developing-in-lightdash

Developing in Lightdash

Build and deploy Lightdash analytics projects. This skill covers the semantic layer (metrics, dimensions, joins) and content (charts, dashboards).

When to Use

• Working with Lightdash YAML files (charts, dashboards, models as code)
• Using the lightdash CLI (deploy, upload, download, preview, lint, sql)
• Defining metrics, dimensions, joins, or tables in dbt or pure Lightdash projects
• Creating or editing charts and dashboards as code

Don't use for: Developing the Lightdash application itself (use the codebase CLAUDE.md), general dbt work without Lightdash metadata, or raw SQL unrelated to Lightdash models.

What You Can Do

Task	Commands	References
Explore data warehouse	lightdash sql to execute raw sql, read .csv results	CLI Reference
Define metrics & dimensions	Edit dbt YAML or Lightdash YAML	Metrics, Dimensions
Create charts	lightdash download, edit YAML, lightdash upload	Chart Types
Build dashboards	lightdash download, edit YAML, lightdash upload	Dashboard Reference
Lint yaml files	lightdash lint	CLI Reference
Set warehouse connection	lightdash set-warehouse from profiles.yml	CLI Reference
Deploy changes	lightdash deploy (semantic layer), lightdash upload (content)	CLI Reference
Test changes	lightdash preview	Workflows

Common Mistakes

Mistake	Consequence	Prevention
Guessing filter values	Case mismatches ('Payment' vs 'payment') cause charts to silently return no data	Always run lightdash sql "SELECT DISTINCT column FROM table LIMIT 50" -o values.csv and use exact values
Not updating dashboard tiles after renaming a chart	Dashboard tile still shows old title — title and chartName are independent overrides that do NOT auto-update	Download the dashboard, find tiles with matching chartSlug, update title and chartName to match
Including unused dimensions in metricQuery	"Results may be incorrect" warning — extra dimensions change SQL grouping and produce wrong numbers	Every dimension in metricQuery.dimensions must appear in the chart config. For cartesian: layout.xField, layout.yField, or pivotConfig.columns
Deploying to wrong project	Overwrites production content	Always run lightdash config get-project before deploying
Missing contentType field	Content type can't be determined without relying on directory structure	Always include contentType: chart, contentType: dashboard, or contentType: sql_chart at the top level

Before You Start

Check Your Target Project

Always verify which project you're deploying to. Deploying to the wrong project can overwrite production content.

lightdash config get-project        # Show current project
lightdash config list-projects      # List available projects
lightdash config set-project --name "My Project"  # Switch project

Detect Your Project Type

The YAML syntax differs significantly between project types.

Type	Detection	Key Difference
dbt Project	Has dbt_project.yml	Metadata nested under meta:
Pure Lightdash	Has lightdash.config.yml, no dbt	Top-level properties

ls dbt_project.yml 2>/dev/null && echo "dbt project" || echo "Not dbt"
ls lightdash.config.yml 2>/dev/null && echo "Pure Lightdash" || echo "Not pure Lightdash"

Syntax Comparison

dbt YAML (metadata under meta:):

models:
  - name: orders
    meta:
      metrics:
        total_revenue:
          type: sum
          sql: "${TABLE}.amount"
    columns:
      - name: status
        meta:
          dimension:
            type: string

Pure Lightdash YAML (top-level):

type: model
name: orders
sql_from: 'DB.SCHEMA.ORDERS'

metrics:
  total_revenue:
    type: sum
    sql: ${TABLE}.amount

dimensions:
  - name: status
    sql: ${TABLE}.STATUS
    type: string

Setting Up Warehouse Connection

If the project needs a different warehouse connection (e.g., switching from Postgres to BigQuery), update it from your profiles.yml:

lightdash set-warehouse --project-dir ./dbt --profiles-dir ./profiles --assume-yes

This reads credentials from profiles.yml, updates the warehouse connection on the currently selected project, and triggers a recompile. Run this before lightdash deploy.

