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Implementing Cloud Design Patterns for AWS

You're reading from   Implementing Cloud Design Patterns for AWS Create highly efficient design patterns for scalability, redundancy, and high availability in the AWS Cloud

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Product type Paperback
Published in Apr 2015
Last Updated in Feb 2025
Publisher
ISBN-13 9781782177340
Length 228 pages
Edition 1st Edition
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Author (1):
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 Young Young
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Young
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Table of Contents (13) Chapters Close

Preface 1. Introduction FREE CHAPTER 2. Basic Patterns 3. Patterns for High Availability 4. Patterns for Processing Static Data 5. Patterns for Processing Dynamic Data 6. Patterns for Uploading Data 7. Patterns for Databases 8. Patterns for Data Processing 9. Patterns for Operation and Maintenance 10. Patterns for Networking 11. Throw-away Environments Index

Sharding write pattern


The previous chapter focused a bit on optimization in terms of splitting the query across cloned instances, which would be only part of a true database scalability concern. The database would still have performance concerns, as there is replication lag coupled with a fragile splitting mechanism through the proxy. The best route to take to reduce all of these concerns, is to architect a distributed database from the very beginning.

In the sharding write pattern, we take the previous concepts a bit further in-depth, by not analyzing the query to determine which instance to execute against. Instead, we use a cluster management tool called MySQL Fabric, which was announced by Oracle in early 2014. Fabric provides a single API to create and manage farms (data centers) of databases or even farms of farms of databases. Using this method, we are able to create multiple database instances, and from a Fabric node, group them into many different layouts to achieve high availability...

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