How do you create a scalable API for handling large numbers of requests?

Here are key steps for creating a scalable API: 1- Define requirements: Understand expected usage patterns (frequency, data size) and plan for future growth. 2- Choose architecture: Consider options like microservices or cloud-native for efficient scaling. 3- Implement caching & load balancing: Reduce database load and distribute requests across multiple servers. 4- Optimize database & queries: Ensure efficient data access and minimize processing time. 5- Monitor & test: Continuously monitor performance and conduct load testing to identify bottlenecks and improve.

it's crucial to define clear expectations for your API's performance. What kind of load should it handle at peak times? What are the response time targets? These questions set the stage for a robust API design.

To create a scalable API for handling large numbers of requests, use a microservices architecture, implement caching, leverage asynchronous patterns, utilize horizontal scaling, optimize code and architecture design, employ pagination, and continuously monitor and fine-tune. These strategies will help ensure that your API can handle increasing traffic loads without compromising performance or reliability.

Just as a great UI is designed for optimal user experience, a great API is designed for optimal consumer experience. An API should be designed in an API-first approach and not tailored for a specific frontend to allow for use and integration by potentially thousands of different consumers. Several points to consider and take into account from the beginning: Estimation of the number of requests per minute, daily. Do we expect usage spikes? Will there be substantial calculations/processing on certain requests? In that case, consider asynchronous processing. Scalable infrastructure : AWS, GCP, Azure. These platforms offer services like auto-scaling, load balancing, and managed databases that can automatically handle increased traffic.

My experience tells me that first you need to understand who is the consumer or potential consumer of your API. This vital knowledge might lead to different architectural approaches to creating your API in the future without unnecessary complexity and duplication to cover more and more growing client base. Understanding the requirements and well-documented contracts in the early stages of design should help you select different architectural approaches to API design such as REST, GraphQL, SOAP, gRPC, WebHooks, and others.

Architecturally, a microservices design can offer the flexibility and scalability needed for high-demand environments. By breaking down the API into smaller, independent services, you can scale components as needed without overhauling the entire system.

● Choosing the right architecture is a critical decision, especially when scalability is a requirement that involves fluctuating numbers of requests. ● Microservice architecture is well-suited for such scenarios, allowing for flexible scaling of pods in Kubernetes based on CPU and memory consumption. ● As the number of requests increases during specific time periods, new pods are automatically spun up to accommodate the demand.

REST, GraphQL, and gRPC offer diverse API design approaches, each with unique strengths. Choosing one depends on specific needs, or combining multiple technologies is an option. REST's simplicity suits traditional web apps and public APIs. GraphQL minimizes over-fetching, ideal for complex scenarios and mobile apps with real-time updates. gRPC prioritizes performance, perfect for high-performance systems. Tools like ApiPlatform facilitate handling REST and GraphQL together for example. With Hasura you can effortlessly generate and manage REST and GraphQL APIs without the complexity of microservices architecture.

Each API architecture has its advantages and disadvantages. You have to choose with a clear understanding of potential bottlenecks or overcomplexity that you will have to deal with using different approaches. There are a few most important consideration points from my experience: business requirements, performance (especially latency for critical applications such as trading or real-time online applications), security, extendability (easy-to-expand contracts), and production ecosystem. For understanding the best approach for your exact problem you can review APIStylebook design guidelines.

There are different techniques to scale an application, usually, they fall into the category of vertical scaling and horizontal scaling. In the first one, you're adding more capabilities to an existing server such as RAM and disk. In the latter, you're adding more instances. If you've chosen an architecture such as microservices, this will help you to use tools like docker and Kubernetes to scale instances horizontally in a dynamic way: The more requests, the more instances will be created to handle the load. All major web services like AWS, Azure, and Google Cloud will allow you to configure this type of scaling.

To create a scalable API, I need to implement caching and load balancing. Caching means storing and reusing data to avoid fetching or generating it again. Load balancing means distributing requests among multiple servers or instances. This way, I can handle a large number of requests efficiently and avoid overloading or crashing. I need to choose a suitable caching mechanism and a load balancing technique, and implement them in my code. I also need to test and optimize the performance of my API.

So caching practice will help in reducing call time because of just getting the data from cache instead of everytime making db, ram connection and all to pull the data. And using the LB engaging with multiple locations deployed applications will serve the better scalable solution. LB handels the request from nearest region deployed application.

Load balancing is another key element. It's not just about distributing traffic evenly—it's about intelligently routing requests to ensure optimal performance and availability. This might involve implementing a combination of DNS round-robin, least connections, or IP hash techniques.

It’s easy to cache RESTful GET requests because their uniqueness or rather their cache key can simply be the request URL itself. Then you can make an automatic cache invalidation with PATCH requests for example. In contrast, GraphQL's single endpoint and varied entity types complicate caching strategies, requiring diverse approaches. - Expiring cache via TTL (Time to Live), but may lead to cache hits too frequently or infrequently. - Incorporating cache invalidation logic into business logic is popular but can be verbose and hard to maintain. - Some systems employ database write log observers to invalidate entities based on updated database rows. Monitor cache hit rates, eviction rates, and cache size to ensure optimal performance.

