Scaling in MicroServices {Must Read}

Scaling is a process of breaking down a software in different units. Scaling also defines in terms of scalability. Scalability is the potential to implement more advance features of the application. It helps to improve security, durability, and maintainability of the application. We have three types of scaling procedures that is followed in the industries.

Scaling, in the context of computing and technology, refers to the process of adjusting the size or capacity of a system to handle changes in workload, users, or demand. Scaling is done to ensure that a system can effectively and efficiently manage increased or decreased resource requirements.

X-Axis Scaling

X-axis scaling is also called as horizontal scaling.

Y-Axis Scaling

Y-axis scaling is also called as a vertical scaling that includes any resource level scaling.

Z-Axis Scaling

X- and Y-axis scaling is pretty much easier to understand. However, one application can also be scaled at the business level, which is called as Z-axis scaling.

Example:

Let’s consider a popular online shopping website like “ShopSmart.” Initially, ShopSmart starts with a small number of users, and the system is set up to handle that level of traffic.

As the popularity of the website grows, more and more users start visiting the site to browse and make purchases.

Now, ShopSmart needs to scale its infrastructure to accommodate the increasing number of users and transactions. There are two common types of scaling:

Vertical Scaling:

Example:

If ShopSmart decides to vertically scale, it might upgrade its existing server by adding more CPU, RAM, or storage capacity. Essentially, it’s making its current server more powerful to handle the growing demand.

Horizontal Scaling:

Example:

Instead of making the existing server more powerful, ShopSmart might opt for horizontal scaling. In this case, it adds more servers to its infrastructure. Each server can handle a portion of the incoming user requests, and a load balancer distributes the traffic among these servers.

Implementation:

ShopSmart could use technologies like containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) to deploy and manage multiple instances of its application across different servers. This allows for more efficient use of resources and better scalability.

In both examples, the goal is to ensure that ShopSmart can provide a smooth and responsive shopping experience for users, even as the number of users and transactions increases over time. Scaling helps prevent issues such as slow response times, system crashes, or downtimes that could negatively impact the user experience.

Scaling is a crucial aspect of managing modern applications, especially in scenarios where user demand can vary significantly, such as during promotions, sales events, or seasonal peaks.

Scenario: E-commerce Microservices Platform

Consider an e-commerce platform that has adopted a microservices architecture. The platform consists of various microservices responsible for different functionalities:

1. Product Service: Manages product information, inventory, and catalog.
2. User Service: Handles user authentication, profile, and account management.
3. Order Service: Processes and manages customer orders.
4. Payment Service: Takes care of payment processing and transactions.
5. Recommendation Service: Provides product recommendations based on user behavior.

1. Vertical Scaling:

• Example: The “Payment Service” experiences increased demand during a flash sale event. To handle the higher load, the platform vertically scales the Payment Service by allocating additional CPU and memory resources to its existing instance.

2. Horizontal Scaling:

• Example: During peak shopping hours, the “Order Service” faces a surge in incoming orders. Instead of vertically scaling a single instance, the platform horizontally scales the Order Service by deploying multiple instances. A load balancer distributes incoming orders among these instances, ensuring even distribution of the workload.

3. Auto-Scaling:

• Example: The “User Service” experiences fluctuating user activity throughout the day. Using auto-scaling policies, the platform automatically adjusts the number of User Service instances based on metrics such as CPU utilization or request rates. This ensures optimal resource usage.

4. Containerization and Orchestration:

• Example: The entire e-commerce platform is containerized using Docker, and Kubernetes is employed for container orchestration. Kubernetes manages the deployment, scaling, and lifecycle of microservices. When demand increases, Kubernetes can automatically scale the instances of each microservice.

5. Database Sharding:

• Example: The “Product Service” interacts with a relational database to store product information. As the number of products grows, the platform adopts database sharding. Product data is horizontally partitioned across multiple databases, with each shard responsible for a subset of the product catalog. This helps distribute the database workload.

6. Content Delivery Networks (CDNs):

• Example: The “Product Service” serves product images and descriptions. To improve content delivery speed globally, the platform leverages a CDN. Images are cached and distributed across a network of servers located worldwide, reducing latency for users accessing product information.

7. Microservices Independence:

• Example: Each microservice, such as the “Recommendation Service,” operates independently. If there is a sudden increase in demand for product recommendations, the platform can scale only the Recommendation Service without affecting the other microservices. This modularity allows for targeted scaling based on individual service requirements.

In this example, the e-commerce platform employs a combination of scaling strategies to ensure that each microservice can handle its specific workload efficiently. The platform adapts to varying demand scenarios, ensuring optimal performance, responsiveness, and resource utilization.