Fix: Terraform SchemaRegistry Compatibility Level Change

Hey guys! Having issues with Terraform not picking up your compatibility level changes in Schema Registry? You're definitely not alone! This article dives into a peculiar problem encountered with the schemaregistry_schema resource in Terraform, specifically version 1.5.0, and Terraform version v1.13.4, where changes to the compatibility_level are not detected in the plan. We'll break down the issue, explore a real-world scenario, and hopefully provide some clarity and potential solutions.

Understanding the Issue

At the heart of the problem is the Terraform schemaregistry_schema resource and its inability to recognize modifications to the compatibility_level attribute. This attribute dictates how schema evolution is handled, ensuring that updates don't break existing data consumers. Ideally, when you switch the compatibility_level (e.g., from BACKWARD to FULL), Terraform should detect this change and reflect it in the plan, prompting an update to the resource. However, in the reported scenario, this isn't happening.

Let's define Compatibility Level: In the context of schema evolution, the compatibility level defines the rules for how a schema can be changed over time. Common levels include BACKWARD, FORWARD, and FULL, each dictating whether newer schemas can read data written by older schemas (BACKWARD), older schemas can read data written by newer schemas (FORWARD), or both (FULL). These levels are crucial for maintaining data integrity in evolving systems.

So, if you are changing a field such as a schema, it gets picked. It seems like an unintended behavior, making schema management trickier than it should be. Now, let's get into the nitty-gritty with a practical example.

Reproducing the Problem: A Step-by-Step Guide

To illustrate the problem, consider the following Terraform configuration:

resource "schemaregistry_schema" "test" {
 subject = "test-01"
 schema_type = "AVRO"
 compatibility_level = "BACKWARD"
 schema = <<EOF
{
 "type": "record",
 "name": "Test",
 "fields": [
 {
 "name": "f1",
 "type": "string"
 }
 ]
}
EOF
}

This code defines a schemaregistry_schema resource named "test" with a subject "test-01", schema type "AVRO", and an initial compatibility_level set to "BACKWARD".

Steps to reproduce the issue:

1. Apply the initial configuration: Run terraform apply to create the schema in the registry.
2. Modify the compatibility_level: Change the compatibility_level from BACKWARD to FULL in your Terraform configuration.
3. Run terraform plan: Execute terraform plan to see if Terraform detects the change.

Expected Behavior: Terraform should identify the change in compatibility_level and include an update in the plan.

Actual Behavior: Terraform reports “No changes. Your infrastructure matches the configuration.”

The plan doesn't detect the change in compatibility_level. This is where the problem surfaces. Terraform seemingly ignores the modification, which can lead to configuration drifts and potential issues in your schema evolution strategy. However, if we modify the schema itself such as adding a new field, Terraform will then recognize the changes including the compatibility level.

To further demonstrate this, let's add a new field to the schema:

resource "schemaregistry_schema" "test" {
 subject = "test-01"
 schema_type = "AVRO"
 compatibility_level = "FULL"
 schema = <<EOF
{
 "type": "record",
 "name": "Test",
 "fields": [
 {
 "name": "f1",
 "type": "string"
 },
 {
 "name": "f2",
 "type": "string"
 }
 ]
}
EOF
}

Running terraform plan now reveals that Terraform does detect a change, and it includes both the schema modification (addition of the f2 field) and the compatibility_level change. This behavior suggests that Terraform's change detection mechanism for compatibility_level is somehow coupled with changes in other attributes, specifically the schema itself.

Diving Deeper: Why is This Happening?

Unfortunately, without access to the provider's source code or detailed debugging information, pinpointing the exact cause is challenging. However, we can speculate on potential reasons:

• Provider Implementation: The issue might stem from how the Terraform provider for Schema Registry is implemented. It's possible that the provider's diffing logic (the part that compares the current state with the desired state) doesn't properly account for changes in compatibility_level unless other attributes are also modified.
• API Behavior: It's conceivable that the Schema Registry API itself doesn't expose changes in compatibility_level as a standalone modification. The provider might rely on reading the entire schema configuration to detect changes, and if the API only flags schema content changes, the compatibility_level change might be missed.
• Terraform Core Bug: Although less likely, there's a slim chance that this behavior is due to a bug in Terraform core itself, particularly in how it handles certain types of resource attributes.

