Alternatives to Scalr

We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

Read the blog post to go more in depth.

We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

To build #Webapps we decided to use Angular 2 with RxJS

#Devops - GitHub , Travis CI , Terraform , Docker , Serverless

We use Terraform because we needed a way to automate the process of building and deploying feature branches. We wanted to hide the complexity such that when a dev creates a PR, it triggers a build and deployment without the dev having to worry about any of the 'plumbing' going on behind the scenes. Terraform allows us to automate the process of provisioning DNS records, Amazon S3 buckets, Amazon EC2 instances and AWS Elastic Load Balancing (ELB)'s. It also makes it easy to tear it all down when finished. We also like that it supports multiple clouds, which is why we chose to use it over AWS CloudFormation.

I use Terraform because it hits the level of abstraction pocket of being high-level and flexible, and is agnostic to cloud platforms. Creating complex infrastructure components for a solution with a UI console is tedious to repeat. Using low-level APIs are usually specific to cloud platforms, and you still have to build your own tooling for deploying, state management, and destroying infrastructure.

However, Terraform is usually slower to implement new services compared to cloud-specific APIs. It's worth the trade-off though, especially if you're multi-cloud. I heard someone say, "We want to preference a cloud, not lock in to one." Terraform builds on that claim.

Terraform Google Cloud Deployment Manager AWS CloudFormation

LaunchDarkly is almost a five year old company, and our methodology for deploying was state of the art... for 2014. We recently undertook a project to modernize the way we #deploy our software, moving from Ansible-based deploy scripts that executed on our local machines, to using Spinnaker (along with Terraform and Packer) as the basis of our deployment system. We've been using Armory's enterprise Spinnaker offering to make this project a reality.