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Hands-On Software Architecture with Golang

Hands-On Software Architecture with Golang

By : Raiturkar
4 (12)
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Hands-On Software Architecture with Golang

Hands-On Software Architecture with Golang

4 (12)
By: Raiturkar

Overview of this book

Building software requires careful planning and architectural considerations; Golang was developed with a fresh perspective on building next-generation applications on the cloud with distributed and concurrent computing concerns. Hands-On Software Architecture with Golang starts with a brief introduction to architectural elements, Go, and a case study to demonstrate architectural principles. You'll then move on to look at code-level aspects such as modularity, class design, and constructs specific to Golang and implementation of design patterns. As you make your way through the chapters, you'll explore the core objectives of architecture such as effectively managing complexity, scalability, and reliability of software systems. You'll also work through creating distributed systems and their communication before moving on to modeling and scaling of data. In the concluding chapters, you'll learn to deploy architectures and plan the migration of applications from other languages. By the end of this book, you will have gained insight into various design and architectural patterns, which will enable you to create robust, scalable architecture using Golang.
Table of Contents (14 chapters)
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Patterns for scaling data performance

So far, we have looked at various fundamental methods of modeling data. In some cases, we need to make a few tweaks to the canonical way of interacting with data to enable performance of a few use cases. This section talks about these types of patterns.

Sharding

A singleton database, however beefy, has limitations in terms of storage space and compute resources. A single server is also not great in terms of availability. Storage systems such as Cassandra distribute data by partitioning data opaquely. However, many systems (including most RDBMS systems) don't partition data internally.

The solution is sharding. This refers to dividing the data store into a set of horizontal partitions...

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