CircleCI

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

We have added very little to the CoffeeScript Hubot application – just enough to allow it to talk to our Hubot workers. The Hubot workers implement our operational management functionality and expose it to Hubot so we can get chat integration for free. We’ve also tailored the authentication and authorization code of Hubot to meet the needs of roles within our team.

For larger tasks, we’ve got an internal #CLI written in Go that talks to the same #API as Hubot, giving access to the same functionality we have in Slack, with the addition of scripting, piping, and all of our favorite #Unix tools. When the Hubot worker recognizes the CLI is in use, it logs the commands to Slack to maintain visibility of operational changes.

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.

When you interact with CircleCI's web application, all of your requests are hitting the #API hosts. We handle the majority of our authentication via #OAuth from GitHub or Bitbucket. We provide programmatic access to everything exposed in the UI through an API token that you can generate once you have authenticated.

Our backend consists of two major pools of machines. One pool hosts the systems that run our site, manage jobs, and send notifications. These services are deployed within Docker containers orchestrated in Kubernetes. Due to Kubernetes’ ecosystem and toolchain, it was an obvious choice for our fairly statically-defined processes: the rate of change of job types or how many we may need in our internal stack is relatively low.

The other pool of machines is for running our users’ jobs. Because we cannot dynamically predict demand, what types of jobs our users need to have run, nor the resources required for each of those jobs, we found that Nomad excelled over Kubernetes in this area.

We’re also using Helm to make it easier to deploy new services into Kubernetes. We create a chart (i.e. package) for each service. This lets us easily roll back new software and gives us an audit trail of what was installed or upgraded.

We are in the process of adopting Next.js as our React framework and using Storybook to help build our React components in isolation. This new part of our frontend is written in TypeScript, and we use Emotion for CSS/styling. For delivering data, we use GraphQL and Apollo. Jest, Percy, and Cypress are used for testing.

Most of CircleCI is written in Clojure and it has been this way since almost the beginning. Early development included Rails, but by the time that CircleCI was released to the public, it was written entirely in Clojure. Clojure is still at our platform’s core. It helps having a common language across much of our stack to allow for our engineers to move between layers of the stack without much overhead.