Testing Terraform With Spock

11 minute read

For many modern programming languages, various levels of automated testing have been around for years. However, for Infrastructure as Code (IaC), and specifically Terraform, the landscape is much less mature. Wouldn’t it be great if we could bring both Java tests and Terraform tests together under the same roof… er… project using a simple and arguably elegant BDD-style testing framework. We can! Enter Spock.

Groovy Language

Credit: The Apache Groovy programming language

The Terraform Testing Landscape

The available testing tools for Terraform mainly consist of

Like Terraform itself, each has their own strengths and weaknesses. But none of them, except for TerraTest are geared towards validating that the infrastructure actually works. The other tools listed are really just compliance testing tools, which is great. Compliance tests are easier and faster to evaluate and they do provide some value. But compliance and $2.00 will get you a cup of coffee at StarBucks if your infrastructure doesn’t actually work.

Other issues with the above infrastructure test frameworks include poor langauge support for the most widely used software development languages and their bespoke configurations. As much as heterogeneous programming language environments are touted, most large IT shops primarily support 1 or 2 development platforms consisting of .Net, Java and/or some flavor of JavaScript. Some traditional Ops engineers staff will likely have experience with Bash and/or Python (and sometimes Ruby) and that’s generally the extent.

The point of the above statements are not meant to poo-poo developers or ops traditionalists. Nor are they meant as a snub to all the other great programming languages that were not mentioned. Rather, they are intended to frame the available Terraform infrastructure testing options in the context of present day IT organization realities.

What About Cross-Functional Teams?

Just because a team consists of team members who can jockey between different langauges, tech stacks and competencies, it doesn’t mean the organization will let them (Boo!). And even if the organization will let them , that doesn’t mean language X or tech stack Y will enjoy support within the organization. And even if those barriers are surmountable, the pain of segmenting infrastructure tests and application tests is real. Different tools, each having different configurations to run different tests for the same logical piece of functionality is a drag factor rooted in complexity.

The Spock Testing Framework

Our tech stack consists of different langauges and tech stacks: JavaScript, Java, Python, Terraform, etc. Different languages and tech stacks means more complexity. However, it’s complexity that must be offset by value. And we continuously look for ways to streamline it. One of those ways is through a single, batteries included build tool (to be covered in a future post). Another way is by streamlining (and combining) application and infrastructure tests. We use Spock to do that within our Java projects.

Why Spock?

Spock is a Groovy BDD-style testing framework based on JUnit. Spock gives you all the great stuff that JUnit offers and all the stuff that’s commonly added onto JUnit (like interactions with mocks) in an all-in-one streamlined testing framework. The Groovy-ness of Spock also allows for some gray-box testing, terseness of syntax not afforded by Java, near 100% compatibility with Java, dynamic types and meta-programming (which should be used sparingly, but which can also be REALLY useful sometimes).

An entire post could be filled with why Spock is such a great (and easy to use) testing framework. But that is not this post. So, without much further delay, let’s discuss how we use it to test Terraform.

Project Setup

At first, we decided on using Gradle for project-based build automation and dependency management. But a few things changed our minds and nudged us toward Maven.

  1. Maven has great support for delegation to NPM and other languages.
  2. There is a Maven plugin for Terraform.

The initial thinking was that there was no way we wanted to do XML programming of any way, shape or form. And that’s kind of what you take on with Maven POMs. But as we progressed, the benefits of using Maven seemed to outweigh the effort required to entend Gradle. So, now we’re using Maven. Less Time Spent on Scaffolding = More Time Spent Creating Value

There’s already some great posts out there about using Terraform with Maven and about using Groovy to test Terraform. This post won’t regurgitate that content. Instead, we’ll now demonstrate how we test Java and Terraform together using Spock with Groovy.

Configuring Maven

First, add the tf-cmd-api, groovy, spock and aws-java-sdk dependencies to the pom.xml. We’ll also add the reflections dependency for a little configuration magic, which will be discussed shortly.


Next, to facilitate the execution of Terraform operations using Maven, let’s also add in the tf-maven-plugin configuration as follows.


Finally, we need to add the Gmavenplus plugin configuration and the Surefire plugin configuration so that our Groovy code is converted into Java and so that Spock Specifications (Spock’s name for test suites) are discovered by Surefire, respectively.


Configuring Spock

It’s great to have all your tests for your project in one place. But it’s not so great if all those tests run every single time. Infrastructure tests take time to run - a lot longer than Java tests. So, you probably don’t want them running every time you run your Java tests. Fortunately, Spock has a very simple mechanism that allows us to run tests of specific types at different times or all-together.

