📚⚡️Vertical scaling: advantages and disadvantages
Data vertical scaling is a database scaling approach in which performance gains are achieved by adding resources (eg, processors, memory) to existing system nodes. This approach focuses on improving system performance by increasing its resources within each node rather than adding new nodes.
Benefits of vertical data scaling:
1. Improved performance: Adding resources to existing nodes allows you to process more data and requests on a single server. This can result in improved responsiveness and overall system performance.
2. Ease of Management: Compared to scaling out (adding new servers), scaling up is less difficult to manage because it doesn't require the same degree of configuration and synchronization across different nodes.
3. Infrastructure Cost Savings: Adding resources to existing servers can be more cost effective than buying and maintaining additional servers.

Disadvantages of vertical data scaling:
1. Resource limit: Vertical scaling has limits determined by the maximum performance and resources of an individual node. Sooner or later, you can reach a point where further increase in resources will not lead to a significant improvement in performance.
2. Single-point-of-failure: If the node to which resources are being added goes down, this can lead to serious problems with the availability of the entire system. In horizontal scaling, the loss of one node does not have such a significant impact.
3. Limited scalability: As the load grows, more and more resources may need to be added, which can eventually become inefficient or costly.
4. Resource limit: With vertical scaling, the resource that is the most bottleneck can only be increased up to a certain limit. If this is the resource that limits performance, then additional resources may be spent inefficiently.