Kafka Troubleshooting in Production

Stabilizing Kafka Clusters in the Cloud and On-premises
1. Auflage 2023
ISBN: 978-1-4842-9489-5
Verlag: Apress

Buch, Englisch, 216 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 453 g

This book provides Kafka administrators, site reliability engineers, and DataOps and DevOps practitioners with a list of real production issues that can occur in Kafka clusters and how to solve them. The production issues covered are assembled into a comprehensive troubleshooting guide for those engineers who are responsible for the stability and performance of Kafka clusters in production, whether those clusters are deployed in the cloud or on-premises. This book teaches you how to detect and troubleshoot the issues, and eventually how to prevent them.
Kafka stability is hard to achieve, especially in high throughput environments, and the purpose of this book is not only to make troubleshooting easier, but also to prevent production issues from occurring in the first place. The guidance in this book is drawn from the author's years of experience in helping clients and internal customers diagnose and resolve knotty production problems and stabilize their Kafka environments. The book is organized into recipe-style troubleshooting checklists that field engineers can easily follow when under pressure to fix an unstable cluster. This is the book you will want by your side when the stakes are high, and your job is on the line. 
What You Will Learn

• Monitor and resolve production issues in your Kafka clusters
• Provision Kafka clusters with the lowest costs and still handle the required loads
• Perform root cause analyses of issues affecting your Kafka clusters
• Know the ways in which your Kafka cluster can affect its consumers and producers
• Prevent or minimize data loss and delays in data streaming
• Forestall production issues through an understanding of common failure points
• Create checklists for troubleshooting your Kafka clusters when problems occur

Who This Book Is For
Site reliability engineers tasked with maintaining stability of Kafka clusters, Kafka administrators who troubleshoot production issues around Kafka, DevOps and DataOps experts who are involved with provisioning Kafka (whether on-premises or in the cloud), developers of Kafka consumers and producers who wish to learn more about Kafka 

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Eldor, Elad

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Betriebssysteme Linux Betriebssysteme, Open Source Betriebssysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Angewandte Informatik

Inhaltsverzeichnis

1. Storage Usage in Kafka: Challenges, Strategies and Best Practices.- 2. Strategies for Aggregation, Data Cardinality and Batching.- 3. Understanding and Addressing Partition Skew in Kafka.- 4. Dealing with Skewed and Lost Leaders.- 5. CPU Saturation in Kafka: Causes, Consequences and Solutions.- 6. RAM Allocation in Kafka Clusters: Performance, Stability and Optimization Strategies.- 7. Disk IO Overload in Kafka: Diagnosing and Overcoming Challenges.- 8. Disk Configuration: RAID10 vs. JBOD.- 9 A Deep Dive into Producer Monitoring.- 10. A Deep Dive into Consumer Monitoring.- 11. Stability issues in On-premises Kafka Data Centers.- 12. Cost Reduction of Kafka Clusters.

Über Autor(innen)

Elad Eldor is a DataOps team leader in the Grow division of Unity (formerly ironSource), working on handling stability issues, improving performance, and reducing the cost of high-scale Kafka, Druid, Presto, and Spark clusters on AWS. He has 12 years of experience as a backend software engineer and six years handling DataOps of big data Linux-based clusters.

Prior to working at Unity, Elad was a Site Reliability Engineer (SRE) at Cognyte, where he developed big data applications and handled the reliability and scalability of Spark and Kafka clusters in production. His main interests are performance tuning and cost reduction of big data clusters.