Creating Lookml Model

Creating LookML Model, Views & Writing LookML

This skill guides you through the process of writing all the LookML (view files and model files) needed to prepare for the dashboard requested by the user during their onboarding interview, configuring the connection mapping in Looker, validating the LookML code, and running inline queries to verify it functions correctly.

Prerequisites & References

• Looker CLI must be installed and authenticated (Step 2 & 3).
• Looker project and BigQuery connection must be created (Step 4 & 5).
• Critical Reference: You MUST read and strictly follow the best practices, naming requirements, and constraints in the lookml-modeling-guidelines skill for writing and verifying all LookML elements.

Instructions

1. Retrieve Onboarding Goal and Schema Specifications

Refer back to the onboarding goal and BigQuery exploration results established with the user during the Step 1 (Discovery) interview in the conversation history. You must recall: 1. The target Dashboard Goal (e.g., Sales Overview, Support Metrics, etc.). 2. The specific GCP Project, Dataset, and Tables (fact and dimension) agreed upon. 3. The target Metrics (measures) and Dimensions to be used.

Note: Do not invent or guess these details. The names of your views, fields, explores, and models must directly map to the schema and tables you discovered in Step 1.

2. Create and Edit View Files (CLI)

For each BigQuery table required to build the target dashboard, you must write the LookML view definition and upload it to the project:

1. Create the views directory: If the views/ directory does not yet exist in the project, create it using the Looker CLI:

   looker-cli project directory create {project_id} views
   

2. Generate View Content: Retrieve the table columns (from Discovery) and write the LookML view file content locally (e.g. to /tmp/{table_name}.view.lkml). Follow the primary key and field definitions guidelines.

3. Upload the View File: Upload the file to your Looker project views folder:

   looker-cli project file create {project_id} views/{table_name}.view.lkml /tmp/{table_name}.view.lkml
   

4. Guideline Reference: You MUST consult and strictly follow the view creation best practices in the lookml-modeling-guidelines skill (Section 4 & 6.C) for all view definitions, primary key rules, and field reference specifications.

3. Create/Update the Model File (CLI)

Write the LookML model definition and upload it to the project root:

1. Write the model LookML structure (defining explores, joins, and includes) to a local file (e.g., /tmp/{model_name}.model.lkml).

   • Specify your database connection: connection: "{connection_name}".
   • Include the specific view files required (e.g., include: "/views/orders.view.lkml", include: "/views/users.view.lkml"). Avoid using broad wildcards like "/views/**/*.view.lkml" to prevent performance bloat and compile errors.
   • Critical for Dashboards: If planning to create LookML dashboards, you MUST include them in the model file by adding include: "/dashboards/**/*.dashboard.lookml".
   • Define explores and joins.

2. Upload the model file to your Looker project root:

   looker-cli project file create {project_id} {model_name}.model.lkml /tmp/{model_name}.model.lkml
   

3. Guideline Reference: You MUST consult and strictly follow the model and explore best practices in the lookml-modeling-guidelines skill (Section 6.A & 6.B) for all include declarations, explore structures, and explicit join relationship definitions.

4. Configure Model Connection in Looker (CLI)

Map your LookML model to the allowed database connection in Looker using the Looker CLI. This step is mandatory for Looker to register the model configuration mapping and enable querying.

1. Write the model configuration payload directly to /tmp/model_config.json:

   {
     "name": "{model_name}",
     "project_name": "{project_id}",
     "allowed_db_connection_names": ["{connection_name}"]
   }
   

2. Register the model configuration mapping (Mandatory Step):

   looker-cli model import /tmp/model_config.json
   

3. Verify the mapping:

   looker-cli model cat {model_name}
   

   Ensure the returned JSON correctly lists your connection in allowed_db_connection_names.

5. Validate the Project's LookML (CLI)

Validate the LookML code to ensure there are no syntax or logic errors:

1. Trigger project validation using the Looker CLI:

   looker-cli api project validate_project {project_id}
   

2. If errors are found, fix them in your local files, upload the updated versions using looker-cli project file update, and re-run validation until clean.

3. Guideline Reference: Under our Zero-Error Policy, you are strictly forbidden from submitting code that fails validation. Refer to lookml-modeling-guidelines (Section 3 & 5) for detailed validation rules and the feedback loop process.

6. Verify by Running Queries (CLI)

Test your connection and LookML model by running a verification query:

1. Use Discovery tools in lookml-modeling-guidelines (Section 2) to find the fully qualified names of the dimensions and measures you defined.

2. Write the JSON query payload directly to /tmp/verify_query.json (requesting your fully qualified dimension and measure, limited to 5 rows):

   {
     "model": "{model_name}",
     "view": "{explore_name}",
     "fields": ["{explore_name}.{dimension_name}", "{explore_name}.{measure_name}"],
     "limit": 5
   }
   

3. Execute the query using the Looker CLI:

   looker-cli query runquery --file /tmp/verify_query.json --format json
   

4. Confirm that the query executes successfully and returns data without database or SQL errors. Refer to lookml-modeling-guidelines (Section 3) for verification guidelines.