Streamline STACKIT Observability Plans In Terraform

Hey everyone! Today, we're diving deep into enhancing observability within your Terraform configurations when working with STACKIT. We'll be discussing a cool way to streamline how you manage observability plans, making your infrastructure management even smoother. Let's jump right in!

Understanding the Problem: The Observability Gap

When you're building out your infrastructure using Terraform, integrating observability tools is crucial. In the context of STACKIT, this often involves setting up an SKE (STACKIT Kubernetes Engine) cluster and an Observability instance. Now, imagine you've got Prometheus running in your cluster, and you need to configure its remoteWrite.queueConfig based on your chosen Observability plan. This is where things can get a bit tricky.

The current workflow often requires you to manually maintain a variable that maps the Observability plan name to its specific details. Think of details like Metric Samples (per minute), Logs (in GB), and Traces (in GB). For instance, the number of Metric Samples per minute is a critical attribute for configuring Prometheus effectively. But maintaining this mapping yourself? It's not the most efficient use of your time, right?

Maintaining this mapping manually introduces several challenges. First, it adds overhead to your Terraform configurations. You need to keep the mapping updated, which can be error-prone and time-consuming. Second, it reduces the readability and maintainability of your code. Imagine someone new joining your team and trying to decipher this manual mapping – it's not the most intuitive process. Third, it creates a potential point of failure. If the mapping is incorrect or outdated, your Prometheus configuration might not align with your Observability plan, leading to performance issues or data loss. Therefore, automating this process and integrating it directly into your Terraform configuration would significantly improve the overall observability setup.

The Proposed Solution: Streamlining Observability Plan Details

So, what's the solution? How can we make this process more efficient and less prone to errors? The core idea is to make these plan details readily available within your Terraform configuration. There are two main approaches we can take:

Option 1: Exposing Plan Details as a Resource Attribute

One way to tackle this is by adding the plan details as a read-only attribute directly to the stackit_observability_instance resource. This means that when you define your Observability instance in Terraform, you can immediately access details like metric_samples_per_minute without needing a separate mapping.

Here’s how it might look in your Terraform code:

resource "stackit_observability_instance" "obs_ske" {
 project_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
 name = "obs-ske"
 plan_name = "Observability-Starter-EU01"
}

output "obs_metric_samples_per_minute" {
 value = stackit_observability_instance.obs_ske.metric_samples_per_minute
}

In this example, stackit_observability_instance.obs_ske.metric_samples_per_minute would directly provide the metric samples per minute for the specified Observability plan. This approach simplifies your configuration and reduces the need for manual mapping.

Option 2: Introducing a New Data Source

Another approach is to introduce a new data source, such as stackit_observability_plan_details. This data source would allow you to fetch the details of an Observability plan based on its name and project ID. This keeps the stackit_observability_instance resource focused on instance management while providing a dedicated way to access plan details.

Here’s how this would look in Terraform:

resource "stackit_observability_instance" "obs_ske" {
 project_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
 name = "obs-ske"
 plan_name = "Observability-Starter-EU01"
}

data "stackit_observability_plan_details" "obs_ske" {
 project_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
 plan_name = stackit_observability_instance.obs_ske.plan_name
}

output "obs_metric_samples_per_minute" {
 value = stackit_observability_plan_details.obs_ske.metric_samples_per_minute
}

In this case, the stackit_observability_plan_details data source fetches the plan details, and you can access metric_samples_per_minute through stackit_observability_plan_details.obs_ske.metric_samples_per_minute. This approach offers a clear separation of concerns and can be particularly useful if you need to access plan details in multiple places within your Terraform configuration.

Benefits of Both Solutions

Both of these solutions offer significant improvements over the manual mapping approach. They reduce the overhead of maintaining variables, improve the readability and maintainability of your code, and minimize the risk of errors. By providing a direct way to access Observability plan details within Terraform, you can streamline your infrastructure management and focus on building awesome applications.

Diving Deeper: A Practical Example

Let's walk through a more detailed example of how you might use the data source variant in a real-world scenario. Imagine you are setting up an SKE cluster and an Observability instance, and you need to configure Prometheus to scrape metrics from your applications. You want to ensure that Prometheus is configured correctly based on the limits of your Observability plan.

