Managing Amazon OpenSearch UI infrastructure as code with AWS CDK

As organizations scale their observability and analytics capabilities throughout a number of AWS Areas and environments, sustaining constant dashboards turns into more and more advanced. Groups typically spend hours manually recreating dashboards, creating workspaces, linking knowledge sources, and validating configurations throughout deployments—a repetitive and error-prone course of that slows down operational visibility.

The subsequent era OpenSearch UI in Amazon OpenSearch Service introduces a unified, managed analytics expertise that decouples from particular person OpenSearch domains and OpenSearch collections. It gives workspaces, devoted staff areas with collaborator administration and a tailor-made surroundings for observability, search, and safety analytics use instances. Every workspace can connect with a number of knowledge sources, together with OpenSearch Service domains, Amazon OpenSearch Serverless collections, and exterior sources reminiscent of Amazon Easy Storage Service (Amazon S3). OpenSearch UI additionally helps entry with AWS IAM Id Middle, AWS Id and Entry Administration (IAM), Id supplier (IdP)-initiated single sign-on (SAML utilizing IAM federation), and AI-powered insights.)-initiated single sign-on (SAML utilizing IAM federation),and AI-powered insights.

On this submit, you’ll discover ways to use the AWS Cloud Improvement Package (AWS CDK) to deploy an OpenSearch UI software and combine it with an AWS Lambda operate that robotically creates workspaces and dashboards utilizing the OpenSearch Dashboards Saved Objects APIs. Utilizing this automation signifies that environments launch with ready-to-use analytics which might be standardized, version-controlled, and constant throughout deployments. which might be standardized, version-controlled, and constant throughout deployments.

Particularly, you’ll discover ways to:

• Deploy an OpenSearch UI software utilizing AWS CDK that in flip makes use of AWS CloudFormation
• Robotically create workspaces and dashboards utilizing a Lambda primarily based customized useful resource
• Generate and ingest pattern knowledge for instant visualization
• Construct visualizations programmatically utilizing the OpenSearch Dashboards Saved Objects API
• Authenticate API requests utilizing AWS Signature Model 4

All of the code samples on this submit can be found on this AWS Samples repository.

Resolution overview

The next structure demonstrates the best way to automate OpenSearch UI workspace and dashboard creation utilizing AWS CDK, AWS Lambda, and the OpenSearch UI APIs.

The workflow flows from left to proper:

1. Deploy stack – Developer runs cdk deploy to launch the infrastructure and create the CloudFormation stack.
2. Create area – CloudFormation creates the OpenSearch area (which serves as the info supply)
3. Create OpenSearch UI app – CloudFormation creates the OpenSearch UI software
4. Set off Lambda – CloudFormation invokes the Lambda operate as a customized useful resource
5. Generate and ingest knowledge – Lambda generates pattern metrics and ingests them into the area
6. Create workspaces and property utilizing saved object API – Lambda creates the workspace, index sample, visualization (pie chart), and dashboard utilizing OpenSearch UI API calls

The result’s a completely configured OpenSearch UI with pattern knowledge and a ready-to-use dashboard automated by infrastructure as code (IaC). The identical workflow can be built-in into present infrastructure for OpenSearch UI purposes to robotically create or replace dashboards throughout future deployments, sustaining consistency throughout environments. consistency throughout environments.

Conditions

To carry out the answer, you want the next stipulations:

• An AWS consumer or function with ample permissions – You’ll want permissions to create and handle AWS sources reminiscent of OpenSearch Service domains, OpenSearch UI purposes, Lambda features, IAM roles and insurance policies, digital personal cloud (VPC) networking parts (subnets and safety teams), and CloudFormation stacks. For testing or proof-of-concept deployments, we suggest utilizing an administrative function. For manufacturing, comply with the precept of least privilege.
• Set up improvement instruments:
• Bootstrap CDK – It is a one-time setup per account or Area:

This creates the mandatory S3 bucket and IAM roles for AWS CDK deployments in your account.

