How can you ensure your application handles concurrent requests?

?? Prioritize scalability from the get-go! I'd recommend a comprehensive strategy for application developers expecting high concurrent user loads. Start by implementing a robust web server leveraging threading, multiprocessing, or event-driven programming techniques. Incorporate a suitable database, ensuring data integrity during simultaneous requests. Integrate caching mechanisms like Redis to optimize data retrieval times. For asynchronous operations, queues like RabbitMQ prove invaluable. Lastly, deploy a load balancer to distribute incoming traffic evenly, safeguarding against server overloads. By merging these strategies, you can seamlessly manage concurrent user requests.

1. Better to start from requirements than to seek problems for your solution. (Parallelism in this case) 2. Better to avoid reinventing the wheel. (Use existing concurrency patterns & libs, there are so many edge cases) 3. Better to measure than to guess. (Auto testing, Profilers, etc) 4. Coroutines/Fibers are lighter than threads

A web server optimizes concurrent request handling. - Web servers are designed for parallel processing. - They manage multiple connections without lag. - Load balancing ensures no single node is overwhelmed. - Efficient threading reduces processing bottlenecks. - Configurations can be tweaked for specific needs. Select the right server, scale seamlessly.

If you're working on something more bespoke than what's already provided by existing software, avoid low-level synchronization primitives like mutexes, semaphores, condition variables, and single-use threads. Instead, favor an architecture for your software built on async/tasks, producer consumer queues, map reduce, and lock-free/concurrent data structures/collections. (Terminology and definitions vary, so this text attempts to be inclusive.) The most common programming errors in multi-threaded or concurrent programming are prevented by using these higher-level abstractions which, under the covers, utilize much of the aforementioned low-level primitives, but provide a synchronization model that encourages robust software development.

-Database connection pooling -Load Balancing : Distribute incoming requests across multiple application instances or servers using load balancers. -Caching -Rate Limiting -Stress Testing : Conduct stress testing and benchmarking to understand how your application behaves under load levels and concurrent requests. This will help you identify performance limitations and areas that need improvement.

The most important rule in concurrent processing — as in much of life — is “Don’t screw it up.” The standard tools you will be using, the database, the web-server, the OS, are all designed for concurrent processing, that’s their shtick; all you have is not screw it up. What does that actually mean? * use the tools they give you: pool management, thread management, memory management. Read and follow the directions. * no global mutable state! Each transaction should treat the platform like a good camper treats the forest: leave it just as they found it. No leaked memory or leaked file-descriptors. * never roll your own multithreading tools. It is much, much harder than it looks. Use standard libraries.

Better to use the best tool for the job than the tools you know. NoSQL stores exist because RDBMS isn't perfect for every situation (eg. At large scale, write-heavy, distributed, embedded, etc)

When you are processing data, especially the one that requires insert and update it is advised to use the lock method which means if one transaction is not completed others should not be processed

Databases are the guardians of your application's data. ????? Transactions ensure data consistency, locks prevent conflicts, and isolation levels define visibility and concurrency control. Choose your database wisely—be it MySQL, PostgreSQL, or MongoDB—based on the specific needs and mechanisms your application demands. ????

Caches are the speed boosters for your application. ???? Whether client-side, server-side, or network-level, they reduce the load on web servers and databases, enhancing response time and scalability. Redis, Memcached, or Varnish—pick a cache that aligns with your application's unique characteristics. Tailoring the cache strategy is key to optimal performance. ????

Every tool has tradeoffs. Eg. Effective caching requires the correct eviction policy to avoid storage exhaustion. Eg. Recalculating some values might be cheaper than a cache lookup.

Having an awareness and basic understanding of hardware caching can also lead to dramatic performance improvements in concurrent applications. For example, CPUs implement cache prefetching of data and instructions. Designing code with this in mind can drastically improve the cache hit ratio leading to significant performance improvements.

Using cache will help but I won't advise this for a company that processes large amounts of requests because if it is not managed properly your system space will be messed up. 1 make sure you delete the catch after each process request

Queues play the orchestrator in managing tasks seamlessly. ???? They decouple heavy operations from your core, ensuring reliability and availability. No more lost or duplicated tasks—queues like RabbitMQ, Kafka, or SQS keep things in order. With features like retry, priority, and delay, queues are the backbone of a well-organized and resilient application workflow. ??

Load balancers are the traffic conductors for a high-performing and resilient application. ???? They distribute requests, boosting capacity, performance, fault tolerance, and availability. Round robin, least connections, or hash algorithms ensure fair delegation to web servers. Choose your load balancer wisely—whether HAProxy, NGINX Plus, or AWS ELB—based on your application's architecture. It's the key to maintaining a balanced and responsive system. ????

Ensuring seamless handling of concurrent requests is a cornerstone of developing robust and scalable applications. Among the various strategies mentioned here and to achieve this, rate limiting stands out as a tried-and-tested measure that I have employed in numerous projects throughout my career. Rate limiting is the practice of controlling the rate at which user requests are processed by the server. This is executed by setting a predefined limit on the number of requests a user can make within a specified timeframe. The essence of rate limiting lies in its simplicity yet the profound impact it has on the stability and fairness in service delivery, especially when dealing with a multitude of concurrent requests.