Scaling a system horizontally means to add more machines or nodes to a system to handle increased load. This approach is also known as "scale-out", and it forms a significant part of modern distributed systems.
Here are step-by-step ways to horizontally scale a system:
1. **Load Balancing:** Utilize a load balancer that distributes network or application traffic across many instances of your application. Each individual instance doesn’t have to be powerful, as increased load can be handled by adding more instances. The load balancer will distribute incoming requests to each instance evenly, which helps prevent any one instance from becoming a bottleneck. Examples of load balancing solutions include Nginx or Apache HTTP Server for software level and AWS Elastic Load Balancer for cloud level.
2. **Stateless Applications:** When scaling out, it’s important to make sure the application is stateless on the server side. This means each request should not depend on the server’s state or previous requests. This feature makes it possible to distribute requests to different instances, as they all reply with the same output when given the same input.
3. **Distributed Data Store:** When a system scales horizontally, data consistency becomes a challenge. Using a distributed data store like Amazon’s DynamoDB or Google’s Bigtable can ensure that data remains consistent across all instances of your application.
4. **Use of Distributed Caches:** To make your system more efficient in terms of reading data, you can use distributed caching systems like Memcached or Redis. They temporarily store data that’s frequently accessed, reducing the read-load on your databases and making retrieval faster.
5. **Implementing an Auto-scaling Policy:** Implement an auto-scaling policy in order to add or reduce instances based on the demand. Especially with cloud computing, such as AWS, Google Cloud, or Azure, this technique can save costs and manage resources effectively. AWS Auto Scaling and Kubernetes’ Horizontal Pod Autoscaler are examples of this.
6. **Partitioning** (also known as sharding): Split large databases into smaller, faster, more easily managed parts called data shards. The shards are spread across multiple servers. One server can store multiple shards, and each shard can store table rows.
For example, a database of online users can be sharded by the user’s geographic location: one shard can contain users from America, another can contain users from Europe, and another can contain users from Asia.
User Database = Shard_America ∪ Shard_Europe ∪ Shard_Asia
Please keep in mind that the appropriate horizontal scaling strategy might vary based on the specific requirements and constraints of the system in question.