GraphQL vs REST API: Which is the Best Choice for Your Tech Stack?
GraphQL vs REST API – a phrase that has left even the most seasoned tech veterans scratching their heads.
You’ve probably stumbled upon countless articles discussing this, but found yourself more confused than before.
This critical decision can make or break your project’s efficiency and scalability.
In this comprehensive guide, we delve deeper than ever into this hot debate.
Instead of overwhelming you with tech jargon, we present a simple, clear, and concise comparison between these two technologies.
We’ll uncover how GraphQL’s efficient data fetching can offer an edge over REST, how REST’s maturity stands up against GraphQL’s innovation, and so much more.
Imagine having complete clarity about the most suitable API for your tech stack.
Imagine confidently leading your team, knowing you’re implementing the best practices to drive your project forward.
Our detailed explanations and real-world examples will empower you to choose between GraphQL and REST API.
So, why wait? Let’s dive in and discover which API – GraphQL or REST – can truly elevate your tech stack to new heights.
By the end of this guide, you’ll have gained expert knowledge and a clear path forward in your tech journey.
Buckle up and prepare for an enlightening journey into the heart of GraphQL and REST API.
Brief Overview of APIs
APIs, or Application Programming Interfaces, serve as the communication channels between different software components, allowing them to interact and exchange data.
They are a set of rules and protocols that specify how software components should interact.
Whether you’re scrolling through your social media feed, checking the weather on your phone, or buying a product online, APIs are at work behind the scenes, seamlessly connecting various systems and services to provide the functionality you need.
REST APIs and GraphQL: A Brief Introduction
REST (Representational State Transfer) APIs have been the standard for web services for many years.
They use HTTP methods (GET, POST, PUT, DELETE, etc.) to perform operations and are built around the concept of resources, which are identified by URLs.
However, REST APIs can lead to inefficiencies such as over-fetching or under-fetching of data.
On the other hand, GraphQL, a newer technology developed by Facebook, allows clients to define the structure of the responses they need, eliminating the inefficiencies associated with REST.
It allows clients to ask for exactly what they need, making it easier to evolve APIs over time and enabling powerful developer tools.
Importance of Choosing the Right API for Your Tech Stack
Choosing the right API for your tech stack is a critical decision that can impact your projects’ overall efficiency, scalability, and long-term evolution.
The right API can make data fetching more efficient, reduce the load on your servers, and lead to faster, more responsive applications.
Additionally, it can also impact the development speed and ease of integration with other services.
Therefore, understanding the differences between REST and GraphQL, and making an informed choice, is crucial for modern developers and tech leaders.
Understanding REST API
Principles of REST
REST, standing for Representational State Transfer, is an architectural style for designing networked applications.
It relies on a stateless, client-server protocol (usually HTTP), where each request from a client contains all the information needed for the server to fulfill that request.
REST is guided by six principles:
- Client-Server Architecture
This principle establishes a clear separation between the client and the server, enhancing the portability of the client-user interface across multiple platforms and improving scalability on the server side.
Each request from the client to the server must contain all the necessary information for the server to understand and fulfil the request.
The server should not store anything about the latest request the client made.
Responses from the server must define themselves as cacheable or non-cacheable to prevent clients from reusing stale or inappropriate data.
- Uniform Interface
This simplifies and decouples the architecture, allowing each part to evolve independently.
- Layered System
This principle allows an architecture to be composed of hierarchical layers by constraining component behaviour.
- Code-On-Demand (optional)
Servers can temporarily extend or customise the functionality of a client by transferring logic to it.
Structure of REST API: Resources, Methods, and Endpoints
REST APIs are designed around resources, which are any kind of object, data, or service that can be accessed by the client.
A resource has an identifier, a URL you can point to, and can be interacted with using HTTP methods (GET to retrieve, POST to create, PUT/PATCH to update, and DELETE to remove data).
The structure of a REST API is determined by its endpoints (the URLs you interact with) and the HTTP methods these endpoints accept.
For example, a REST API for managing a blog might have a “/posts” endpoint that accepts GET requests to retrieve posts and POST requests to create new posts.