Get the pattern code

Clone the pattern implementation from GitHub:

git clone https://github.com/aws-samples/sample-automate-opensearch-ui-dashboards-deployment.git 
cd opensearch-dashboard-automation-sample 

The repository incorporates:

opensearch-dashboard-automation-sample/ 
├── cdk/ 
│   ├── bin/ 
│   │   └── app.ts                           # CDK app entry level 
│   └── lib/ 
│       └── dashboard-stack.ts               # OpenSearch area, Lambda, and customized useful resource 
└── lambda/ 
    ├── dashboard_automation.py              # Most important Lambda for workspace and dashboard automation 
    ├── sigv4_signer.py                      # AWS SigV4 signing utility 
    └── necessities.txt                     # Python dependencies

This pattern demonstrates the best way to deploy an OpenSearch UI software, create a workspace, ingest pattern knowledge, and robotically generate visualizations and dashboards utilizing IaC.

After cloning the repository, you possibly can deploy the stack to robotically create your first OpenSearch workspace and dashboard with pattern knowledge.

Understanding the answer

Earlier than deploying, let’s look at how the answer works. The next steps clarify the structure and automation logic that may execute robotically if you deploy the AWS CDK stack. The subsequent part incorporates the precise deployment instructions you’ll run.

Provision OpenSearch UI sources

The AWS CDK integrates seamlessly with AWS CloudFormation. This implies you possibly can outline your OpenSearch sources and automation workflows as IaC. On this answer, AWS CDK provisions the OpenSearch area, OpenSearch UI software, and a Lambda primarily based customized useful resource that performs the automation logic.

When deploying OpenSearch UI automation, the order of useful resource creation is necessary to appropriately resolve dependencies. The really useful order is as follows:

1. Create the Lambda execution function – Required for entry to AppConfigs and APIs
2. Create the OpenSearch area – Serves as the first knowledge supply
3. Create the OpenSearch UI software – References the Lambda function in its AppConfigs
4. Create the Lambda operate – Defines the automation logic
5. Create the customized useful resource – Triggers the Lambda automation throughout stack deployment

The next code snippet (from cdk/lib/dashboard-stack.ts) exhibits the important thing infrastructure definitions:

export class OpenSearchDashboardStack extends cdk.Stack { 
  constructor(scope: Assemble, id: string, props?: OpenSearchDashboardStackProps) { 
    tremendous(scope, id, props); 
 
    const masterUserArn = props?.masterUserArn ||  
      `arn:aws:iam::${this.account}:function/Admin`; 
 
    // Step 1: Create IAM Position for Lambda FIRST 
    const dashboardRole = new iam.Position(this, 'DashboardLambdaRole', { 
      assumedBy: new iam.ServicePrincipal('lambda.amazonaws.com'), 
      inlinePolicies: { 
        OpenSearchAccess: new iam.PolicyDocument({ 
          statements: [ 
            new iam.PolicyStatement({ 
              actions: ['opensearch:ApplicationAccessAll'], 
              sources: ['*'] 
            }), 
            new iam.PolicyStatement({ 
              actions: ['es:ESHttpPost', 'es:ESHttpPut', 'es:ESHttpGet'], 
              sources: [`arn:aws:es:${this.region}:${this.account}:domain/*`] 
            }) 
          ] 
        }) 
      } 
    }); 
 
    // Step 2: Create OpenSearch Area 
    const opensearchDomain = new opensearch.Area(this, 'OpenSearchDomain', { 
      model: opensearch.EngineVersion.OPENSEARCH_2_11, 
      capability: { dataNodes: 1, dataNodeInstanceType: 'r6g.massive.search' }, 
      // ... further configuration 
    }); 
 
    // Step 3: Create OpenSearch UI Utility 
    const openSearchUI = new opensearch.CfnApplication(this, 'OpenSearchUI', { 
      appConfigs: [ 
        { 
          key: 'opensearchDashboards.dashboardAdmin.users', 
          value: `["${masterUserArn}"]` // Human customers 
        }, 
        { 
          key: 'opensearchDashboards.dashboardAdmin.teams', 
          worth: `["${dashboardRole.roleArn}"]` // Lambda function 
        } 
      ], 
      dataSources: [{ dataSourceArn: opensearchDomain.domainArn }], 
      // ... further configuration 
    }); 
 
