Rill Canvas

Instructions for developing a canvas dashboard in Rill

Introduction

Canvas dashboards are free-form dashboard resources that display custom chart and table components laid out in a grid. They enable building overview and report-style dashboards with multiple visualizations, similar to traditional business intelligence tools.

Canvas dashboards differ from explore dashboards in important ways:

• Explore dashboards: Best for explorative analysis, drill-down investigations, and letting users freely slice data by any dimension.
• Canvas dashboards: Best for fixed reports, executive summaries, or combining multiple metrics views into a single view.

Canvas dashboards are lightweight resources found downstream of metrics views in the project DAG. Each component within a canvas fetches data individually, typically from a metrics view resource.

When to use canvas dashboards:

• Building executive summaries with KPIs and multiple visualizations
• Creating report-style dashboards with markdown explanations
• Comparing metrics across different metrics views
• Designing custom layouts not possible with explore dashboards

Canvas Structure

A canvas dashboard is defined in a YAML file with type: canvas. Here is the basic structure (most canvas dashboards work great without any of the optional properties here):

type: canvas
display_name: "Sales Overview Dashboard"

# Optional filter settings
filters:
  enable: true
  pinned:
    - region
    - product_category

# Optional time range presets
time_ranges:
  - P7D
  - P30D
  - P90D
  - inf

# Optional maximum dashboard width
max_width: 1400

# Optional theme reference
theme: my_theme

# Default time settings for all components
defaults:
  time_range: P7D
  comparison_mode: time

# Optional security access control
security:
  access: "'{{ .user.domain }}' == 'company.com'"

# Required dashboard content organized in rows
rows:
  - height: 240px
    items:
      - width: 12
        kpi_grid:
          metrics_view: sales_metrics
          measures:
            - total_revenue
            - order_count

  - height: 400px
    items:
      - width: 6
        line_chart:
          metrics_view: sales_metrics
          title: "Revenue Trend"
          x:
            type: temporal
            field: event_time
          y:
            type: quantitative
            field: total_revenue
      - width: 6
        bar_chart:
          metrics_view: sales_metrics
          title: "Revenue by Region"
          color: primary
          x:
            type: nominal
            field: region
            limit: 10
            sort: -y
          y:
            type: quantitative
            field: total_revenue

Layout System

Canvas dashboards use a 12-unit grid system for layout.

Row Configuration

Each row defines a horizontal section with a specific height:

rows:
  - height: 240px    # Row height in pixels
    items:
      # Components go here

Recommended row heights:

• Markdown headers: 40px - 80px
• KPI grids: 128px - 240px (depending on number of measures)
• Charts and visualizations: 300px - 500px
• Leaderboards: 300px - 450px
• Tables: 300px - 500px

Item Widths

Items within a row share the 12-unit width:

rows:
  # Full width (1 component per row)
  - items:
    - width: 12
      markdown:
        content: "# Dashboard Title"

  # Half width (2 components per row)
  - items:
    - width: 6     
      line_chart:
        # ...
    - width: 6
      bar_chart:
        # ...

  # Third width (3 components per row)
  - items:
    - width: 4
      donut_chart:
        # ...
    - width: 4
      bar_chart:
        # ...
    - width: 4
      area_chart:
        # ...

Width guidelines:

• width: 12 - Full width; use for KPI grids, markdown headers, wide charts
• width: 6 - Half width; use for side-by-side comparisons
• width: 4 - Third width; use for three equal charts
• width: 3 - Quarter width; use for four small components (minimum practical width)

Dashboard Composition Best Practices

When building a new canvas dashboard, follow this recommended structure:

1. Row 1 - Context: Start with a markdown component providing dashboard title and overview
2. Row 2 - Key Metrics: Add a KPI grid with 2-4 of the most business-relevant measures
3. Row 3 - Primary Analysis: Split into two halves:
   • Left (width 6): A leaderboard showing top entities by a key dimension
   • Right (width 6): A time-series chart (line_chart or stacked_bar) showing trends
4. Additional Rows: Add 1-2 more rows with relevant charts based on the data

