What are the best strategies for optimizing API performance and scalability in your career path?

Before you jump on optimization, it's essential to thoroughly understand the specific requirements of the API. There are some things on which you have to keep an eye such as analyzing usage patterns, identifying performance bottlenecks, and anticipating future growth demands. By understanding the API's needs, you can tailor your optimization strategies accordingly.

I have seen multiple times in my career the overall application infra setup worsening the API performance/experience. In a cloud-native world, its almost inevitable to have your application fronted by an application gateway and other API management services. Team must make sure, these components can scale effectively and doesn't adversely affect the overall execution time. e.g. SSL termination at gateway can do wonders on load. Now getting to the core API part, these few things will help you improve the overall application throughput and API performance: 1. Using caching 2. Async APIs 3. Concurrency and parallelization within API logic effectively 4. Robust retry logic 5. Following HTTP RFC standards properly while designing APIs etc

Of course, I recommend that you first choose the framework you know best to release the API. Understand the architecture first, build the Database architecture, design the project based on the X(what is) design pattern, and start working in agreement with the team.

Understand the data and the objective of the API first then proceed with the performance with respect to 1. Db queries 2. Infrastructure like servers and db configuration 3. The code quality 4. Remove any non essential code. 5. Caching 6. Try to reduce frequent round trip 7. Loose coupling by using Message broken and Webhooks 8. Implementing proper rate limit if required 9. Prevention data bombardment like limiting the output data. These are a few quick ones.

In my opinion Functional requirements define what the API does and how the API will be used. The way in which the API will be used affects several issues such as the technology choices, regulatory issues, and security. For example, an API that’s being used to perform financial transactions will have more constraints than one delivering advertisements.

A cache is a collection of items. You can think of a cache as a stockpile of something that you need to keep handy for quick access in a time of need. For example, a cache of medicines or a stock of food items. When it comes to software engineering, the meaning isn’t so different. Think about a bunch of bestseller products on an e-commerce website, a list of country codes, or the most popular posts on your blog. What do they have in common? They are all frequently needed by the users. Going to the DB every single time a user asks for these frequently accessed items is a serious drag on performance. Hence, the need for caching.

Another important technique is “load shedding.” If a system receives API requests at a higher rate than it can process, they may build up in a queue, and in the worst case the queue becomes deep enough that by the time any request is serviced, the client making the request has already timed out, so that no useful work is done even though the system is running at maximum. Load shedding avoids this by adding a layer as early as possible that checks whether the system is close to being overloaded, and if it is, rejecting requests before beginning to process them. This ensures that the requests that are accepted complete before timing out, so the system delivers useful work even when overloaded.

Caching significantly improves API response times by storing frequently accessed data in a temporary storage. This reduces the need to repeatedly fetch the same information from the original source, such as a database or external service. By serving cached data, the API can conserve valuable resources, such as CPU cycles and bandwidth, that would otherwise be expended on redundant requests. This leads to more efficient resource utilization. Rate limiting is a critical defense mechanism to protect the server from being overwhelmed by too many requests in a short period. It prevents abuse, accidental or intentional, and helps maintain the overall health and availability of the API.

Data caching is a huge benefit. Caching is used to speed up the project and optimize it. You can use Redis, Memory Cache and etc. Technologies and your resources are also important.

API rate limiting refers to setting a threshold or limit over the number of times an API can be accessed by its users. The limits can be decided in multiple ways. User-based Limits Location-based Limits Server-based Limits It’s very necessary to protect your resources from being overexploited by users with malicious intent.

Another thing not mentioned is synthetic monitoring. By having automated checks that hit your APIs for the different parts of your user journey every once in a while, you can get identify outages or issues with your APIs immediately, some times even before your users

The golden rule. I would recommend you to read the book "The Standard" by Hassan Rezk habib Write a test in your project Before your project tests you.

