Vertical scaling involves adding more power (CPU, RAM) to an existing machine. It is a straightforward approach but has limitations in terms of physical hardware constraints.

Simplicity: Easy to implement as it involves upgrading the existing server.
Cost: Can be expensive due to the high cost of powerful hardware.
Downtime: May require downtime during upgrades.
Limitations: Limited by the capacity of a single machine.

Horizontal Scaling (Scale-Out):

Horizontal scaling involves adding more machines to a system. It is often used in distributed systems and cloud computing environments.

Flexibility: Allows for incremental growth by adding more servers.
Cost-Effective: Can be more cost-effective as it uses commodity hardware.
Redundancy: Provides high availability and fault tolerance.
Complexity: More complex to manage due to distributed nature.

Adding More RAM

Overview:

Increasing the RAM of a server to handle more concurrent processes and improve performance.


        // Pseudocode for increasing RAM
        Server server = new Server();
        server.addRAM(32); // Add 32GB RAM
        server.restart();

Benefits:

Improves application performance by allowing more data to be processed in-memory.

Drawbacks:

Limited by the maximum RAM capacity supported by the server's motherboard.

Upgrading CPU

Overview:

Replacing the existing CPU with a faster one to improve processing speed.


        // Pseudocode for upgrading CPU
        Server server = new Server();
        server.upgradeCPU("Intel i9"); // Upgrade to Intel i9
        server.restart();

Benefits:

Enhances computational power and reduces processing time for tasks.

Drawbacks:

May require compatible motherboard and cooling system.

Adding More Servers

Overview:

Incorporating additional servers to distribute the load and enhance system capacity.


        // Pseudocode for adding servers
        Cluster cluster = new Cluster();
        cluster.addServer(new Server());
        cluster.balanceLoad();

Benefits:

Increases system's capacity to handle more requests simultaneously.

Drawbacks:

Requires load balancing and may increase network latency.

Database Sharding

Overview:

Splitting a database into smaller, more manageable pieces called shards.


        // Pseudocode for sharding database
        Database db = new Database();
        db.shard("user_data", "region");

Benefits:

Improves performance and scalability by distributing data across multiple servers.

Drawbacks:

Increases complexity of data management and requires consistent hashing.

Load Balancing

Overview:

Distributing incoming network traffic across multiple servers to ensure no single server becomes overwhelmed.


        // Pseudocode for load balancing
        LoadBalancer lb = new LoadBalancer();
        lb.addServer(new Server());
        lb.distributeRequests();

Benefits:

Enhances reliability and availability by preventing server overload.

Drawbacks:

May introduce a single point of failure if not properly configured.