Choosing chart types:

• Time-series analysis: Use line_chart or area_chart with temporal x-axis
• Categorical comparisons: Use bar_chart or stacked_bar with nominal x-axis
• Part-to-whole: Use donut_chart or stacked_bar_normalized
• Two-dimensional patterns: Use heatmap
• Dual-metric comparison: Use combo_chart for two measures with different scales
• Funnel analysis: Use funnel_chart to visualize sequential stage drop-offs
• Two-measure relationships: Use scatter_plot to explore correlation, clusters, or outliers between two numeric measures

Field guidelines

The field names are case sensitive and should match exactly to the fields present in the metrics view.

Time dimension restrictions: The time dimension (timeseries field from the metrics view) is special and can ONLY be used in the x-axis field for temporal charts. Never use the time dimension in:

• Leaderboard dimensions
• Color fields
• Any other dimension configuration

Component Types

Markdown

Add text content, headers, and documentation:

markdown:
  content: |
    ## Dashboard Overview

    This dashboard tracks key sales metrics across all regions.

    ---
  alignment:
    horizontal: left    # left, center, right
    vertical: middle    # top, middle, bottom

Best practices:

• Use markdown for dashboard titles, section headers, and explanatory text
• Add blank lines between markdown elements for proper rendering
• Use --- for horizontal rules to separate sections

KPI Grid

Display key metrics with comparison values and sparklines:

kpi_grid:
  metrics_view: sales_metrics
  measures:
    - total_revenue
    - order_count
    - average_order_value
    - customer_count
  comparison:
    - delta           # Absolute change
    - percent_change  # Percentage change
    - previous        # Previous period value
  sparkline: right    # right, bottom, none

With dimension filters:

kpi_grid:
  metrics_view: sales_metrics
  measures:
    - total_revenue
    - order_count
  dimension_filters: region IN ('North America', 'Europe')
  comparison:
    - percent_change
  sparkline: bottom
  hide_time_range: true

Leaderboard

Display ranked dimension values by measures:

leaderboard:
  metrics_view: sales_metrics
  title: "Top Products"
  description: "Products ranked by total revenue"
  dimensions:
    - product_category
  measures:
    - total_revenue
    - order_count
  num_rows: 10

With multiple dimensions:

leaderboard:
  metrics_view: sales_metrics
  dimensions:
    - region
    - product_category
  measures:
    - total_revenue
    - average_order_value
    - order_count
  num_rows: 7

Important: Never use time dimensions in leaderboard dimensions. Leaderboards are for categorical ranking, not time-series analysis.

Line Chart

Show trends over time:

line_chart:
  metrics_view: sales_metrics
  title: "Revenue Trend"
  color: primary
  x:
    field: order_date
    type: temporal
    limit: 30
  y:
    field: total_revenue
    type: quantitative
    zeroBasedOrigin: true

With color dimension breakdown:

line_chart:
  metrics_view: sales_metrics
  title: "Revenue by Region"
  color:
    field: region
    type: nominal
    limit: 5
    legendOrientation: top
  x:
    field: order_date
    type: temporal
  y:
    field: total_revenue
    type: quantitative
    zeroBasedOrigin: true

With custom color mapping:

line_chart:
  metrics_view: sales_metrics
  title: "Performance Comparison"
  color:
    field: status
    type: nominal
    colorMapping:
      - value: "active"
        color: hsl(120, 70%, 45%)
      - value: "inactive"
        color: hsl(0, 70%, 50%)
  x:
    field: event_date
    type: temporal
  y:
    field: event_count
    type: quantitative

Bar Chart

Compare values across categories:

bar_chart:
  metrics_view: sales_metrics
  title: "Revenue by Product Category"
  color: hsl(210, 70%, 50%)
  x:
    field: product_category
    type: nominal
    limit: 10
    sort: -y
    labelAngle: 0
  y:
    field: total_revenue
    type: quantitative
    zeroBasedOrigin: true

