What will be better Laravel or Node.js to handle a logistics portal which displays thousands (20-50k) of delivery data information in an interactive table (searchable, filterable), live delivery tracking, basic user management, and report creation?
Data comes usually in CSV (manually uploaded or via API from courier companies). Live tracking uses checks tracking numbers on the courier page using API.
Hey! So I am planning to make an e-commerce website with React Native as my frontend technology stack and MongoDB as my database. I was wondering what will be the best REST framework to use for my backend that will simply serve the frontend. Is Django rest framework a good option or should I go with Flask? I am currently leaning towards flask as the development team is well versed in it. Please help!
If the application is simple such as less endpoint or simple logic, I'd suggest Flask. But what you're building is quite a large system that contains many logics and a lot of models. So I'd suggest you with Django and Django REST framework. Django ORM is also one of the best ORM in the world as well.
I am starting a new project to build a simple ERP system for small businesses, where the owners can also manage orders on their phones.
So I'm a little confused. Please advice.
There is no problem to keep using node.js for your backend. Keep in mind that you already have expertise in it, so you could focus on development instead of to learn a new syntax/framework. There are good libraries in node.js that could help you in the development (services, validations, integrations, etc) also keeps you with a single language to the whole system. Django, as far as I know, it will provide a solid base for you, but it could be too much for your purpose, also could be more complex than you could need. Java provides to you many frameworks to simplify your integrations also could achieve a good performance. Anyway, I recommend you to follow using node.js, since you already know the syntax/platform.
I recommend Node.js because you are creating a new application and will likely not have a huge workload, at least not initially. Node is incredibly fast and used in more SPA's than any other solution, but as the CPU load increases, the efficiency starts to drop off.
I could see how scalability could eventually become a concern if your SaaS platform becomes extremely popular, but considering that you already know Node, I think it would be wiser to shorten your time to market and develop a quality product rather comfortably.
Otherwise you also run the risk of security flaws due to inexperience with a new framework and that is not something you want when you are handling other people's data.
Hello, Node.js is simply a better option than python if you wish to make your application real-time operations. Also Node.js is a better choice than python for server side development.
But let's get your problem now. For most ERP projects, Node.js is a better choice. Also, since you are already familiar with Node.js, continue with it. Personally, I think Node.js is way better than Django mainly because JS is the god of ERP projects. Java is a good counterpart though.
I personally suggest NodeJs as you are also familiar with it. Even nodeJS has its own strong frameworks such as NestJS, Loopback etc. And the community is pretty much strong though. If you are looking for a faster development , then always you can go for NodeJS. And its pretty fast though.
Will you build it from scratch? There are some open source ERP/CRM solutions that you can use as a base for your solution. SugarCrm is an example. By looking at those, you can then decide which language you'll use for the backend.
Hey if you are allready familar with nodejs then just go with it. There are some very nice frameworks out there that can be hold with the big ones.
Examples: AdonisJS or SailsJS
AdonisJS is even very similar like django.
I can recommend you a flexible constructor for this purpose. To create a system, you only need sql, and you can connect to any database without any problems. Please see the introductory article about the features, and if you are interested, I can provide access to the test site. My contacts for communication are on the site page https://falconspace.site/docs/vvedenie-v-falcon-space--c-chego-nachat https://falconspace.site/for-it
We decided to migrate our web framework from Django to Flask for a number of reasons. Firstly, Flask is a light-weight framework which makes it easier to use for simpler cases and to build a quick prototype. It gives developers full freedom for what they want their application to look like. With a coherent and neat API, Flask allows easy and flexible configurations along with integrated unit testing support which makes it a great tool. By using Flask, a simple application can be later changed to add more functionality and make it complex. It provides flexibility to expand the application quickly which is harder to do with Django where requirements change dynamically. Moreover, Flask is a minimal yet powerful platform with easy database integration which gives us the opportunity to easily develop and maintain applications. On the other hand, Django has a high learning curve and is a higher entry point for simple solutions. In addition to that, Django requires roughly more than 2 times more lines of code than Flask for the same functionality. Django is suited for bigger projects that need a lot of functionality. For simpler projects like our application, the features might be an overkill. Putting all these factors into consideration, we decided it was best to switch to Flask from Django
We choose Next.js for our React framework because it's very minimal and has a very organized file structure. Also, it offers key features like zero setups, automatic server rendering and code splitting, typescript support. Our app requires some loading time to process the video, server-side rendering will allow our website to display faster than client-side rending.