● When architecting scalable solutions, it's always beneficial to explore methods to enhance performance and consider alternatives to reduce redundant data requests. ● Caching emerges as the optimal solution in scenarios where users make numerous API calls. ● For example, rather than validating credentials with every request, caching allows us to cache the validation results. ● This eliminates the overhead for subsequent requests.

As my mentor always told me, usually the bottleneck is always in the database. For complex cases sometimes the ORM is not the best option and you need an optimized version of a query, but there are other options to consider too, such as creating smart indexes and materialized views of joined tables.

1️⃣ Use efficient data structures and algorithms to optimize your queries. 2️⃣ Implement caching using tools like Redis to reduce database load. 3️⃣ Use database indexing to speed up data retrieval. 4️⃣ Consider using a load balancer to distribute traffic and prevent server overload. 5️⃣ Implement rate limiting to prevent abuse and ensure fair usage. 6️⃣ Consider horizontal scaling by adding more servers as your user base grows.

● When dealing with large database sizes, it's crucial to practice optimization in database design. ● For instance, partitioning can be implemented where data is divided based on time, such as by month. ● This approach enhances query performance by allowing developers to focus on querying specific partitions rather than searching the entire table.

Here our focus should be more on read based schema instead of write based schema. This means the database is optimised for the read operations. This might include denormalizing the database to combine information which is very frequently fetched and changed.

➩ Choose the appropriate database technology: relational or non-relational. - Require ACID properties? Opt for an RDBMS system. - Need scalability and flexibility? Use a NoSQL database like MongoDB. ➩ Identify frequently queried fields. Create indexes on those columns to speed up search operations. Avoid over-indexing to prevent slowdowns in write operations and excessive disk space consumption. ➩ Avoid queries like "SELECT *"; retrieve only necessary columns and avoid unnecessary joins, scans, or sorts. ➩ Utilize caching mechanisms like Redis or Memcached for read-heavy workloads. ➩ Use the LIMIT/OFFSET clause to restrict the number of rows returned. ➩ Optimize query predicates for efficient filtering.

Multi layered testing is required for any internal or external API solution. Building up a valid schema of expected responses and validating data can help with both manual and automated testing. With a scalable API you will want to use a suitable automated API testing library. For example you can use Mocha Chai for JavaScript Applications. Further more DevOps controllers such as Jenkins can fully automate , test &deploy your API in a scalable fashion, ensuring the newest deployments conform to your testing standards.

APIs should also be tested for negative cases and benchmarked for wrong inputs. Many a times benchmarking is done for correct inputs and APIs fail when they recieve large number of incorrect requests.

Define API requirements, considering functionality, data format, and usage patterns. Choose a suitable architecture (REST, GraphQL, or gRPC) based on simplicity and efficiency. Implement caching and load balancing for improved performance and redundancy. Optimize database and queries, selecting an appropriate type and designing an efficient schema. Monitor and test the API using tools for logging, tracing, profiling, and various testing methods. Continuously update and improve the API, following best practices, and incorporating user feedback for relevance and competitiveness.

Creating a scalable API is a journey of continuous improvement. It requires a strategic blend of technology and foresight. As you build or refine your API, keep these considerations in mind to ensure that it grows alongside your business needs.

A common misconception is that scale is strictly about code and hardware performance/design. To effectively scale anything you need to factor the human aspects into the design as the cost of staff can quickly overwhelm the cost of hardware. Will your design work with 1, 10, 100+ engineers working on it? Is your process and code clean enough to maintain? Are there rules in place to reduce tech debt? Are you using tools that are well understood? If neglected, it doesn't matter how well optimized your code or design is, you will likely reach the limit of your team.

APIs should be horizontally scalable and service which is hosting the API should be zero down time deployable i.e when there is an ongoing API request being served by a service and the service is restarted, API should not fail and handle this case gracefully

API systems, especially those that are open to everyone on the web have to be protected from API abuse. There are a few examples from my experience that I suggest to everyone to keep in mind during the architecture of good API: * You might be a target of a denial of the service attack (DoS). Consider protecting your vital services from such attacks. * Rate limit abuse is a widely discussed problem. If each call of your system costs money to your business or can damage your application you have to prevent such abuse. * Data scraping, also known as web scraping might be a violation of your user agreement and can damage your business. * You need to prevent well-known but still dangerous attacks like XSRF/CSRF and others.

Learn from the industry and apply to your product. Facebook uses Tupperware container control system that has been in use and evolving since 2011. They also employ Katran and Proxygen for scaling, both are open source. Google, on the other hand, utilizes Borg and Omega cluster container control systems. Instead of grabbing the Borg code, cleaning it up, and dumping it onto GitHub, Google created Kubernetes from scratch. Kubernetes is the best choice among open source solutions, however it has a downside: it tops out at managing about 5,000 nodes, which is nowhere near the scalability of Borg or Tupperware. The clusters in a Google and Facebook datacenters span around 100,000 nodes.