With color dimension:

bar_chart:
  metrics_view: sales_metrics
  title: "Revenue by Category and Region"
  color:
    field: region
    type: nominal
    limit: 5
  x:
    field: product_category
    type: nominal
    limit: 8
    sort: -y
  y:
    field: total_revenue
    type: quantitative

Stacked Bar

Show cumulative values across categories or time:

stacked_bar:
  metrics_view: sales_metrics
  title: "Revenue Over Time by Region"
  color:
    field: region
    type: nominal
    limit: 5
  x:
    field: order_date
    type: temporal
    limit: 20
  y:
    field: total_revenue
    type: quantitative
    zeroBasedOrigin: true

With multiple measures:

stacked_bar:
  metrics_view: sales_metrics
  title: "Cost Breakdown Over Time"
  color:
    field: rill_measures
    type: value
    legendOrientation: top
  x:
    field: order_date
    type: temporal
    limit: 20
  y:
    field: cost_of_goods
    fields:
      - cost_of_goods
      - shipping_cost
      - marketing_cost
    type: quantitative
    zeroBasedOrigin: true

Stacked Bar Normalized

Show proportional distribution (100% stacked):

stacked_bar_normalized:
  metrics_view: sales_metrics
  title: "Revenue Share by Region"
  color:
    field: region
    type: nominal
    limit: 5
  x:
    field: order_date
    type: temporal
    limit: 20
  y:
    field: total_revenue
    type: quantitative
    zeroBasedOrigin: true

With custom color mapping for measures:

stacked_bar_normalized:
  metrics_view: inventory_metrics
  title: "Inventory Status Distribution"
  color:
    field: rill_measures
    type: value
    legendOrientation: top
    colorMapping:
      - value: "in_stock"
        color: hsl(120, 60%, 50%)
      - value: "low_stock"
        color: hsl(45, 90%, 50%)
      - value: "out_of_stock"
        color: hsl(0, 70%, 50%)
  x:
    field: report_date
    type: temporal
    limit: 20
  y:
    field: in_stock
    fields:
      - in_stock
      - low_stock
      - out_of_stock
    type: quantitative

Area Chart

Show magnitude over time with optional stacking:

area_chart:
  metrics_view: sales_metrics
  title: "Order Volume Over Time"
  color: primary
  x:
    field: order_date
    type: temporal
    limit: 30
  y:
    field: order_count
    type: quantitative
    zeroBasedOrigin: true

With color dimension:

area_chart:
  metrics_view: sales_metrics
  title: "Revenue by Channel"
  color:
    field: sales_channel
    type: nominal
    limit: 4
  x:
    field: order_date
    type: temporal
    limit: 20
  y:
    field: total_revenue
    type: quantitative
    zeroBasedOrigin: true

Donut Chart

Show proportional breakdown:

donut_chart:
  metrics_view: sales_metrics
  title: "Revenue by Region"
  innerRadius: 50
  color:
    field: region
    type: nominal
    limit: 8
    sort: -measure
  measure:
    field: total_revenue
    type: quantitative
    showTotal: true

Heatmap

Show patterns across two dimensions:

heatmap:
  metrics_view: activity_metrics
  title: "Activity by Day and Hour"
  color:
    field: event_count
    type: quantitative
  x:
    field: day_of_week
    type: nominal
    limit: 7
  y:
    field: hour_of_day
    type: nominal
    limit: 24
    sort: -color

With custom color range:

heatmap:
  metrics_view: performance_metrics
  title: "Performance Score Matrix"
  color:
    field: score
    type: quantitative
    colorRange:
      mode: scheme
      scheme: sequential
  x:
    field: category
    type: nominal
    limit: 10
  y:
    field: subcategory
    type: nominal
    limit: 15

With custom Vega-Lite config for colors:

heatmap:
  metrics_view: utilization_metrics
  title: "Resource Utilization"
  vl_config: |
    {
      "range": {
        "heatmap": ["#F4A261", "#D63946", "#457B9D"]
      }
    }
  color:
    field: utilization_rate
    type: quantitative
  x:
    field: resource_name
    type: nominal
    limit: 20
  y:
    field: time_slot
    type: nominal
    limit: 12

