What is Django?
What is Node.js?
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Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.
In late 2015, following the Series G, Pinterest began migrating their web experience to React, primarily because they “found React rendered faster than our previous template engine, had fewer obstacles to iterating on features and had a large developer community.”
The legacy setup consistent of Django, Python and Jinja on the backend, with Nunjucks handling template rendering on the client side. They wanted to move to React for handling template rendering across the board, but if they “switched the client-side rendering engine from Nunjucks to React, [they’d] also have to switch [their] server-side rendering, so they could share the same template syntax.”
Now, when a user agent makes a request, a latent module render requests that it needs data via an API call. Concurrently, a separate network call is made “to a co-located Node process to render the template as far as it can go with the data that it has.”
Node then responds with rendered templates, and along with a “holes” array to indicate what data was still needed to complete the render. Finally, the Python webapp makes an API call to fetch the remaining data, and each module is sent back to Node as completely independent module requests/in parallel/.
With this framework in place, Pinterest developers are in the process of replacing Nunjucks code with React components throughout the codebase.
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.
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!