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Monitoring is crucial since you get to know what event caused that error. It's easier for you to fix this error because you can literally replay this event on a test environment or development environment. One can also setup alerts and define them using varius levels that inform the development team about an error that just occured or process that failed and whether it's a priority or not

For testing, adopt a comprehensive strategy that includes unit testing, integration testing, and end-to-end testing. Automated testing tools help identify bugs and ensure code integrity. Create test cases covering various scenarios, including edge cases and potential failure points. Monitoring is critical for real-time visibility into system health. Use monitoring tools to track key performance indicators (KPIs), such as response times, error rates, and resource utilization. Establish alerting thresholds to detect anomalies or potential issues promptly. Monitor external dependencies, like APIs or databases, to address issues proactively.

You can try out optimizing the codebase by eliminating redundant or inefficient code segments. Developers have to apply appropriate data structures and algorithms to minimize processing time. In addition, they can optimize database queries to retrieve only the necessary data, reducing the amount of data transferred and processed.

Optimizing your API's code and database is like fine-tuning the engine of a high-performance car. Following coding standards ensures a smooth and efficient operation, while database optimization acts as the fuel, enhancing overall speed and scalability. It's not just about writing code; it's about crafting a well-oiled system . Like a skilled driver on a racetrack, implementing best practices ensures your API performs at its peak, delivering a seamless experience for users and developers alike.

First steps to optimize your code and database. design the project based on the design pattern. Code based on SOLID. Use the DRY, KISS, FIRE principles. update your packages to new technologies. write a test. split the project into micor services. Implement a separate database for each service and keep learning.

Using microservices leads to code perfection in many cases. if you work in local companies, your company may not use cloud technologies, because some companies do not believe in the security of cloud technologies or consider them expensive to use. The use of Cloud Technologies, in my opinion, will greatly help in the Horizontal and Vertical expansion of this program.

Cloud services provide on-demand access to computing resources, storage, and services. Choose a cloud provider based on specific needs, such as AWS, Azure, or Google Cloud. Utilize Infrastructure as Code (IaC) for automated provisioning, ensuring reproducibility and scalability. Leverage serverless computing for event-driven tasks, reducing operational overhead and costs. Microservices architecture involves breaking down applications into smaller, independent services. Each microservice focuses on a specific business capability and communicates through APIs. This enhances agility, allowing independent development and deployment. Containerization, using tools like Docker, ensures consistency across different environments.

Microservices are the key for creating state of the art software architectures. Through using microservices, service redundancies can be limited to an absolute minimum. Microservices enable software architects to provide components that could be consumed across multiple applications in such a way that they can be easily integrated into multiple applications. The code of the microservice no longer has to be maintained in multiple applications in parallel, but only in one central location.

Cloud services offer beneficial features like autoscaling, pay-as-you-go pricing, and failover across regions. They simplify installation of services like databases, serverless offerings, API Management, or Kubernetes clusters. Microservices, while popular, require careful planning, coordination, and monitoring, which could be challenging for less experienced teams. Starting with a monolithic architecture with independent modules is advisable. As scaling needs grow, transition your most used modules into separate services, optimizing your system for performance and maintainability.

Keeping a constant view of incoming feedback from the field is vital in understanding the bottlenecks in any system. However, feedback does not give you a technical pointer to 'how' a specific issue can be resolved or optimized. API performance should be dealt with from the ground up as part of the design and review process itself where the team analyzes the current and projected load patterns for at least 3 years ahead and ensure the design + infra. provisioning is flexible enough that it can be auto-scaled to meet the growing needs of load and geo-distribution. Fine tuning these aspects is something that can be done from the feedback guidance.

As a backend application developer, I build, maintain and test APIs (NodeJS, Typescript, AWS) for a living. One thing I've found insightful is that there's always a way to make your work better. Recently, I was working on a project which had multiple AWS cloud services components. In one of the API calls, I was calling the AWS Secrets Manager and instantiating a Document DB connection inside the lambda function. This resulted in increased response time for the API. After doing some analysis, I found out that migrating the project to ES 6 syntax instead of the commonJS module, allows me to use the top-level await functionality. Having the AWS SM call and documentDB connection on the top level, reduced overall API response time tremendously.

Coarse-grained web services. For example: operate on a whole array or structure in one go, instead of needing a round trip for each element.