What are common bottlenecks in Kafka Streams applications?
An example of an AI chatbot conversation about identifying bottlenecks in Kafka Streams applications.
What are common bottlenecks in Kafka Streams applications?
Common bottlenecks in Kafka Streams include processing latency, state store performance, and network issues.
How can I identify these bottlenecks in my application?
You can use metrics from Kafka Streams, such as processing time and throughput, to identify where delays occur.
What tools can help monitor these metrics?
Tools like Confluent Control Center, Prometheus, and Grafana can help you monitor Kafka Streams metrics effectively.
Are there best practices to optimize performance?
Yes, best practices include optimizing state store configurations, using appropriate serialization, and scaling your application.
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