Infrastructure Scaling 101: Must-Have Tools and Technologies

Over my 10 years working at different companies and organizations, I've seen all sorts of ways people tackle scalability. I've often seen firsthand how stuff inside the company can really hold things back, and how even the perfect solution sometimes just doesn't fly.

So I thought I'd write some articles on this topic as we teamed up with LUMENAX. I'd love to hear what you think, too!

What is Scaling?

First things first, let's define scaling. There are many different aspects, but for me, scaling isn't just about getting bigger. It's about smart growth that keeps things working well as you expand (without breaking down or losing efficiency).

When Companies Need to Scale

With this as a base, let's dive into what I consider the 4 most important categories where companies usually get lost:

1. Product success and market fit

   • When a company's product hits the mark or they expand their product line, it often leads to challenges in managing a growing customer base:

     • Manual onboarding becomes unmanageable, requiring process automation

     • Surge in customer support requests for operational problem-solving (e.g., data recovery, performance issues)

     • Increase in feature services or deployments, along with maintenance needs

2. Employee turnover due to burnout

   • When staff feel overwhelmed or overworked, it can lead to higher turnover rates

3. Rising employee costs and operational efficiency

   • As the company grows, there's often a need to improve operational efficiency to manage increasing employee costs

4. Talent shortage

   • Difficulty in finding and hiring the right talent to support growth

Which strategies to use from a technical perspective

• Scaffolding is especially important during initial phase of building your infrastructure. Scaffolding allows you to reuse community knowledge of dozens of contributors where problems are covered with focus on best-practices, and will save you heaps of time while would you reinventing wheel. With this approach, you can focus on more unique challenges that existing services or tools may not fully address.

  
  

  Key Areas Where Scaffolding is Applied:

  • Application Development: Scaffolding through libraries and frameworks (e.g., React, Django) allows for rapid development of features and functionality without reinventing the wheel.

  • Infrastructure Automation: Tools like Ansible or Terraform modules serve as scaffolding for automating the creation and management of cloud resources, such as virtual machines, storage, and networks.

  • Internal Developer Platforms: Scaffolding within internal platforms enables the provisioning of entire infrastructures, where services, databases, networking, and workloads can be efficiently deployed and managed.

  
  

  Action point: Use existing tools and frameworks for scaffolding instead of building custom solutions.

• Using Infrastructure as Code (IaC) to describe your infrastructure ensures that all team members have consistent visibility into the infrastructure resources. It’s crucial to design your IaC repository in a clear and readable way, making it easy to understand the logical structure of your modules and the input values they depend on. If you opt for a declarative approach, tools like Terraform, along with wrappers such as Terragrunt or Terramate, or even Crossplane (for GitOps-based infrastructure management), can help you organize your IaC repository according to best practices. For teams with a development background, imperative tools like Pulumi, or hybrid tools like Ansible (which support both imperative and declarative approaches), may be more suitable. Ultimately, it’s best to choose a tool your team is most comfortable with.

  
  

  Action point: Adopt Infrastructure as Code (IaC) using the tools and frameworks that best match your team's skillset,

• Hand in hand with your IaC is the need for a place where you can run your code for provisioning the infrastructure. For that purpose, CI/CD runners serve as the backbone of the automation process in a CI/CD pipeline. Rather than delving into what CI/CD is or should contain, it's more important to focus on the challenges you'll encounter, particularly those related to scaling. As your infrastructure grows and more team members start contributing, you will repeatedly encounter the question of where your CI/CD runners should be executed. The right solution depends on the current stage of your infrastructure's development, ranging from the initial setup to when multiple contributors are working in parallel:

  • Hosted runners: This is the fastest solution where you just need to configure your pipeline CI/CD pipeline, and you are ready to go. Hosted runners are great for smaller projects or the early stages of infrastructure development, as they require minimal setup and maintenance. However, they might become limiting or more costly as your workloads grow in complexity and volume, as they are typically billed based on compute minutes and often have pricing per user.

  • Self-hosted runners: As your pipeline demands increase, more users start working with your IaC, and you begin to surpass the compute time limits of the starting tiers, it’s likely the right time to invest some of your resources into configuring a few VMs to set up self-hosted runners. This will provide greater control over the environment, better performance, and potentially lower costs in the long run, especially as your workloads continue to grow.

  • Autoscaling runners in a Kubernetes cluster: For more advanced stages, especially when scalability and efficiency become top priorities, deploying autoscaling runners in a Kubernetes cluster is a smart move. With runners in a Kubernetes cluster, you can easily scale the number of runners up or down according to your team's usage, effectively handling peaks in demand. It provides a balance between resource efficiency and scalability, making it suitable for larger teams and more complex projects. However, it requires experience with Kubernetes and adds an additional layer of infrastructure to maintain.

