What is GitHub?
Who uses GitHub?
Why developers like GitHub?
Here are some stack decisions, common use cases and reviews by companies and developers who chose GitHub in their tech stack.
I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.
I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!
I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.
Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.
Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.
With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.
If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.
We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.
As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).
When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.
The core Shopify app has remained a Rails monolith, but we also have hundreds of other Rails apps across the organization. These are not microservices, but domain-specific apps: Shipping (talks with various shipping providers), Identity (single sign on across all Shopify stores), and App Store to name a few. Managing a hundred apps and keeping them up to date with security updates can be tough, so we've developed ServicesDB, an internal app that keeps track of all production services and helps developers to make sure that they don't miss anything important.
ServicesDB keeps a checklist for each app: ownership, uptime, logs, on-call rotation, exception reporting, and gem security updates. If there are problems with any of those, ServicesDB opens a GitHub issue and pings owners of the app to ask them to address it. ServicesDB also makes it easy to query the infrastructure and answer questions like, “How many apps are on Rails 4.2? How many apps are using an outdated version of gem X? Which apps are calling this service?”.
Quasar Framework FeathersJS Node.js Vue.js SendinBlue Zeit Now GitHub
It was almost too easy to build a complete Feathers Rest API combined with Quasar SSR and reactive form that we are serving through an i-frame within our main site for serving our newsletter signup and opt-in page. Total time: 15 hrs. Check it out:
When we've gone through code reviews (every code and config change goes through a code review) and feel good about the level of automated testing (no one can sign off on their own code's functionality; a quality assurance engineer or other developer has to verify functionality), we merge our changes via a bot that interacts with GitHub (the bot maintains our versions and change logs). After Buildkite has a green build and our binary is shipped to our repo servers, we are good to roll out deploys to our data centers and to keep pushing the needle on the performance of our system.
#Languages #DataStores #Databases #InMemoryDatabases
Heroku Docker GitHub Node.js hapi Vue.js AWS Lambda Amazon S3 PostgreSQL Knex.js Checkly is a fairly young company and we're still working hard to find the correct mix of product features, price and audience.
We are focussed on tech B2B, but I always wanted to serve solo developers too. So I decided to make a $7 plan.
Why $7? Simply put, it seems to be a sweet spot for tech companies: Heroku, Docker, Github, Appoptics (Librato) all offer $7 plans. They must have done a ton of research into this, so why not piggy back that and try it out.
Enough biz talk, onto tech. The challenges were:
- Slice of a portion of the functionality so a $7 plan is still profitable. We call this the "plan limits"
- Update API and back end services to handle and enforce plan limits.
- Update the UI to kindly state plan limits are in effect on some part of the UI.
- Update the pricing page to reflect all changes.
- Keep the actual processing backend, storage and API's as untouched as possible.
In essence, we went from strictly volume based pricing to value based pricing. Here come the technical steps & decisions we made to get there.
- We updated our PostgreSQL schema so plans now have an array of "features". These are string constants that represent feature toggles.
- The Vue.js frontend reads these from the vuex store on login.
- Based on these values, the UI has simple
v-ifstatements to either just show the feature or show a friendly "please upgrade" button.
- The hapi API has a hook on each relevant API endpoint that checks whether a user's plan has the feature enabled, or not.
Side note: We offer 10 SMS messages per month on the developer plan. However, we were not actually counting how many people were sending. We had to update our alerting daemon (that runs on Heroku and triggers SMS messages via AWS SNS) to actually bump a counter.
What we build is basically feature-toggling based on plan features. It is very extensible for future additions. Our scheduling and storage backend that actually runs users' monitoring requests (AWS Lambda) and stores the results (S3 and Postgres) has no knowledge of all of this and remained unchanged.
Hope this helps anyone building out their SaaS and is in a similar situation.
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