Category: Graphql vs. rest api architecture

The usage of APIs has seen a tremendous growth. As mobile apps became more popular, there was an obvious need to de-link the backend of the application from the front-end. Using APIs as the backend made developing and launching mobile apps easier. This important development made its entry in the web app development space too, as client-server model became more popular here.

graphql vs. rest api architecture

Once again, APIs were the important components in the server-end. The result of all these is that APIs are now present in most of the software that are developed nowadays. In this guide, we compare the two, so that you can make an informed decision. This will help you to appreciate the challenges they intend to address and how they fit in your context.

Using these protocols involved several complexities, e. This made updates to the interfaces costly when you used CORBA, moreover, the interfaces had a high degree of specificity. This meant that if you were rolling out a new version of the interface, then all clients needed to upgrade at once.

If you had to implement SOAP, then you had to implement a complete protocol including error handling, security etc. However, the rigid contract between the client and server increased the complexity. In summary. In summary, the protocols prior to REST had rigid contracts between the client and server, and any minor change required all clients to upgrade at once. The RESTful architecture emerged inand it offered a simpler way for a computer to interact with another.

This architecture utilizes the HTTP protocol for communication between computers, and this communication is stateless, i.

REST API vs GraphQL - Comparison

The client stores the client session, therefore, the server can service any client at any time. This was an improvement over the earlier protocols since it helped in scaling the APIs. With the emergence of RESTful architecture, application systems could now be loosely coupled.

Business contracts between two organizations change often, and the RESTful architecture made it easier for them to implement these changes.

Even more importantly, RESTful APIs help the client and server to be loosely coupled, thus making mobile and web app development much easier. While REST offered many advantages to developers and became the de facto standard for businesses that deploy APIs, it also has a few disadvantages. These disadvantages arise from the fact that the server creates the representation of the resource, and the response to the client uses that.

We have seen a sharp increase in the usage of mobile devices since Some mobile-devices were low-powered, moreover, the mobile networks were slow. Developers also had to design the API endpoints keeping the front-end views in mind, and changes to the front-end views required changes to the API endpoint. The result was less flexibility. Many different front-end frameworks emerged with the growth of mobile usage, moreover, there was an increase in platforms running client applications.

The latest stable release of GraphQL was released in June GraphQL servers are available for multiple popular languages, e. The popularity of GraphQL grew rapidly.

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They can coexist in one project. Over to the differences now. The other key different concerns with the shape and size of the resource, and in the case of REST, the server determines it. You write a query specifying what information you want to retrieve. This means that developers using this API should know exactly what should they call, and what to expect as response.GraphQL has been released only as a specification. This means that GraphQL is in fact not more than a long document that describes in detail the behaviour of a GraphQL server.

All three architectures represent major use cases of GraphQL and demonstrate the flexibility in terms of the context where it can be used. This architecture will be the most common for greenfield projects. In the setup, you have a single web server that implements the GraphQL specification. This is called resolving the query. It then constructs the response object as described in the official specification and returns it to the client.

This means it can potentially be used with any available network protocol. A standard greenfield architecture with one GraphQL server that connects to a single database.

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This is particularly compelling for companies with legacy infrastructures and many different APIs that have grown over years and now impose a high maintenance burden. One major problem with these legacy systems is that they make it practically impossible to build innovative products that need access to multiple systems. This way, new client applications can be developed that simply talk to the GraphQL server to fetch the data they need. The GraphQL server is then responsible for fetching the data from the existing systems and package it up in the GraphQL response format.

GraphQL allows you to hide the complexity of existing systems, such as microservices, legacy infrastructures or third-party APIs behind a single GraphQL interface. When a query is received by the server, it will resolve it and either retrieve the required data from the connected database or some of the integrated APIs. Both approaches can also be combined and the GraphQL server can fetch data from a single database as well as from an existing system - allowing for complete flexibility and pushing all data management complexity to the server.

