正文
上篇教程我们讲了如何添加数据源，配置客户端，这节我们继续来实现 terraform provider
实现数据源客户端功能
数据源使用可选 Configure 方法从提供程序提取已配置的客户端。提供程序配置 HashiCups 客户端，数据源可以保存对该客户端的引用以执行其操作。
打开 internal/provider/coffees_data_source.go 文件。
允许您的数据源类型存储对 HashiCups 客户端的引用，方法是将该 coffeesDataSource 类型替换为以下内容。
// coffeesDataSource is the data source implementation.
type coffeesDataSource struct {
client *hashicups.Client
}
将文件开头 import 的语句替换为以下内容。
import (
"context"
"github.com/hashicorp-demoapp/hashicups-client-go"
"github.com/hashicorp/terraform-plugin-framework/datasource"
"github.com/hashicorp/terraform-plugin-framework/datasource/schema"
)
通过将 var ( ... ) 语句替换为以下内容，确保数据源实现 DataSourceWithConfigure 接口。
// Ensure the implementation satisfies the expected interfaces.
var (
_ datasource.DataSource = &coffeesDataSource{}
_ datasource.DataSourceWithConfigure = &coffeesDataSource{}
)
通过将 Configure 方法添加到数据源类型，从提供程序获取 HashiCups 客户端。
// Configure adds the provider configured client to the data source.
func (d *coffeesDataSource) Configure(_ context.Context, req datasource.ConfigureRequest, resp *datasource.ConfigureResponse) {
if req.ProviderData == nil {
return
}
client, ok := req.ProviderData.(*hashicups.Client)
if !ok {
resp.Diagnostics.AddError(
"Unexpected Data Source Configure Type",
fmt.Sprintf("Expected *hashicups.Client, got: %T. Please report this issue to the provider developers.", req.ProviderData),
)
return
}
d.client = client
}
实现数据源 schema
数据源使用该 Schema 方法来定义可接受的配置和状态属性名称和类型。咖啡数据源需要将具有各种属性的咖啡列表保存到状态。
将数据源 Schema 的方法替换为以下内容。
// Schema defines the schema for the data source.
func (d *coffeesDataSource) Schema(_ context.Context, _ datasource.SchemaRequest, resp *datasource.SchemaResponse) {
resp.Schema = schema.Schema{
Attributes: map[string]schema.Attribute{
"coffees": schema.ListNestedAttribute{
Computed: true,
NestedObject: schema.NestedAttributeObject{
Attributes: map[string]schema.Attribute{
"id": schema.Int64Attribute{
Computed: true,
},
"name": schema.StringAttribute{
Computed: true,
},
"teaser": schema.StringAttribute{
Computed: true,
},
"description": schema.StringAttribute{
Computed: true,
},
"price": schema.Float64Attribute{
Computed: true,
},
"image": schema.StringAttribute{
Computed: true,
},
"ingredients": schema.ListNestedAttribute{
Computed: true,
NestedObject: schema.NestedAttributeObject{
Attributes: map[string]schema.Attribute{
"id": schema.Int64Attribute{
Computed: true,
},
},
},
},
},
},
},
},
}
}
实现数据源数据模型
使用以下命令将数据模型类型添加到数据源。
// coffeesDataSourceModel maps the data source schema data.
type coffeesDataSourceModel struct {
Coffees []coffeesModel `tfsdk:"coffees"`
}
// coffeesModel maps coffees schema data.
type coffeesModel struct {
ID types.Int64 `tfsdk:"id"`
Name types.String `tfsdk:"name"`
Teaser types.String `tfsdk:"teaser"`
Description types.String `tfsdk:"description"`
Price types.Float64 `tfsdk:"price"`
Image types.String `tfsdk:"image"`
Ingredients []coffeesIngredientsModel `tfsdk:"ingredients"`
}
// coffeesIngredientsModel maps coffee ingredients data
type coffeesIngredientsModel struct {
ID types.Int64 `tfsdk:"id"`
}
将文件开头 import 的语句替换为以下内容。
import (
"context"
"fmt"
"github.com/hashicorp-demoapp/hashicups-client-go"
"github.com/hashicorp/terraform-plugin-framework/datasource"
"github.com/hashicorp/terraform-plugin-framework/datasource/schema"
"github.com/hashicorp/terraform-plugin-framework/types"
)
实现读取功能
数据源使用该 Read 方法根据架构数据刷新 Terraform 状态。 hashicups_coffees 数据源将使用配置的 HashiCups 客户端调用 HashiCups API 咖啡列表终结点，并将此数据保存到 Terraform 状态.
read 方法遵循以下步骤：
- Reads coffees list. 该方法调用 API 客户端 GetCoffees 的方法。
- Maps response body to schema attributes 该方法读取咖啡后，会将 []hashicups.Coffee 响应映射到， coffeesModel 以便数据源可以设置 Terraform 状态
- Sets state with coffees list.
将数据源 Read 的方法替换为以下内容。
// Read refreshes the Terraform state with the latest data.
func (d *coffeesDataSource) Read(ctx context.Context, req datasource.ReadRequest, resp *datasource.ReadResponse) {
var state coffeesDataSourceModel
coffees, err := d.client.GetCoffees()
if err != nil {
resp.Diagnostics.AddError(
"Unable to Read HashiCups Coffees",
err.Error(),
)
return
}
// Map response body to model
for _, coffee := range coffees {
coffeeState := coffeesModel{
ID: types.Int64Value(int64(coffee.ID)),
Name: types.StringValue(coffee.Name),
Teaser: types.StringValue(coffee.Teaser),
Description: types.StringValue(coffee.Description),
Price: types.Float64Value(coffee.Price),
Image: types.StringValue(coffee.Image),
}
for _, ingredient := range coffee.Ingredient {
coffeeState.Ingredients = append(coffeeState.Ingredients, coffeesIngredientsModel{
ID: types.Int64Value(int64(ingredient.ID)),
})
}
state.Coffees = append(state.Coffees, coffeeState)
}
// Set state
diags := resp.State.Set(ctx, &state)
resp.Diagnostics.Append(diags...)
if resp.Diagnostics.HasError() {
return
}
}
生成并安装更新的提供程序。
go install .
验证数据源
cd examples/coffees
此目录中的 main.tf Terraform 配置文件从新 hashicups_coffees 数据源读取数据并输出该数据。
terraform {
required_providers {
hashicups = {
source = "hashicorp.com/edu/hashicups"
}
}
}
provider "hashicups" {
host = "http://localhost:19090"
username = "education"
password = "test123"
}
data "hashicups_coffees" "edu" {}
output "edu_coffees" {
value = data.hashicups_coffees.edu
}
运行 Terraform 计划。Terraform 将报告从 HashiCups API 检索到的数据
terraform plan
#...
data.hashicups_coffees.edu: Reading...
data.hashicups_coffees.edu: Read complete after 0s
Changes to Outputs:
+ edu_coffees = {
+ coffees = [
+ {
+ description = ""
+ id = 1
+ image = "/hashicorp.png"
+ ingredients = [
+ {
+ id = 6
},
]
+ name = "HCP Aeropress"
+ price = 200
+ teaser = "Automation in a cup"
},
#...
You can apply this plan to save these new output values to the Terraform state,
without changing any real infrastructure.
───────────────────────────────────────────────────────────────────────────────
Note: You didn't use the -out option to save this plan, so Terraform can't
guarantee to take exactly these actions if you run "terraform apply" now.