Our basic approach is this:

  1. Identify the types of tests you want to support
  2. Create annotations for each of those test types
  3. Configure Spock to select the correct test types based on defaults, properties and/or command line arguments

Let’s assume the project holds different types of automated tests: unit tests, integration tests, synchronous infrastructure tests and asynchronous infrastructure tests. Unit tests should be the default type of tests run - they run the fastest and more often than not, unit tests are what we want to validate on local builds. Then we have integration tests - tests that validate the integration between the current project and other stuff, synhcronous infrastructure tests - infrastructure tests we can get feedback on relatively quickly and finally, asynchronous infrastructure tests - infrastructure tests that have a relatively long feedback cycle (e.g. testing S3 replication).

Creating The Annotations

Add a single Annotations.groovy file in a test package as follows.

package com.bluflametech.test //your package name goes here

import java.lang.annotation.ElementType
import java.lang.annotation.Retention
import java.lang.annotation.RetentionPolicy
import java.lang.annotation.Target

@interface AlwaysTest { }

@interface UnitTest { }

@interface IntegrationTest { }

@interface SyncInfraTest { }

@interface AsyncInfraTest { }
Configuring Spock

Add a single SpockConfig.groovy file in the test/resources directory as follows.

import com.bluflametech.test.AlwaysTest
import com.bluflametech.test.UnitTest
import org.reflections.Reflections

import java.lang.annotation.Annotation
import java.util.stream.Collectors

def testAnnotations = (new Reflections('com.bluflametech.test'))
        .filter({ clazz -> !clazz.simpleName.toLowerCase().startsWith('always')})
                {Class<?> clazz -> clazz.simpleName.toLowerCase().dropRight(4)},
                {Class<?> clazz -> clazz}))

def testTypes = System.getProperty('testTypes')?.split(',\\s*')?.collect {testAnnotations[it.toLowerCase()]}

runner {

  testTypes?.each { testType ->
      include.annotations << testType
  include.annotations << AlwaysTest
  testTypes ?: include.annotations << UnitTest

Deconstructing the code, we can see that it does the following.

def testAnnotations = ... creates a Map of all annotations in the specified package. Assuming the above code that created the annotations, the resulting map is as follows.

  unit: UnitTest,
  integration: IntegrationTest,
  syncinfra: SyncInfra,
  asyncinfra: AsyncInfra

def testTypes = ... gets the testTypes system property, which can be passed in like mvn test -DtestTypes=unit,integration from the command line.

The runner closure configures the Spock runner, which will add each annotation associated with its key value (above) specified in the testTypes parameter. If no testTypes are specified, Spock specificiations annotated with @UnitTest are run by default. And anything annotated with @AlwaysTest is always run as part of a test execution. This allows stuff like linting to be configured in terms of Spock specifications.

Adding Some Syntactic Sugar

All the basics for wiring up Terraform tests with Spock are included in the tf-cmd-api dependency that was added above. However, that library lacks the terseness and the elegance that Spock provides. In short, it’s not very Groovy. Fortunately, Groovy comes with some really powerful features that can help out.

First, let’s talk about what’s missing.

1. Destroy on cleanup

A typical Terraform test has 3 parts: 1. provision infrastructure using Terraform 2. evaluate provisioned infrastructure 3. tear down provisioned infrastructure. The infrastructure for each test might be a little different, how it’s evaluated will be different, but the need to destroy the infrastructure is a constant. Why not just make it happen without having to add extra code?

2. Instantiate Terraform operations by default

The tf-cmd-api library requires Terraform operations to be instantiated as follows.

TerraformInit init = new TerraformInit(TF_ROOT_DIR)
TerraformInit apply = new TerraformInit(TF_ROOT_DIR)

Wouldn’t it be nice if they were just wired up and made available?

3. Intuitive usage of Terraform operations

Without extension, the above Terraform operations would be used as follows.

apply.execute([tfVars: props])

That’s not bad. But we can do better.

terraform.apply([tfVars: props])

Now, that looks more intuitive. Let’s make it happen!

A Terraform Specification for Spock

Test suites in Spock are Specifications. They are created by extending the Spock Specification class. So, why not have a TerraformSpecification class that wires up some Terraform functionality?

