tf-datasource-gen

name	tf-datasource-gen
description	generate terraform datasources based on openapi spec.

Terraform Datasource Generator

Instructions

0. First inspect the repo for existing datasource code, schema files, api structs, provider registrations, and acceptance tests for the feature. For each step 1 through 12 if the datasource already satisfies the user request skip the step completely, make no edits and proceed to the next step. Do not make any minor edits or fixes to existing code if the datasource already satisfies the user request.

   For example:

   • If api structs exist, skip creating them and use the existing structs
   • If a datasource already exists, skip recreating it unless explicitly asked to edit it.
   • If an acceptance test already exists, skip creating a duplicate and extend coverage only if asked.

1. datasource code should be in internal/datasources/

2. schema for datasource should be in its own file with format _schema.go

   add validation for organization_id, project_id and cluster_id if present. for example with organization_id

capellaschema.AddAttr(attrs, "organization_id", snapshotBackupBuilder, requiredStringWithValidator())

func requiredStringWithValidator() *schema.StringAttribute {
    return &schema.StringAttribute{
        Required:   true,
        Validators: []validator.String{stringvalidator.LengthAtLeast(1)},
    }
}

3. implement one or two datasources depending on the spec provided.

   • the first is to get a specific resource. use the get endpoint. if there is no get endpoint then skip this implementation. for example if the feature is Buckets then need bucket.go to get a specific bucket.

   • the second datasource is to list all resources. use the list endpoint. if there is no list endpoint then skip this implementation. the file name should have a plural resource name. for example if the feature is Buckets then need buckets.go to list all buckets.

4. create struct with feature name that embeds Data struct. for example if the feature is Buckets then need this struct

type Buckets struct {
    *providerschema.Data
}

5. need New function. for example if feature is Buckets then need this function

func NewBuckets() datasource.DataSource {
    return &Buckets{}
}

6. type should implement interfaces datasource.DataSource and datasource.DataSourceWithConfigure. must use type conversion of nil to assert that the type implements the interfaces.

   for example for Buckets

var (
    _ datasource.DataSource              = (*Buckets)(nil)
    _ datasource.DataSourceWithConfigure = (*Buckets)(nil)
)

7. need Metadata function. for example with Buckets

func (d *Buckets) Metadata(_ context.Context, req datasource.MetadataRequest, resp *datasource.MetadataResponse) {
    resp.TypeName = req.ProviderTypeName + "_buckets"
}

8. need Configure function. for example with Buckets

func (d *Buckets) Configure(_ context.Context, req datasource.ConfigureRequest, resp *datasource.ConfigureResponse) {
    if req.ProviderData == nil {
        return
    }

    data, ok := req.ProviderData.(*providerschema.Data)
    if !ok {
        resp.Diagnostics.AddError(
            "Unexpected Data Source Configure Type",
            fmt.Sprintf("Expected *providerschema.Data, got: %T. Please report this issue to the provider developers.", req.ProviderData),
        )

        return
    }

    d.Data = data
}

9. generate necessary structs to handle API response. put structs in internal/api/ use ClientV1 struct to make API calls with retry logic. for example:

response, err := s.ClientV1.ExecuteWithRetry

10. register the datasource in internal/provider/provider.go in func (p *capellaProvider) DataSources

    for example with buckets need datasources.NewBuckets,

11. create acceptance tests for both datasources in acceptance_tests/ with format _test.go. for example if feature is Buckets then need buckets_test.go

12. acceptance tests should run in parallel. that is use resource.ParallelTest()