Combo Chart

Combine bar and line on dual axes:

combo_chart:
  metrics_view: sales_metrics
  title: "Revenue and Order Count"
  color:
    field: measures
    type: value
    legendOrientation: top
  x:
    field: order_date
    type: temporal
    limit: 20
  y1:
    field: total_revenue
    type: quantitative
    mark: bar
    zeroBasedOrigin: true
  y2:
    field: order_count
    type: quantitative
    mark: line
    zeroBasedOrigin: true

With custom color mapping:

combo_chart:
  metrics_view: funnel_metrics
  title: "Conversions and Conversion Rate"
  color:
    field: measures
    type: value
    legendOrientation: top
    colorMapping:
      - value: "Conversions"
        color: hsl(210, 100%, 73%)
      - value: "Conversion Rate"
        color: hsl(280, 70%, 55%)
  x:
    field: event_date
    type: temporal
    limit: 30
  y1:
    field: conversions
    type: quantitative
    mark: bar
  y2:
    field: conversion_rate
    type: quantitative
    mark: line

Scatter Plot

Visualize the relationship between two measures, with each point representing a data value. Points can be optionally colored by a dimension and sized by a measure. Useful for correlation analysis and identifying outliers across two numeric metrics.

Interactive controls (rendered automatically):

• Shift + Drag: pan the plot
• Scroll wheel: zoom in/out
• Double-click: reset zoom and pan to default view

scatter_plot:
  metrics_view: bids_metrics
  title: "Bids vs Clicks by Advertiser"
  x:
    field: total_bids
    type: quantitative
    zeroBasedOrigin: false
  y:
    field: total_clicks
    type: quantitative
    zeroBasedOrigin: false
  dimension:
    field: advertiser_name
    limit: 10
    type: nominal
  size:
    field: total_impressions
    type: quantitative
  color:
    field: device_os
    limit: 5
    type: nominal
    legendOrientation: top

Scatter-specific fields:

• x (required): Measure on the x-axis. Must be type: quantitative.
• y (required): Measure on the y-axis. Must be type: quantitative.
• dimension (optional): Nominal dimension; each distinct value renders as a separate point. Use limit to cap the number of points.
• size (optional): Measure that controls point radius. Must be type: quantitative.
• color (optional): A color string or a nominal dimension field object to color points by.

Notes:

• Setting zeroBasedOrigin: false on both axes is usually preferred for scatter plots, since the interesting range often does not include zero.
• comparison_time_range is not supported for scatter plots.

Funnel Chart

Show flow through stages or conversion processes:

funnel_chart:
  metrics_view: conversion_metrics
  title: "Conversion Funnel"
  breakdownMode: dimension
  color: stage
  mode: width
  stage:
    field: funnel_stage
    type: nominal
    limit: 10
  measure:
    field: user_count
    type: quantitative

With multiple measures breakdown:

funnel_chart:
  metrics_view: engagement_metrics
  title: "Engagement Funnel"
  breakdownMode: measures
  color: value
  mode: width
  measure:
    field: impressions
    type: quantitative
    fields:
      - impressions
      - clicks
      - signups
      - purchases

Breakdown modes and color options:

• breakdownMode: dimension with color: stage (different colors per stage) or color: measure (similar colors by value)
• breakdownMode: measures with color: name (different colors per measure) or color: value (similar colors by value)

Pivot

Create pivot tables with row and column dimensions:

pivot:
  metrics_view: sales_metrics
  title: "Sales by Region and Category"
  row_dimensions:
    - region
    - product_category
  col_dimensions:
    - quarter
  measures:
    - total_revenue
    - order_count
    - average_order_value

Simple pivot (rows only):

pivot:
  metrics_view: sales_metrics
  row_dimensions:
    - region
  col_dimensions: []
  measures:
    - total_revenue
    - order_count
    - margin_rate

Table

Display tabular data with specified columns:

table:
  metrics_view: sales_metrics
  title: "Product Performance"
  description: "Detailed breakdown of product metrics"
  columns:
    - product_name
    - product_category
    - total_revenue
    - order_count
    - average_price