package com.bluflametech.test

import com.deliveredtechnologies.terraform.TerraformUtils
import com.deliveredtechnologies.terraform.api.TerraformApply
import com.deliveredtechnologies.terraform.api.TerraformInit
import com.deliveredtechnologies.terraform.api.TerraformOutput
import com.deliveredtechnologies.terraform.api.TerraformPlan
import groovy.json.JsonSlurper
import spock.lang.Specification

abstract class TerraformSpecification extends Specification {

  private String tfRootDirUnderTest
  protected Map<String, Closure<?>> terraform

    String destroyPlanFile = 'destroy.tfplan'
    tfRootDirUnderTest = tfRootDir
    def apply = new TerraformApply(tfRootDirUnderTest)
    def plan = new TerraformPlan(tfRootDirUnderTest)
    def output = new TerraformOutput(tfRootDirUnderTest)
    def init = new TerraformInit(tfRootDirUnderTest)

    terraform = [
        init: { init.execute([:]) },
        apply: { props ->
          Properties planProps = props.clone() as Properties
          planProps << [destroyPlan: 'true']
          planProps << [planOutputFile: destroyPlanFile]
          JsonSlurper slurper = new JsonSlurper()
        destroy: { apply.execute([plan: destroyPlanFile]) }

  def cleanup() {

  private void removePlans() {
    File dir = TerraformUtils.getTerraformRootModuleDir(tfRootDirUnderTest).toFile()
    File[] planFiles = dir.listFiles((FilenameFilter) { foo, name -> (name as String).endsWith('.tfplan') })
    planFiles.each { file -> file.delete() }
  private String getTfRootDir() {
    String className = this.getClass().simpleName
    String stackName = className[0].toLowerCase() +
        className.dropRight(4)[1..-1].replaceAll('[A-Z]') { matcher ->
    File example = new File("src/main/tf-examples/${stackName}")
    if (example.exists() && example.directory) {
      return "tf-examples/${stackName}"


Let’s start at the bottom. getTfRootDir() resolves the Terraform root module directory based on the name of the specification. By convention, specifications end with the suffix Spec. getTfRootDir() amends that convention by matching everything before that suffix with a root module directory.


Class Name Root Module Directory
BucketSpec bucket
BucketWithReplicaSpec bucket_with_replica

The search order for the Terraform root module directories is first _src/main/tf-examples/_ and then _src/main/tf/_.

Now, let’s move to the top of that file where the protected terraform instance variable is declared. This allows access to Terraform commands in the spec like terraform.init(). Notice that it’s a map of closures - this is done so that we can control how the commands are executed.

Moving down from there is the initialization block. Spock does not allow subclasses of its Specification class to declare their own constructors. So, instead of a constructor, we use an initialization block. This is where we can load up the terraform closure map with Terraform operations.

Since we expect the Terraform init operation to be called with no arguments, we supply an empty map to its call and wrap that call in a no argument closure. Next, we define terraform.apply to be a Terraform apply operation, the creation of a Terraform destroy plan (so that tfvars don’t have to be supplied to tear down the infrastructure on cleanup) and a Json object representation of terraform output as the return value. Finally, we define the terraform.destroy operation to be a Terraform apply using the destroy plan that was created on terraform.apply.

Just below the initialization block is the cleanup() method. The cleanup() method is a special method for Spock that is executed following every test, whether the test succeeds or fails. It’s a perfect place to destroy the Terraform infrastructure that was provisioned during the test. Additionally, in the cleanup() method and in the initialization block, we can guard against stale plans by ensuring that any plans are cleaned up. Thus keeping inline with a cardinal rule of testing: Always clean up after yourself.

Writing a Spock Test for Terraform

Now that we’ve added our dependencies to Maven, configured Spock and added some syntactic sugar to make testing more DRY, elegant and intuitive, let’s take a look at what a (simple) Terraform test with Spock might look like.

class BucketSpec extends TerraformSpecification {

  def "bucket tf module creates a non-kms encrypted private bucket"() {

    AmazonS3 s3 = AmazonS3ClientBuilder.standard().withRegion('us-east-1').build()

    Faker faker = new Faker(new Random(System.currentTimeMillis()))

    def tfvars = [
        region: 'us-east-1',
        bucket_name: "bftest-${faker.funnyName().name().toLowerCase().replaceAll('[.\']', '').replaceAll('[\\s]', '-')}" as String,
        product: 'bluflametech',
        env: 'shared'


    def output = terraform.apply([tfVars: tfvars])


    //validate outputs
    output.details.value.bucket_name == tfvars.bucket_name
    output.details.value.bucket_region == tfvars.region
    output.details.value.website_endpoint == 'none'
    output.details.value.kms_key_arn == 'none'

    //validate deployment assumptions in AWS
    with(s3.getBucketTaggingConfiguration(tfvars.bucket_name).tagSet) { bucket ->
      bucket.getTag('product') == tfvars.product
      bucket.getTag('env') == tfvars.env

    GetPublicAccessBlockResult publicAccessBlockResult =
        s3.getPublicAccessBlock((new GetPublicAccessBlockRequest()).withBucketName(tfvars.bucket_name))

Pretty cool, right? We think so.

However, you may have noticed that even after the (albeit brief) discussion on compliance testing above, the example directly above is, in fact, a compliance test. The good news is that Spock, the Terraform Maven plugin and its libraries can do more - much more.

But that will have to wait for a future post.