Guide to overcome design limitations: Decouple components: Break down monolithic architecture into microservices, enabling independent scaling and deployment. Implement caching: Leverage caching mechanisms (e.g., Redis, Memcached) to reduce database queries and improve performance. Use message queues: Decouple services using message queues (e.g., RabbitMQ, Apache Kafka) to handle high volumes of requests. Leverage cloud services: Utilize cloud providers' auto-scaling features and managed services (e.g., AWS RDS, Google Cloud SQL) to simplify scalability. Monitor and analyze: Use monitoring tools (e.g., Prometheus, Grafana) to identify bottlenecks and optimize system performance. These steps will definitely help

Learn from Huygens principle. Current point at the obstacle is the point to avoid to proceed but keep on moving on remaining other points.

Improving scalability of a system starts by defining what scalability means for you Start off with a simple problem statement, For example: I want the system to be able to handle X requests per second. The scalability of your system is directly proportional to how your system handles its most expensive operation. Can you parallelize the operation across multiple hosts? Is there any form of caching that will help reduce the I/O time? Does adding more compute power improve your systems efficiency? All of these are possible ways to scale your system. And as the saying goes, there no single silver bullet in System Designs, so choose what is applicable to your system. Finally, monitor how you're performing with the changes,

To overcome scalability issues, start by defining the specific areas where your system struggles under load. Focus on splitting complex tasks into smaller, independent services that can scale separately. Use load balancers to distribute traffic efficiently and prioritize lightweight solutions like caching to handle frequent requests faster. Optimize your database with partitioning or sharding for better performance. Finally, ensure your system is fault tolerant by designing for flexibility, allowing you to adapt and grow as demands change.

Before reaching out to the scalability topic. One needs to first focus on performance optimization by identifying possible bottlenecks in the system. Critical path could be one way to find or dig out through logs. Then, apply possible optimal solutions to address this issue. Once applications become stable and most processes optimized then get onto the scaling part like adopting cloud (Horizontal or vertical scaling) otherwise performance degrade in multiplication factors like x^n where x - number of performance issues and n- number of machines once scaled horizontally. Saving (money and resources) will be more by x^n factor just by considering performance optimization.