At the beginning of October, the OpenSauced engineering team completed a weeks-long
migration of our infrastructure, data, and pipelines to Microsoft Azure. Before this move, we had several bespoke container Apps on Digital Ocean alongside managed PostgreSQL databases.
This setup worked well for a while and was a great way to bootstrap. But, because we lacked GitOps, infrastructure-as-code (IaC) tooling, and a structured method for storing secrets in those early days, our app configurations could be brittle, prone to breaking during upgrades or releases, and difficult to scale in a streamlined manner.
We ultimately decided to migrate our core backend infrastructure from DigitalOcean to Azure, consolidating everything into a unified environment. This move allowed us to capitalize on our existing Azure Kubernetes Service (AKS) infrastructure and fully commit to Kubernetes as our primary service and container orchestration platform.
Azure Kubernetes Service for container runtimes
If you've read any of my previous engineering deep dives (including Technical Deep Dive: How We Built the Pizza CLI Using Go and Cobra, How we use Kubernetes jobs to scale OpenSSF Scorecard, and How We Saved 10s of Thousands of Dollars Deploying Low Cost Open Source AI Technologies At Scale with Kubernetes), you know that we already deploy several AI services and core data pipelines on AKS (primarily the services that power StarSearch).
To simplify our infrastructure and make the most of our existing compute resources in our AKS clusters, we adopted a "monolithic cluster" approach. This means we’re deploying all infrastructure, APIs, and services to the same AKS clusters, centralizing control, management, deployment, and scaling.
The benefits are clear: we avoid the complexity of multi-cluster management, consolidate our networking within a single region, and streamline operations for our small, agile engineering team.
However, this approach has trade-offs we may need to tackle in the future. As OpenSauced grows and scales, we’ll need to reassess and likely adopt a multi-region or multi-cluster strategy to support a globally distributed network. This decision was made with a conscious understanding of the scalability challenges we may face in the future, but for now, this approach gives us the flexibility and simplicity we need.
Choosing a Kubernetes Ingress controller
With AKS now handling all our backend infrastructure, including public-facing APIs, we needed an ingress solution for routing external traffic into our clusters. This also required load balancing, firewall management, Let's Encrypt certificates for SSL, and security policies.
We chose Traefik as our Kubernetes ingress controller. Traefik, a popular choice in the Kubernetes community, is an "application proxy" that offers a rich set of features while being easy to set up. With Traefik, what could have been a complex, error-prone task became an intuitive and streamlined integration into our infrastructure.
Using Pulumi for infrastructure as code and deployment
A key part of our migration was adopting Pulumi as our infrastructure-as-code solution. Before this, our infrastructure setup was a bit ad-hoc, with various configurations and third-party services stitched together manually. When we needed a new cloud service or we were ready to deploy some new API service, we'd piece-meal the different bits together in cloud dashboards and build some custom automation in GitHub actions. While this worked in the very early stages of OpenSauced, it quickly became brittle and hard to manage at scale or across an engineering team.
Pulumi offers several benefits that have already had a noticeable impact on our workflows and engineering culture:
- Environment Reproducibility: We can easily create and replicate environments, whether spinning up a new Kubernetes cluster or a full staging environment. It’s as simple as creating a new Pulumi stack.
- Simple, Consistent Deployments: Deployments are straightforward, repeatable, and integrated into our CI/CD pipelines.
- State and Secret Management: Pulumi provides a built-in mechanism for storing state and secrets, which can be securely shared across the entire engineering team.
- GitOps Compatibility: By leveraging Pulumi’s tight integration with Git, we can adopt deeper GitOps workflows, bringing more automation and consistency to our infrastructure management.
Overall, Pulumi has significantly reduced the friction around infrastructure management and deploying new services, allowing us to focus on what really matters — building OpenSauced!
Azure Flexible servers for managed Postgres
For the data layer at OpenSauced (including user data, user assets, and GitHub repository metadata), we previously used DigitalOcean’s managed PostgreSQL service. For our migration to Azure, we opted for Azure Database for PostgreSQL with the Flexible Server deployment option.
This service gives us all the benefits of a managed database solution, including automated backups, restoration capabilities, and high availability. The bonus here is that we can co-locate our data with our AKS clusters in the same region, ensuring low-latency networking between our services on-cluster and the database.
Looking ahead, as our user base grows, we’ll need to explore data replication and distribution to additional regions to enhance availability and redundancy. But for now, this managed solution meets our needs and positions us well for future scalability.
Hats off to the Azure Postgres team on enabling a smooth and near zero downtime migration of our data. All in all, using Azure's provided migration tools, moving everything over took less than 5 minutes. We completed the production migration with minimal end user impact. Because we used Pulumi to configure all our containers on-cluster and also deploy the Postgres flexible servers, we could quickly and easily re-deploy our containers with different configurations to be ready to use the new databases.
Between our Kubernetes environment, Pulumi IaC tooling, and Azure's sublime migration tools, we were able to complete a full production migration seamlessly.
Grafana Observability
As part of this migration, we also made some enhancements to our observability stack to ensure that our backend infrastructure is properly monitored. We use Grafana for observability, and during the migration, we deployed Grafana Alloy on our clusters. Alloy integrates seamlessly with Prometheus for metrics and Loki for log aggregation, giving us a powerful observability framework.
With these tools in place, we have a comprehensive view of our system’s health, allowing us to monitor performance, detect anomalies, and respond to issues before they impact our users. Additionally, our integration with Grafana’s on-call and alerting features enable our engineering team to respond to incidents and ensure OpenSauced stays healthy.
A huge thank you to our Microsoft Azure partners in enabling us to make this transition, providing their expertise, and supporting us along the way!!
As always, stay saucy friends!!
Top comments (0)