  • An alternative to Kubernetes is deploying CI/CD runners on spot instances. This approach can significantly reduce costs, as spot instances are typically offered at a fraction of the regular price, and they allow you to scale runners up or down dynamically based on pipeline demand. However, it's essential to keep in mind that spot instances can be terminated at short notice, making them unsuitable for workloads that are inflexible, stateful and fault-intolerant.

  
  

  Action point: Select the CI/CD runner setup that best fits your team’s current stage and scalability needs, and as you reach the limits of your existing setup, regularly reevaluate your runners to ensure they continue to meet your scaling, cost, and performance requirements.

• If you are running your workloads on Kubernetes, GitOps has become the standard practice. It differs from the historically well-known push method by adopting a pull-based approach, where a GitOps controller continuously checks your source-of-truth repository for changes that need to be applied to your cluster. With the GitOps approach, you can manage not only workloads running on your clusters but also any Kubernetes resources used within your cluster. Current key players in this space that you should consider are ArgoCD, FluxCD, and the newest addition, Sveltos:

  • It allows you to make changes at scale across multiple clusters with a single commit.

  • It provides platform engineers with clear observability into which services are deployed, their configurations, and in which clusters they are applied.

  • GitOps also encompasses concepts such as source of truth, auditing, revisions, self-healing, disaster recovery, and continuous deployment, which would otherwise need to be handled separately.

  
  

  Action point: If you are using Kubernetes, adopt GitOps to manage your infrastructure and establish your source of truth within a version control system.

  
  

• Automate your repetitive, one-time actions typically executed by humans, such as processing files, handling data transformations, triggering workflows, responding to events, and integrating cloud services. These automated tasks may vary depending on your workload environment, with different tools and approaches being more suitable for cloud-native versus on-premises or hybrid setups:

  • Use Kubernetes operators to automate specific human operations instead of relying on manual actions by engineers. Operators can react to state changes in your custom resource definitions, extending the capabilities of standard Kubernetes components.

  • As an analogy to operators, we typically utilize scripts that are orchestrated by management tools like Ansible and Puppet. These scripts are then managed by DevOps platforms like Jenkins, GitHub, or GitLab CI, where you can run them as one-time actions, triggers, or scheduled tasks according to your needs. However, this approach requires more engineering effort.

  • Another analogy to Operators are serverless functions on cloud providers. These are often seen as a simpler alternative, as they eliminate the need to manage the execution environment for scripts, leaving that responsibility to the cloud provider. Serverless functions are particularly useful when you need to automate tasks that are closely integrated with the services provided by your chosen cloud platform.

  
  

  Action point: Automate repetitive tasks using tools that best suit your environment, such as Kubernetes operators, serverless functions, or orchestration tools like Ansible or Puppet, to minimize manual effort and enhance efficiency.

• Don't be a knowledge silo! Document everything you come across to unblock your teammates and speed up their work. Whether it's related to provisioning/onboarding processes, upgrading infrastructure resources, or solving specific operational issues, this is critical. As you scale, it's essential that your team functions as one, with everyone sharing the necessary knowledge.

  
  

  Action point: Ensure you document all processes, solutions, and learnings to prevent knowledge silos.

• Centralise the storage of secrets and credentials. As your infrastructure and applications resources grows we need to cover many critical aspects like access control, automated rotations, audit logging, dynamic creation, etc. Managing these becomes increasingly difficult if your credentials are spread across multiple systems, each with its own methods for handling these aspects, which adds operational burdens.

  
  

  Action point: Plan to centralize secrets storage early, before your secrets become scattered across multiple systems.

• Testing sets are essential for verifying important features after upgrades. This is especially important to identify any feature degradation or discrepancies, allowing you to react quickly and execute a rollback before customers notice any issues. Two key types of testing sets include smoke tests and load tests, both serving distinct purposes in maintaining system stability.

  • Smoke Tests: These are a basic set of tests that check the most critical functionalities of your application to ensure they work correctly after an upgrade or deployment. Think of them as a "sanity check" – they help you quickly verify that the essential features (such as logging in, accessing key pages, or basic CRUD operations) are functioning as expected. Smoke tests are typically executed first, providing a quick assessment to determine if the system is stable enough for further, more comprehensive testing.

  • Load Tests: These tests evaluate how your system performs under heavy traffic, simulating a high number of users, requests, or data processing to identify any potential performance issues. This helps in identifying performance bottlenecks, ensuring your system can handle real-world demands without degradation or failure.

