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Amazon DynamoDB - The Definitive Guide

You're reading from   Amazon DynamoDB - The Definitive Guide Explore enterprise-ready, serverless NoSQL with predictable, scalable performance

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781803246895
Length 414 pages
Edition 1st Edition
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Author (1):
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Aman Dhingra Aman Dhingra
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Aman Dhingra
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Table of Contents (24) Chapters Close

Preface 1. Part 1:Introduction and Setup
2. Chapter 1: Amazon DynamoDB in Action FREE CHAPTER 3. Chapter 2: The AWS Management Console and SDKs 4. Chapter 3: NoSQL Workbench for DynamoDB 5. Part 2: Core Data Modeling
6. Chapter 4: Simple Key-Value 7. Chapter 5: Moving from a Relational Mindset 8. Chapter 6: Read Consistency, Operations, and Transactions 9. Chapter 7: Vertical Partitioning 10. Chapter 8: Secondary Indexes 11. Part 3: Table Management and Internal Architecture
12. Chapter 9: Capacity Modes and Table Classes 13. Chapter 10: Request Routers, Storage Nodes, and Other Core Components 14. Part 4: Advanced Data Management and Caching
15. Chapter 11: Backup, Restore, and More 16. Chapter 12: Streams and TTL 17. Chapter 13: Global Tables 18. Chapter 14: DynamoDB Accelerator (DAX) and Caching with DynamoDB 19. Part 5: Analytical Use Cases and Migrations
20. Chapter 15: Enhanced Analytical Patterns 21. Chapter 16: Migrations 22. Index 23. Other Books You May Enjoy

Using DAX for high-volume delivery

After learning about various aspects and concepts of DAX, let’s compare its performance with reading from the DynamoDB web service directly. As a reminder, DynamoDB stores data on warm Solid-State Drives (SSDs) under the hood, and all reads require seeking these SSDs for data before decrypting them using your specified encryption context type (3). In contrast, with DAX, the cached data is stored in memory, which results in reading speeds that are tens of times faster.

For the purposes of this demonstration, I will be creating a new DynamoDB table called chapter14 with a partition key attribute named PK and no sort key attribute. This means that each unique value of the PK attribute will identify a single item in the table. Using the same DAX cluster that I created in the previous section, I will measure the end-to-end latency of performing reads from an EC2 instance in the same VPC as the DAX cluster. While I am not a professional programmer...

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