How can you optimize the performance of your back-end architecture?

Web-socket : if you want to continuously send data between client and server then Websocket is the best. If you want to send large data over the network, you can achieve chunk-by-chunk transfer using WebSockets. This approach allows you to break down the data into smaller, manageable chunks, transfer each chunk sequentially, and then reconstruct the data on the receiving end. This method ensures efficient and reliable data transfer, even for large payloads, while maintaining the benefits of WebSocket's low-latency, persistent connection. Upgrading to HTTP2 - it uses binary format instead of text based, supports header compression - multiplexing: allows multiple requests and responses to be sent concurrently over a single TCP connection.

Selecting the ideal tech stack is the cornerstone for optimizing back-end architecture, acting as your project's backbone. Carefully weigh each component, from programming languages to databases, for compatibility and scalability, ensuring seamless integration and adaptability to future technological advancements, allowing your project to flourish in the dynamic digital ecosystem.

One thing I have found useful is always having the scope of an upgrade. If some frameworks end their support with some client-libs, we should not hesitate to upgrade or switch out to different stacks. Keeping your code modular with high cohesion and loose coupling will always be fruitful in the long runs.

Here are some strategies and best practices to consider when optimizing your back-end architecture. Monitor: Track performance. Cache: Store frequently used data. Optimize DB: Improve database queries. Load Balance: Distribute traffic. Scale: Add resources or servers. Async Processing: Offload tasks. CDN: Serve static assets efficiently. Compression: Reduce file sizes. API Efficiency: Minimize requests. Sharding: Divide large databases. Resource Management: Prevent leaks. Code Efficiency: Trim unnecessary code. Updates: Keep software current. Distributed Cache: Share data. Failover & Redundancy: Ensure uptime. Benchmark: Measure impact. Content Delivery: Optimize content. CDN for DBs: Enhance database performance.

Database Optimization: Optimize database queries, schema design, and connection pooling. Load Balancing: Distribute traffic using load balancers for fault tolerance and even request distribution. Horizontal Scaling: Add more servers or instances to handle increased loads. Asynchronous Processing: Offload time-consuming tasks to background workers or queues. Content Delivery: Serve static assets through CDNs to reduce server load and latency. Compression: Enable data compression for faster data transfer. CDNs: Leverage CDNs for caching static assets and lowering latency. Content Aggregation: Combine multiple assets into single requests for efficiency. Database Sharding: Consider database sharding for improved performance with large datasets.

Before adding any caching layer, think about what can you do with the current solution, to avoid adding the cache layer. Usually, teams are not ready to handle all the issues that will appear after introducing any type of caching. Managing the cache invalidation/expiration policy, and avoiding concurrent requests to build the same piece of cache - those are just example issues you'll need to face while working with any kind of caching. From my experience, I highly encourage you to first perform the analysis of your system and find the bottlenecks. Perhaps there are some quick fixes you can apply, before starting the journey with cache.

Eventually the design of the business services with its defined products are an amalgamation of group of microservices acting together that mold into monetizable solutions. Consumption patterns will primarily drive or kill the service offerings. As an enhanced backend design solution design; relying on the costly data search and access from the permanent storage systems may not scale well, instead try caching based on MRU (most recently used data) and augment cache with indexed pre-loaded data slivers [first few bits of everything] to keep the consumer hooked and will improve greatly the experience (assuming client-side browser/device caching and edge caching is already in the play)

Companies seems to easily grab to quick fix solutions for scalability, neglecting the quality of the work the developers did. If you neglect the quality of the work, fixing speed with caching and load balancing will backfire in the long run.

Caching has been one of my secret weapons in the realm of application performance, especially in scenarios that involve frequent data retrieval. With caching, instead of always querying the database, the system first checks temporary storage (or cache) for product details. If the details are found in the cache, they're returned, bypassing the database entirely. If they aren't cached, they're retrieved from the database, presented to the user, and simultaneously stored in the cache for upcoming requests. For me, Redis is an unbitable db for caching. Note: You need to be really careful with the caching mechanism.