But how do we gain this flexibility with GraphQL? As you learned in the previous chapter, the payload of a GraphQL query or mutation consists of a set of fields. The sole purpose of a resolver function is to fetch the data for its field. It thus resolves the query and is able to retrieve the correct data for each field.

graphql vs. rest api architecture

Once all resolvers returned, the server will package data up in the format that was described by the query and send it back to the client. The above screenshot contains some of the resolved field names. Each field in the query corresponds to a resolver function.

The GraphQL calls all required resolvers when a query comes in to fetch the specified data. GraphQL is particularly great for frontend developers since it completely eliminates many of the inconveniences and shortcomings that are experienced with REST APIs, such as over- and underfetching.

Complexity is pushed to the server-side where powerful machines can take care of the heavy computation work. All the lower-level networking tasks as well as storing the data should be abstracted away and the declaration of data dependencies should be the dominant part.

They provide the abstraction that you need to be able to focus on the important parts of your application rather than having to deal with the repetitive implementation of infrastructure. GraphQL Fundamentals. Core Concepts. Big Picture Architecture. How to GraphQL. Advanced GraphQL. More GraphQL Concepts.I have tried to incorporate the constructive feedback on this article and provide as many unbiased insights as possible. Keep in mind that is still a critical reviewbased on my experience running and working with all the different kind of APIs in production either directly or with dozens of our clients.

And I am sure you know the problems with these APIs first hand. These problems are part of the reason for GraphQL existence. And it makes complete sense! If you have the luxury being able to study the architectural styles or hire someone to train you, the chances are you are an architect.

And as architect your role is to understand the benefits and trade-offs of different architectural styles and the implications of applying the styles constraints on the product you are building.

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Of course, different products need different API architectural styles to reach the desired properties and business objectives. And as always, there is no silver bullet. As an architect, it is your duty to have multiple styles under your belt and to know which one to use and when. Besides, it takes educated clients to consume the API correctly. The reward for the martyrdom and discipline is a system that scales, performs and its components can evolve independently.

But, in our fast-paced world, where many apps last only months and where we optimize for now, not later, these seem not to be the properties worth pursuing. If you have decided to go with the microservices architecture, REST is probably the best style to enable for independent evolution of services and context separation. It almost feels like REST and microservices architecture were meant for each other.

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It took me years to fully comprehend the REST, the mechanics and benefits of the server-driven application state interaction, affordance-centric design and the reality of frameworks and implementations. A good HTTP API gives you all the benefits of the existing internet infrastructure but it still tightly couples clients with servers making an independent evolution a chore.

Furthermore, it still requires a significant amount of understanding of the protocol on both producer and consumer parts. It also rewards you with effortless consistency between the APIs. The cost you pay is that your clients have to read the docs GraphQL schema at the development time and hardcode the affordances queries and mutations.

This hardcoding of the development-time knowledge leads to clients tightly coupled to the server, but the same also applies to so-called-REST APIs. Using GraphQL also leaves you vulnerable to what HTTP protocol and the whole internet infrastructure already solved: Scaling, performance, network communication mechanics, and concepts like content, language negotiation, and many others.

Adding the functionality that otherwise comes with HTTP leads to bike-shedding. The one I want to address is about the over-fetching. Often, we see the claim that GraphQL has the benefit over REST that its clients are not over-fetching data from the server by being able to specify only the attributes they want to receive.

Other good examples are colossal responses justified by the chattiness of the HTTP1. Besides, how is fetching few more fields with the out-of-the-box caching less performant than fetching only the fields you want but all the way from the origin every single time?

With GraphQL you can defer the moment of understanding how users consume your API until you start profiling the queries, evaluating their complexity and identifying the slow queries. In either case, at some point, you will have to understand the user needs and design your API and its implementation accordingly. It would be foolish to think you can create a well-performant API for every use case. You have to make design choices. There is no escaping to this.

What aspect are you buying when you say yes to a particular style?People today do not like to be confined, if I talk about development teams, they would hold up flags stating the same. Talking specifically about web developments, there are a lot of areas to explore and a lot of technologies to help you do that.

So, why limit yourself, when you don't have to? This is possible because the presentation layer and the backend content become two separate entities. Drupal acts as the content repository and a frontend technology of your liking takes care of, of course, the frontend part of website architecture. To provide a connection between the separated development aspects of the project, enters the API.