With dimension filters:

table:
  metrics_view: sales_metrics
  title: "North America Sales"
  columns:
    - product_name
    - total_revenue
    - order_count
  dimension_filters: region IN ('North America')

Image

Display external images:

image:
  url: https://example.com/logo.png
  alignment:
    horizontal: center
    vertical: middle

Custom Chart

Build fully custom visualizations using Metrics SQL queries and Vega-Lite specifications. Use this when the built-in chart types are insufficient and you need complete control over the visualization.

Custom charts use metrics_sql to query data from metrics views and vega_spec to define the Vega-Lite visualization. The data from each query is available in the Vega-Lite spec as named datasets: query1, query2, etc.

metrics_sql Query Language

metrics_sql lets you write SELECT queries against metrics views as virtual tables. Each metrics view exposes its dimensions and measures as columns.

Query rules:

• Table names: use the name of any valid metrics view in the project
• Columns: only reference dimension and measure names defined in that metrics view's schema
• Measures are pre-aggregated; never wrap them in SUM(), COUNT(), AVG(), or other aggregate functions
• Grouping is implicit by selected dimensions; you do not need GROUP BY unless combining with expressions like date_trunc()
• Use date_trunc('<grain>', <time_dimension>) for time bucketing (grain: minute, hour, day, week, month, quarter, year)
• Always include ORDER BY for deterministic results
• Use LIMIT to keep result sets reasonable (default to 50 for top-N queries, 500 for time series)
• Do not alias column names unless strictly required for disambiguation in multi-query specs
• Canvas-level time and dimension filters are injected automatically at runtime; do not add WHERE clauses for them
• You may write multiple queries against different metrics views; results are bound as query1, query2, etc.

Example queries:

Single view:

SELECT publisher, total_bids, bid_price FROM bids_metrics ORDER BY total_bids DESC LIMIT 20

Time series:

SELECT date_trunc('day', __time) as day, impressions, revenue FROM ad_metrics ORDER BY day

Cross-view (two queries):

-- query1:
SELECT campaign, spend FROM spend_metrics ORDER BY spend DESC LIMIT 10
-- query2:
SELECT campaign, conversions FROM conversion_metrics ORDER BY conversions DESC LIMIT 10

Vega-Lite Specification Rules

• Generate a valid Vega-Lite v5 JSON specification
• Bind data with {"name": "query1"}, {"name": "query2"}, etc. Do not include "data": {"values": [...]} sections; data comes from query results
• Set "width": "container" and "height": "container" so the chart fills its parent
• Always include "autosize": {"type": "fit"} at the top level of the spec
• Use display_name values from the metrics view schema for axis titles, legend titles, and tooltip labels
• Apply format_d3 or format_preset from measure metadata to axis and tooltip format strings
• Pick the best mark type for the data: bar, line, area, point, rect (heatmap), arc (pie/donut), etc.
• Include tooltips with all relevant fields and human-readable formatting
• Use a clean, professional color scheme; prefer Rill's categorical palette when possible
• For temporal axes: set "type": "temporal" and choose an appropriate timeUnit
• For categorical axes: sort by the primary measure descending unless the user specifies otherwise
• For layered or multi-view charts, use the "layer" or "concat" composition operators
• Avoid unnecessary chart junk: remove gridlines on categorical axes, use concise axis labels

YAML Examples

Simple custom chart with one query:

custom_chart:
  metrics_sql:
    - |
      SELECT publisher, total_bids, bid_price
      FROM bids_metrics
      ORDER BY total_bids DESC
      LIMIT 20
  vega_spec: |
    {
      "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
      "width": "container",
      "height": "container",
      "autosize": {"type": "fit"},
      "data": {"name": "query1"},
      "mark": "bar",
      "encoding": {
        "x": {"field": "publisher", "type": "nominal", "sort": "-y", "axis": {"labelAngle": -45}},
        "y": {"field": "total_bids", "type": "quantitative", "title": "Total Bids"},
        "tooltip": [
          {"field": "publisher", "type": "nominal"},
          {"field": "total_bids", "type": "quantitative", "title": "Total Bids"},
          {"field": "bid_price", "type": "quantitative", "title": "Bid Price", "format": ",.2f"}
        ]
      }
    }