Common Use Cases of REST API
REST APIs have wide-ranging use cases, as they can be used to create web services, mobile services, and even APIs for third-party clients. Here are a few common use cases:
- Web Services: REST APIs have been widely used to build web services due to their simplicity and alignment with the web’s architecture.
- Mobile Services: As mobile applications often require a backend for data storage and other tasks, REST APIs have become a common choice for such backends.
- Third-party APIs: Many companies provide REST APIs that enable third-party developers to integrate with their services.
The RESTful approach is about leveraging existing protocols and standards, which makes it an excellent choice for public-facing APIs and integrations between different systems.
Principles of GraphQL
Developed by Facebook in 2012 and open-sourced in 2015, GraphQL is a powerful query language for APIs.
It differs from REST by empowering clients to request the precise data they need, leading to more efficient network communication.
The principles guiding GraphQL include:
GraphQL’s structure follows the relationships between objects, mirroring the frontend needs, and making it easy to request complex data structures.
- Strong Typing
Every GraphQL API conforms to a strongly typed schema, which means the API’s shape, and the interactions permitted with it, are explicit and clear.
- Client-Specified Responses
Instead of the server dictating the returned data, clients request exactly what they need, which helps avoid over-fetching or under-fetching data.
GraphQL APIs are self-documenting. They fully describe all possible API operations and data types through introspection, enabling powerful tooling and improving the developer experience.
Structure of GraphQL: Type System, Queries, and Mutations
GraphQL is built around three main components:
- Type System: At the heart of every GraphQL API is a defined schema using GraphQL’s type system.
The schema defines what queries clients can make, the types of data they can fetch, and how they can interact with it.
- Queries: Queries are requests to read or fetch data.
They’re analogous to the ‘GET’ method in REST, allowing clients to specify exactly what data they need.
- Mutations: Mutations in GraphQL are used to modify server-side data.
They are similar to the ‘POST’, ‘PUT’, and ‘DELETE’ methods in REST.
Mutations change data and return the new data to the client.
Common Use Cases of GraphQL
GraphQL can be used wherever a client needs to fetch data efficiently. Some common use cases are:
- Single-Page Applications (SPAs): SPAs can make complex queries to a backend server, and GraphQL allows the client to tailor requests only to fetch what’s needed.
- Mobile Applications: Similar to SPAs, mobile applications can also take advantage of GraphQL’s efficient data loading to reduce network costs and improve performance.
- Microservices Architecture: GraphQL can be used as a gateway to aggregate data from different microservices, presenting clients with a unified API.
- Real-time Data Updates: With the concept of GraphQL subscriptions, real-time data updates can be handled efficiently.
- Third-party APIs: Companies can offer a GraphQL API as an alternative to a REST API for third-party developers, providing them with more flexibility and efficiency in data retrieval.
GraphQL offers a more efficient data-fetching mechanism as a powerful alternative to REST, leading to improved performance and better usage of network resources.
It is strong typing and introspective nature enable excellent developer tooling and improved developer experience.
GraphQL vs Rest API: Explain Like I Am 5
Imagine you’re a chef (the client) and want to make a fancy dish for your customers (the users). Now, you need ingredients (data) from the grocery store (the server).
In the world of APIs, there are two main ways to get these ingredients: REST and GraphQL.
If REST is your shopping method, you’d have to go to different sections of the store to get each ingredient.
Need eggs? Go to the dairy section. Need flour? Go to the baking aisle. This could result in several trips back and forth.
But what if you could simply hand over your shopping list to the store clerk (GraphQL) and get everything you need in a single trip?
That’s what GraphQL allows you to do.
It helps to reduce your time and effort by only fetching the specific ingredients you need.
It’s efficient and could save you from the trouble of over-fetching (buying more than you need) or under-fetching (forgetting to buy some things).
Comparative Analysis: GraphQL vs REST API Performance
Performance is a key metric when evaluating any technology, and APIs are no exception. Let’s delve deep into comparing the performance aspects of GraphQL and REST APIs.
Data Fetching: Over-fetching and Under-fetching in REST vs Efficient Data Fetching in GraphQL
One of the main advantages of GraphQL over REST is its efficiency in data fetching.
