Startups Using Memcached in Boston

Ruby / Rails – We use Ruby and Rails for our web facing applications, as well as the API underlying the platform. The entire platform is deployed to Amazon AWS. We haven’t made the jump to Rails 5 yet for existing applications, but it’s on the radar.

AWS – The entire drizly platform is built on AWS. We use a variety of different services, including SNS, Redshift and Lambda, which are used heavily in our data pipeline.

React – Where possible new frontend development is done in React. We have some SPA style projects built in React, as well as a significant number of components on our e-commerce site utilizing react.

Mysql/Postgres/Redshift – Depending on the application we use a slightly different data-store. We like to be flexible, and find the right solution for the problem at hand.

SQS – We utilize SQS heavily as a background job processing system. We’ve found it to be stable, and durable, and performant enough for our use cases.

Ruby On Rails – We use RoR for our core site implementation, enabling users to search for medical Providers and easily leave ratings & reviews. Our web pages are built using Bootstrap, HAML, SASS and Javascript technologies. We’ve built a data model system in Python that replaces ActiveRest. That model system is shared as a reusable Library by many of our other applications.

Postgres & Redis – All this is backed up by RDS instances in AWS running PostgresDB. We heavily use Redis and SOLR for data caching and queue management.

Flask/Python – The rest of our apps and services – Email systems, data analysis, internal tools – all run in Python based Flask/Flask-Restless environments.

ELK – Our logging system is run as an Elasticsearch-Logstash-Kibana stack utilizing Filebeat and Logspout for streaming the log output. From this stack we’ve also created a comprehensive Technical SEO Dashboard where we can monitor crawlers and their activity and measure the cause & effect on new site features.

DevOps – Our apps are deployed using Docker Swarm orchestration via Ansible scripts for independence from specific cloud providers. We’ve built a structure with Docker in a Blue/Green deployment methodology so there is zero downtime when releasing code updates. The system is front ended with Jenkins-CI for automated execution of Unit/Integration/Acceptance test suites.

Ruby on Rails – We use Rails for most of our services. It’s easy to read, easy to test, reasonably fast to learn, and opinionated in ways that we find helpful.

Elixir – We’re using Elixir for select services where high concurrency is important.

React – We are using React on new frontend features because it’s stateless paradigm makes for code that is easier to reason about and winds up with fewer bugs. It’s also nice to have a single framework across our services, so folks don’t have to learn an entirely new system every time they work on something different.

MariaDB – It seems like everyone is moving to NoSQL data stores, but we love SQL! It turns out that databases that have been around for several decades are very good at what they do — indexing, locking, transacting — and using this proven technology means we get a lot of DBMS features “for free” that NoSQL variants force you to build yourself. We do have a service at scale beyond what a single SQL database can support, and in that instance we are sharded across several database instances.

Docker – All new application servers that we build are containerized and thus entirely immutable. This eliminates an entire class of problems that arise when servers are otherwise left in an unexpected state. We never have to worry about rogue processes, old open ports, or artifacts on the file system impacting a newly-deployed set of code.

MySQL – MySQL is used to provide the main data storage for all business critical information such as user data, jobs, candidates, assessments meta-data etc. We use NDB cluster as well as full redundancy real-time back-up server. Additionally the data is archived hourly, daily and weekly. When it comes to data security – nothing is ever too much.

MongoDB – Thousands of data points a minute are streaming to our servers in the form of user responses to pre-employment assessment answers. This data constitutes the main material for later analytics. Mongo’s Sharding technique allows us to employ multiple low cost instances to handle all this data in parallel fashion. Like MySQL data, No-SQL data is fully redundant and backed up on regular basis.

Python/R – Both Python and R are used to automate the data analytics, required for creating job-success predictions. While Python provides a much more versatile and reliable development environment (especially with modules like NumPy, Pandas, etc), R still has advantages in certain areas. Python’s rpy2 module make the two work together pretty decently.

Apache/PHP – Since our web application is a single-page app, the web service is mainly used as a REST-style backend that interacts with the browser by sending back-and-forth JSON packages. Memcached allows to maintain single state between all web instances. Other great tools like WKPDF (that is used for server-side web rendering) for creating downloadable materials, etc.

JavaScript/Web MVP – On the client we took a rather unorthodox approach of creating our own MVP framework that connects seamlessly with the backend, and makes the entire development cycle much faster. The framework that we created (ElementsJS) makes use of jQuery as well as multiple open-source jQuery plug-ins, while binding them together in a simple to use JavaScript API.