Architect Azure Environment - Performance & Scalability

Scaling and performance optimization are about matching the resources available to an application with the demand it is receiving. Performance optimization includes scaling resources, identifying and optimizing potential bottlenecks, and optimizing your application code for peak performance.

Scaling

Compute resources can be scaled in two different directions:

• Scaling up is the action of adding more resources to a single instance like CPU, memory etc.
• Scaling out is the addition of instances.

Performance optimization

When optimizing for performance, you'll look at network and storage to ensure performance is acceptable. Both can impact the response time of your application. Selecting the right networking and storage technologies for your architecture will help you ensure you're providing the best experience for your consumers.

Performance optimization will also include understanding how the applications themselves are performing. Errors, poorly performing code, and bottlenecks in dependent systems can all be uncovered through an application performance management tool.

Scalability and performance patterns and practices

1. Caching

Use caching in your architecture can help improve performance. Caching is a mechanism to store frequently used data or assets (web pages, images) for faster retrieval. Caching can be used at different layers of your application. You can use caching between your application servers and a database, to decrease data retrieval times. You could also use caching between your end users and your web servers, placing static content closer to the user and decreasing the time it takes to return web pages to the end user. This also has a secondary effect of offloading requests from your database or web servers, increasing the performance for other requests.

2. Autoscaling

Autoscaling is the process of dynamically allocating resources to match performance requirements and can be de-allocated when no longer needed.

3. Decouple resource-intensive tasks as background jobs

Many types of applications require background tasks that run independently of the user interface (UI). Examples include batch jobs, intensive processing tasks, and long-running processes such as workflows. Background jobs can be executed without requiring user interaction--the application can start the job and then continue to process interactive requests from users. This can help to minimize the load on the application UI, which can improve availability and reduce interactive response times.

4. Use a messaging layer between services

Adding a messaging layer in between services can have a benefit to performance and scalability. Adding a messaging layer creates a buffer for requests between the services so that requests can continue to flow in without error if the application can’t keep up. As the application works through the requests, they will be answered in the order in which they were received.

5. Performance monitoring

Look across all layers of your application and identify and remediate performance bottlenecks in your application. These bottlenecks could be poor memory handling in your application, or even the process of adding indexes into your database. It may be an iterative process as you relieve one bottleneck and then uncover another that you were unaware of.

6. Data partitioning

In many large-scale solutions, data is divided into separate partitions that can be managed and accessed separately. The partitioning strategy must be chosen carefully to maximize the benefits while minimizing adverse effects. Partitioning can help improve scalability, reduce contention, and optimize performance.


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