API Platform vs Node.js: What are the differences?
API Platform and Node.js can be primarily classified as "Frameworks (Full Stack)" tools.
Node.js is an open source tool with 35.4K GitHub stars and 7.78K GitHub forks. Here's a link to Node.js's open source repository on GitHub.
According to the StackShare community, Node.js has a broader approval, being mentioned in 5477 company stacks & 20011 developers stacks; compared to API Platform, which is listed in 6 company stacks and 6 developer stacks.
What is API Platform?
What is Node.js?
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The core Web application of Raygun is still a Microsoft ASP.NET MVC application. Not too much has changed from a fundamental technology standpoint. We originally built using Mono, which just bled memory and would need to be constantly recycled. So we looked around at the options and what would be well suited to the highly transactional nature of our API. We settled on Node.js, feeling that the event loop model worked well given the lightweight workload of each message being processed. This served us well for several years.
When we started to look at .NET Core in early 2016, it became quite obvious that being able to asynchronously hand off to our queuing service greatly improved throughput. Unfortunately, at the time, Node.js didn’t provide an easy mechanism to do this, while .NET Core had great concurrency capabilities from day one. This meant that our servers spent less time blocking on the hand off, and could start processing the next inbound message. This was the core component of the performance improvement.
We chose .NET because it was a platform that our team was familiar with. Also we were skilled enough with it to know many performance tips and tricks to get the most from it. Due to this experience, it helped us get to market faster and deliver great performance.
When starting a new company and building a new product w/ limited engineering we chose to optimize for expertise and rapid development, landing on Rails API, w/ AngularJS on the front.
The reality is that we're building a CRUD app, so we considered going w/ vanilla Rails MVC to optimize velocity early on (it may not be sexy, but it gets the job done). Instead, we opted to split the codebase to allow for a richer front-end experience, focus on skill specificity when hiring, and give us the flexibility to be consumed by multiple clients in the future.
We also considered .NET core or Node.js for the API layer, and React on the front-end, but our experiences dealing with mature Node APIs and the rapid-fire changes that comes with state management in React-land put us off, given our level of experience with those tools.
We're using GitHub and Trello to track issues and projects, and a plethora of other tools to help the operational team, like Zapier, MailChimp, Google Drive with some basic Vue.js & HTML5 apps for smaller internal-facing web projects.
Back at the start of 2017, we decided to create a web-based tool for the SEO OnPage analysis of our clients' websites. We had over 2.000 websites to analyze, so we had to perform thousands of requests to get every single page from those websites, process the information and save the big amounts of data somewhere.
Very soon we realized that the initial chosen script language and database, PHP, Laravel and MySQL, was not going to be able to cope efficiently with such a task.
By that time, we were doing some experiments for other projects with a language we had recently get to know, Go , so we decided to get a try and code the crawler using it. It was fantastic, we could process much more data with way less CPU power and in less time. By using the concurrency abilites that the language has to offers, we could also do more Http requests in less time.
Unfortunately, I have no comparison numbers to show about the performance differences between Go and PHP since the difference was so clear from the beginning and that we didn't feel the need to do further comparison tests nor document it. We just switched fully to Go.
There was still a problem: despite the big amount of Data we were generating, MySQL was performing very well, but as we were adding more and more features to the software and with those features more and more different type of data to save, it was a nightmare for the database architects to structure everything correctly on the database, so it was clear what we had to do next: switch to a NoSQL database. So we switched to MongoDB, and it was also fantastic: we were expending almost zero time in thinking how to structure the Database and the performance also seemed to be better, but again, I have no comparison numbers to show due to the lack of time.
As of now, we don't only use the tool intern but we also opened it for everyone to use for free: https://tool-seo.com
Since 2011 our frontend was in Django monolith. However, in 2016 we decide to separate #Frontend from Django for independent development and created the custom isomorphic app based on Node.js and React. Now we realized that not need all abilities of the server, and it is sufficient to generate a static site. Gatsby is suitable for our purposes. We can generate HTML from markdown and React views very simply. So, we are updating our frontend to Gatsby now, and maybe we will use Netlify for deployment soon. This will speed up the delivery of new features to production.
StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.
Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!
#StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
I just finished a web app meant for a business that offers training programs for certain professional courses. I chose this stack to test out my skills in graphql and react. I used Node.js , GraphQL , MySQL for the #Backend utilizing Prisma as a database interface for MySQL to provide CRUD APIs and graphql-yoga as a server. For the #frontend I chose React, styled-components for styling, Next.js for routing and SSR and Apollo for data management. I really liked the outcome and I will definitely use this stack in future projects.
For many(if not all) small and medium size business time and cost matter a lot.
That's why languages, frameworks, tools, and services that are easy to use and provide 0 to productive in less time, it's best.
Maybe Node.js frameworks might provide better features compared to Rails but in terms of MVPs, for us Rails is the leading alternative.
Amazon EC2 might be cheaper and more customizable than Heroku but in the initial terms of a project, you need to complete configurationos and deploy early.
Advanced configurations can be done down the road, when the project is running and making money, not before.
Finally, comunication and keeping a good history of conversations, decisions, and discussions is important so we use a mix of Slack and Twist
When we started thinking about technology options for our own Design System, we wanted to focus on two primary goals
- Build a design system site using design system components - a living prototype
- Explore new ways of working to position our technical capabilities for the future
We have a small team of developers responsible for the initial build so we knew that we couldn’t spend too much time maintaining infrastructure on the Backend. We also wanted freedom to make decisions on the Frontend with the ability to adapt over time.
For this first iteration we decided to use Node.js, React, and Next.js. Content will be managed via headless CMS in prismic.io.
- Next.js so that we can run React serverside without worrying about server code.
- prismic.io so that our content is accessible via API and our frontend is fully independent.
Possible pros for Python / Django: - easy syntax, easier to learn for me as a beginner - fast development, earlier release - libraries for mathematical and scientific computation
Which software would you use in my case? Are my arguments for Python/NodeJS right? Which kind of database would you use?
Thank you for your answer!
Mixmax was originally built using Meteor as a single monolithic app. As more users began to onboard, we started noticing scaling issues, and so we broke out our first microservice: our Compose service, for writing emails and Sequences, was born as a Node.js service. Soon after that, we broke out all recipient searching and storage functionality to another Node.js microservice, our Contacts service. This practice of breaking out microservices in order to help our system more appropriately scale, by being more explicit about each microservice’s responsibilities, continued as we broke out numerous more microservices.
As Mixmax began to scale super quickly, with more and more customers joining the platform, we started to see that the Meteor app was still having a lot of trouble scaling due to how it tried to provide its reactivity layer. To be honest, this led to a brutal summer of playing Galaxy container whack-a-mole as containers would saturate their CPU and become unresponsive. I’ll never forget hacking away at building a new microservice to relieve the load on the system so that we’d stop getting paged every 30-40 minutes. Luckily, we’ve never had to do that again! After stabilizing the system, we had to build out two more microservices to provide the necessary reactivity and authentication layers as we rebuilt our Meteor app from the ground up in Node.js. This also had the added benefit of being able to deploy the entire application in the same AWS VPCs. Thankfully, AWS had also released their ALB product so that we didn’t have to build and maintain our own websocket layer in Amazon EC2. All of our microservices, except for one special Go one, are now in Node with an nginx frontend on each instance, all behind AWS Elastic Load Balancing (ELB) or ALBs running in AWS Elastic Beanstalk.
We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.
To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas
To build #Webapps we decided to use Angular 2 with RxJS
#Devops - GitHub , Travis CI , Terraform , Docker , Serverless
At IT Minds we create customized internal or #B2B web and mobile apps. I have a go to stack that I pitch to our customers consisting of 3 core areas. 1) A data core #backend . 2) A micro #serverless #backend. 3) A user client #frontend.
For the Data Core I create a backend using TypeScript Node.js and with TypeORM connecting to a PostgreSQL Exposing an action based api with Apollo GraphQL
For the micro serverless backend, which purpose is verification for authentication, autorization, logins and the likes. It is created with Next.js api pages. Using MongoDB to store essential information, caching etc.
Finally the frontend is built with React using Next.js , TypeScript and @Apollo. We create the frontend as a PWA and have a AMP landing page by default.
I have benchmarked Node.js and other popular frameworks using a real life application example. You can find the results here: https://firstname.lastname@example.org/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.