Book Image

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
Book Image

Solutions Architect's Handbook

By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect gives you the flexibility to work with cutting-edge technologies and define product strategies. This handbook takes you through the essential concepts, design principles and patterns, architectural considerations, and all the latest technology that you need to know to become a successful solutions architect. This book starts with a quick introduction to the fundamentals of solution architecture design principles and attributes that will assist you in understanding how solution architecture benefits software projects across enterprises. You'll learn what a cloud migration and application modernization framework looks like, and will use microservices, event-driven, cache-based, and serverless patterns to design robust architectures. You'll then explore the main pillars of architecture design, including performance, scalability, cost optimization, security, operational excellence, and DevOps. Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). Finally, you'll get to grips with the documentation of architecture design and the soft skills that are necessary to become a better solutions architect. By the end of this book, you'll have learned techniques to create an efficient architecture design that meets your business requirements.
Table of Contents (18 chapters)

Job observer pattern

In the job observer pattern, you can create an Auto Scaling group, based upon the number of messages in the queue to process. The job observer pattern helps you to maintain performance through increasing or decreasing the number of server instances used in job processing.

The following diagram depicts the job observer pattern:

Job observer pattern architecture

In the preceding architecture, the first fleet of Amazon EC2 servers is on the left-hand side, running batch jobs and putting messages in the queue, for example, image metadata. The second fleet of EC2 servers on the right-hand side is consuming and processing those messages, for example, image encoding. As the message reaches a certain threshold, Amazon CloudWatch triggers Auto Scaling to add the additional server in the consumer fleet to speed up the job processing. Auto Scaling also removes additional servers when the queue depth goes below the threshold.

The job observer pattern computes scale with job...