GraphQL vs Tornado

Overview

Tornado

Stacks530

Followers409

Votes167

GitHub Stars22.3K

Forks5.5K

GraphQL

Stacks34.9K

Followers28.1K

Votes309

GraphQL vs Tornado: What are the differences?

Data Communication Model: GraphQL is a query language that allows clients to request only the data they need, reducing over-fetching and under-fetching issues common in REST APIs. On the other hand, Tornado is a Python web framework and asynchronous networking library, focused on building scalable WebSocket services, making it suitable for real-time applications.
Type System: GraphQL enforces a strong type system, where the schema defines the structure of the data available and the queries that can be made. Tornado, as a web framework, does not have a built-in type system like GraphQL. Developers using Tornado have the flexibility to define data structures and types as needed within their application logic.
Query Language: GraphQL has its own query language for retrieving data, providing a clear and efficient way for clients to request specific data. Tornado, being a Python web framework, does not have a standardized query language like GraphQL. Developers using Tornado typically rely on Python code to interact with data sources and APIs.
Real-Time Capabilities: Tornado is well-suited for building real-time applications due to its asynchronous nature and support for WebSockets. This allows Tornado applications to handle a large number of simultaneous connections efficiently. GraphQL, while capable of handling real-time data with subscriptions, is primarily designed for fetching data in a more controlled and structured manner.
Community and Ecosystem: GraphQL has a growing community and widespread adoption, leading to a rich ecosystem of tools, libraries, and resources for developers. In contrast, Tornado, being more focused on asynchronous networking, may have a smaller community compared to GraphQL, resulting in fewer resources and support available.
Scalability: GraphQL offers a more fine-grained approach to fetching data, allowing clients to specify their data requirements precisely, thereby reducing the chances of over-fetching or under-fetching. Tornado, with its asynchronous architecture, can handle high loads and concurrent connections efficiently, making it a suitable choice for scalable web applications that require real-time capabilities.

In Summary, GraphQL and Tornado differ in their approach to data communication, type systems, query languages, real-time capabilities, community support, and scalability, catering to different use cases and developer preferences.

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CLI (Node.js)

Manual

Detailed Comparison

Tornado	GraphQL
By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user.	GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012.
-	Hierarchical;Product-centric;Client-specified queries;Backwards Compatible;Structured, Arbitrary Code;Application-Layer Protocol;Strongly-typed;Introspective
Statistics
GitHub Stars 22.3K	GitHub Stars -
GitHub Forks 5.5K	GitHub Forks -
Stacks 530	Stacks 34.9K
Followers 409	Followers 28.1K
Votes 167	Votes 309
Pros & Cons
Pros 37 Open source 31 So fast 27 Great for microservices architecture 20 Websockets 17 Simple Cons 2 Event loop is complicated	Pros 75 Schemas defined by the requests made by the user 63 Will replace RESTful interfaces 62 The future of API's 49 The future of databases 12 Get many resources in a single request Cons 4 More code to type. 4 Hard to migrate from GraphQL to another technology 2 Takes longer to build compared to schemaless. 1 No support for caching 1 No built in security
Integrations
Python	No integrations available

What are some alternatives to Tornado, GraphQL?

Node.js

Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

Rails

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

Django

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

Laravel

It is a web application framework with expressive, elegant syntax. It attempts to take the pain out of development by easing common tasks used in the majority of web projects, such as authentication, routing, sessions, and caching.

.NET

.NET is a general purpose development platform. With .NET, you can use multiple languages, editors, and libraries to build native applications for web, mobile, desktop, gaming, and IoT for Windows, macOS, Linux, Android, and more.

ASP.NET Core

A free and open-source web framework, and higher performance than ASP.NET, developed by Microsoft and the community. It is a modular framework that runs on both the full .NET Framework, on Windows, and the cross-platform .NET Core.

Symfony

It is written with speed and flexibility in mind. It allows developers to build better and easy to maintain websites with PHP..

Spring

A key element of Spring is infrastructural support at the application level: Spring focuses on the "plumbing" of enterprise applications so that teams can focus on application-level business logic, without unnecessary ties to specific deployment environments.

Spring Boot

Spring Boot makes it easy to create stand-alone, production-grade Spring based Applications that you can "just run". We take an opinionated view of the Spring platform and third-party libraries so you can get started with minimum fuss. Most Spring Boot applications need very little Spring configuration.

Android SDK

Android provides a rich application framework that allows you to build innovative apps and games for mobile devices in a Java language environment.

Related Comparisons

GraphQL vs Tornado: What are the differences?

Data Communication Model: GraphQL is a query language that allows clients to request only the data they need, reducing over-fetching and under-fetching issues common in REST APIs. On the other hand, Tornado is a Python web framework and asynchronous networking library, focused on building scalable WebSocket services, making it suitable for real-time applications.
Type System: GraphQL enforces a strong type system, where the schema defines the structure of the data available and the queries that can be made. Tornado, as a web framework, does not have a built-in type system like GraphQL. Developers using Tornado have the flexibility to define data structures and types as needed within their application logic.
Query Language: GraphQL has its own query language for retrieving data, providing a clear and efficient way for clients to request specific data. Tornado, being a Python web framework, does not have a standardized query language like GraphQL. Developers using Tornado typically rely on Python code to interact with data sources and APIs.
Real-Time Capabilities: Tornado is well-suited for building real-time applications due to its asynchronous nature and support for WebSockets. This allows Tornado applications to handle a large number of simultaneous connections efficiently. GraphQL, while capable of handling real-time data with subscriptions, is primarily designed for fetching data in a more controlled and structured manner.
Community and Ecosystem: GraphQL has a growing community and widespread adoption, leading to a rich ecosystem of tools, libraries, and resources for developers. In contrast, Tornado, being more focused on asynchronous networking, may have a smaller community compared to GraphQL, resulting in fewer resources and support available.
Scalability: GraphQL offers a more fine-grained approach to fetching data, allowing clients to specify their data requirements precisely, thereby reducing the chances of over-fetching or under-fetching. Tornado, with its asynchronous architecture, can handle high loads and concurrent connections efficiently, making it a suitable choice for scalable web applications that require real-time capabilities.

GraphQL vs Tornado

Overview