I'll share some technologies which could help: 1. Load Balancer with the right scheduling. 2. Spark for Large Scale Data processing. 3. Kafka and Flink for streaming data near real-time. 4. Work on DB modeling, use scalable DB.

To keep the software scalable and efficient under increasing user traffic, I design the system using microservices architecture for independent scaling of components. I implement load balancing to distribute traffic evenly across servers and utilize caching to reduce database load. Leveraging cloud infrastructure allows for dynamic resource allocation. Continuous performance monitoring helps identify and address bottlenecks promptly.

While infra and hardware spec upgrade is the most common goto item that gives immediate results but a code refactor before may deliver a long term solution without adding to the infra cost. Lets look at what we are fetching, is there some unwanted data can we split the service?

First, identify the components of the application that are resource-intensive when processing requests. If the application is a stateless microservice, infrastructure scaling with a load balancer and horizontal auto-scaling can be effective. Adding a caching mechanism may also be beneficial if caching is possible. For monolithic applications, consider isolating the high-demand components and converting them into stateless microservices that can be auto-scaled. However, auto-scaling can increase the load on the database, so additional resources or multiple databases might be necessary. To reduce database overload, implement a primary database for writes and a secondary/read replica for reads during auto-scaling.

We had a similar use case in one of our solutions delivered. Here are my two cents - 1. Deploy application close to the region, data center and implement geo residency if it's being used globally or in a few regions. 2. Think about vertical calling as opposed to horizontal calling 3. Implement data replication at database and decoupled transactional vs reporting analytics data 4. Host and deploy apps, web apis on web containers or on AKS with load balancer enabled 5. Distribute load depending upon user and transaction volume on Microservices 6. Use cloud native monitoring services for concurrent user volume and take decisions accordingly.

It is important to figure what’s runs hot by looking at the tools like datadog. Load balancing don’t always work if the software isn’t written correctly to scale horizontally. Maybe some data needs to be cached or denormalize.

I would recommend below 1. Always do regular code reviews and see if there are any performance bottle necks and rectify them. 2. Monitor the logs for any performance bottle necks and usage patterns which includes rate limiting which allows proactive scaling. 3. Making sure indexes created on DB and do query optimizations. 4. Caching frequently used data to reduce the DB calls. 5. Making sure the load balancer distributes the load to all instances.

1.Implementing load balancing across servers. 2.Using caching for frequently accessed data. 3.Optimizing database queries and indexing. 4.Designing the system to be modular for easy scaling. 5.Leveraging cloud services for auto-scaling based on demand.