Caching can improve your back-end performance by storing frequently accessed data in a fast memory. However, caching can also lead to memory leaks if you don’t control the size and validity of your cache. To prevent this, you need to have cleanup policies for your cache. A deprecation policy specifies when the cached data should be invalidated or updated. A cleanup policy specifies how the expired or unused cached data should be reclaimed or deleted. You can use tools like Redis or Memcached, and techniques like TTL, LRU, or cache eviction to implement these policies. By having deprecation and cleanup policies for your cache, you can ensure that your cache is always fresh and optimal, and that your backend is free of memory leaks.

HTTP is incredibly common and relatively easy to implement communication protocol, however there are other protocols that might fit better depending on requirements. For example, HTTP sends payload data in human readable form which makes debugging easier, however gRPC, on the other hand, sends roughly twice as less data as HTTP, but it's encoded in binary non human readable format which improves response time and reduces digital waste. It's all about trade-offs that should be accepted in alignment with the constraints.

I'm not sure about the microservices part. Most projects can be done without complicating things with microservices. Microservices should be included at a time when scalability is the most important thing in development. There are applications with millions of users that use the Modular Monolith architecture. I would always go with that. As for RESTful services, the implementation of APIs has become a common thing in the industry - there is almost no backend that does not have its own API. It is very important to work on the optimization of Endpoints. If REST services are not suitable, you should consider other implementation methods, such as GraphQL.

When working with RESful APIs you can Use caching strategically. Implement caching mechanisms like Redis or Memcached to reduce database loads and improve response times for requests that don't require fresh data. Cache at multiple levels - CDN, application server, database queries. Also you can Optimize database queries and indexes. Tune your database queries and make sure appropriate indexes are in place to improve query performance. Avoid expensive joins and scans where possible. You do have the option to Implement request throttling. Set limits on the number of requests per time period to prevent any one client overwhelming your APIs.

Choosing a microservice is not always a good option. An appropriate architecture should be chosen depending on the scale of the project

Implementing RESTful APIs and microservices is crucial for optimizing back-end architecture. RESTful APIs follow standards for web service design, using HTTP methods for CRUD operations and JSON/XML for data exchange. They enhance modularity, maintainability, and testability. Microservices decompose the back-end into smaller, independent units communicating via APIs, boosting scalability, resilience, and adaptability.

To optimize database performance we can - implement sharding, optimize database schema, leverage caching mechanisms, use database partitioning, optimize indexing strategies, scale horizontally, implement connection pooling, & proactively tune & monitor performance (with a tool like Sysbench).

Before diving into database optimization, make sure you are well versed with database monitoring and analysis tools and that you can asses the performance of your queries. Tools provided by your cloud provider (in case the DB is managed in the cloud), or event tools provided by the database such as metrics, query plans and more, these will make sure the query/index improvement you add actually improve performance.

Don't use ORM for readonly queries, when you return what you fetch. Prefer optimistic over pesimistic transactions unless you have insights about the benefits of the second in a specific use case. Add index to columns frequently used in queries (don't add an index on every column). Modify the queries to use the index

Everything starts with data and the place where it is stored - the database. This is the first place where optimization should go. You need to master concepts like indexing, sharding, caching mechanisms, database partitioning, implement connection pooling. If this part of the process is done correctly, you won't have many problems with the code.

Database queries are the instructions that guide interactions with the database, and indexes play a significant role in enhancing data retrieval speed. When not optimized, queries can consume excessive resources and time, potentially impacting your back-end's performance. To optimize your queries, consider implementing best practices such as using prepared statements, minimizing unnecessary joins, limiting the data returned to only what's needed, and implementing pagination for handling large datasets. These practices can help keep your queries efficient.

When developing a .NET 6 Web API, there are several important aspects to consider for API security: Authentication: Implementing authentication is essential to prevent unauthorized access and protect sensitive resources. In your APIs, you can utilize various authentication mechanisms such as JSON Web Tokens (JWT), OAuth, or custom authentication schemes. Authorization Implementing authorization ensures that users have appropriate access rights to specific API endpoints or resources. Rate Limiting Rate limiting helps prevent abuse, excessive traffic, and denial-of-service (DoS) attacks. It ensures fair usage of your API's resources and prevents it from being overwhelmed.