Regardless of the root cause, the practical implication is clear: you can't reliably change the compatibility_level using Terraform without also modifying the schema content. This workaround is far from ideal, as it forces unnecessary schema changes just to update the compatibility setting.

Potential Workarounds and Solutions

While a definitive fix likely requires an update to the Terraform provider, here are some potential workarounds and mitigation strategies:

1. Modify Schema Content: As demonstrated, the most reliable workaround is to introduce a minor, inconsequential change to the schema content whenever you need to update the compatibility_level. This could involve adding a comment, reformatting the JSON, or adding and removing a dummy field. While not elegant, this ensures Terraform detects the change.

   resource "schemaregistry_schema" "test" {
    subject = "test-01"
    schema_type = "AVRO"
    compatibility_level = "FULL"
    schema = <<EOF
   {
    "type": "record",
    "name": "Test",
    "fields": [
     {
      "name": "f1",
      "type": "string"
     }
    ],
    "description": "Dummy change to trigger update" // Added a comment
   }
   EOF
   }
   

   Adding a description field, even with a simple comment, forces Terraform to recognize a change in the schema and apply the updated compatibility level.

2. Lifecycle ignore_changes: You could try using the lifecycle block with ignore_changes to prevent Terraform from managing the schema attribute altogether. This would allow you to manage compatibility_level independently, but it also means you'd need to manage schema changes outside of Terraform, which might not be desirable.

   resource "schemaregistry_schema" "test" {
    subject = "test-01"
    schema_type = "AVRO"
    compatibility_level = "FULL"
    schema = <<EOF
   {
    "type": "record",
    "name": "Test",
    "fields": [
     {
      "name": "f1",
      "type": "string"
     }
    ]
   }
   EOF
    lifecycle {
     ignore_changes = [
      schema,
     ]
    }
   }
   

   By ignoring changes to the schema, Terraform won't detect modifications to the schema content, allowing you to change the compatibility level without triggering a schema update. However, this means you need to manage schema evolution separately. This approach can be suitable if your schema evolution is managed through a different process.

3. External Data Source: Another approach involves using an external data source to track the desired compatibility_level and then use a local variable to set the resource's compatibility_level. This can add complexity, but it provides more control over the update process.

   This workaround could involve using a data source to fetch the desired compatibility level from an external source (e.g., a configuration file or environment variable). You can then use this value to set the compatibility_level in your resource.

   data "external" "compatibility_level" {
    program = ["/bin/sh", "-c", "echo '{ \"level\": \"FULL\" }'"]
   }
   
   resource "schemaregistry_schema" "test" {
    subject = "test-01"
    schema_type = "AVRO"
    compatibility_level = data.external.compatibility_level.result.level
    schema = <<EOF
   {
    "type": "record",
    "name": "Test",
    "fields": [
     {
      "name": "f1",
      "type": "string"
     }
    ]
   }
   EOF
   }
   

   In this example, an external data source is used to read the compatibility level from a shell command. This allows you to change the compatibility level independently of the schema content.

4. Raise an Issue: The most crucial step is to report this behavior to the maintainers of the Terraform provider for Schema Registry. This increases the chances of a proper fix being implemented in a future release. Providing a clear and reproducible test case (like the one outlined above) will help the maintainers understand and address the issue more effectively.

Key Takeaways

The inability of the Terraform schemaregistry_schema resource to detect compatibility_level changes is a significant issue that can lead to configuration drifts and unexpected behavior. While workarounds exist, they are not ideal. Here's a quick recap:

• Problem: Terraform doesn't detect changes to compatibility_level unless the schema content is also modified.
• Workaround: Modify schema content (e.g., add a comment) to trigger an update.
• Better Solutions: Consider alternative management approaches such as external data sources or ignoring schema changes if suitable for your workflow.
• Best Practice: Report the issue to the provider maintainers to facilitate a proper fix.

Final Thoughts

Managing schemas and their compatibility levels is crucial for data-intensive applications. While Terraform provides a powerful way to automate infrastructure, quirks like this one can make the process more challenging. By understanding the issue and implementing appropriate workarounds, you can mitigate the risk and ensure your schema evolution strategy remains on track. Don't forget to raise the issue with the provider maintainers – your contribution can help improve the tool for everyone!

Stay tuned for updates, and happy Terraforming, folks! Remember to always validate your plans and apply changes cautiously, especially when dealing with critical infrastructure components like schema registries.