GraphQL allows the client to request exactly what it needs, which can lead to fewer bytes being transferred over the network and potentially faster response times.
A common issue with REST APIs is the over-fetching and under-fetching of data. Over-fetching occurs when the client downloads more information than it needs, resulting in unnecessary data transfer and slower performance.
On the other hand, under-fetching occurs when a single endpoint doesn’t provide enough information, forcing the client to make multiple requests, which can also slow down performance.
In contrast, GraphQL eliminates these issues by allowing clients to specify exactly what data they need.
This can make the responses faster and use less network bandwidth. (source)
Speed and Performance: REST vs GraphQL
The speed of REST and GraphQL APIs heavily depends on the specifics of their implementation, including the efficiency of the database queries, the server’s power, the network’s latency, and more.
In general, GraphQL can potentially have an edge due to its efficiency in data fetching.
But if poorly designed, GraphQL can also lead to inefficient database queries or large payloads, slowing down performance.
REST has a predictable performance pattern – each endpoint corresponds to a specific database query.
It’s easier to optimise the performance at the level of individual endpoints, but the performance can degrade when multiple round trips are required.
GraphQL, on the other hand, can have varying performance depending on the complexity of the query.
For simple queries, GraphQL’s performance can be on par with REST.
But for complex queries, if not properly optimized, GraphQL’s performance can degrade due to the need to resolve multiple fields, especially if these fields require database access. (source)
REST APIs have built-in HTTP caching mechanisms that can boost performance by storing responses in cache and reusing them for subsequent requests.
GraphQL, by default, doesn’t benefit from HTTP caching mechanisms due to the way it handles requests. However, solutions like persisted queries, client-side caching (like Apollo Client), or application-level caching can be used to implement effective caching with GraphQL.
In conclusion, the performance of both GraphQL and REST APIs depends largely on the specifics of their implementation and the use case at hand.
However, GraphQL’s efficiency in data fetching can potentially give it an edge in many scenarios.
Still, careful design and optimization are crucial to ensure good performance in both cases.
Flexibility: Fixed Data Structures in REST vs Customizable Queries in GraphQL
REST APIs are based on fixed data structures (endpoints).
An API designer decides what data is available on each endpoint, and the client’s ability to influence the returned data is limited.
In contrast, GraphQL offers much more flexibility.
It allows the client to decide exactly what data it needs, reducing unnecessary data transfers, and can even aggregate responses across multiple services.
This enables the client to shape responses according to its exact requirements.
Versioning: Multiple Versions in REST vs Single Evolving Version in GraphQL
In REST, changes in the structure of the data often lead to versioning.
This means that if a client needs additional data, a new version of the API might need to be created.
Managing and maintaining multiple API versions can become complex and troublesome over time.
GraphQL handles this differently. Instead of versioning the API, it supports a continuously evolving API schema.
If new fields are added, older clients remain unaffected as they only query the data they need.
This eliminates the need for versioning and the challenges associated with it, while still allowing for growth and change.
Understanding these differences between REST and GraphQL is crucial for developers and businesses alike, as the right API choice can significantly impact application performance, efficiency, and long-term maintenance.
Therefore, a careful comparative analysis like this one can be instrumental in making an informed decision.
In short, the performance of both GraphQL and REST APIs depends largely on the specifics of their implementation and the use case at hand.
However, GraphQL’s efficiency in data fetching can give it an edge in many scenarios.
Still, careful design and optimisation are crucial to ensure good performance in both cases.
Example of REST API Implementation
Imagine a simple blogging platform with posts and users.
The REST API for this might include endpoints like ‘/users‘, ‘/users/:id‘, ‘/users/:id/posts‘, and ‘/posts‘. To get information about a user and their posts, the client might first GET ‘/users/:id‘ to fetch user details and then GET ‘/users/:id/posts‘ to fetch the user’s posts, resulting in multiple roundtrips to the server.
In this example, we are fetching the details of the user with an id of 1 and also fetching the posts of the user with an id of 1. These are two separate network requests to the REST API, and each returns a response which is then logged to the console.
