What is Algolia?
Who uses Algolia?
Why developers like Algolia?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Algolia in their tech stack.
Shortly after I joined Algolia as a developer advocate, I knew I wanted to establish a place for the community to congregate and share their projects, questions and advice. There are a ton of platforms out there that can be used to host communities, and they tend to fall into two categories - real-time sync (like chat) and async (like forums). Because the community was already large, I felt that a chat platform like Discord or Gitter might be overwhelming and opted for a forum-like solution instead (which would also create content that's searchable from Google).
I looked at paid, closed-source options like AnswerHub and ForumBee and old-school solutions like phpBB and vBulletin, but none seemed to offer the power, flexibility and developer-friendliness of Discourse. Discourse is open source, written in Rails with Ember.js on the front-end. That made me confident I could modify it to meet our exact needs. Discourse's own forum is very active which made me confident I could get help if I needed it.
It took about a month to get Discourse up-and-running and make authentication tied to algolia.com via the SSO plugin. Adding additional plugins for moderation or look-and-feel customization was fairly straightforward, and I even created a plugin to make the forum content searchable with Algolia. To stay on top of answering questions and moderation, we used the Discourse API to publish new messages into our Slack. All-in-all I would say we were happy with Discourse - the only caveat would be that it's very helpful to have technical knowledge as well as Rails knowledge in order to get the most out of it.
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.
Although we were using Elasticsearch in the beginning to power our in-app search, we moved this part of our processing over to Algolia a couple of months ago; this has proven to be a fantastic choice, letting us build search-related features with more confidence and speed.
Elasticsearch is only used for searching in internal tooling nowadays; hosting and running it reliably has been a task that took up too much time for us in the past and fine-tuning the results to reach a great user-experience was also never an easy task for us. With Algolia we can flexibly change ranking methods on the fly and can instead focus our time on fine-tuning the experience within our app.
Memcached is used in front of most of the API endpoints to cache responses in order to speed up response times and reduce server-costs on our side.
Redesigned the Zephir documentation website. This included converting all the documentation to markdown in its own repository. We connected the documentation to Crowdin for our translations/localization service. Our repositories are all on GitHub .
A new application was created to handle the website. The application was developed using nanobox.io locally, with Phalcon and PhpStorm as the editor. We also integrated with Algolia for our search using their docsearch service.
The stack was deployed to our servers and is now live!
Phalcon Zephir #Documentation
On Meetupfinder.com, we needed a way to find geographic coordinates for various cities around the world. Algolia Places made it very easy to us to implement this is.
Algolia powers many aspects of our feed and Stack Decisions. When you @ mention a tool or tag a topic, their #SearchAsAService engine delivers results quickly and accurately. #StackDecisionsLaunch
- Database search
- Search as you type
- Analytics dashboard
- Search in any language
- Understand users mistakes
- Smart Highlighting
- Realtime indexing
- Protect your indexes from misuse
- Discover realtime faceting
- Search objects by location