Securing your back-end and data is paramount in web development, as it safeguards sensitive information and maintains user trust. Here are some additional steps to enhance security: -Implement Cross-Origin Resource Sharing (CORS) -Regularly Update Dependencies -API Rate Limiting -Security Audits and Penetration Testing -User Input Validation -Session Management -Data Backups -Monitoring and Intrusion Detection -User Education -Compliance with Regulations By taking these comprehensive security measures, you can better protect your back-end and the data it manages, reducing the risk of security breaches and ensuring a safer user experience.

To secure back-end effectively, consider the following best practices: 1. Implement the HTTPS protocol to encrypt data in transit. 2. Encrypt data at rest to safeguard it from potential breaches. 3. Validate and sanitize all inputs to prevent SQL injection and XSS. 4. Implement secure authentication and authorization mechanisms, to ensure that only authorized users can access specific resources. 5. Proper error handling and logging 6. Apply best practices to secure your database, including using strong passwords, restricting user privileges, performing regular data backups, and keeping your database software up to date. 7. Stay vigilant about applying security patches and updates to address known vulnerabilities.

Securing your back-end and data is paramount for optimal architecture. Utilize HTTPS, encrypt data in transit/at rest, validate inputs, and employ robust authentication (e.g., JWT, OAuth). Follow database security best practices like strong passwords, restricted privileges, backups, and timely updates to ensure data integrity and user safety.

Use industry best-practices such as not having connection string in your code base and use secure storage for sensitive information such as Azure Key Vault, AWS Secrets Manager, etc..

A smart approach I've found useful is to write your tests before you begin coding. This helps you spot any unusual situations you might have missed and ensures you cover all the important cases. Ensuring comprehensive coverage of various scenarios is particularly beneficial, especially when considering future refactoring efforts.

Monitoring and testing is core to every system. Good monitoring can provide us with insights otherwise hard to come by, and can prove to be crucial when trying to detect bottlenecks and bugs. Testing is often neglected by developers, and in other cases done in ways that are not effective or useful. When talking about Unit tests be extra careful to not test the implementation of the framework with excessive mocking, instead, focus on core features and business logic.

Also, implemeting a distribute tracing mechanism could help you track down bugs or performance issues since it allows you to observe your system, specifically how flows in your system work, which parts of your cpde are being ran, tracking incoming requests all the way from gateway to tge database calls.

Invite or hire white hat hackers and also one or two QA engineers with vague API description and let them create test and load plans that you aren't expecting.

Can use performance measurement tool like K6 and probably integrate with your CI/CD pipeline to have objective measurements to your systems.

There is no one size fits all when it comes to choosing the right stack. That being said: the right back-end stack can support a business for many years to come.

In picking the right stack, It is also important to consider the non-technical aspects of your project. For instance, if you're building your startup on a budget, you should consider how much it would cost to hire a developer of a certain skill set. For instance, you can use both golang and nodejs to build performant web applications. However, finding a Go developer may be more challenging and costly than finding a Nodejs developer. It is better to hire a senior Nodejs dev who can make your project highly performant than a junior Go developer just for the sake of building your project in Go. Decisions like this can significantly impact the overall success of your project.

Build with your business in mind. The best stack and the most well written code don't mean anything if your business still has to run through 20 steps to make use of it

Know that the product owners tend to move faster than your preferred backend stack. Consider areas that are more flexible and those that solidify the design. From 22 years on projects, shifts in requirements won't stop but an ability to communicate the effect of change is often low. Invest in better communication and space for wrong decisions.

The popularity or novelty of a technology does not automatically make it the best answer when choosing a stack for the development of your project. It is better to understand well the requirements of the same and the resources available to carry it out before choosing one just because it is in trend. If not, ask Cobol, C ++ or php.