Here's how you can design a scalable and efficient system with key considerations.

I believe Scalability and Efficiency of a real time software system must balance out each other depending on the needs and target users (let's say 100k and increasing). We need to analyze how our system should behave. Should it be fast for all the users or compromise response time for efficiency or try to achieve both in real time by keeping your costs in check. - Consider IAAS/PAAS services to scale up as needed and achieve max utilization of resources. - Achieve both resource efficiency and faster response times by implementing sharding, asynchronous events, load balancing, caching, etc., - With increasing userbase, system needs robust security measures as well. - Backups and more Function instances to make sure system's available

I designed the system used for IBM Global Small Business for interaction with customers. It was not a good system for the IBM employees tasked with implementing it. Customer facing systems in IBM are usually critically bad for the people facing customers, I knew that coming into the job. I tried to make it easy; instead of 27 screens, they faced only 5. Maybe more, for billing. It was not 5. But it was not 27. So I am, thankful, and I hope they are too,

Clearly outline what scalability and efficiency mean for your system. Scalability: Handle more transactions, users, or data. Efficiency: Achieve faster response times or lower operational costs. Set specific, measurable goals to guide design decisions. Example: Support X users concurrently, process Y transactions per second, maintain Z percent uptime.

It is crucial to consider key metrics and continuously collect user feedback. A scalable design should always incorporate the monitoring and analysis of essential metrics to ensure performance, availability, and efficiency. Additionally, user feedback is vital for identifying improvement areas and adapting the system to real needs, ensuring constant evolution aligned with user expectations. These metrics should be updated constantly.

In my journey of more than ten years as a Software Engineer, I've been working with several Fintechs in the financial market in Brazil and Latin America, I've learned that, first and foremost, we need to understand user behavior and product objectives. My thinking is always to hope for the best and prepare for the worst. Start small and then scale according to needs, so you can think more confidently about whether you need to improve data resources, scale vertically or horizontally, when you face an unexpected increase in resources, it's very important to be ready to reduce costs and improve efficiency, and you can do this by identifying bottlenecks, either in the code or in the project configurations.

At StackSpot, we usually do not opt for microservices initially. There is a pattern: we always first architect a monolith and then, as the system grows, we modularize the services. This is mainly because, at the beginning of a system, we lack data, and the business tends to be very volatile. In an ambiguous scenario, starting with microservices can lead to chaos.

Choose Architecture: Selecting the right architecture is crucial for scalability and efficiency. Microservices: Allows individual components to be scaled independently. Factors to Consider: Load balancing, state management, and communication patterns. Task Distribution: Supports distributing tasks across multiple servers to optimize resources and reduce bottlenecks.

Efficient Database design needs more emphasis on user needs, targeted response times, relation between tables, updates, query structures, etc., I would go with a combination of SQL & NoSQL databases and run a schedule job to refresh data in NoSQL based Cache allowing for faster retrievals mimicking hot reload. For the RDBMS database, depending on the above-mentioned factors, I would go for duplications in data vs relations. Minimal joins, atomic transactions, database sharding improve scalability and efficiency. Backups, indexing depths, database load and query structures are some important things to consider while designing the system.

Efficient indexing enhances scalability. Appropriate normalization improves system performance. Sharding can significantly boost scalability. Consider data access and update patterns in design. Opt for a schema with minimal joins and simple transactions. Implement caching to reduce database load and improve response times.

It is necessary to keep in mind that a relational database does not always apply to all scenarios. It is also important to know that the type of database is also linked to its availability. I believe that in complex scenarios, it makes perfect sense to use mixed approaches. Additionally, we cannot forget about data that often resides in databases but, due to its nature, could be stored in files or cloud storage.

Efficiency Improvement: Incorporate asynchronous processing to boost efficiency. Decoupling Tasks: Separate time-consuming tasks from the main execution flow. Concurrent Handling: Allow the system to manage other requests while tasks are being processed. Message Queues: Use message queues and event-driven architectures for managing asynchronous jobs. User Experience: Enhance user experience by reducing wait times. Resource Utilization: Optimize server resource usage.

True, Asynchronous processing and event-driven architectures improve system efficiency by multi-fold. We have redesigned some core business logic services of our Angular-java website to change from legacy synchronous API calls to a combination of AWS Lambda, SQS, SNS, and Glue to achieve over 20% more resource efficiency, even with a 10M+ user base. Cloud services would be a great support to the application performance in such architectures and tremendously reduce costs and attain max utilization and upscaling as and when required.

Track Performance: Implement robust monitoring to track system performance and scalability. Use Monitoring Tools: Gain insights into resource usage, response times, and system throughput. Identify Bottlenecks: Use data to identify and optimize system bottlenecks. Proactive Monitoring: Anticipate issues before they impact users. Ensure Efficiency: Keep your system efficient as it scales.

At StackSpot, we focus on the following metrics: Performance Metrics: CPU, memory, I/O, latency, throughput. Health Indicators: Service status, response times, error rates. Logs and Events: Detailed information about operations, exceptions, and critical events. Alerts are configured to monitor these metrics and to identify improvements whenever necessary.

Here are my considerations based on Stackspot. For us most important thing is feedback: Feedback Collection: Regularly collect feedback from all stakeholders, including the developers of the product, who are the first users. This approach allows for quick and valuable feedback. Data Analysis: Use data and metrics to identify areas for improvement and gain insights into performance and issues. Iteration: Implement changes based on feedback and data analysis. Continuous iteration allows for testing new ideas and adjusting processes. Including the developers of the product as the first users facilitates the rapid implementation of improvements before the system is made available to end users.