Introduction

Managing Elasticsearch clusters can sometimes feel overwhelming. As an engineer, I spent countless hours juggling multiple Confluence pages to keep track of configurations for deploying or troubleshooting clusters.

Deploying Elasticsearch became simpler with Kubernetes, but configuration tasks remained an issue. But then, everything changed with the release of the Terraform Provider for Elasticsearch.

This tool revolutionized how I managed my Elasticsearch configurations, making it easier to deploy, re-deploy, and manage multiple clusters with consistent settings. However, as you scale to multiple clusters, new challenges arise—such as configuration drift, versioning, and promotion through different environments.

If you’re looking to simplify your Elasticsearch configurations while leveraging opinionated baselines and defaults, this guide is for you.

Why Use Terraform to Manage Elasticsearch

While there are several ways to manage Elasticsearch configurations, such as Ansible, ECK Stack, and stackconfig policies, I’ve found that these options often come with more drawbacks than benefits.

Here are a few of the challenges:

  • Lack of straightforward validation tools: It’s tough to keep code clean and error-free without easy PR validation.
  • Delayed error detection: Some configurations can only be validated after reaching a cluster or pod.
  • Limited local testing: Testing from your local machine can be a real headache.

This is where the Terraform Provider for Elasticsearch truly shines:

  • Local Testing: You can easily test configurations right from your laptop.
  • Instant Feedback: Run Terraform against your cluster as part of a PR, and get immediate feedback on errors, ensuring a clean main branch.
  • Rapid Deployment: Terraform applies your configurations almost instantly, giving you real-time results.

Getting Started with Terraform

If you’re new to Terraform, I won’t dive into the basics here, but I highly recommend checking out the following resources:

Creating Reusable Terraform Modules

Now, let’s get to the exciting part: reusable Terraform modules. I’m launching a GitHub project to create standardized, reusable modules that anyone can use to streamline their Elasticsearch deployments.

Quick Note: As of now, there are three modules available, and I’m actively working on expanding this project.

You might wonder, “Why use these modules when there are examples in the documentation?” Let me share why these modules can be a game-changer, especially when managing multiple clusters.

Currently, I manage around 60 Elasticsearch clusters. While that might seem manageable, it’s enough to cause headaches. By moving common configurations into a dedicated template repository or folder, you can:

  • Standardize: Enforce consistent standards across all clusters from a master template
  • Version Control: Easily bump and promote template versions as needed.
  • Simplify Deployments: Unsure about which ILM or component templates to deploy? Use variables as feature flags to automatically configure everything.
  • Prevent Configuration Drift: Keep all clusters in sync effortlessly.
  • Establish a Baseline: Ensure every cluster starts with the same configuration; customization happens later.
  • Test with Confidence: Run rigorous tests against your template modules to ensure they’re reliable and error-free, eliminating the risk of copy-paste errors.

Demo Time

Elasticsearch offers comprehensive documentation, but navigating it to figure out certain configurations can be a headache.

Take component templates, for example. Elasticsearch provides a variety of default component templates based on the features you enable. However, I often see people making the mistake of editing these default templates directly. What many don’t realize is that nearly all of these component templates reference a corresponding @custom template, designed specifically for overriding settings.

The challenge? These @custom templates often aren’t created by default. You have to track down which @custom templates are being referenced, create them (preferably in code), and then edit them to ensure future upgrades don’t wipe out your configurations.

To make things even more complicated, there are over a hundred @custom component templates managing the settings for more than a hundred potential indexes. Managing this across multiple clusters sounds like a daunting task.

This is where a standardized component templates module can save the day. It comes with most of the @custom templates pre-created, ready to be activated with a simple feature flag.

here is an example

module “component_templates” { source = “../modules/component_templates” apm_enabled = true kubernetes_enabled = true }

This will deploy 30 component templates for you, without having to poke into any code at all.

Wrapping Up

If this sounds interesting to you, check out the GitHub repository below. Your feedback and contributions are highly welcomed and appreciated.

GitHub