Apache Kafka - Real-time Stream Processing (Master Class) - Supressing Intermediate Results

The lecture discusses suppressing intermediate results in stream processing, focusing on a use case involving application monitoring with Kafka streams. It explains how to count heartbeats, send alerts, and handle continuous updates. The lecture highlights the importance of suppressing intermediate results to avoid false alerts and ensure accurate monitoring. A practical example demonstrates the implementation, emphasizing the need for a grace period and suppression to manage intermediate results effectively.

What is the purpose of the heartbeat events in the monitoring system?

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Explain how the application determines if an application is down based on heartbeats.

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What is the significance of allowing late arrival of heartbeats by 10 seconds?

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Describe the process of grouping and counting heartbeats by application ID.

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What is the role of the suppress method in the context of this application?

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What happens when the final count of heartbeats is less than three?

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How does the application handle false alerts caused by continuous intermediate results?

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