Explore techniques for resolving issues with Python Kafka Producers in this informative video. Learn to leverage native monitoring capabilities and Confluent Cloud's Metrics API while examining how linger.ms affects latency and batch sizes. Discover the importance of monitoring key metrics, utilizing on_delivery and stats_cb callbacks, and making HTTP requests to the Confluent Cloud Metrics API. Investigate the impact of varying linger.ms settings and implement troubleshooting strategies using Python logging. Gain insights into enterprise-level monitoring infrastructure and best practices for maintaining robust Kafka producer applications.
Resolving Issues with Python Kafka Producers - Monitoring and Troubleshooting
Overview
Syllabus
- Intro
- What is linger.ms in Kafka producers
- What to Monitor
- on_delivery and stats_cb callbacks
- Confluent Cloud Metrics API HTTP Request
- Varying linger.ms
- Troubleshooting using python logging
- Monitoring Infrastructure in the Enterprise
- Closing
Taught by
Confluent