    // Step 4: Create Lambda Operate 
    const dashboardFn = new lambda.Operate(this, 'DashboardSetup', { 
      runtime: lambda.Runtime.PYTHON_3_11, 
      handler: 'dashboard_automation.handler', 
      code: lambda.Code.fromAsset('../lambda'), 
      timeout: cdk.Period.minutes(5), 
      function: dashboardRole 
    }); 
 
    // Step 5: Create Customized Useful resource 
    const supplier = new cr.Supplier(this, 'DashboardProvider', { 
      onEventHandler: dashboardFn 
    }); 
 
    new cdk.CustomResource(this, 'DashboardSetupResource', { 
      serviceToken: supplier.serviceToken, 
      properties: { 
        opensearchUIEndpoint: openSearchUI.attrDashboardEndpoint, 
        domainEndpoint: opensearchDomain.domainEndpoint, 
        domainName: opensearchDomain.domainName, 
        workspaceName: 'workspace-demo', 
        area: this.area 
      } 
    }); 
  } 
}

These are some necessary implementation notes:

• The Lambda function have to be created earlier than the OpenSearch UI software so its Amazon Useful resource Identify (ARN) may be referenced in dashboardAdmin.teams
• The Lambda function contains each opensearch:ApplicationAccessAll (for OpenSearch UI API entry) and es:ESHttp* permissions (for ingesting knowledge into the OpenSearch area)
• The customized useful resource allows the automation operate to run throughout deployment, passing each OpenSearch UI and OpenSearch area endpoints as parameters

Authenticate with OpenSearch UI APIs

When programmatically interacting with the OpenSearch UI (Dashboards) APIs, correct authentication is required so your Lambda operate or automation script can securely entry the APIs. The OpenSearch UI makes use of AWS Signature Model 4 (SigV4) authentication—much like the OpenSearch area APIs—however with just a few necessary distinctions.

When signing OpenSearch UI API requests, the service identify have to be opensearch, not es. It is a frequent supply of confusion: the OpenSearch area endpoint nonetheless makes use of the legacy service identify es, however the OpenSearch UI endpoints require opensearch. Utilizing the incorrect service identify will trigger your requests to fail authentication, even when the credentials are legitimate.

For POST, PUT, or DELETE requests, embrace the next headers to fulfill the OpenSearch UI API safety necessities:

The SigV4 signing course of robotically computes this physique hash when utilizing the botocore AWSRequest object, sustaining request integrity and stopping tampering throughout transmission.

The next code snippet (from lambda/sigv4_signer.py) demonstrates the best way to signal and ship a request to the OpenSearch UI API:

def get_common_headers(physique: bytes = b"{}") -> Dict[str, str]: 
    """ 
    Get frequent headers for OpenSearch UI API requests. 
     
    Args: 
        physique: Request physique bytes to hash 
         
    Returns: 
        Dictionary of required headers 
    """ 
    body_hash = hashlib.sha256(physique).hexdigest() 
    return { 
        "Content material-Sort": "software/json", 
        "x-amz-content-sha256": body_hash, 
        "osd-xsrf": "osd-fetch", 
        "osd-version": "3.1.0", 
    } 
 
 
def make_signed_request( 
    methodology: str, 
    url: str, 
    headers: Dict[str, str], 
    physique: bytes = b"", 
    area: str = None, 
) -> Any: 
    session = boto3.Session() 
    if not area: 
        area = session.region_name 
     
    # Create AWS request 
    request = AWSRequest(methodology=methodology, url=url, knowledge=physique, headers=headers) 
     
    # Signal with SigV4 utilizing 'opensearch' service identify (not 'es') 
    credentials = session.get_credentials() 
    SigV4Auth(credentials, "opensearch", area).add_auth(request) 
     
    # Ship request utilizing URLLib3Session 
    http_session = URLLib3Session() 
    return http_session.ship(request.put together()) 

This utility operate indicators the request utilizing the right service identify (opensearch), attaches the required headers, and sends it securely to the OpenSearch UI endpoint.

