HEAVY.AI Docs
v8.1.0
v8.1.0
  • Welcome to HEAVY.AI Documentation
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    • Overview
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      • Hardware Reference
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      • Free Version
      • Installing on Docker
        • HEAVY.AI Installation using Docker on Ubuntu
      • Installing on Ubuntu
        • HEAVY.AI Installation on Ubuntu
        • Install NVIDIA Drivers and Vulkan on Ubuntu
      • Installing on Rocky Linux / RHEL
        • HEAVY.AI Installation on RHEL
        • Install NVIDIA Drivers and Vulkan on Rocky Linux and RHEL
      • Getting Started on AWS
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      • Getting Started on Azure
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  • Loading and Exporting Data
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      • Kafka
      • Using HeavyImmerse Data Manager
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  • SQL
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      • Geospatial Capabilities
        • Uber H3 Hexagonal Modeling
      • Functions and Operators
      • System Table Functions
        • generate_random_strings
        • generate_series
        • tf_compute_dwell_times
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        • tf_geo_rasterize
        • tf_geo_rasterize_slope
        • tf_graph_shortest_path
        • tf_graph_shortest_paths_distances
        • tf_load_point_cloud
        • tf_mandelbrot*
        • tf_point_cloud_metadata
        • tf_raster_contour_lines; tf_raster_contour_polygons
        • tf_raster_graph_shortest_slope_weighted_path
        • tf_rf_prop_max_signal (Directional Antennas)
        • ts_rf_prop_max_signal (Isotropic Antennas)
        • tf_rf_prop
      • Window Functions
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  • HeavyImmerse
    • Introduction to HeavyImmerse
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    • Measures and Dimensions
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    • Using Filters
    • Using Cross-link
    • Chart Animation
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    • SQL Editor
    • Customization
    • Joins (Beta)
    • Chart Types
      • Overview
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    • Deprecated Charts
      • Bar
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      • Stacked Bar
    • HeavyIQ SQL Notebook
  • HEAVYIQ Conversational Analytics
    • HeavyIQ Overview
      • HeavyIQ Guidance
  • HeavyRF
    • Introduction to HeavyRF
    • Getting Started
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  • 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
      • Linear Regression
      • Random Forest Regression
      • Decision Tree Regression
      • Gradient Boosting Tree Regression
    • Principal Components Analysis
  • Python / Data Science
    • Data Science Foundation
    • JupyterLab Installation and Configuration
    • Using HEAVY.AI with JupyterLab
    • Python User-Defined Functions (UDFs) with the Remote Backend Compiler (RBC)
      • Installation
      • Registering and Using a Function
      • User-Defined Table Functions
      • RBC UDF/UDTF Example Notebooks
      • General UDF/UDTF Tutorial Notebooks
      • RBC API Reference
    • Ibis
    • Interactive Data Exploration with Altair
    • Additional Examples
      • Forecasting with HEAVY.AI and Prophet
  • APIs and Interfaces
    • Overview
    • heavysql
    • Thrift
    • JDBC
    • ODBC
    • Vega
      • Vega Tutorials
        • Vega at a Glance
        • Getting Started with Vega
        • Getting More from Your Data
        • Creating More Advanced Charts
        • Using Polys Marks Type
        • Vega Accumulator
        • Using Transform Aggregation
        • Improving Rendering with SQL Extensions
      • Vega Reference Overview
        • data Property
        • projections Property
        • scales Property
        • marks Property
      • Migration
        • Migrating Vega Code to Dynamic Poly Rendering
      • 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
    • FAQs
    • Troubleshooting
    • Vulkan Renderer
    • Optimizing
    • Known Issues and Limitations
    • Logs and Monitoring
    • Archived Release Notes
      • Release 6.x
      • Release 5.x
      • Release 4.x
      • Release 3.x
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  • Quick Links
  • Vega Tutorials
  • Vega Reference
  • Migration
  • Try Vega
  • Additional Resources
  • Standards
  • Related Vega Implementations
  • Vega Concepts
  • OmniSci APIs
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  1. APIs and Interfaces

Vega

PreviousODBCNextVega Tutorials

Vega is a visualization specification language that describes how to map your source data to your viewing area. By creating a JSON Vega specification structure, you define the data and the transformations to apply to the data to produce meaningful visualizations. The specification includes the geometric shape that represents your data, scaling properties that map the data to the visualization area, and graphical rendering properties.

HEAVY.AI uses Vega for backend rendering. Using the API, the client sends the Vega JSON to the backend, which renders the visualization and returns a PNG image for display. See the for backend rendering examples.

The topics in this guide define and describe the HEAVY.AI implementation of Vega, and provide examples you can use as a basis for your own visualizations:

  • - Introduces you to Vega specification patterns so you can start creating visualizations quickly and easily. Each tutorial has example code that demonstrates a particular feature or pattern. Tutorials start with basic Vega concepts and an introduction to the API for communication with the backend. Other tutorials provide more in-depth information about specific Vega implementations.

  • - Describes the HEAVY.AI implementation of Vega specification syntax and associated rules. Also includes links to Vega standards and related specifications.

  • - If you are upgrading to Release 5.2 or higher, you need to migrate any code that renders polygons in cached mode to dynamic poly rendering.

  • - Try the HEAVY.AI Vega engine and work with various examples. See your changes to Vega code in real time.

Quick Links

Additional Resources

Standards

Related Vega Implementations

Vega Concepts

OmniSci APIs

and properties

property

property

property

property

HEAVY.AI Connector
HEAVY.AI Charting Examples
Tutorials
Reference
Code Migration
Try Vega
Vega Tutorials
Vega at a Glance
Getting Started with Vega
Getting More from Your Data
Creating More Advanced Charts
Using the poly Marks Type
Vega Accumulator
Using Transform Aggregation
Improving Rendering with SQL Extensions
Vega Reference
width
height
data
projections
scales
marks
Migration
Migrating Vega Code to Dynamic Poly Rendering
Try Vega
The JavaScript Object Notation (JSON) Data Interchange Format
Vega – A Visualization Grammar
Introducing d3-scale
d3-scale on GitHub
MapD Connector API