To target a specific project:

lightdash set-warehouse --project-dir ./dbt --profiles-dir ./profiles --project <uuid> --assume-yes

Core Workflows

Verify Filter Values Before Using Them

CRITICAL: Never guess filter values. Case mismatches (e.g., 'Payment' vs 'payment') cause charts to silently return no data.

Before writing any string filter, query actual values from the warehouse:

lightdash sql "SELECT DISTINCT category FROM payments LIMIT 50" -o category_values.csv

Read the CSV and use the exact values in your filter YAML. This applies to all equals/notEquals filters with string values — in charts and dashboards.

Editing Metrics & Dimensions

1. Find the model YAML file (dbt: models/*.yml, pure Lightdash: lightdash/models/*.yml)
2. Edit metrics/dimensions using the appropriate syntax for your project type
3. Validate: lightdash lint (pure Lightdash) or dbt compile (dbt projects)
4. Deploy: lightdash deploy

See Metrics Reference and Dimensions Reference for configuration options.

Editing Charts

1. Download: lightdash download --charts chart-slug
2. Edit the YAML file in lightdash/ directory
3. Verify filter values: If you added or changed filters, use lightdash sql to check actual column values (see Common Mistakes)
4. Update dashboard tiles: If you changed the chart's name or purpose, download any dashboards that reference it and update their tile title and chartName properties to match (see Common Mistakes)
5. Lint: lightdash lint to validate before uploading
6. Upload: lightdash upload --charts chart-slug (and any modified dashboards)

Dashboard tiles have their own titles. A saved_chart tile's title and chartName properties are independent overrides — they do NOT auto-update when you rename the chart. If you change a chart from "Total Revenue" to "Gross Profit" but don't update the dashboard tile, the dashboard will still display "Total Revenue". Always download the dashboard, find tiles with matching chartSlug, and update their title and chartName to match.

# Dashboard tile — title and chartName must be updated manually when chart changes
tiles:
  - type: saved_chart
    properties:
      chartSlug: total-revenue-kpi
      title: "Gross Profit"        # ← Update this when chart name/purpose changes
      chartName: "Gross Profit"    # ← Update this too

Editing Dashboards

1. Download: lightdash download --dashboards dashboard-slug
2. Edit the YAML file in lightdash/ directory
3. Verify filter values: If you added or changed filters, use lightdash sql to check actual column values (see Common Mistakes)
4. Lint: lightdash lint to validate before uploading
5. Upload: lightdash upload --dashboards dashboard-slug

Creating New Content

Charts and dashboards are typically created in the UI first, then managed as code:

1. Create in UI
2. lightdash download to pull as YAML
3. Edit and version control
4. lightdash lint to validate before uploading
5. lightdash upload to sync changes

Testing with Preview

For larger changes, test in isolation:

lightdash preview --name "my-feature"
# Make changes and iterate
lightdash stop-preview --name "my-feature"

CLI Quick Reference

Command	Purpose
lightdash deploy	Sync semantic layer (metrics, dimensions)
lightdash upload	Upload charts/dashboards
lightdash download	Download charts/dashboards as YAML
lightdash lint	Validate YAML locally
lightdash preview	Create temporary test project
lightdash sql "..." -o file.csv	Run SQL queries against warehouse
lightdash run-chart -p chart.yml	Execute chart YAML query against warehouse

See CLI Reference for full command documentation.

Semantic Layer

The semantic layer defines your data model. See individual references for full configuration:

• Tables Reference — queryable entities, labels, joins
• Metrics Reference — aggregated calculations (count, sum, average, min, max, number, etc.)
• Dimensions Reference — attributes for grouping/filtering (string, number, boolean, date, timestamp)
• Joins Reference — cross-table relationships

Chart Types

All charts share a common base structure:

contentType: chart              # Required: chart, dashboard, or sql_chart
chartConfig:
  type: <type>
  config: {}        # Type-specific — see individual references
dashboardSlug: my-dashboard  # Optional: scopes chart to dashboard (won't appear in space)
metricQuery:
  exploreName: my_explore     # Required: which explore to query
  dimensions:
    - my_explore_category
  filters: {}
  limit: 500
  metrics:
    - my_explore_total_sales
  sorts: []
name: "C

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