Note: This is a simplified examples. In a real-world application, you would likely want to do something more complex with the data, such as rendering it in a user interface.
Example of GraphQL Implementation
In contrast, with a GraphQL API for the same blogging platform, the client could make a single request specifying exactly what it needs. The GraphQL query might look like this:
This query retrieves the id, name, and all posts (including their title and content) for a user in a single request.
This query fetches the user with id of 1, their name, and all their posts (including the title and content of each post) in a single network request. The response from the server is then logged to the console.
Transitioning from REST to GraphQL: A Real-World Example
Transitioning from REST to GraphQL is a process that should be done gradually.
The main steps usually include setting up a GraphQL server, defining a schema, and rewriting individual REST endpoints as GraphQL resolvers.
For example, consider a company with a REST API for their online store.
Over time, they start to feel the pain of over-fetching and under-fetching data with their mobile clients.
To solve this, they introduce a GraphQL server as a layer before their existing REST API.
Now, instead of the mobile clients hitting the REST API directly, they hit the GraphQL server, which in turn calls the appropriate REST endpoints.
This setup allows them to continue using their stable REST API while gradually transitioning towards GraphQL.
They only rewrite the parts of their API to GraphQL when they touch those parts of the codebase for other reasons, reducing the risk and cost associated with a big rewrite.
This is a common pattern for teams transitioning from REST to GraphQL, and it demonstrates the flexibility of GraphQL and how it can coexist with existing technologies.
Choosing the Right API for Your Tech Stack
The decision between REST and GraphQL should be made after carefully evaluating your project requirements, your team’s expertise, and the long-term vision for your application.
Here are some scenarios where one might be a better fit than the other:
When to Choose REST API: Situations and Scenarios
REST might be a better choice in the following situations:
- Simple Use Case: If your application’s data requirements are simple and unlikely to change frequently, REST could be sufficient.
- Limited Resources: REST can be more straightforward to set up and requires less processing power on the server side, making it a good choice if you’re resource-constrained.
- Caching Needs: HTTP caching is well-established and can be leveraged easily in REST to improve performance.
- Public API for Third-Party Developers: If you’re building an API for third-party developers, REST can be a safe choice due to its widespread acceptance and understanding.
When to Choose GraphQL: Situations and Scenarios
Consider GraphQL in these situations:
- Efficient Data Loading: If your application needs to load large volumes of data and network efficiency is a concern, GraphQL’s ability to fetch only the needed data in a single request can be a significant advantage.
- Frequent Schema Changes: If your data structure changes frequently, GraphQL’s flexible schema can save you from the headaches of versioning and data migrations.
- Complex Systems with Multiple Microservices: GraphQL can be an excellent choice for complex systems with multiple microservices, as it allows you to create a unified API layer that hides the complexity of underlying systems.
- Developer Experience and Tooling: If having a strong developer experience and good tooling is important for your team, GraphQL’s self-documenting nature and strong type system provide a superior developer experience.
The choice between REST and GraphQL isn’t binary or permanent.
They can be used together, and you can transition from one to the other over time.
Remember, the best choice is the one that suits your specific needs and provides the most significant benefits to your project and team.
Transitioning from REST to GraphQL
The transition from REST to GraphQL can be made smoother by following certain steps and preparing for potential challenges.
Here are some insights from a backend engineering perspective:
Steps to Make a Smooth Transition
- Understand GraphQL Fundamentals: Before anything else, your team needs to understand the basics of GraphQL, including its type system, queries, mutations, and resolvers.
You can use libraries like Apollo Server or Express-GraphQL to ease the process.
- Define the Schema: Write down your data types, queries, and mutations in GraphQL’s Schema Definition Language (SDL).
- Create Resolvers: Define functions that will fetch the data for each field in your schema.
- Adapt Your Frontend: Modify your frontend application to send GraphQL queries and handle the responses.
- Incremental Adoption: Instead of a big bang approach, transition your API one endpoint at a time.
This incremental approach is one of the biggest strengths of GraphQL.
Potential Challenges and How to Overcome Them
- Learning Curve: GraphQL comes with a steep learning curve, but this can be mitigated by investing in training and building a culture of learning and mentorship within your team.
