Learn how to leverage the native monitoring capabilities of the Python Kafka producer along with Confluent Cloud’s Metrics API while exploring how linger.ms affects latency and batch sizes.

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RELATED RESOURCES
► Confluent Developer: https://cnfl.io/3VYWxow
► Python Kafka client - https://cnfl.io/3xxYxuI
► Confluent Cloud Metrics API - https://cnfl.io/3RK3p6U
► Knight Capital - https://www.cnbc.com/2012/08/02/the-knight-fiasco-how-did-it-lose-440-million.html

CHAPTERS

00:00 - Intro

00:26 - What is linger.ms in Kafka producers

02:02 - What to Monitor

03:18 - on_delivery and stats_cb callbacks

04:43 - Confluent Cloud Metrics API HTTP Request

06:42 - Varying linger.ms

07:34 - Troubleshooting using python logging

10:17 - Monitoring Infrastructure in the Enterprise

11:46 - Closing

ABOUT CONFLUENT
Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit www.confluent.io.

#streamprocessing #kafka #apachekafka #confluent