Create workspace and dashboard with pattern knowledge

The Lambda operate (lambda/dashboard_automation.py) automates the complete technique of provisioning a workspace, producing pattern knowledge, and creating visualizations and dashboards by the OpenSearch UI APIs. Go to the next lists of APIs:

Comply with these steps:

1. Find or create a workspace. Every dashboard within the OpenSearch UI should exist inside a workspace. The operate first checks whether or not a workspace already exists and creates one if needed. The workspace associates a number of knowledge sources (for instance, an OpenSearch area or OpenSearch Serverless assortment):

   def get_or_create_workspace(endpoint: str, area: str,  
                              data_source_id: str, workspace_name: str) -> Non-obligatory[str]: 
       """Get present workspace or create new one (idempotent).""" 
       # Test for present workspace 
       workspace_id = find_workspace_by_name(endpoint, area, workspace_name) 
       if workspace_id: 
           return workspace_id 
    
       # Create new workspace 
       url = f"https://{endpoint}/api/workspaces" 
       payload = { 
           "attributes": {"identify": workspace_name, "options": ["use-case-observability"]}, 
           "settings": {"dataSources": [data_source_id]} 
       } 
       response = make_signed_request("POST", url, get_common_headers(), json.dumps(payload).encode(), area) 
       return response.json()["result"]["id"]

   This logic allows repeated deployments to stay idempotent; the Lambda operate reuses present workspaces somewhat than creating duplicates.

2. Generate and ingest pattern knowledge. To make the dashboards significant upon first launch, the Lambda operate generates a small dataset simulating HTTP request metrics and ingests it into the OpenSearch area utilizing the Bulk API:

   def generate_sample_metrics(num_docs: int = 50) -> record: 
       """Generate practical HTTP API request metrics.""" 
       endpoints = ["/api/users", "/api/products", "/api/orders"] 
       status_codes = [200, 201, 400, 404, 500] 
       status_weights = [0.70, 0.15, 0.08, 0.05, 0.02]  # Sensible distribution 
    
       paperwork = [] 
       for i in vary(num_docs): 
           paperwork.append({ 
               "@timestamp": generate_timestamp(), 
               "endpoint": random.selection(endpoints), 
               "status_code": random.selections(status_codes, weights=status_weights)[0], 
               "response_time_ms": random.randint(20, 500) 
           }) 
       return paperwork

   The operate then ingests this knowledge into the area:

   def ingest_sample_data(domain_endpoint: str, area: str, paperwork: record) -> bool:
       """Ingest paperwork utilizing OpenSearch bulk API."""
       index_name = f"application-metrics-{datetime.utcnow().strftime('%Y.%m.%d')}"
       bulk_body = "n".be a part of([
           f'{{"index":{{"_index":"{index_name}"}}}}n{json.dumps(doc)}'
           for doc in documents
       ]) + "n"
   
       url = f"https://{domain_endpoint}/_bulk"
       response = make_domain_request("POST", url, headers, bulk_body.encode(), area)
       return 200 <= response.status_code < 300

   This allows every deployment to incorporate pattern analytics knowledge that instantly populates the dashboard upon first login.