An API layer is a necessity when going headless, because it transmits all the information from the front to the backend and vice-versa. Although the purpose of all three is the same, they are quite different from one another. Today, we would be highlighting their meanings, their pros and cons and all the visible distinctions they have. Yes, they are different too. Before that it is essential to understand their history, origin and what they were intended for because the differences actually start from there.

In simple terms, it provided an easy path for one computer to interact with another by utilising an HTTP protocol. The communication between the two computers is not stored on the server, meaning it is stateless; rather the client sessions are stored on a client-side server. REST offers a great deal of functionality without a lot of effort. Built in May ofit was designed as an encoding scheme, eliminating the need for ad-hoc code for every application to communicate with servers, which use a defined way for the same.

It not only formats the way a request should be written, but the responses also come in a formatted manner. Built in by Facebook, it acts as a cross-platform data query and manipulation language. It was inafter GraphQL was open-sourced that it became truly popular. Now that we know the basics of all the three APIs, let us have a look at their popularity status, before beginning the comparison. One of the most important aspects for an API is the way its fetches data.

It could require one or more requests. Therefore, this aspect is also referred to as its request efficiency. This works perfectly as long as you only need to retrieve a single piece of data like an article.

However, if you need more than that, the number of requests you would have to type in separately would be equivalent to the amount of data you need. No sugar-coating here, there are going to be a lot of round trips. And the problem with a lot of round trips is a lot of extra information you do not even need. As a result, the said application will not get everything it needs in a single trip, making multiple trips the only option. It's safe to say that REST over-fetches and the verbose responses can be a problem.Skip to main content.

Search form Search. Usually, when people start looking for something new, it means that current solution is not enough. But is that so? GraphQL leverages requests of two types — queries retrieving data from the server, and mutations changing the data. API Protocol Comparison If we start comparing the existing API protocols, we can easily see how the concepts and approaches of API architecture have evolved over time and what issues each new protocol has been solving. The amount of data transferred within the SOAP structure was enormous, thus requiring more resources and slowing down the communication.

With the advance of mobile applications, this approach has quickly become inefficient, as it affected their performance. REST with its endpoint requests seemed to be a silver bullet, thus has rapidly turned into the protocol of choice among open API architecture developers.

Below is an example of a SOAP request and response. As you can see, both messages are quite extensive and contain a lot of data. It contains all properties of the requested resource, no matter whether the user needs it or not. When Facebook guys started looking for a different way of fetching data from the server, they were trying to resolve the problem of under-fetching or over-fetching that the existing API protocols had.

graphql vs. rest api architecture

For example, in REST, if you requested a list of products and their prices, you would also get product descriptions and images, if available. At the same time, if prices were quoted in another source, you would have to make another query to retrieve them. GraphQL was designed to take care of this problem. When you make a data query in GraphQL, you specify exactly what you wish to receive.

Such results are achieved through shifting the data definition functions to the client side, while in REST, data is defined on the server side. Moreover, it can fetch data from different databases by a single query. Another thing you should consider while choosing the API protocol is data caching. Caching eliminates the need to send full requests and responses in case data has been cached from previous requests.

GraphQL is the better REST

At the same time, GraphQL has no inherent caching and requires additional mechanisms on the client side, such as Apollo Client. At the same time, GraphQL is much more lightweight, thus reducing the network payload. Both protocols declare data types explicitly, which ensures better communication between applications. Having mentioned caching as one of the points worth taking into account, we have to admit that in this respect both SOAP and GraphQL have nothing to boast of.

Neither of them has own caching mechanisms and needs to rely on additional tools. With no versioning, the developers using GraphQL cannot know when they need to update their application with the API change Client-side data definition allows fetching exactly the requested data.

graphql vs. rest api architecture

This data fetching approach greatly reduces network payload and increases application performance No caching mechanism. Caching setup requires implementation of additional tools Single endpoint used for retrieving data Need to learn a new API protocol. This is the big question, as we compare the protocols and find their advantages and disadvantages for a single ultimate purpose — deciding which of them to use in a particular case.

