What strategies can you use to design a REST API for high performance?

To design a high-performance REST API, utilize caching mechanisms to reduce response times and minimize server load. Implement efficient data retrieval techniques like pagination and selective field fetching to optimize API performance. Additionally, employ asynchronous processing for long-running tasks and use HTTP caching headers to leverage client-side caching for static resources, ensuring scalability and responsiveness of the API.

Estoy de acuerdo pero estaría mejor escrito de esta manera, ya que cuenta con algunos errores gramáticales: Antes de comenzar a dise?ar tu API, es crucial establecer objetivos claros en términos de rendimiento. Esto podría incluir métricas como el tiempo de respuesta esperado, la tasa de transferencia de datos, el número máximo de solicitudes concurrentes que puede manejar la API, etc. Establecer estos objetivos te ayudará a orientar tus decisiones de dise?o y optimización.

Define Goals ?? Before diving into the technicalities, clearly define the performance goals for your REST API. Understand the expected load, response times, and data throughput. Performance goals should align with your business objectives and user expectations. This clarity will guide the architectural decisions you make, ensuring that the API is built with performance in mind from the ground up. Without clear goals, it's easy to overlook critical performance considerations or invest in unnecessary optimizations. ??

One thing that I try to analyse is the load from 5 years from now, and always having that in mind while designing an API. So I want my APIs to be quicker and as loosely coupled as possible. Reducing DB interactions as much as possible, before sending out the response. Having a clear plan for authentication and authorisation of the requests coming in.

It depends on what you're trying to accomplish, but generally, my solution has been not to build a REST API. Traditional APIs have issues of over or under fetching data. What this means is that sometimes they return more data than you intend to share while other times they return too little data resulting in additional calls. Both these scenarios are sub-optimal when it comes to performance. My team and I have predominantly switched to GraphQL which allows us to directly control the what and how much when it comes to data. Beyond this adjusted approach, general concepts like caching, whether by client, cdn, or other will generally help for more static data.

Agreed, Data should be sent as minimal as possible, pagination is definitely a go to solution to solve that. For endpoints which use high data processing before sending out the response. These endpoints should be gathered and moved out as schedulers which will update the DB in the given timeframe. We can then use the on_demand section for these high data processing sections to decide whether to go for the latest processed data or little older data which has been updated by schedulers.

Using REDIS for the most used endpoint response for each account is the way to go. This will reduce network i/o, memory i/o. For example, if your app is showing some metrics in the home page which needs updating every time the user logs in. That endpoint can be cached into redis as a key value pair and can be used as a local cache.

Caching is definitely a must to do especially when the cost of hitting the backend SOR ( system of records ) are more. Cache like hazelcast IMDG might work like a charm which definitely improve the API response time because we are not hitting the back end SOR. TTL ( time to live) can be configured accordingly. Cache synchronization is must as well which will drastically improve the performance of any REST API. Offloading the traffic from SOR is must especially the SOR is relatively a huge database which contains over millions of records.

Having a good error handling mechanism is the first and foremost thing to have when thinking about scaling. How fast your API can send out an error response when something goes wrong. We should not continue to serve for the requests which turn into failure. Having this along with good monitoring stack like ELK to get to know about the bottlenecks and resolving that, overtime you can achieve the scale you need.

Monitoring is as important as development. Humans are not machines, so no matter how perfect the developer is there is a high possibility he too made a mistake. Having monitoring stack like ELK along with APM(application performance monitoring) and having a perfect logging mechanism and using filebeat to fetch the logs and analysing the logs and in the end connecting everything with the alerts to your developer group, if we continue to do this we can make sure that we can solve the issues in quick time. CPU and Request counts need to be monitored for the hosted system and auto scaling needs to be activated based on the CPU usage or no of request counts.

By enabling request and response compression using any of zipping algorithms like GZips, This reduces bandwidth usage and improves data transfer speed over the network.