Time series custom chart:

custom_chart:
  metrics_sql:
    - |
      SELECT date_trunc('day', __time) as day, impressions, revenue
      FROM ad_metrics
      ORDER BY day
      LIMIT 500
  vega_spec: |
    {
      "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
      "width": "container",
      "height": "container",
      "autosize": {"type": "fit"},
      "data": {"name": "query1"},
      "layer": [
        {
          "mark": {"type": "line", "color": "#4C78A8"},
          "encoding": {
            "x": {"field": "day", "type": "temporal", "title": "Date"},
            "y": {"field": "impressions", "type": "quantitative", "title": "Impressions"}
          }
        }
      ],
      "encoding": {
        "tooltip": [
          {"field": "day", "type": "temporal", "title": "Date"},
          {"field": "impressions", "type": "quantitative", "title": "Impressions"},
          {"field": "revenue", "type": "quantitative", "title": "Revenue", "format": "$,.2f"}
        ]
      }
    }

Custom chart with multiple queries (cross-view):

custom_chart:
  metrics_sql:
    - |
      SELECT campaign, spend
      FROM spend_metrics
      ORDER BY spend DESC
      LIMIT 10
    - |
      SELECT campaign, conversions
      FROM conversion_metrics
      ORDER BY conversions DESC
      LIMIT 10
  vega_spec: |
    {
      "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
      "width": "container",
      "height": "container",
      "autosize": {"type": "fit"},
      "layer": [
        {
          "data": {"name": "query1"},
          "mark": {"type": "bar", "opacity": 0.7},
          "encoding": {
            "x": {"field": "campaign", "type": "nominal", "sort": "-y"},
            "y": {"field": "spend", "type": "quantitative", "title": "Spend"},
            "color": {"datum": "Spend"}
          }
        },
        {
          "data": {"name": "query2"},
          "mark": {"type": "line", "point": true},
          "encoding": {
            "x": {"field": "campaign", "type": "nominal"},
            "y": {"field": "conversions", "type": "quantitative", "title": "Conversions", "axis": {"orient": "right"}},
            "color": {"datum": "Conversions"}
          }
        }
      ]
    }

Additional guidelines:

• The vega_spec field must contain valid JSON (not YAML) as a string
• The prompt field is optional and stores the user's natural language description for reference
• When refining an existing custom chart, apply targeted edits to the existing SQL and spec rather than regenerating from scratch

Field Configuration

Data Types

• nominal: Categorical data (strings, categories). Use for dimensions.
• temporal: Time-based data (dates, timestamps). Use for time dimensions.
• quantitative: Numerical data (counts, amounts). Use for measures.
• value: Special type for multiple measures. Use only in color field with rill_measures.

Axis Properties

x:
  field: category_name       # Field name from metrics view
  type: nominal              # Data type
  limit: 10                  # Max values to display
  sort: -y                   # Sort order (see below)
  showNull: true             # Include null values
  labelAngle: 45             # Label rotation angle

Sort Options

• "x" or "-x": Sort by x-axis values (ascending/descending)
• "y" or "-y": Sort by y-axis values (ascending/descending)
• "color" or "-color": Sort by color field (heatmaps)
• "measure" or "-measure": Sort by measure (donut charts)
• Array of values: Custom sort order (e.g., ["Mon", "Tue", "Wed"])

Y-Axis Properties

y:
  field: total_revenue
  type: quantitative
  zeroBasedOrigin: true      # Start y-axis at zero

Multiple measures:

y:
  field: revenue
  fields:
    - revenue
    - cost
    - profit
  type: quantitative

Color Configuration

Simple color string:

color: primary              # Named color
color: secondary
color: "#FF5733"            # Hex color
color: hsl(210, 70%, 50%)   # HSL color

