Skip to content

Latest commit

 

History

History
286 lines (204 loc) · 7.91 KB

terraform.md

File metadata and controls

286 lines (204 loc) · 7.91 KB
Raw

MODIFYING THE TERRAFORM SCRIPTS

The deployment process is reliant on the files in the terraform and terragrunt directories.

All .tf files in the terraform folder are used to generate or obtain resources or variables. Each .tf file is scanned on run, and any resources it describes are created or updated.

Key files are as follows:

main.tf					- Specifies the resources used by the application

policies.tf				- Contains policy documents referenced by resources in main.tf

variables.tf			- Variable declarations - note that all terraform variables must be 
						  declared before usage

variables.auto.tfvars	- A collection of parameters that are common among all environments

The .hcl files in the terragrunt folder are used to specify paramenters which differ between environments. They also specify the terraform repository to be used when deploying

Documentation on how resources are specified can be found at: https://registry.terraform.io/providers/hashicorp/aws/5.6.0/docs

CONFIGURATION OF GIT AND TERRAFORM

In order to run the terragrunt scripts, you must first configure your git repo and terraform repository.

The terraform repository must be configured once per environment. Git only needs to be configured once per fork.

To configure the terraform repository for a new environment:

  1. Go to https://app.terraform.io/session and log in or create an account

  2. Create a new workspace with a unique name, selecting "API-driven workflow" when asked to choose workflow type.

  3. In your workspace, go to the "Variables" tab, and create two new sensitive environment variables: AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY

  4. Copy in the relevant access key id and secret access key for an AWS user with appropriate permissions to create, modify, and delete resources

To configure the git repo:

  1. Go to https://app.terraform.io/app/settings/tokens and create an api token

  2. Store the value of the API token in a secure location

  3. Copy that value into a new GitHub Secret (Settings -> Secrets -> Create New Secret) with the name TF_API_TOKEN

CREATING A NEW ENVIRONMENT

CDK

Before creating a new environment, the CDK script for that environment must be run in order to create and get the name for the relevant ClamAV stack and queue.

The name of the stack to be used should be like WfdmClamavStack{ENV}

The correct queue to be used will be named starting with: wfdmclamavstack{env}-...

S3

Create a copy of one of the 'wfdm-s3-bucket-{env}' buckets, which contain the source code to be used.

Once that is done, the steps are as follows:

  1. Copy one of the deploy_{env}.yml files in .github\workflows, and rename it appropriately.

  2. Replace references to the previous environment with references to the new one. Values that should be updated are:

name:

on: workflow_dispatch: tags:

jobs: environment: name:

jobs: steps: working-directory:

NOTE: failure to replace values may cause conflicts and naming collisions on run

  1. Copy one of the terragrunt/{env} folders, renaming it accordingly

  2. In its contained terragrunt.hcl folder, update parameters as needed. Note that the following parameters MUST be updated for the new environment to permit clamAV to work and to prevent naming collisions:

target_env

env_lowercase

env_full

clamAVStackName

clamstackQueue

You must also update the workspace name specified in the generate "backend" { ... } statement. Failure to do so will result in the new environment overwriting the existing environment instead of deploying seperately from it.

EXECUTING A DEPLOYMENT WITH GITHUB ACTIONS

In GitHUB, go to the Actions tab.

Select the workflow you would like to use, then click "Run Workflow." This will open the terraform terminal and run the deployment script in it.

Note that configurations specified in the workflow will overwrite manual changes that have been made, if any are present. The terraform terminal will note any changes or updates that it needs to make, and provide continuous information on the state of the deployment.

CONFIGURATION OF OPENSEARCH AFTER DEPLOYMENT

When newly deployed or updated, the opensearch domain will default to using AWS IAM authentication only, and will only work through the API

To configure it to allow use of the both the UI and connection from the API, you will need to make the following changes:

  1. Go to the Amazon OpenSearch Service dashboard.

  2. Select the wf1-wfdm-opensearch-{env} domain and begin editing the security configuration

  3. Make the following changes:

    1. Under fine-grained access control, change the Master User option from IAM to "Create Master User"

    2. Create a master user with name opensearch-{env} and a secure password

    3. Under Access policy, select "Only use fine-grained access control." This is necesary to allow access to the UI.

    4. Amazon will display an alert that some elements of your access policy will be cleared. This is working as intended. Accept, and then save your security settings.

  4. Go to the dashboard url for your opensearch domain to access the Kibana dashboard. Login with the master user

  5. Decline / close any prompts that appear on login

  6. In the Kibana dashboard, open the menu by clicking the "hamburger" menu icon in the top left. Go to security > users and create a user named "opensearch-add-index-lambda-user." Then, add it to the all_access role in security > roles > all_access > manage mapping

  7. Go to security > users > opensearch-{env} and add the arns for the API user and the indexing lambda function role as Backend Roles to the user

    - The ARNs can be found by viewing the user / role in the amazon IAM console

- The API user will be named like **WF1-WFDM-{env}-Document-API**

- The Lambda function role will be named like **WF1-WFDM-iam_role_lambda_index_searching-{env}**

  8. Either through Postman or the Kibana Dev Tools, create an index using the following POST request:

    PUT /wf1-wfdm-opensearch-{env} { "mappings":{ "properties":{ "key":{ "type":"text" }, "absoluteFilePath":{ "type":"text" }, "fileContent":{ "type":"text" }, "lastModified":{ "type": "date" }, "lastUpdatedBy":{ "type":"text" }, "mimeType":{ "type":"text" }, "fileName":{ "type":"text" }, "fileRetention":{ "type":"text" }, "fileLink":{ "type":"text" }, "fileSize":{ "type":"text" }, "scanStatus":{ "type":"text" }, "metadata":{ "type":"nested", "properties":{ "metadataValue":{ "type":"text" }, "metadataName":{ "type":"text" } } }, "security":{ "type":"nested", "properties":{ "displayLabel":{ "type":"keyword" }, "securityKey":{ "type":"keyword" } } }, "securityScope":{ "type":"nested", "properties":{ "displayLabel":{ "type":"keyword" }, "canReadorWrite":{ "type":"keyword" } } }, "filePath": { "type": "text", "fields": { "tree": { "type": "text", "analyzer": "custom_path_tree" } } } } }, "settings": { "analysis": { "analyzer": { "custom_path_tree": { "tokenizer": "custom_hierarchy" }, "custom_path_tree_reversed": { "tokenizer": "custom_hierarchy_reversed" } }, "tokenizer": { "custom_hierarchy": { "type": "path_hierarchy", "delimiter": "/" }, "custom_hierarchy_reversed": { "type": "path_hierarchy", "delimiter": "/", "reverse": "true" } } } } }