    
    

  Action point: Implement smoke and load tests to verify key functionalities and performance after upgrades before customers notice any issues.

• Last but not least, the important pillars of any infrastructure are the domains of observability, alerting, and monitoring. These domains are crucial for ensuring stability, reliability, and responsiveness, and they should always be considered from the very beginning when designing your infrastructure. Integrating them early on helps you build a system that can effectively handle growth, detect issues promptly, and maintain performance as it scales. Here are a few key points that should be covered from the perspective of these domains:

  • Centralized Logging: Achieve comprehensive visibility into your infrastructure's health and performance by collecting logs from all sources and real-time metrics data, using tools like Promtail and Loki, or cloud provider solutions such as AWS CloudWatch Logs, Azure Monitor Logs, or Google Cloud Logging.

  • Automated Metrics Monitoring: Ensure continuous tracking of key performance indicators (KPIs) such as CPU usage, memory, latency, and errors to maintain real-time insights into system health. Using tools like Prometheus, Datadog, AWS CloudWatch, Azure Monitor Metrics, or Google Cloud Monitoring. Additionally, design your applications to expose custom metrics relevant to their performance and functionality. This approach provides a more comprehensive understanding of your application's behavior and helps identify potential issues early on.

  • Scalable Alerting: Establish an alerting system that scales with your infrastructure, promptly notifying your team of critical issues while minimizing unnecessary noise as your infrastructure grows, using tools like Prometheus Alertmanager, AWS CloudWatch Alarms, Azure Monitor Alerts, Google Cloud Alerting, or advanced solutions like Robusta. These advanced tools not only provide alerting capabilities but also offer context-rich notifications and automated remediation, and can be integrated with team collaboration platforms like Slack or Microsoft Teams to ensure your team receives actionable alerts for quicker incident response.

  • Real-Time Dashboards: Implement dynamic dashboards for real-time visualization of data, enabling swift detection of anomalies and informed decision-making for scaling. Many tools, like Grafana, Datadog, AWS CloudWatch, Azure Monitor, and Google Cloud Monitoring, offer predefined dashboards for popular applications, allowing you to quickly gain insights without needing to create dashboards from scratch, which accelerates the setup process and provides instant visibility into key metrics.

  • Distributed Tracing: Gain clarity on request flows and interactions between microservices to identify performance bottlenecks and understand system dependencies in complex environments. To achieve effective distributed tracing, ensure your logs are structured with traceable fields, such as unique request IDs or correlation IDs, that can be consistently passed across your microservices per request. Use tools like Jaeger, Zipkin, OpenTelemetry, or AWS X-Ray to collect, visualize, and analyze trace data, making it easier to track each request's journey through your system and quickly diagnose issues.

Action point: Leverage predefined configurations and templates for observability, monitoring, and alerting, and document particular alerts with runbooks. There are many more domains and topics to consider, but these are what I see as the most essential. I plan to cover each of these topics in detail in separate blog posts, but I hope I've provided a solid foundation for the key areas that need to be tackled during your scaling phase.

My Role in Scaling

You might ask, "What do I do with all this?" Sometimes, purely nothing, because it's not my job. You heard that right. My job is not to resolve everything; I'm focusing only on what I know best, and that's the technical part of scaling. Other parts of the process outside of my scope are taken by folks who are experts in their respected fields. Together, we bring value as we resolve impediments and complex problems holistically.

If you're interested in how we do it, follow us and hear more!

There are many more domains and topics to consider, but these are what I see as the most essential. I plan to cover each of these topics in detail in separate blog posts, but I hope I've provided a solid foundation for the key areas that need to be tackled during your scaling phase.

Let's Scale Together

Scaling is a journey, not a destination. As we've explored these strategies, it's clear that effective scaling requires a holistic approach, combining technical expertise with business acumen and a deep understanding of your team's capabilities.

At LUMENAX, we're passionate about helping businesses navigate the complexities of scaling. Whether you're a startup experiencing rapid growth or an established company looking to optimize your operations, we have the expertise to guide you through each stage of your scaling journey.

Ready to Scale Your Business?

If you're facing any of the challenges we've discussed or you're looking to proactively prepare for future growth, we're here to help. Our team of experts can work with you to implement these strategies and tailor them to your specific needs.

🚀 Take the First Step:

• Schedule a free consultation to discuss your scaling challenges and goals

• Let's create a customized scaling strategy that works for your unique business

Remember, successful scaling is about learning, adapting, and growing together. Let's build a community of knowledge and support each other in our scaling journeys.