Field-based color:

color:
  field: region
  type: nominal
  limit: 10
  legendOrientation: top    # top, bottom, left, right, none

Custom color mapping:

color:
  field: status
  type: nominal
  colorMapping:
    - value: "success"
      color: hsl(120, 70%, 45%)
    - value: "warning"
      color: hsl(45, 90%, 50%)
    - value: "error"
      color: hsl(0, 70%, 50%)

Color scheme:

color:
  field: score
  type: quantitative
  colorRange:
    mode: scheme
    scheme: sequential

Special Field: rill_measures

Use rill_measures in the color field when displaying multiple measures in stacked charts:

color:
  field: rill_measures
  type: value
  legendOrientation: top
y:
  field: revenue
  fields:
    - revenue
    - cost
    - profit
  type: quantitative

Advanced Features

Dimension Filters

Filter component data without affecting other components:

kpi_grid:
  metrics_view: sales_metrics
  measures:
    - total_revenue
  dimension_filters: region IN ('North America') AND status IN ('active')

Time Range Override

Override the default time range for a specific component:

heatmap:
  metrics_view: activity_metrics
  time_range:
    preset: last_7_days
  # ... other config

Time Filters

Override time settings with detailed control:

stacked_bar:
  metrics_view: sales_metrics
  time_filters: tr=P12M&compare_tr=rill-PY&grain=week
  # ... other config

Vega-Lite Configuration

Customize chart appearance with Vega-Lite config:

bar_chart:
  metrics_view: sales_metrics
  vl_config: |
    {
      "axisX": {
        "grid": true,
        "labelAngle": 45
      },
      "range": {
        "category": ["#D63946", "#457B9D", "#F4A261", "#2A9D8F"]
      }
    }
  # ... other config

Complete Example

type: canvas
display_name: "Monthly Business Report"

defaults:
  time_range: P30D
  comparison_mode: time

max_width: 1400
theme: corporate_theme

rows:
  - height: 100px
    items:
      - width: 12
        markdown:
          content: |
            # Monthly Business Report

            Comprehensive overview of business performance metrics.

            ---
          alignment:
            horizontal: center
            vertical: middle

  - height: 50px
    items:
      - width: 12
        markdown:
          content: "## Key Metrics"
          alignment:
            horizontal: left
            vertical: middle

  - height: 200px
    items:
      - width: 12
        kpi_grid:
          metrics_view: business_metrics
          measures:
            - revenue
            - profit
            - customers
            - orders
          comparison:
            - percent_change
            - previous
          sparkline: right

  - height: 50px
    items:
      - width: 12
        markdown:
          content: "## Revenue Analysis"
          alignment:
            horizontal: left
            vertical: middle

  - height: 400px
    items:
      - width: 8
        combo_chart:
          metrics_view: business_metrics
          title: "Revenue and Profit Margin"
          color:
            field: measures
            type: value
            legendOrientation: top
          x:
            field: report_date
            type: temporal
            limit: 30
          y1:
            field: revenue
            type: quantitative
            mark: bar
          y2:
            field: profit_margin
            type: quantitative
            mark: line

      - width: 4
        donut_chart:
          metrics_view: business_metrics
          title: "Revenue by Segment"
          innerRadius: 50
          color:
            field: customer_segment
            type: nominal
            limit: 5
          measure:
            field: revenue
            type: quantitative
            showTotal: true

  - height: 50px
    items:
      - width: 12
        markdown:
          content: "## Regional Performance"
          alignment:
            horizontal: left
            vertical: middle

  - height: 350px
    items:
      - width: 6
        leaderboard:
          metrics_view: business_metrics
          dimensions:
            - region
          measures:
            - revenue
            - profit
            - order_count
          num_rows: 8

      - width: 6
        heatmap:
          metrics_view: business_metrics
          title: "Revenue by Region and Product"
          color:
            field: revenue
            type: quantitative
          x:
            field: product_category
            type: nominal
            limit: 8
          y:
            field: region
            type: nominal
            limit: 6