3. Create a visualization. After the index sample is offered, the Lambda operate creates a pie chart visualization that exhibits HTTP standing code distribution:

   def create_visualization(endpoint: str, area: str,  
                           workspace_id: str, index_pattern_id: str) -> Non-obligatory[str]: 
       """Create pie chart exhibiting HTTP standing code distribution.""" 
       url = f"https://{endpoint}/w/{workspace_id}/api/saved_objects/visualization" 
    
       vis_state = { 
           "title": "HTTP Standing Code Distribution", 
           "sort": "pie", 
           "aggs": [ 
               {"id": "1", "type": "count", "schema": "metric"}, 
               { 
                   "id": "2", 
                   "type": "terms", 
                   "schema": "segment", 
                   "params": {"field": "status_code", "size": 10} 
               } 
           ] 
       } 
    
       payload = { 
           "attributes": { 
               "title": "HTTP Standing Code Distribution", 
               "visState": json.dumps(vis_state), 
               "kibanaSavedObjectMeta": { 
                   "searchSourceJSON": json.dumps({ 
                       "index": index_pattern_id, 
                       "question": {"question": "", "language": "kuery"} 
                   }) 
               } 
           } 
       } 
    
       response = make_signed_request("POST", url, get_common_headers(), json.dumps(payload).encode(), area) 
       return response.json().get("id") 
    

   This visualization will later be embedded inside a dashboard panel.

4. Create the dashboard. Lastly, the Lambda operate creates a dashboard that references the visualization created within the earlier step:

   def create_dashboard(endpoint: str, area: str,  
                       workspace_id: str, viz_id: str) -> Non-obligatory[str]: 
       """Create dashboard containing the visualization.""" 
       url = f"https://{endpoint}/w/{workspace_id}/api/saved_objects/dashboard" 
    
       # Outline panel structure for the visualization 
       panels_json = [{ 
           "version": "2.11.0", 
           "gridData": {"x": 0, "y": 0, "w": 24, "h": 15, "i": "1"}, 
           "panelIndex": "1", 
           "embeddableConfig": {}, 
           "panelRefName": "panel_1" 
       }] 
    
       payload = { 
           "attributes": { 
               "title": "Utility Metrics", 
               "description": "HTTP request metrics dashboard", 
               "panelsJSON": json.dumps(panels_json), 
               "optionsJSON": json.dumps({"darkTheme": False}), 
               "model": 1, 
               "timeRestore": False, 
               "kibanaSavedObjectMeta": { 
                   "searchSourceJSON": json.dumps({"question": {"question": "", "language": "kuery"}}) 
               } 
           }, 
           "references": [{ 
               "name": "panel_1", 
               "type": "visualization", 
               "id": viz_id 
           }] 
       } 
    
       response = make_signed_request("POST", url, get_common_headers(), json.dumps(payload).encode(), area) 
       return response.json().get("id") 
    

This completes the dashboard creation course of, offering customers with an interactive visualization of software metrics as quickly as they entry the workspace.

The total implementation, together with logging, error dealing with, and helper utilities, is offered within the AWS Samples GitHub repository.

Deploy the infrastructure with AWS CDK

With the AWS CDK stack and Lambda automation in place, you’re able to deploy the total answer and confirm that your OpenSearch UI dashboard is created robotically.

Deploy the stack

From the foundation listing of the cloned repository, navigate to the AWS CDK folder and deploy the stack utilizing your IAM consumer ARN from the Conditions part:

cd cdk 
npm set up 
npx cdk bootstrap  # First time solely 
npx cdk deploy -c masterUserArn=arn:aws:iam::123456789012:consumer/your-username

The deployment course of usually takes 20–25 minutes as a result of AWS CDK provisions the OpenSearch area, OpenSearch UI software, Lambda operate, and customized useful resource that runs the automation.

Confirm the deployment

After the deployment completes:

1. Open the OpenSearch UI endpoint displayed within the AWS CDK output.
2. Register utilizing your IAM credentials.
3. Change to the newly created workspace-demo workspace.
4. Open the Utility Metrics dashboard.
5. View the pie chart visualization that shows the distribution of HTTP standing codes from the pattern knowledge.

The dashboard robotically shows a pie chart visualization populated with artificial software metrics, demonstrating how the Saved Objects API can be utilized to bootstrap significant analytics dashboards instantly after deployment.

Enhancement 1: Simplify dashboard creation with Saved Object Import API

As your OpenSearch Dashboards evolve, managing advanced dependencies between index patterns, visualizations, and dashboards can develop into more and more troublesome. Every dashboard typically references a number of saved objects, and manually recreating or syncing them throughout environments may be time-consuming and error inclined.

