For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
  • Welcome
    • Overview
    • Release Notes
  • Installation and Configuration
    • System Requirements
    • Installation
    • Services and Utilities
    • Executor Resource Manager
    • Configuration Parameters
    • Security
    • Distributed Configuration
  • Loading and Exporting Data
    • Supported Data Sources
    • Command Line
  • SQL
    • Data Definition (DDL)
    • Data Manipulation (DML)
  • HeavyImmerse
    • Introduction to HeavyImmerse
    • Admin Portal
    • Control Panel
    • Working with Dashboards
    • Measures and Dimensions
    • Using Parameters
    • Using Filters
    • Using Cross-link
    • Color Consistency
    • Chart Animation
    • Multilayer Charts
    • SQL Editor
    • Customization
    • Joins (Beta)
    • Chart Types
    • Deprecated Charts
    • HeavyIQ SQL Notebook
  • HeavyIQ Conversational Analytics
    • HeavyIQ Overview
    • HeavyIQ Model Overview (HeavyLM)
  • HeavyRF
    • Introduction to HeavyRF
    • Getting Started
    • HeavyRF Table Functions
  • HeavyConnect
    • HeavyConnect Release Overview
    • Getting Started
    • Best Practices
    • Examples
    • Command Reference
    • Parquet Data Wrapper Reference
    • ODBC Data Wrapper Reference
    • Raster Data Wrapper Reference
  • HeavyML (BETA)
    • HeavyML Overview
    • Clustering Algorithms
    • Regression Algorithms
    • Principal Components Analysis
  • Python / Data Science
    • Data Science Foundation
    • JupyterLab Installation and Configuration
    • Using HEAVY.AI with JupyterLab
    • Python User-Defined Functions (UDFs) with RBC
    • Ibis
    • Interactive Data Exploration with Altair
    • Additional Examples
  • APIs and Interfaces
    • heavysql
    • Thrift
    • JDBC
    • ODBC
    • Vega
      • Vega Tutorials
      • Vega Reference Overview
        • data Property
        • projections Property
        • scales Property
        • marks Property
      • Migration
      • Try Vega
    • RJDBC
    • SQuirreL SQL
    • heavyai-connector
  • Tutorials and Demos
    • Loading Data
    • Using Heavy Immerse
    • Hello World
    • Creating a Kafka Streaming Application
    • Getting Started with Open Source
    • Try Vega
  • Troubleshooting and Special Topics
    • Vulkan Renderer
    • Optimizing
    • Known Issues and Limitations
    • Logs and Monitoring
    • Archived Release Notes
NVIDIANVIDIA
Developer-friendly docs for your API
Privacy Policy | Your Privacy Choices | Terms of Service | Accessibility | Corporate Policies | Product Security | Contact

Copyright © 2026, NVIDIA Corporation.

LogoLogoDocumentation
On this page
  • Specification Language Syntax
  • Format Rules
APIs and InterfacesVega

Vega Reference Overview

||View as Markdown|
Previous

Improving Rendering with SQL Extensions

Next

data Property

A Vega specification is a JSON-formatted structure that describes a visualization, which can be sent to the back end for rendering. This document introduces the the Vega specification syntax and provides links to topics that provide more details about each Vega property.

For examples of using Vega, see Tutorials. You can also see and edit examples in Try Vega.

Specification Language Syntax

The Vega specification includes properties for describing the source data, mapping the data to the visualization area, and visual encoding. The root Vega specification supported by OmniSci has the following JSON structure and top-level properties:

{
"width": <number>,
"height": <number>,
"data": [],
"projections": [],
"scales": [],
"marks": []
}
PropertyTypeDescription
width and heightunsigned integer

Visualization area width and height, in pixels. Both properties are required.
Example: Set the viewing area width to 384 pixels and the height to 564 pixels:

dataarray

Source data. The Vega data model uses tabular data, similar to a spreadsheet. Organized in rows with any number of named columns.
JSON format:

projectionsarray

Projection data. Maps longitude and latitude data to projected x and y coordinates.
JSON format:

scalesarray

Data-to-visualization area mapping. Maps visually encoded data values to pixel positions with attributes, such as color.
JSON format:

marksarray

Geometric primitive used to visually encode data.
JSON format:

Format Rules

  • Property names are case-sensitive.
  • Property values are typed.
  • Unsupported properties are ignored by the rendering engine.