- Server Side: \
For the server, we decide to mainly focus on
Pythonsince it is the most popular language for machine learning and our product will be focused on computer vision based on machine learning. Besides, every member of the team is proficient in
Pythonand capable to contribute to both development and testing. Moreover, there are lots of machine learning libraries available for
Pythonand we will be using
PyTorch. Another factor that leads to this decision is Dr. Tsai provides us an existing solution for other sports and it is using
PyTorchand it is working pretty well. So we decide to stay with these tools to develop our product so that it is much easier not only for us to get started but also for our primary customer to accept and use it as a solution. Since we are building a backend server, we will also use
Flaskwhich is a microframework for
Pythonweb development. We have considered using
Flaskis more lightweight than
Flaskcould help to keep the code simple and clean. Although
Flaskis a microframework, it has a large number of libraries and it could be as powerful as
Djangowith those libraries while not containing any unwanted functionalities. As for deployment, we decide to deploy our web app on
Herokufor the demonstration purpose of the course. The biggest advantage of
Herokuis it is completely free but it could fulfill our needs in the early stage of development. However, it might be a good idea to deploy it in CSI’s server for the production version since the data it collects and stores could potentially contain sensitive and confidential information.
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Flask is a light, yet powerful Python web framework perfect for quickly building smaller web applications. It's a "micro-framework" that's easy to learn and simple to use, so it's perfect for those new to web development as well as those looking to rapidly develop a web application.
I have benchmarked Node.js and other popular frameworks using a real life application example. You can find the results here: https://email@example.com/web-rest-api-benchmark-on-a-real-life-application-ebb743a5d7a3
We decided to move the provisioning process to an API-driven process, and had to decide among a few implementation languages:
- Go, the server-side language from Google
We built prototypes in both languages, and decided on NodeJS:
- NodeJS is asynchronous-by-default, which suited the problem domain. Provisioning is more like “start the job, let me know when you’re done” than a traditional C-style program that’s CPU-bound and needs low-level efficiency.
- NodeJS acts as an HTTP-based service, so exposing the API was trivial
Getting into the headspace and internalizing the assumptions of a tool helps pick the right one. NodeJS assumes services will be non-blocking/event-driven and HTTP-accessible, which snapped into our scenario perfectly. The new NodeJS architecture resulted in a staggering 95% reduction in processing time: requests went from 7.5 seconds to under a second.
The server side of Trello is built in Node.js. We knew we wanted instant propagation of updates, which meant that we needed to be able to hold a lot of open connections, so an event-driven, non-blocking server seemed like a good choice. Node also turned out to be an amazing prototyping tool for a single-page app. The prototype version of the Trello server was really just a library of functions that operated on arrays of Models in the memory of a single Node.js process, and the client simply invoked those functions through a very thin wrapper over a WebSocket. This was a very fast way for us to get started trying things out with Trello and making sure that the design was headed in the right direction. We used the prototype version to manage the development of Trello and other internal projects at Fog Creek.
All backend code is done in node.js
We have a SOA for our systems. It isn't quite Microservices jsut yet, but it does provide domain encapsulation for our systems allowing the leaderboards to fail without affecting the login or education content.
We've written a few internal modules including a very simple api framework.
I don't know how well this will scale if/when I have hundreds of people connected simultaneously, but I suspect that when that time comes, it may be just a matter of increasing the hardware.
Used node.js server as backend. Interacts with MongoDB using MongoSkin package which is a wrapper for the MongoDB node.js driver. It uses express for routing and cors package for enabling cors and eyes package for enhancing readability of logs. Also I use nodemon which takes away the effort to restart the server after making changes.
Django takes the hassle out of building an enterprise web application using Python.
- admin app for administration
- ORM for deploying against different database vendors
- social auth package for authentication with enterprise IdP
- guardian package for authorization
I use Flask for times when I need to create a REST API that interfaces with other Python code, or there is no specific reason why I'd want to use Node.JS. I prefer Flask because of its small learning curve, allowing me to get started coding as quickly as possible
Our backend was written in Django. We took advantage of the ready-to-go admin interface as a go-to solution for the client to be able to authorize his users, as well as other functionality, while most of the work was done through the Django Rest Framework.
Hands down the best Python web framework I've used. Very easy to extend and add apps and go from 0 to full project quickly and painlessly. I built a fully authenticated project with a single endpoint in less than 30 minutes.
This lightweight web framework enables quick REST API development while enabling easy clustering, and the usage of multiple worker processes required to scale the REST API service to meet high volume requirements.
정말 편리하고 많은것을 알아서 제공해 주는 프레임워크 이다. 책의 예제만 진행해서 많이 써보지는 못했지만, 쉽게 쉽게 웹을 개발 할 수 있는 점이 매력적 이다. 게다가 orm 이 기본으로 내장 되어 있고 db 도 sqlite 가 기본으로 되어있어. 그냥 django 만 설치하면 바로 웹개발이 가능하다.
django는 저의 무기입니다.
django 이외에 flask로 간단한 restful api를 만들면서 느낀점은 framework 보다 언어가 중요하다는것을 알았고 django가 얼마나 큰 framework인지 알게되었습니다.
저는 signal 사용을 좋아합니다.
Service to query NOAA weather forecasts data and service to build tidal current forecast maps using AWS EC2 and Geoserver
Flask drives our APIs, both the Website APIs and the majority of the REST Messaging APIs