To simplify this course of, we suggest utilizing the Saved Objects Import/Export API. You should use this API to bundle whole dashboards, together with their dependent objects, right into a single transferable artifact. By utilizing this strategy, you possibly can model, migrate, and deploy dashboards throughout environments as a part of your CI/CD workflow, sustaining consistency and lowering operational overhead.

Export your dashboard

You’ll be able to export dashboards straight from the OpenSearch UI or use saved object export API:

1. Open Stack Administration after which Saved Objects
2. Choose the dashboard and associated objects (for instance, visualizations and index patterns)
3. Select Export
4. Save the exported file as dashboard.ndjson

This file incorporates saved objects serialized in newline-delimited JSON (NDJSON) format, prepared for versioning or deployment automation.

Import dashboards programmatically

You’ll be able to programmatically import the NDJSON file right into a goal workspace utilizing the Saved Objects import API:

# Pseudo code for import operate 
def import_dashboard(workspace_id, ndjson_file): 
    # Learn the exported dashboard file 
    dashboard_config = read_file(ndjson_file) 
     
     
    # POST to import to opensearch ui endpoint 
    url = f"{opensearch_ui_endpoint}/w/{workspace_id}/api/saved_objects/_import" 
    response = make_signed_request("POST", url, dashboard_config) 
     
    return response.success 

By utilizing this strategy, you possibly can deal with dashboards as deployable property, precisely like software code. You’ll be able to retailer your exported dashboards in supply management, combine them into your AWS CDK or CloudFormation pipelines, and robotically deploy them to a number of environments with confidence.

Enhancement 2: Improved safety configurations

In some instances, you may wish to enhance the safety configuration of your OpenSearch UI software, otherwise you is perhaps coping with OpenSearch domains which were deployed with further safety configurations. On this part, we talk about how one can enhance the safety configuration of your OpenSearch UI software and nonetheless obtain IaC with AWS CDK. Extra particularly, we clarify how one can arrange your OpenSearch UI software when your OpenSearch area is in a VPC and when fine-grained entry management is enabled.

When the OpenSearch Area resides inside a VPC, further configurations can be wanted to correctly join along with your dashboard.

Allow communication between Lambda features used to ingest knowledge and the OpenSearch area within the VPC

When the OpenSearch Service area resides in a VPC, the Lambda features that ingest knowledge into the area should be capable of talk with it. Probably the most easy approach of doing that is to permit the Lambda operate to be executed inside the similar VPC as your OpenSearch Service area and provides it the identical safety group. An instance is supplied within the GitHub repository.

1. Permit HTTPS communications from purchasers making an attempt to speak along with your OpenSearch Service area. On this instance, the shopper can be utilizing the identical safety group used within the OpenSearch Service area:

   openSearchSecurityGroup.addIngressRule(
     openSearchSecurityGroup,
     ec2.Port.tcp(443),
     'Permit inbound HTTPS site visitors from itself',
   );

2. Add this managed coverage to the function assumed by the Lambda operate to permit it entry to the VPC:

   iam.ManagedPolicy.fromAwsManagedPolicyName('service-role/AWSLambdaVPCAccessExecutionRole')

3. Specify the VPC and the safety group your Lambda operate can be utilizing. On this case, the VPC is identical one utilized by your OpenSearch Service area:

   const dashboardFn = new lambda.Operate(this, 'DashboardSetup', {
     // ... further configuration
     vpc: vpc,
     securityGroups: [openSearchSecurityGroup]
   });

