Alternatives to Keepalived

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.

We're using Git through GitHub for public repositories and GitLab for our private repositories due to its easy to use features. Docker and Kubernetes are a must have for our highly scalable infrastructure complimented by HAProxy with Varnish in front of it. We are using a lot of npm and Visual Studio Code in our development sessions.

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

• Web: a mix of JavaScript/ES6 and React.
• Desktop: And Electron to ship it as a desktop application.
• Android: a mix of Java and Kotlin.
• iOS: written in a mix of Objective C and Swift.

• The core application and the API written in PHP/Hack that runs on HHVM.
• The data is stored in MySQL using Vitess.
• Caching is done using Memcached and MCRouter.
• The search service takes help from SolrCloud, with various Java services.
• The messaging system uses WebSockets with many services in Java and Go.
• Load balancing is done using HAproxy with Consul for configuration.
• Most services talk to each other over gRPC,
• Some Thrift and JSON-over-HTTP
• Voice and video calling service was built in Elixir.

• Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.

• For server configuration and management we use Terraform, Chef and Kubernetes.
• We use Prometheus for time series metrics and ELK for logging.

Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.

We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.

We switched to Traefik so we can use the REST API to dynamically configure subdomains and have the ability to redirect between multiple servers.

We still use nginx with a docker-compose to expose the traffic from our APIs and TCP microservices, but for managing routing to the internet Traefik does a much better job

The biggest win for naologic was the ability to set dynamic configurations without having to restart the server