Therefore, we have put together a small list of recommended use cases for each of the API protocols we have analyzed. SOAP can be used in enterprise solutions with formal contract-based exchange formats. Besides, it can cope with higher loads and process data quicker than SOAP. GraphQL can show the best performance when the number of queries needs to be reduced to the absolute minimum. With its single query addressing multiple resources, GraphQL can be a good match to this challenge.It offers some great ideas, such as stateless servers and structured access to resources.

GraphQL was developed to cope with the need for more flexibility and efficiency! The same screen also displays the names of the last 3 followers of that user. With REST, you have to make three requests to different endpoints to fetch the required data. The server then responds with a JSON object where these requirements are fulfilled. Using GraphQL, the client can specify exactly the data it needs in a query. One of the most common problems with REST is that of over- and underfetching.

This happens because the only way for a client to download data is by hitting endpoints that return fixed data structures. Overfetching means that a client downloads more information than is actually required in the app. Imagine for example a screen that needs to display a list of users only with their names. This response however might contain more info about the users that are returned, e. The client will have to make additional requests to fetch everything it needs. This can escalate to a situation where a client needs to first download a list of elements, but then needs to make one additional request per element to fetch the required data.

As an example, consider the same app would also need to display the last three followers per user. This is handy since it allows for the client to get all required information for a particular view by simply accessing the corresponding endpoint.

With every change that is made to the UI, there is a high risk that now there is more or less data required than before. Consequently, the backend needs to be adjusted as well to account for the new data needs. This kills productivity and notably slows down the ability to incorporate user feedback into a product. With GraphQL, this problem is solved.

Thanks to the flexible nature of GraphQL, changes on the client-side can be made without any extra work on the server.

Since clients can specify their exact data requirements, no backend engineer needs to make adjustments when the design and data needs on the frontend change. This can for example help in evolving an API and deprecating specific fields that are not requested by any clients any more. With GraphQL, you can also do low-level performance monitoring of the requests that are processed by your server.

Instrumenting and measuring performance of these resolvers provides crucial insights about bottlenecks in your system. This schema serves as the contract between the client and the server to define how a client can access the data. Frontend teams can easily test their applications by mocking the required data structures.

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Once the server is ready, the switch can be flipped for the client apps to load the data from the actual API. GraphQL Fundamentals. Core Concepts. Big Picture Architecture.And as always, the choice should depend on the type of project that you are building. REST stands for Representational State Transfer, which means that the server transfers a representation of the state of the requested resource to the client after the API is called.

API is an application program interface used for communication between two software, mostly backend and frontend part of the application, but not only. Sometimes API interacts between two backends or backend and frontend of totally different applications.

Consider Twitter, where resources can be, for example, a user or a tweet. An endpoint consists of root-endpoint and path which determines the resource we are asking for. In this step, you have to consider what action will be performed. Headers are used to pass additional data for different purposes like authentication or content type. And the last element is the body, which contains data that we want to send to the server.

When we define those queries, we are defining the shape of data that we will get as a response. Another feature of GraphQL is the hierarchical nature, which means that data in the queries and responses reflect natural relationships between the objects. When we are using GraphQL, we need to define schemas, which are models of data that can be requested. The schema describes what fields the object has with the types, and it also defines what kind of queries can be sent. GraphQL may have three basic types of operations: query - to read data mutation - to save data subscription - to receive real-time data.

Requests are defined based on the queries or mutations. We can ask for different resources on the same endpoint, just chaining the query. Overfetching happened in a situation when we have to get more data that we exactly need. Imagine a situation when we need to list posts of certain users. We need user data and posts. In that case, we have to call two endpoints for one view.

START USING HTTP API // HTTP API Vs REST API // Theory and Demo

In GraphQL, we can create the query in a way that provides all the necessary data for a certain view, not too much, not too little. It makes the code less readable and maintainable. But if you have time to research and learn, you could think of building your solution with REST.

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RxJS: Observable vs Subject - tutorial for beginners. How GraphQL works? Amount of endpoints 2. Data fetching 3. Versioning Conclusion.