The digital world is changing faster than ever. In an environment where new frameworks, technologies and tools are coming out almost every minute, scalability is not just an empty phrase, but the bare minimum to allow the development of an IT project. In this busy and ever-changing landscape, GraphQL architecture is steadily gaining popularity. Why? Thanks to its flexibility and precise data fetching mechanism. However as applications grow in scale and gain more users, the efficient scaling of GraphQL APIs becomes not only important, but actually crucial to ensure consistent performance and reliability.
In this short blog, we'll explore a variety of practices that allow you to smoothly adapt to the rapidly changing digital environment. From leveraging caching mechanisms and query optimization, to exploring advanced tools that allow you to expand your infrastructure as your applications grow - all to show how important it is to ensure the consistency, performance and reliability of your GraphQL API.
Let's start with the topic closest to my heart, due to the fact that our tool is used mainly (but not only) for designing GraphQL schemas.
Efficiently designed schema is the key
The foundation of a performant GraphQL API lies in its schema design*. When thinking of your schema there are a lot of things you need to cover (or at least be aware of), but to fully enjoy the DX and the speed of working with GraphQL lest start with data fetching. The main points I want to cover here are:
- over-fetching & under-fetching: which is handled out of the box with the GraphQL queries system which enables retrieving only the required data, preventing unnecessary data retrieval or messing up your network bandwidth.
and its biggest nightmare which is:
- deep nesting: when planing your GraphQL schema avoid going to deep or your will bury yourself with nested queries that can lead to performance bottlenecks and may nullify the highly optimized fetching system which you're getting for free in GraphQL! Keeping query depth shallow will let you enjoy faster query execution and reduce logic complexity for your team.
*if following the schema first path; schema first is our preferred approach here at GraphQL Editor & the entire blog post may be seen as biased in favor of schema first
Data fetching, batching & caching
When we talk about making things run faster, getting data quickly is super important. Using some basic data caching & batching techniques you can really amp up how well your API works, giving it a serious speed boost without much hassle.
- use DataLoader: DataLoader is a utility that can be used as part of an application's data fetching layer to provide a simplistic and consistent API for different remote data sources (web services, databases etc.) through batching and caching.
- implement layered caching: Using caching in different places, like on the client's side, the server's side or CDN (if used) can really help as it stops things from being fetched over and over and makes the general server response time way faster.
- pick right cache eviction policy: Once again caching is critical for any high-performance system so make sure to use a cache eviction policy (when things which are not needed anymore are kicked out of the cache) that fits your project. Keep in mind you need to figure out how to keep all the different caches in sync so they have the same information. This way, you make sure everything stays up-to-date and works smoothly.
- batch your data: Merge multiple data requests into a single query to minimize round trips and improve efficiency, leveraging GraphQL's ability to fetch multiple resources in a single request.
Monitoring, optimization, load balancing & horizontal scaling
Continuous monitoring and optimization are required to keep your app running fast and smooth. Good monitoring tools, load balancing and horizontal scaling are your pals when it comes to handling peak traffic... and it also helps to retain customers as bad latency can wreck customer experience even for the best designed, shiny app. Keep your eyes on:
- profiling & monitoring: regularly check and analyze query execution times using monitoring tools to pinpoint and address performance issues effectively.
- resolver optimization: always aim to improve the efficiency of resolver functions to boost query performance. Don't wait and try to immediately address any issues with slow-performing queries for better overall system responsiveness.
- load balancing: most cloud infrastructures have a load balancing feature available out of the box, but if you're working on your own infrastructure (which is becoming a new trend after the "serverless hype" era), make every effort to evenly distribute incoming requests across multiple servers to prevent overload, ensuring a balanced workload and maintaining system responsiveness.
- horizontal scaling: scale your application by adding more server instances to handle increased demands and manage them efficiently through tools which help achieve seamless orchestration like K8s the open-source system for automating deployment, scaling, and management of containerized applications.
Don't rush with GraphQL adoption
As always, the old saying that haste makes waste rings true. Rather than rush, take your time to give yourself and your users peace of mind. Transitioning from REST to GraphQL is one thing, overcoming obstacles that you didn't foresee at the beginning (let's not kid ourselves, there is no such thing as smooth transition and if there's even a small chance that something might go wrong you can be sure it will) so be prepared to handle one of the greatest scalability challenges which is:
- backward compatibility: with GraphQL you get backward compatibility out of the box and new fields can be added to the schema without impacting existing queries. This is in contrast to REST, where changes to the API may disrupt existing clients. So when you finally decide to transition from REST to GraphQL remember to ensure backward compatibility.
Teamwork makes the dream work - collaborate!
Having effective communication and maintaining detailed documentation - these are the two keys for ensuring your team enjoys effective collaboration and is working effectively. Find a platform that can streamline and simplify communication over your schema (the most important piece of your GraphQL project) leave no room for understatement as the key to your project's success is:
- teamwork: GraphQL is all about collaboration between frontend and backend teams, helping develop a common understanding and an efficient query optimization approach. This is where our GraphQL Editor can help increase the work effectiveness of teams through such features as Live Collaboration that allows multiple team members to work on a GraphQL schema & in general through its visual features that provide a better picture of the schema overall.
From niche to mainstream
It's been 8 years since Facebook released GraphQL and the days when it was some fresh, niche technology are long gone. By now it has moved way past the hype phase, and has easily maintained its status as the main competitor to the steadily declining REST over the past few years. Hardly surprising taking into account the various GraphQL benefits mentioned above, especially with the schema-first approach I've recommended earlier. So while rushing into adoption isn't a good idea, considering it definitely is!
Top comments (0)