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When a product recall affects a particular manufacturing batch, the important question is not simply who bought the product: it is which customers purchased that exact batch. Adarsh Kurumali’s one-day RecallRadar prototype demonstrates how Java, Kafka on Confluent Cloud, and Flink SQL can connect simulated purchase events with fictional recall announcements and produce customer-specific alert events.
It is a learning project, not a live consumer-safety service. Its most important lesson is that matching two records is straightforward; handling them correctly when they arrive in either order—and preserving the state needed to match them—is the harder part.
What RecallRadar does
RecallRadar consumes two kinds of simulated events: retail purchases and product recall announcements. A Java detection service checks whether a purchase and a recall share both a productId and a batchId. When they do, it emits an alert tied to the customer in the purchase record.
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The distinction between product and batch is essential. If a recall applies only to one manufacturing batch, matching on product alone could flag customers whose purchases are not implicated. The simplified examples in Kurumali’s account include customer, product, and batch identifiers for a purchase, and product and batch identifiers for a recall; they are illustrative, not a complete event schema.
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How the event flow is organized
The project uses four Kafka topics, each with a distinct role:
| Topic | Purpose |
|---|---|
rr-purchases |
Simulated retail purchase events. |
rr-recalls |
Fictional product recall announcements. |
rr-alerts |
Customer-specific alert events emitted by the Java detector. |
rr-recall-impact |
Recall-impact analytics generated by Flink SQL. |
The purchase and recall streams feed the detector. Its alert stream supports customer-level alert views and a separate Flink SQL analytics pipeline. A Spring Boot dashboard displays results, but it calculates its displayed metrics independently; it does not read the Flink output topic. This separation matters when interpreting the demo: having an analytics topic does not mean the dashboard uses its output.
Why event arrival order changes the design
A detector cannot assume that the purchase will always arrive before the recall. There are two cases to account for:
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The purchase arrives first
The system must retain enough purchase information to find it when a matching recall arrives later. When that recall appears, the detector can compare product and batch identifiers and emit an alert for the purchase’s customer.
The recall arrives first
A later purchase may still be for the recalled batch. The detector must therefore have access to the earlier recall when processing that purchase, rather than checking only the latest incoming event.
Kurumali’s account captures the general problem: “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 is simple; deciding what records to retain, how to find them, and how long they remain useful is the more consequential design work.
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What the prototype’s state does—and does not—guarantee
The matching state in RecallRadar is held in memory. Kurumali notes that restarting the service can lose that state. The account does not demonstrate durable recovery, replay behavior, or protection against duplicate alerts after events are replayed. Those are unresolved production concerns, not capabilities to infer from a successful demo.
For a real deployment, a team would need to decide how matching data survives restarts, how long relevant records are retained, how recovered or replayed events are handled, and how duplicate notifications are prevented. The right choices depend on the system’s requirements; the project account does not specify a production design for them.
Customer alerts and impact analytics answer different questions
An individual alert can answer, “Which customer’s purchase matched this recall?” Aggregate analytics can answer questions about the overall impact of a recall. RecallRadar uses the alert stream for both kinds of downstream work, but through separate paths: the dashboard calculates its own displayed metrics, while Flink SQL produces analytics on rr-recall-impact.
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Metric definitions also matter. The number of alert events is not necessarily the number of distinct affected customers: a customer may be associated with multiple purchases or alerts. Any dashboard or report should label which measure it displays rather than treating those counts as interchangeable.
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When investigating a result in a streaming pipeline like this one, follow the event path in order instead of starting with the dashboard:
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productIdandbatchId. - Verify the input topic. Check whether the event reached
rr-purchasesorrr-recalls, as appropriate. - Check the detector’s matching path. Establish whether the counterpart event was available, including cases where it arrived earlier and must be retained.
- Inspect the alert stream. If the detector found a match, look for the resulting customer-specific event on
rr-alerts. - Check the consumer independently. Confirm whether the dashboard is displaying its own calculated metrics or whether the question concerns the separate Flink output on
rr-recall-impact.
Kurumali also describes encountering a Kafka metadata timeout with a 60000 ms setting while debugging. That figure is an incident from the project account, not a performance result or a general recommendation to configure every Kafka client with that timeout.
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What a one-day demo proves—and what it does not
RecallRadar shows how a small event-driven prototype can connect simulated retail purchases, fictional recalls, matching logic, alerts, a dashboard, and a separate aggregation pipeline. It does not establish that the system is accurate, reliable, scalable, or suitable for making actual consumer-safety decisions. The one-day duration is the author’s description of the build, not a benchmark.
- The transactions are simulated and the recalls fictional.
- The service creates alert events but does not deliver real email or SMS notifications.
- Matching state is in memory, with restart loss identified as a limitation.
- The dashboard does not consume the Flink analytics output.
- The cloud resources were shut down after submission, so the account does not establish that a live demo remains available.
Before using a system for real recall decisions, Kurumali identifies durable state, failure recovery, duplicate-alert prevention, verified recall data, auditability, and reliable notification delivery as issues to address. These are the author’s disclosed production concerns, not a complete safety or regulatory review.
Further reading
For readers who want broader Kafka background, O’Reilly’s Kafka: The Definitive Guide, 2nd Edition covers producers and consumers, event-driven applications, data pipelines, deployment, and stream processing. The publisher identifies this edition as November 2021. It is optional reading, not a prerequisite for understanding the RecallRadar project.
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