First, you would define your SKE cluster and Observability instance:

resource "stackit_ske_cluster" "my_cluster" {
 project_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
 name = "my-cluster"
 plan_name = "stackit-kubernetes-developer"
 kubernetes_version = "1.28"
 zone = "eu-central-1a"
 node_pools = [
 {
 name = "default-pool"
 machine_type = "c1.2"
 replicas = 3
 },
 ]
}

resource "stackit_observability_instance" "obs_instance" {
 project_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
 name = "obs-instance"
 plan_name = "Observability-Starter-EU01"
}

Next, you would use the stackit_observability_plan_details data source to fetch the plan details:

data "stackit_observability_plan_details" "obs_plan" {
 project_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
 plan_name = stackit_observability_instance.obs_instance.plan_name
}

Now, you can use the data.stackit_observability_plan_details.obs_plan.metric_samples_per_minute attribute to configure Prometheus. For example, you might use a Helm chart to deploy Prometheus and configure its settings:

resource "helm_release" "prometheus" {
 name = "prometheus"
 chart = "prometheus"
 repository = "https://prometheus-community.github.io/helm-charts"
 namespace = "monitoring"
 values = [
 jsonencode({
 prometheus:
 prometheusConfig:
 global:
 scrape_interval: "30s"
 external_labels:
 cluster: "my-cluster"
 remote_write: [
 {
 url: "your-prometheus-remote-write-url"
 queue_config:
 capacity: 200 # Example value
 max_samples_per_send: data.stackit_observability_plan_details.obs_plan.metric_samples_per_minute
 },
 ],
 })
 ]
}

In this example, max_samples_per_send is dynamically set based on the metric_samples_per_minute attribute from the Observability plan details. This ensures that Prometheus is configured correctly according to your plan's limits, preventing potential issues.

By integrating the Observability plan details directly into your Terraform configuration, you can automate the configuration of your monitoring tools, reduce manual errors, and ensure that your infrastructure is set up correctly from the start.

Exploring Alternative Solutions

Of course, there are always alternative ways to approach a problem. In this case, the primary alternative is the one we're already trying to move away from: maintaining the mapping variable yourself. While this isn't ideal, it's worth acknowledging that it's a viable option, especially if the number of Observability plans you're working with is small and the details don't change frequently.

Maintaining the mapping variable involves creating a data structure (like a map or dictionary) in your Terraform code that associates plan names with their details. You would then reference this variable when configuring your monitoring tools. While this approach works, it has the drawbacks we discussed earlier: increased overhead, reduced readability, and potential for errors.

Another alternative could be to use an external data source or API to fetch the plan details. This might involve querying a STACKIT API or a custom API that you've set up. While this approach could provide more flexibility, it also adds complexity to your Terraform configuration and introduces dependencies on external systems. Therefore, it's generally better to stick with solutions that are tightly integrated with Terraform, like the resource attribute or data source options we discussed earlier.

Ultimately, the best solution depends on your specific needs and constraints. However, for most users, either exposing plan details as a resource attribute or introducing a new data source will provide a more efficient and reliable way to manage Observability plans in Terraform.

Final Thoughts: Why This Matters

So, why are we even talking about this? Why does it matter whether we maintain a manual mapping or use a data source? The answer boils down to efficiency, reliability, and maintainability. In the world of infrastructure as code, automation is king. The more you can automate, the less time you spend on manual tasks and the fewer errors you'll make.

By integrating Observability plan details directly into your Terraform configuration, you're taking a step towards true automation. You're ensuring that your monitoring tools are configured correctly from the start, without any manual intervention. This not only saves you time and effort but also reduces the risk of misconfiguration and downtime.

Moreover, this approach makes your Terraform code more readable and maintainable. When someone else looks at your code, they can easily see how your monitoring tools are configured and why. This is crucial for collaboration and long-term maintainability. Let's be honest; clear, concise, and self-documenting code is a gift to your future self and your teammates.

In conclusion, enhancing observability in Terraform with STACKIT is all about making your life easier and your infrastructure more reliable. By adopting solutions like exposing plan details as a resource attribute or introducing a new data source, you can streamline your workflow, reduce errors, and build a more robust and maintainable infrastructure. So, go ahead, give it a try, and let's make our infrastructure management a little bit smoother, one Terraform configuration at a time!

Additional Information

None. But if you have any questions or want to share your experiences, feel free to leave a comment below!