Before diving into horizontal scaling, let's first discuss what actually we mean by "scaling".
Horizontal Scaling
Imagine you've initially boosted the processing power of your web application's server through vertical scaling. However, as your application grows, you've hit the performance limit of that single powerful server. Now, simply upgrading to an even more robust server isn't a sustainable solution, as it will eventually max out as well, and transitioning to a new server causes downtime and incurs additional costs.
The next step is to implement horizontal scaling. Instead of relying on a single powerful machine, you introduce multiple machines to collectively handle the increasing workload. This approach ensures that your application can efficiently distribute tasks among these machines, allowing for better scalability and improved performance without the limitations of a single server.
Advantages:
Improved Performance and Scalability: Distribution of workload among multiple machines helps handle a large number of requests.
Cost effective: It can be cost-effective as we can use commodity hardware, and adding new machines when needed can be more cost effective than setting up a single powerful machine.
Fault Tolerance: Even if one of the machine fails, other machines are still active and will handle the requests.
Simplified Maintenance: Maintenance upgrades in horizontal scaling is easy as we can perform updates on each machine one by one, by which our application works perfectly on other devices.
Elasticity: Horizontal Scaling allows for dynamic scaling based on demand i.e we can add or remove machines depending on the workload.
Disadvantages:
Data Consistency: Making sure that each machine has the latest version of data available can be challenging. It can sometimes lead to complexities.
Initial Setup Complexity: Setting up horizontal scaling can be challenging, especially when considering load balancing, data partition and synchronization.
Not suitable for all: There can be cases where some applications can't be divided into smaller tasks that can run concurrently.
Network Bottlenecks: Depending on the architecture and communication patterns, horizontal scaling might lead to increased network traffic and bottlenecks.
Scenarios where Horizontal Scaling is useful:
E-Commerce Platforms: E-commerce platforms often face fluctuations in traffic. Horizontal scaling enables these platforms to handle this increased demand without comprising performance.
Micro-services Architecture: Systems built using a micro-service architecture can leverage horizontal scaling for individual service.
Batch Processes: Applications that perform batch processing tasks, like data analysis, can benefit from horizontal scaling to distribute the workload.
Stateless Applications: Applications where each request is independent and doesn't rely on the stored session data are well suited for horizontal scaling.
Containerized Environments: Containers, managed by tools like Docker and orchestrated by platforms like Kubernetes, facilitate horizontal scaling. Containers can be deployed and scaled independently, making it straightforward to handle varying workloads.