As a new engineer, understanding auto-scaling is important for several reasons. It is a key component of cloud native application development, allowing for greater scalability and cost efficiency, better collaboration and DevOps culture, and better availability and resilience. Auto-scaling automatically adjusts the number of resources used by an application based on current demand, ensuring the system can handle a large number of requests while minimizing cost. It also makes it easier for different teams and developers to work together on the same application. Auto-scaling is a powerful tool for building and deploying applications in a cloud environment, and is essential for any engineer working in the field today.
As a new engineer, understanding the concept of auto-scaling is important for several reasons.
First, auto-scaling is a key component of cloud native application development. It is the process of automatically adjusting the number of resources (such as servers) used by an application based on the current demand. By understanding how auto-scaling works, you will be able to build, deploy, and manage cloud-native applications more effectively.
Second, auto-scaling allows for greater scalability and cost efficiency. By automatically adjusting the number of resources used by an application, it ensures that the system can handle a large number of requests while minimizing the cost of running the application. This makes it easy to handle the increasing demand for more computing power and storage.
Third, auto-scaling allows for better collaboration and DevOps culture. By automating the process of scaling resources, it becomes easier for different teams and developers to work together on the same application.
Fourth, auto-scaling allows for better availability and resilience, by automatically adjusting resources when traffic increases, it ensures that the application is always available to handle requests, and when traffic decreases, it reduces the resources which allows to save cost.
In summary, as a new engineer, understanding the concept of auto-scaling is important because it is a key component of cloud native application development, allows for greater scalability and cost efficiency, better collaboration and DevOps culture, and better availability and resilience. It is a powerful tool for building and deploying applications in a cloud environment and is essential for any engineer working in the field today.
Learning Materials
Here’s a list to get you started learning about auto-scaling. Note that some of these links may not be free and may require a subscription or payment. I receive no affiliate payments for these links.
Beginner:
- “An Introduction to AWS Auto Scaling” by simplilearn: https://www.simplilearn.com/tutorials/aws-tutorial/aws-auto-scaling
- “Cloud Autoscaling Explained” by Densify: https://www.densify.com/articles/autoscaling
Intermediate:
- “Auto-Scaling in Kubernetes” by Kubernetes: https://kubernetes.io/blog/2016/07/autoscaling-in-kubernetes/
- “Overview of autoscale in Microsoft Azure” by Microsoft: https://learn.microsoft.com/en-us/azure/azure-monitor/autoscale/autoscale-overview
Advanced:
- “Types of AWS Auto Scaling and Best Practices” by developer.com: https://www.developer.com/web-services/aws-auto-scaling-types-best-practices/
- “A Detailed Guide to How to Scale Microservices” by OpsLevel: https://www.opslevel.com/blog/detailed-guide-to-how-to-scale-microservices
- “Azure Best Practices for Autoscaling” by Microsoft: https://learn.microsoft.com/en-us/azure/architecture/best-practices/auto-scaling
Videos to Watch
Kubernetes cluster autoscaling for beginners
Kubernetes is a powerful tool for deploying applications, but it’s important to understand the resource requirements of the applications and set resource requests and limits to allow the scheduler to make informed decisions when placing pods onto nodes. This will help ensure that the nodes are not over-utilized and that applications are given the resources they need.
A Possible Learning Path
Hands-on experience: Start by setting up a simple Kubernetes cluster and experimenting with different auto-scaling mechanisms such as Kubernetes Horizontal Pod Autoscaler and Cluster Autoscaler. This can be done by following tutorials and guides and deploying these services on a cloud platform like AWS, Azure, or GCP.
Theoretical learning: Once you have a basic understanding of auto-scaling, you can begin to explore the underlying concepts and technologies such as Kubernetes Horizontal Pod Autoscaler and Cluster Autoscaler. This can be done through online resources such as tutorials, courses, and documentation provided by Kubernetes, as well as books and blogs on the topic.
Understanding the principles and best practices: Auto-scaling is an important aspect of a microservices architecture, so it’s important to understand the key principles and best practices of auto-scaling, such as scaling policies, monitoring, and alerting.
Joining a community: Joining a community of Kubernetes enthusiasts will help you connect with other people who are learning and working with auto-scaling for Kubernetes. This can be done through online forums, meetups, and social media groups.
Practice, practice, practice: As with any new technology, the best way to learn is by doing. The more you practice deploying and using auto-scaling mechanisms in a Kubernetes cluster, the more comfortable and proficient you will become with the technology.
A Note from the Architect
Before I explain an auto-scaler, let me quickly tell you the story of the Porsches crashing into walls every 15 minutes.
Back when I was a newer developer working for the e-commerce department of an electronics parts seller, we had a web outage. We had no idea why the webserver was down. This was in the olden days when the best monitoring tool we had was named ELMA. It was better for debugging and development than anything you would want to put into production.
The e-commerce website was down and we developers were scrambling to find out why. The president of the company was standing in our cubicles, shaking his head and looking at his watch. Every few minutes he would proclaim loudly, “There goes another one.”
After a while, one of our more senior developers asked, “’Another one,’ what, sir?”
He replied, “You just crashed a brand new Porsche into a brick wall.”
That’s how much money we were losing every 15 minutes while the site was down.
We found out that the problem had nothing to do with our code. It was an issue with our servers. We didn’t have enough. We had recently opened up to new markets overseas and this added traffic was crashing our old Internet Information Server systems. We didn’t have auto-scaling.
Auto-scaling is like having a personal trainer for your infrastructure. It makes sure that your infrastructure is always in tip-top shape by automatically adjusting the number of resources (like servers or containers) based on the current demand.
In traditional IT environments, auto-scaling is often done manually, which can be time-consuming and error-prone. But in a cloud-native environment like Kubernetes, auto-scaling is built into the platform and can be easily configured with Kubernetes resources. This makes it a lot more efficient and reliable.
With Kubernetes, you can set up auto-scaling rules that specify how many resources you want to have available for a given service. For example, you can set a rule that says “if the CPU usage goes above 80% for more than 5 minutes, then add another server.” And when the demand goes down, the system will also automatically scale down the resources. This way, you can ensure that you always have the right amount of resources to handle the current load and also save money by not having unnecessary resources running.
Another benefit of using auto-scaling in a cloud-native environment is that it allows you to handle unexpected traffic spikes, such as a viral social media post or a news article. This way, you don’t have to worry about your service going down because of a sudden increase in traffic.
So, using auto-scaling in a cloud-native environment has many benefits, including increased efficiency, reliability and cost savings. Plus, it’s like having a personal trainer that makes sure your infrastructure is always ready for action. It might also save on the number of Porsches you crash into brick walls.