Authorize OpenSearch UI service for VPC endpoint entry

For the OpenSearch Service area to be accessible to your dashboard, VPC endpoint entry have to be enabled. This may be achieved by utilizing a customized useful resource, as proven within the following configuration:

const authorizeOpenSearchUIVpcAccess = new cr.AwsCustomResource(this, 'AuthorizeOpenSearchUIVpcAccess', {
  onUpdate: {
    service: 'OpenSearch',
    motion: 'authorizeVpcEndpointAccess',
    parameters: {
      DomainName: opensearchDomain.domainName,
      Service: 'software.opensearchservice.amazonaws.com',
    },
    physicalResourceId: cr.PhysicalResourceId.of(`${opensearchDomain.domainName}-VpcEndpointAccess`),
  },
  coverage: cr.AwsCustomResourcePolicy.fromStatements([
    new iam.PolicyStatement({
      actions: ['es:AuthorizeVpcEndpointAccess'],
      sources: [opensearchDomain.domainArn],
    }),
  ]),
});

Allow fine-grained entry management

If you use fine-grained entry management together with an OpenSearch UI, you’ve got extra management over which operations are allowed for every consumer. This may be particularly helpful if you wish to restrict your customers’ actions past the admin, learn, or write permissions that include OpenSearch UI. Distinctive roles may be created and mapped to a number of customers to realize exact management over who can entry what performance.

Within the earlier sections, the identical Lambda was used to make requests to each the OpenSearch Service area and the OpenSearch UI. Nonetheless, in conditions the place the primary function isn’t the identical between the OpenSearch Service area and the OpenSearch UI, we suggest making a Lambda operate for every function. Once more, when deploying OpenSearch UI automation, the order of useful resource creation is necessary to appropriately resolve dependencies. As illustrated beforehand, the really useful order is as follows:

1. Create the dashboard Lambda execution function – Required for entry to AppConfigs and APIs
2. Create the OpenSearch area major function – Required for area creation and APIs
3. Create the OpenSearch area – Serves as the first knowledge supply
4. Create the OpenSearch area Lambda operate – Defines the automation logic for the OpenSearch area
5. Create the OpenSearch area customized sources – Triggers the Lambda automation throughout stack deployment
6. Create the OpenSearch UI software – References the Lambda function in its AppConfigs
7. Create the OpenSearch UI Lambda operate – Defines the automation logic for the OpenSearch UI
8. Create the OpenSearch UI customized useful resource – Triggers the Lambda automation throughout stack deployment

When creating the OpenSearch Service area, specify the fine-grained entry management parameter, as follows:

// Step 3: Create OpenSearch Area
const opensearchDomain = new opensearch.Area(this, 'OpenSearchDomain', {
  // ... further configuration
  // Allow Nice-Grained Entry Management in your OpenSearch Area
  fineGrainedAccessControl: {
    masterUserArn: openSearchMasterRole.roleArn,
  }
});

The Lambda operate accountable for speaking with the OpenSearch Service area ought to have the mandatory permissions to put in writing to it. The next is a configuration instance the place the Lambda operate assumes the area’s major function:

// Step 4: Create Lambda Operate for OpenSearch Area
const domainFn = new lambda.Operate(this, 'DomainSetup', {
  // ... further configuration
  function: openSearchMasterRole
});

Then, add the customized sources to create the roles and function mappings, as wanted:

// Step 5: Create Customized Sources for OpenSearch Area
const domainProvider = new cr.Supplier(this, 'DomainProvider', {
  onEventHandler: domainFn
});

// A customized useful resource to create roles (Non-obligatory)
new cdk.CustomResource(this, 'DomainRoleSetupResource', {
  serviceToken: domainProvider.serviceToken,
  // ... further configuration
});

// A customized useful resource to create function mappings (Non-obligatory)
new cdk.CustomResource(this, 'DomainRolesMappingSetupResource', {
  serviceToken: domainProvider.serviceToken,
  // ... further configuration
});

Create further roles within the OpenSearch Service area (Non-obligatory)

If you wish to grant particular permissions to some customers, we suggest creating roles for them. This may be achieved by making the next requests to the OpenSearch Service area endpoint.

For extra details about the roles endpoint, evaluate the Create function within the OpenSearch documentation.