- Performance Considerations: GraphQL queries can be complex and potentially strain your database if not optimised.
Using libraries like DataLoader to batch and cache requests can help alleviate this.
- Security: A poorly designed GraphQL schema or resolver can expose your API to security risks.
Ensuring proper authentication, authorisation, and validation measures are in place is crucial.
- Tooling and Support: While a strong community backs GraphQL, it still lacks some of the extensive tooling and support available for REST.
Research and select your tooling carefully to ensure it fits your needs.
In short, transitioning from REST to GraphQL should be approached as a journey rather than a destination.
It involves learning, experimenting, and adapting to maximise the advantages of this powerful technology.
Why is Autify Network using GraphQL instead of REST APIs?
Currently, most middleware applications have two choices when designing or implementing API services.
These can either be built with REST or explore the emerging GraphQL technology.
Although REST is widely considered the standard method for API development, GraphQL is believed to overcome the main drawbacks of REST, especially data fetching issues.
In the later section, you will share why Autify Network chose GraphQL over traditional REST.
Architectural Difference between REST and GraphQL
The main difference between REST and GraphQL APIs technology is the availability of the number of endpoints when they are handling requests.
GraphQL tries to collect every request in one place, whereas the REST is built such that some specific endpoints handle each resource.
The REST configuration creates a complicated situation whenever a change occurs on the back end. It will cause an adjustment to the front end to query the exact endpoint for the desired resource.
On the other hand, GraphQL will take some time when the request is handled at a single endpoint to find the right resource.
The below image illustrates the difference between REST and GraphQL architecture.
Data Fetching Flow
The main advantage of GraphQl over REST is that REST responses contain too much data or sometimes not enough data, which creates the need for another request.
GraphQL solves this problem by fetching only the exact and specific data in a single request.
Graphql being a strictly typed language, helped us mitigate so many bugs prior testing phase.
This was one of the reasons for us to go with GraphQL.
Use of Multiple Data Sources
GraphQL allows multiple resource requests in a single query call, which saves time and bandwidth by reducing the number of network round trips to the server.
It also helps prevent waterfall network requests, where you must resolve dependent resources on previous requests.
Consider a scenario where you need to fetch blog posts, photos, videos and a bunch of other things simultaneously.
If you had to do this, you would make network calls one after another, just like how a waterfall flows.
But the GraphQL API endpoint only takes one API endpoint request call.
We’re using a modern, flexible tech stack.
With this in mind, GraphQL caught our attention.
It offers efficient, declarative data fetching and versioning capabilities.
It reduces round trips with batched queries.
GraphQL also integrates well with microservices. Real-time updates with subscriptions are another key feature.
These traits make GraphQL an ideal solution for building scalable, performant APIs.
Strong Types Throughout The App
We use typescript as our main language, and having type safety throughout the application was a must for Autify Network.
And using GraphQL. It also ensures that we can be sure of what we deliver not only on the code level but also at the API level.
A variety of Supported Type Definitions in GraphQL also helps us speed up our development.
The main GraphQL quality is less loquacious than traditional REST API.
GraphQL treats performance as its top priority while REST is focused on keeping services reliability as their main objective.
Even if a REST API returns only a basic partial, it still transfers more data, while GraphQL always aims for the smallest possible request.
For example, if the client needs a field, they request it, and if the API adds a new field, clients don’t get it unless it’s being added to the GraphQL query.
No wasted bits over the wire
APIs where the client needs to GET/author first and then fetch each book individually via GET /author/:id/books/:id endpoint.
This results in n+1 queries, a well-known performance issue faced in REST APIs.
While REST API calls are chained on the client side before the final representation can be formed for display, in GraphQL, it’s simplified by enabling the server to combine all the data for the client within a single query, resulting in fewer bits being transferred over the wire.
Although REST has some pros over GraphQL. For example, caching is much easier in APIs using REST than in GraphQL (even though we have a data loader now).
Error handling is another interesting issue.
By default, monitoring REST APIs is easier because of standardised HTTP codes.
In the case of GraphQL, we either get 200s or 400s. GraphQL allows even for some errors to be returned as 200s. However, those are still things that can be fixed easily.
