Kafka Troubleshooting in Production
Apress (Verlag)
978-1-4842-9489-5 (ISBN)
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
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.
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.
Erscheinungsdatum | 30.11.2023 |
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Zusatzinfo | 74 Illustrations, color; 17 Illustrations, black and white; XX, 216 p. 91 illus., 74 illus. in color. |
Verlagsort | Berkley |
Sprache | englisch |
Maße | 178 x 254 mm |
Themenwelt | Mathematik / Informatik ► Informatik ► Betriebssysteme / Server |
Mathematik / Informatik ► Informatik ► Datenbanken | |
Mathematik / Informatik ► Informatik ► Netzwerke | |
Mathematik / Informatik ► Mathematik | |
Schlagworte | Apache Kafka • Big Data • Big Data Clusters • DataOps • data pipelines • DevOps • Kafka Clusters • Processing at Scale • Real-Time Data Processing • root cause analysis • Site Reliability Engineering • SRE • Streaming data |
ISBN-10 | 1-4842-9489-0 / 1484294890 |
ISBN-13 | 978-1-4842-9489-5 / 9781484294895 |
Zustand | Neuware |
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