# Pseudo code to create a job
def create_role(domain_endpoint: str, area: str, 
                        new_role_name: str) -> bool:
    """Create a brand new function"""
    url = f"https://{domain_endpoint}/_plugins/_security/api/roles/{new_role_name}"

    payload = {
        "description": "",
        "cluster_permissions": [
            // ... Permisions
        ],
        "index_permissions": [
            {
                "index_patterns": [
                    // ... Index patterns
                ],
                "fls": [],
                "masked_fields": [],
                "allowed_actions": [
                    // ... Allowed actions
                ],
            },
        ],
    }
    
    response = make_domain_request("PUT", url, headers, json.dumps(payload).encode(), area)
    return response.success

Create function mappings within the OpenSearch area in your dashboard customers (Non-obligatory)

Customers may be mapped to a number of roles to manage their entry to the OpenSearch Service area, which can be mirrored within the OpenSearch UI dashboard related to the area.

For extra details about the rolesmapping endpoint, evaluate the Create function mapping within the OpenSearch documentation.

# Pseudo code to create a job mapping
def create_role_mapping(domain_endpoint: str, area: str, 
                        new_role_name: str) -> bool:
    """Create a brand new function mapping"""
    url = f"https://{domain_endpoint}/_plugins/_security/api/rolesmapping/{new_role_name}"

    payload = {
        "backend_roles": [
            "",
            "",
        ],
    }

    response = make_domain_request("PUT", url, headers, json.dumps(payload).encode(), area)
    return response.success

These are some necessary implementation notes:

• By default, the OpenSearch Area will create a job mapping for its major consumer, underneath all_access and security_manager. Should you modify these mappings, we suggest maintaining the primary consumer within the record to stop unintentional lack of entry.
• When fine-grained entry management is used, if a consumer opens the OpenSearch UI with out being mapped to a job within the OpenSearch Area, they are going to be unable to visualise or modify the info situated within the OpenSearch Area, even when they’re a part of the OpenSearch UI’s admin group. For that reason, we suggest creating customized sources so as to add the suitable function mappings. OpenSearch UI admins will nonetheless be capable of make adjustments to the OpenSearch UI dashboards.
• When programmatically interacting with the OpenSearch Area APIs, correct authentication is required so your Lambda operate or automation script can securely entry the APIs. The OpenSearch Area makes use of SigV4 authentication. When signing the OpenSearch Area API requests, the service identify have to be es.

Value issues

This answer makes use of a number of AWS companies, every with its personal price element:

• Amazon OpenSearch Service – That is the primary price driver. Expenses are primarily based on occasion sort, variety of nodes, and Amazon Elastic Block Retailer (Amazon EBS) storage. For testing, you should utilize a smaller occasion (for instance, t3.small.search) or delete the area after use to attenuate price.) or delete the area after use to attenuate price.
• AWS Lambda – The automation operate runs solely throughout deployment and incurs minimal prices for just a few brief invocations.
• AWS CDK and CloudFormation – Create short-term IAM roles and Amazon S3 deployment property with negligible price.

For pricing particulars, discuss with Amazon OpenSearch Service Pricing.

Clear Up

To keep away from incurring ongoing prices, clear up the sources created by this answer if you’ve accomplished your testing.Open your mission listing and destroy the AWS CDK stack:

This command removes the sources provisioned by the AWS CDK stack, together with:

• The Amazon OpenSearch Service area
• The OpenSearch UI software
• The AWS Lambda operate and customized useful resource
• IAM roles and insurance policies related to the deployment

By cleansing up, you cease the associated prices and preserve a tidy, cost-efficient AWS surroundings.

Extra sources

Conclusion

By integrating the Saved Objects API with the next-generation Amazon OpenSearch UI, you possibly can programmatically create whole analytics experiences—together with workspaces, pattern knowledge, visualizations, and dashboards—straight out of your IaC.

This strategy brings the ability of IaC to your analytics layer. Utilizing AWS CDK and AWS Lambda, you possibly can model, deploy, and replace dashboards persistently throughout environments, lowering handbook setup whereas enhancing reliability and governance. With this automation in place, your groups can deal with insights somewhat than setup—delivering observability-as-code that scales along with your group.

Concerning the authors