So the key point here is because we are using multiple client-to-server communication thus, we went for Graphql.
Future of APIs: Beyond REST and GraphQL
As we continue to evolve in the digital age, new trends and technologies are shaping the future of APIs beyond the realms of REST and GraphQL.
Understanding these can help prepare your tech stack for the future.
Emerging Trends and Technologies in API Development
gRPC: gRPC is a high-performance, open-source framework developed by Google.
It uses Protocol Buffers as its interface definition language and supports several programming languages, making it highly versatile.
gRPC has powerful features such as bi-directional streaming and flow control, making it a potent choice for API development, especially for microservices.
AsyncAPI: As we move towards event-driven architectures and real-time data flow, AsyncAPI is emerging as a promising alternative.
It’s an open-source initiative that provides a specification and a suite of tools to define and work with event-driven APIs.
APIs for Machine Learning and AI: As AI and Machine Learning continue to advance, we see a growing trend in APIs providing access to these technologies.
These APIs allow developers to leverage complex AI and ML models without needing to understand the underlying details.
WebAssembly (Wasm): WebAssembly is an emerging trend that could impact how APIs are built and used, especially on the front-end.
It allows high-level languages (like C, C++, and Rust) to run in the browser at near-native speed.
Preparing Your Tech Stack for the Future
As we look towards the future of API development, here are a few ways to ensure your tech stack stays relevant:
- Continuous Learning: Technology evolves rapidly, and the key to staying current is continuous learning.
Encourage your team to stay updated with the latest trends and technologies.
- Experimentation: Don’t be afraid to experiment with new technologies as they emerge.
Implementing small proof-of-concept projects can provide valuable insights.
- Architect for Change: Design your systems to be adaptable to change.
This means adhering to principles like modularity, separation of concerns, and loose coupling.
- Invest in Automation: As your APIs grow and evolve, investing in automation can help manage the complexity.
This includes automated testing, deployment, monitoring, and documentation generation.
- Strong Foundation: While keeping an eye on the future is important, it’s equally vital to have a strong foundation in current technologies and best practices.
A deep understanding of REST and GraphQL will provide a solid basis for exploring future API technologies.
Overall, the future of APIs is looking bright and filled with potential. By staying informed, flexible, and ready to learn, your tech stack will be well-prepared for whatever comes next.
As we wrap up our discussion on REST and GraphQL, it’s important to reflect on the insights gathered and to consider how they impact your choice of API for your tech stack.
5 Key Takeaways: GraphQL vs REST API
- Efficient Data Loading: GraphQL excels at efficient data fetching, preventing over-fetching and under-fetching issues that are common in REST.
- Versioning and Flexibility: GraphQL allows for more flexibility and avoids the need for versioning unlike REST APIs, which often require multiple versions to accommodate changes.
- Learning Curve and Support: REST is more straightforward and widely known, leading to a lesser learning curve and extensive support.
On the other hand, GraphQL, despite its steep learning curve, has a growing community and strong developer experience.
- Consider Your Specific Needs: The choice between REST and GraphQL should ultimately depend on your project’s specific needs, the complexity of your data, and your team’s expertise.
- Performance Tuning: With GraphQL, developers can fine-tune their queries to fetch precisely what they need, allowing for optimization in performance. In contrast, REST API’s fixed data structure might result in additional network calls, thereby affecting the performance.
The choice of API – REST or GraphQL – carries substantial implications for your tech stack.
It impacts the efficiency of data fetching, the scalability of your application, and the ease of development for your team.
Your API acts as a vital intermediary between your users and your application, which makes choosing the right one critical.
However, the decision isn’t binary or permanent.
You can utilize both APIs in different parts of your application depending on the specific needs, or transition from one to another as your application evolves.
Moreover, it’s essential to keep an eye on the future as emerging technologies continue to reshape the landscape of API development.
Continuous learning, experimentation, and a flexible architecture are vital to ensuring your tech stack can adapt to future changes.
In conclusion, making the right API choice involves understanding the trade-offs, aligning them with your project requirements, and being ready to evolve with the changing tech landscape.