checked against the already-known recall. A detector that only compares each new event with the immediately preceding event can miss a valid match.
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As Kurumali puts it, “When related events can arrive in either order, processing the latest event is not enough. The system also needs access to relevant earlier information.” The equality check on product and batch is straightforward; retaining or recovering the relevant records is the more consequential design problem.
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State is the prototype’s important limitation
RecallRadar keeps its matching state in memory, according to the project account. That makes the demo easier to build, but a service restart can lose the records it needs to correlate future events with earlier ones. Kurumali’s broader observation is useful beyond this project: “In an event-driven system, deciding what to remember can be just as important as deciding what to process next.”
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The account identifies durable state, retention and replay, failure recovery, and duplicate-alert prevention as production concerns; it does not demonstrate implemented solutions for them. Replaying an event after a failure, for example, can be a correctness issue if it produces a second customer alert. A real deployment would need explicit recovery and deduplication behavior, not merely a successful match during an uninterrupted demo.
Alert counts and affected-customer counts answer different questions
An alert event count is not necessarily a count of distinct people. If a customer has multiple purchases or more than one matching alert, total alert events and distinct affected customers can diverge. A dashboard or impact report should name the metric it displays rather than treating “alerts” and “customers affected” as interchangeable.
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The separate Flink aggregation and dashboard calculation also mean that metric definitions and data paths should be checked independently. The project description does not establish that the dashboard consumes Flink’s aggregate output.
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For a prototype built around this flow, investigate one event at a time and follow it through the system. This sequence helps distinguish a producer or routing problem from a correlation or display problem:
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- Check the input event. Confirm that the purchase or recall was produced with the identifiers expected by the detector, especially the matching
productIdandbatchId. Treat the project’s simplified examples as illustrations, not a full schema specification. - Verify the input topic. Establish that purchase events reach
rr-purchasesand recall announcements reachrr-recalls, rather than assuming that a producer’s successful action proves the event reached the intended stream. - Check both arrival orders. Determine whether the purchase or recall arrived first, then verify that the counterpart remains available to the detector when the second event is processed.
- Inspect the match and output. Check whether the Java service found equality on both identifiers and, if so, whether it emitted a customer-specific event to
rr-alerts. - Follow each consumer separately. A dashboard issue and an analytics issue are not necessarily the same fault: the dashboard computes its metrics independently, while Flink SQL writes analytics to
rr-recall-impact.
The project account also mentions a Kafka metadata timeout example of 60000 ms as a debugging incident. It is an incident detail, not a measured latency, throughput, or performance result.
What the one-day prototype demonstrates—and what it does not
The project is a learning prototype built for Confluent AI Developer Day, not a demonstrated consumer-safety service. Its transactions are simulated and its recalls fictional. It generates alert events but does not send real email or SMS, and the author says the cloud resources were shut down after submission. The account does not provide independently validated measurements of latency, accuracy, scale, or reliability.
Before using a system like this for actual safety decisions, Kurumali identifies verified recall data, durable state, failure recovery, duplicate-alert prevention, auditability, and dependable notification delivery as issues to address. These are limitations and recommendations disclosed in the project account, not a complete regulatory or safety assessment.
Kafka is not automatically the right choice for every recall workflow. The account notes that a conventional database query may be reasonable in some systems; continuous processing and independent reactions by multiple systems are reasons a streaming design may be worth considering. The architecture should follow the operational need, not the appeal of a particular stack.
Further reading
For readers who want a broader Kafka reference after exploring the project, O’Reilly’s Kafka: The Definitive Guide, 2nd Edition covers producers and consumers, event-driven applications, data pipelines, deployment, and stream processing. O’Reilly identifies the second edition as published in November 2021. It is optional background reading, not a requirement for running this prototype.
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