Wie können Sie ein System entwerfen, das Millionen von Anfragen pro Sekunde verarbeiten kann?

To handle millions of requests per second, prioritize a distributed architecture with load balancing, caching, and horizontal scaling. Employ microservices for modularity, ensuring fault tolerance and redundancy. Use high-performance databases, employ asynchronous processing for time-consuming tasks, and implement efficient content delivery networks. Monitoring and auto-scaling mechanisms are crucial for dynamic demand. Regularly optimize code and infrastructure for performance. Security measures, such as rate limiting and encryption, should be robust. Testing at scale is imperative. Continuous improvement and adaptability are key aspects for sustained reliability in such a high-throughput system.

In order to manage a high throughput of millions of requests every second, we should also have asynchronous processing for time-consuming tasks, and implement efficient content delivery networks. it's essential to focus on a distributed system design that incorporates load balancing, caching strategies, and horizontal scaling techniques.

To handle tons of requests every second, build a clever system. Spread the workload well, use databases that share info smoothly, and make your code run fast. Use cloud services that can grow when needed. Save data cleverly, and speed things up with networks that deliver content quickly. Keep an eye on how your system is doing, and make it better over time. It's all about using smart tech and improving things as you go!

From my perspective as an infrastructure engineer, it is important to distribute your system across multiple servers or nodes. This helps distribute the load and allows for better scalability and Employ intelligent load balancing algorithms that consider servers health and capacity.

In my experience the type of transaction is the first requirement as it will drive the decision making process for numerous reasons. For example HIPPA data requirements require different solutions and scalability options than internal accounting invoice data. Once this is known the additional definitions of scalability, environment elasticity, and security can begin to be answered. Everything else related to load balancing, monitoring and performance goals can then begin to be addressed that include but are not limited to acceptable minimum latency, what data to be masked and at what level of detail.

Layered, microservices, serverless need to be balanced with very minimum bottleneck possibilities on each of the functionality. Pleace also consider, secure by design approach because your security posture started on the architecture level.

The orchestrators design combined with anycast. Network slicing enables operators to create virtual slices with tailored QoS. Orchestrator addressing resource management, access control, scheduling, and resource migration. Lagrangian relaxation (LR) handles complexity, relaxing constraints for efficient solution. Experiments prove they achieve near-optimal revenue while meeting QoS. LR and anycast combination holds potential for ISP network efficiency and profitability Anycast integration enhances resource provisioning, QoS, latency reduction, scalability, and cost-effectiveness reaffic to closest resource, ensuring efficiency. As a scalable, flexible, adapt to network changes, boosting ISP performance, cost-effectiveness.

simple yet powerful system design wit fewer connectors for request must be taken in design consideration with tools such as nginx which is apt for larger environment and can be used in different roles and function

Microservices Magic ✨ Go for Microservices! 🚀 They're like system LEGO bricks - small, independent, and super flexible. Each part can do its thing without messing with the others. Easy to scale, update, and deploy. Sure, there's a bit of chatting between services, but the perks of agility and independence totally make up for it. It's like a high-five for your system's scalability and maintainability! 🌟

Layered Architecture: Suitable for simpler applications but may introduce latency and bottlenecks. Microservices Architecture: Offers scalability and independent deployment but adds network overhead and complexity. Serverless Architecture: Great for cost-effectiveness and scalability, but limits control and may introduce cold starts.

Please also look at the standard architectures utilized by similar organizations, as well as knowledge from indigenous institutions. Identify the gaps and create an optimization approach. Do NOT fear to innovate and apply the innovation.

Optimize for Super Speed! Caching: Store popular data in a fast space (like memory). Lightens the database load, boosts response time, and saves bandwidth. Load Balancing: Spread requests across servers like a pro. Algorithms like round-robin keep things efficient, boosting performance and scalability. Sharding: Break data into manageable chunks. Supercharges throughput, minimizes contention, and powers up parallel processing. Optimization is like giving your system a turbo boost, but watch out for inconsistency hiccups and tricky synchronization challenges! 🚀

Caching: Use in-memory or SSD caching for frequently accessed data. Load Balancing: Implement algorithms like round-robin or least connections for distributing requests. Sharding: Split data into manageable chunks for increased throughput and reduced contention.

Ensure each of the component working as it was designed by ensurong the communication between component with proper session and auth mechanism to finally ensure the data processing journey will run as it is. Fraudster will never follow your defined journey, they will try to break it.

Detailing the data since the begining will help to design the strategy to secure the data. Secure by design. Encryption strategy need to defined on the architecture level. You may possible transmit the processing data without encryption if you planned to implemented a clean ACL on the private env system. This will keep the latency maintain and securing the the data/aystem at the same time. You need to defined another strategy if your system communicated in public. Storing the data with encrytion need also to consider how the encryption services will run. Where you put the key? Built in or centralize key system? If centralize, you also need to ensure the system running with HA to avoid single point of failure.

TIER 0,1!are by far the best approach regarding the security core architecture. Fail over, load balancers, wAF, round robin technics, honeypots, many instances , CDN etc are the way to go "cloud" . Your custom designed.cloud I have seen cloudflare,.Azure, Amazon and other established leaders on sectors bands fail. Design your "own cloud"

Fortify Your System: Security 101 🔒 Encryption: Shield your data with AES or RSA magic. Note: May impact processing time and storage space. Authentication: Confirm user identity with passwords, tokens, or certificates. Heads up: May introduce some latency and complexity. Authorization: Set user permissions with ACLs or RBAC. Caution: Adds maintenance and testing overhead. Secure your system like a digital fortress, but be aware of the trade-offs. Safety first! 🛡️

Encryption: Secure data using algorithms like AES or RSA. Authentication: Verify user identity with passwords, tokens, or certificates. Authorization: Use ACLs or RBAC for defining user permissions and roles.

Not just If the data is related to PII/FII, your system accessible on public, you need to consider to do the vulnerability assesment Test and Penetration Testing also. Many company have an awesome data processing system but somehow not considering the security aspect much. Sometimes the data processing journey can be exploited easily. Sometime naked API is everywhere. Sometimes using the component with 0 day, unremediated CVE on the software version etc.

Load Testing: Simulate normal and peak conditions. Stress Testing: Test the system's limits. Chaos Engineering: Introduce failures to test resilience. Continuous Feedback Loop: Regularly update the system based on test results and monitoring data.

Test, Tweak, Triumph! 🚀 Load Testing: Simulate normal, peak, and failure scenarios. Uncover how your system handles the heat. Stress Testing: Push it to the limits! Identify bugs, bottlenecks, and vulnerabilities. Chaos Engineering: Embrace controlled chaos to find weaknesses. Strengthen your system's resilience. Iteration: Learn from testing, monitoring, and logging. Tweak and fine-tune based on feedback. Testing isn't just finding bugs; it's sculpting a powerhouse system. Iterate, refine, and watch your creation soar! 🛠️

A "top down" architecture, starting from DNS and using a set of 3+ instances( per each of webserver, waf, cdn etc) , load balanced, and geo-located correctly accordingly with the (filtered) source of the requests load. Additionally I would strongly recommend using a Honeypot instance so that you keep a lot of "unwanted" traffic out from your "money" sites.. with this way you will save resources for serving- servicing where you actually want - should to