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Hands-On Linux for Architects

You're reading from   Hands-On Linux for Architects Design and implement Linux-based IT solutions

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789534108
Length 380 pages
Edition 1st Edition
Tools
Concepts
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Authors (2):
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Denis Salamanca Denis Salamanca
Author Profile Icon Denis Salamanca
Denis Salamanca
Esteban Flores Esteban Flores
Author Profile Icon Esteban Flores
Esteban Flores
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Toc

Table of Contents (22) Chapters Close

Preface 1. Section 1: High-Performance Storage Solutions with GlusterFS FREE CHAPTER
2. Introduction to Design Methodology 3. Defining GlusterFS Storage 4. Architecting a Storage Cluster 5. Using GlusterFS on the Cloud Infrastructure 6. Analyzing Performance in a Gluster System 7. Section 2: High-Availablility Nginx Web Application Using Kubernetes
8. Creating a Highly Available Self-Healing Architecture 9. Understanding the Core Components of a Kubernetes Cluster 10. Architecting a Kubernetes Cluster 11. Deploying and Configuring Kubernetes 12. Section 3: Elastic Stack
13. Monitoring with the ELK Stack 14. Designing an ELK Stack 15. Using Elasticsearch, Logstash, and Kibana to Manage Logs 16. Section 4: System Management Using Saltstack
17. Solving Management Problems with Salty Solutions 18. Getting Your Hands Salty 19. Design Best Practices 20. Assessments 21. Other Books You May Enjoy

Parallel I/O

I/O refers to the process of requesting and writing data to a storage system. The process is done through I/O streams, where data is requested one block, file, or object at a time.

Parallel I/O refers to the process where multiple streams perform operations concurrently on the same storage system. This increases performance and reduces access times, as various files or blocks are read or written at the same time.

In comparison, serial I/O is the process of performing a single stream of I/O, which could lead to reduced performance and increased latency or access times.

Storage clusters, such as GlusterFS, take advantage of parallel I/O, since data is spread through multiple nodes, allowing for numerous clients to access data at the same time without any drop in latency or throughput.

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