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WeAreDevelopers LIVE • Dec 15, 2023

Tips, Techniques, and Common Pitfalls Debugging Kafka

Developersteve

Stop manually piecing together logs during 2 AM Kafka crashes. Zero-code OpenTelemetry auto-instrumentation seamlessly traces asynchronous payloads, instantly isolating execution bottlenecks without refactoring application logic.

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#1 about 6 min

Introduction to Apache Kafka as architecture glue

An overview of Kafka's origin and its role as event-driven glue in distributed systems.

#2 about 2 min

Real-world Apache Kafka production use cases

How companies like Netflix, Uber, and British Gas utilize Kafka for real-time data streaming.

#3 about 2 min

Comparing Kafka and MQTT for smart devices

The architectural differences between Kafka and MQTT regarding latency, throughput, and packet size.

#4 about 3 min

Building Kafka producers and consumers in Python

How to use Python libraries to build foundational producer and consumer services.

#5 about 3 min

Deep dive into Netflix routing and data streams

Breaking down the complex routing and data streaming layers triggered by platform events.

#6 about 2 min

Advantages of adding Kafka to streaming architecture

The overarching benefits of high capacity, durability, and interoperability that Kafka brings to platforms.

#7 about 4 min

Common challenges in distributed service deployments

Highlighting the complexities of monitoring, load balancing, and scaling clustered applications.

#8 about 3 min

Using distributed tracing instead of server logs

How tracing tools provide contextual error mapping rather than digging through isolated application logs.

#9 about 5 min

Gathering metrics natively with OpenTelemetry frameworks

How the CNCF OpenTelemetry project standardizes application logging models across programming languages.

#10 about 3 min

Simplifying Kubernetes monitoring with Lumigo operators

How to automate instrumentation inside Kubernetes namespaces without modifying deployment code.

#11 about 2 min

Injecting telemetry into Docker and ECS containers

Adding monitoring logic to Python environments within standalone containers using environmental variables.

#12 about 1 min

Automated distributed tracing on AWS Lambda functions

Tracing cloud invocations accurately through simple UI flags and preconfigured Lambda layers.

#13 about 4 min

Setting up the Kafka Python Docker demo

Configuring a multi-broker cluster utilizing Docker Compose, Python, and the Flask web framework.

#14 about 5 min

Deploying the Kafka cluster to Docker Compose

Reviewing Docker deployment flags and executing the containerized background broker nodes.

#15 about 8 min

Executing Flask and exploring live tracing metrics

Simulating application load to generate data traces and examine system runtime performance.

#16 about 2 min

Final takeaways on application telemetry and tracing

The importance of building for scale and standardizing tracing metrics across complex container environments.

#17 about 7 min

Audience questions on brokers and monitoring tools

Discussing Confluent compatibility, maintaining broker metrics, and effective tools to diagnose Kafka stream errors.

Matching moments

3:06 min

Core concepts of Apache Kafka and topic topologies

Denis Washington +1 · WWC 2021

3:45 min

Reviewing core Apache Kafka architecture and distributed fundamentals

Kirill Kulikov · LIVE

12:33 min

Exploring advanced observability stacks and distributed infrastructure challenges

Pawel Piwosz · LIVE

1:37 min

Introduction to Apache Kafka benchmarking and performance analysis

Kirill Kulikov · LIVE

3:08 min

Live dashboard demonstration of tracing errors and system metrics

Iulia Feroli Iulia Feroli · LIVE

1:13 min

Overcoming integration challenges with Kafka Streams microservices

Denis Washington +1 · WWC 2021

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