How can you design a scalable solution for sudden demand?

Scalability is all about how you can meet varying upward demands and for that, the key input is what is the demand going to be? Just yesterday we saw a major streaming service crashed probably because it didn’t plan for the sudden demand (unless there are other technical glitches). The understanding of what is going to be the (varying) demand is a task more for the business team. The technology team needs to align by providing the most practical and cost effective right-sized solution. It’s a good news that almost all modern technology platforms offer such elasticity on demand. So in my opinion the challenge in designing a scalable system to meet the sudden demand is more with understanding the demand and less with technology.

While demand is one dimension, we would also need the type of trade or business we are guaging the requirement for, as this would also add a slice of other tangibles we would miss to consider.

In order for you to know if it's scalable you have to know the user requirements and have a great handle on the peak demands of the system. Doesn't do any good to provide a secure, well-designed architecture if it can't handle the "rush hour" loads of peak demand without breaking a sweat. While you may have extra cycles to burn in lower activity times, you'll reap the rewards of system performance and scalability, especially in a transactional and time-sensitive environment, if you plan properly.

understanding demand patterns and sources is crucial for designing a scalable solution. It helps in capacity planning, resource allocation, and proactive scaling to ensure your system can handle varying loads effectively. This knowledge enables you to make informed decisions about infrastructure, architecture, and performance optimization.

System should be build to be scalable and elastic. Have the data about your system how much it can scale under the load and then at what level it need to consider the elasticity. If the above information is available then it could be used to compare the historical data and do the forecasting of the upcoming coming demand and do the changes to the system as needed.

Choosing the optimal architecture depends on specific use cases, desired performance levels, and budget considerations for specific events. Horizontal scaling, vertical scaling, microservices, serverless, and hybrid architectures all have their strengths and weaknesses. It's essential to evaluate which one aligns with your system's unique demands and constraints. For instance, mission-critical applications may require more costly but high-performance solutions, while cost-efficiency might be prioritized for less demanding scenarios.

Creating a modularity where the parts can be scaled is important to deciding the scalability of architecture. For this we need- a) modularity in architecture b) virtualisation and containerisation c) scalable algorithms

Selecting the appropriate architecture hinges on your particular use case, business objectives, and the trade-offs you are prepared to accept. It's common to blend different architectures to craft a comprehensive and streamlined system. Factors to weigh include operational overhead, scalability requirements aligned with future growth, supportability constraints, and compatibility with existing systems and technologies.

A Stateless containerized micro service is best suited to handle such traffic burst. Leveraging Kubernetes auto scaler is one option amongst other popular options. Scale can be on CPU, memory or custom metric depending on the application.

Selecting the right architecture is a pivotal decision. Each of these options comes with its own trade-offs in terms of cost, complexity, and scalability. Careful consideration of your system's specific needs, budget, and long-term goals is crucial when making this choice. Your chosen architecture will impact how you handle scalability, availability, and maintainability, so it's essential to align it with your business objectives and technical requirements.

1. Load balancing has to be planned according to product e.g. event streaming, sales events etc). Load balancing may need to be tested and pre-warmed if spikes are expected.. Caching is a double edged sword and need to carefully thought through from- 1. Caching hit ratios 2. External vs Internal caching 3. Warm up 4. Partition for specific requests 5. Compression 6. Granularity for size of data 7. Security

Load balancing and caching are indeed essential techniques in achieving scalability. Load balancing ensures even distribution of requests, optimizing resource usage and increasing fault tolerance. Caching, on the other hand, reduces the need to repeatedly fetch the same data, which can significantly enhance response times and alleviate backend load. When implemented effectively, these strategies can enhance system performance and ensure a smoother user experience, especially during peak demand periods.

Considering Sudden Demand or seasonal traffic e.g. Christmas, thanksgiving day and new year. if it's more transactions / real time oriented / user driven / experience caching and load balancing implementation helps in terms of more read vs write requests.

Analyze your application's network traffic and availability needs, then choose a load balancing algorithm like Round Robin, Least Connections, or Weighted Round Robin. Establish multiple instances or pods across servers or clusters for efficient load distribution. Implement controls to monitor and address the health of these instances. Identify segments benefiting from caching, like static assets or frequently accessed content. Develop an efficient caching strategy, exploring intelligent options for dynamic content to balance performance gains with data accuracy. Choose an appropriate cache invalidation method when content changes occur, ensuring users receive the most up-to-date information

Software reliability engineering will need to be done to ensure monitoring and testing. Stack by stack, heart beats will need to be set up on a command and control dashboard using dedicated tools, processes and teams.

Together, monitoring and testing form a critical feedback loop. They empower you to fine-tune your system, preemptively address issues, and enhance its overall efficiency. This vigilance serves as a safeguard, ensuring that your users or customers enjoy a seamless experience, free from performance hiccups or security vulnerabilities.

User acceptance testing is as important as technical test execution. If people can't easily use or learn to use the applications built, then the utility of the technical development will be limited and ROI won't be achieved.

Monitoring and testing are ongoing processes that are vital for the long-term success of any scalable system. Regularly tracking performance and conducting various tests under different conditions help you identify potential weaknesses or areas for improvement. It also allows you to proactively address issues, fine-tune your system, and ensure that it continues to meet the demands of your users or customers. Continuous monitoring and testing are essential for maintaining a robust and reliable system.

Yes quality of system can be improved through monitoring system functional and non functional perspective in continuous and iterative manner in test environment and production

User adoption is critical to the success of all solution design and development. Ensuring that user voice is captured early and through all stages of development is critical and developing an adoption plan is a proven practice to help realize the value of any innovation.

The system design should not be limited to servers, RAM, and databases. There are a few other aspects we should consider like Security, Authentication and authorization, Latency, Caching, Disaster recovery, etc.

Scalability/elasticity is more than simply the obvious hardware (servers, storage, networking etc.) but MUST include the targeted applications ability to scale and perhaps most importantly of all is deploying a world class automation end orchestration engine(s) with as perfect as possible a CMDB underpinning it all. Remember resizing to normal is equally vital!

When designing a scalable solution, in addition to understanding demand and selecting the right architecture, consider security, cost management, data handling, failure recovery, version control, thorough documentation, scaling policies, user experience, compliance, team skills, feedback loops, and environmental impact. Scalability is an ongoing process, so regularly reevaluate and adapt your architecture and strategies to meet changing demands and technologies while keeping costs in check and ensuring a positive user experience.

We can bring and build all the best of best architecture patterns or niche tech stack to achieve the desired quality attributes but the solution is only as good as the weakest link of the chain i.e., underlying system (SoR) for data. In any given enterprise there is always a huge dependency on the legacy software/systems for key business critical data. So to tackle these kind of legacy SoR dependency your org needs to adopt better data management capabilities like EDW (Enterprise data ware house), data lake, ODL (operational data layer). Also robust data ingestion patterns from SoR to Data layer which can be used for transactional purposes. This ensures there is a good foundation for scalability/availability & other quality attributes