HEAVY.AI Docs
v8.1.0
v8.1.0
  • Welcome to HEAVY.AI Documentation
  • Overview
    • Overview
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  • Installation and Configuration
    • System Requirements
      • Hardware Reference
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      • Licensing
    • Installation
      • 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
      • Getting Started on GCP
      • Getting Started on Azure
      • Getting Started on Kubernetes (BETA)
      • Upgrading
        • Upgrading HEAVY.AI
        • Upgrading from Omnisci to HEAVY.AI 6.0
        • CUDA Compatibility Drivers
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    • Services and Utilities
      • Using Services
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    • Executor Resource Manager
    • Configuration Parameters
      • Overview
      • Configuration Parameters for HeavyDB
      • Configuration Parameters for HEAVY.AI Web Server
      • Configuration Parameters for HeavyIQ
    • Security
      • Roles and Privileges
        • Column-Level Security
      • Connecting Using SAML
      • Implementing a Secure Binary Interface
      • Encrypted Credentials in Custom Applications
      • LDAP Integration
    • Distributed Configuration
  • Loading and Exporting Data
    • Supported Data Sources
      • Kafka
      • Using HeavyImmerse Data Manager
      • Importing Geospatial Data
    • Command Line
      • Loading Data with SQL
      • Exporting Data
  • SQL
    • Data Definition (DDL)
      • Datatypes
      • Users and Databases
      • Tables
      • System Tables
      • Views
      • Policies
      • Comment
    • Data Manipulation (DML)
      • SQL Capabilities
        • ALTER SESSION SET
        • ALTER SYSTEM CLEAR
        • DELETE
        • EXPLAIN
        • INSERT
        • KILL QUERY
        • LIKELY/UNLIKELY
        • SELECT
        • SHOW
        • UPDATE
        • Arrays
        • Logical Operators and Conditional and Subquery Expressions
        • Table Expression and Join Support
        • Type Casts
      • Geospatial Capabilities
        • Uber H3 Hexagonal Modeling
      • Functions and Operators
      • System Table Functions
        • generate_random_strings
        • generate_series
        • tf_compute_dwell_times
        • tf_feature_self_similarity
        • tf_feature_similarity
        • 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
      • Reserved Words
      • SQL Extensions
      • HeavyIQ LLM_TRANSFORM
  • HeavyImmerse
    • Introduction to HeavyImmerse
    • Admin Portal
    • Control Panel
    • Working with Dashboards
      • Dashboard List
      • Creating a Dashboard
      • Configuring a Dashboard
      • Duplicating and Sharing Dashboards
    • Measures and Dimensions
    • Using Parameters
    • Using Filters
    • Using Cross-link
    • Chart Animation
    • Multilayer Charts
    • SQL Editor
    • Customization
    • Joins (Beta)
    • Chart Types
      • Overview
      • Bubble
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      • Combo
      • Contour
      • Cross-Section
      • Gauge
      • Geo Heatmap
      • Heatmap
      • Linemap
      • Number
      • Pie
      • Pointmap
      • Scatter Plot
      • Skew-T
      • Table
      • Text Widget
      • Wind Barb
    • Deprecated Charts
      • Bar
      • Combo - Original
      • Histogram
      • Line
      • Stacked Bar
    • HeavyIQ SQL Notebook
  • HEAVYIQ Conversational Analytics
    • HeavyIQ Overview
      • HeavyIQ Guidance
  • 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
      • 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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On this page
  • Assumptions
  • Preparation
  • Update and Reboot
  • Create the HEAVY.AI User
  • Installation
  • Install NVIDIA Drivers ᴳᴾᵁ ᴼᴾᵀᴵᴼᴺ
  • Installing with APT
  • Installing with a Tarball
  • Configuration
  • Set Environment Variables
  • Initialization
  • Activation
  • Configure Firewall ᴼᴾᵀᴵᴼᴺᴬᴸ
  • Licensing HEAVY.AI ᵉᵉ⁻ᶠʳᵉᵉ ᵒⁿˡʸ
  • Final Checks
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  1. Installation and Configuration
  2. Installation
  3. Installing on Ubuntu

HEAVY.AI Installation on Ubuntu

This is an end-to-end recipe for installing HEAVY.AI on a Ubuntu 22.04 machine using CPU and GPU devices.

The order of these instructions is significant. To avoid problems, install each component in the order presented.

Assumptions

These instructions assume the following:

  • You are installing on a “clean” Ubuntu 22.04 host machine with only the operating system installed.

  • Your HEAVY.AI host only runs the daemons and services required to support HEAVY.AI.

  • Your HEAVY.AI host is connected to the Internet.

Preparation

Prepare your Ubuntu machine by updating your system, creating the HEAVY.AI user (named heavyai), installing kernel headers, installing CUDA drivers, and optionally enabling the firewall.

Update and Reboot

  1. Update the entire system:

sudo apt update
sudo apt upgrade

2. Install the utilities needed to create Heavy.ai repositories and download archives:

sudo apt install curl
sudo apt install libncurses5

3. Install the headless JDK and the utility apt-transport-https:

sudo apt install default-jre-headless apt-transport-https

4. Reboot to activate the latest kernel:

sudo reboot

Create the HEAVY.AI User

Create a group called heavyai and a user named heavyai, who will be the owner of the HEAVY.AI software and data on the filesystem.

  1. Create the group, user, and home directory using the useradd command with the --user-group and --create-home switches.

sudo useradd --user-group --create-home --group sudo heavyai

2. Set a password for the user:

sudo passwd heavyai

3. Log in with the newly created user:

sudo su - heavyai

Installation

Install the HEAVY.AI using APT and a tarball.

The installation using the APT package manager is recommended to those who want a more automated install and upgrade procedure.

Install NVIDIA Drivers ᴳᴾᵁ ᴼᴾᵀᴵᴼᴺ

If your system uses NVIDIA GPUs, but the drivers not installed, install them now. See Install NVIDIA Drivers and Vulkan on Ubuntu for details.

Installing with APT

Download and add a GPG key to APT.

curl https://releases.heavy.ai/GPG-KEY-heavyai | sudo apt-key add -

Add a source apt depending on the edition (Enterprise, Free, or Open Source) and execution device (GPU or CPU) you are going to use.

echo "deb https://releases.heavy.ai/ee/apt/ stable cuda" \
| sudo tee /etc/apt/sources.list.d/heavyai.list
echo "deb https://releases.heavy.ai/ee/apt/ stable cpu" \
| sudo tee /etc/apt/sources.list.d/heavyai.list
echo "deb https://releases.heavy.ai/os/apt/ stable cuda" \
| sudo tee /etc/apt/sources.list.d/heavyai.list
echo "deb https://releases.heavy.ai/os/apt/ stable cpu" \
| sudo tee /etc/apt/sources.list.d/heavyai.list

Use apt to install the latest version of HEAVY.AI.

sudo apt update
sudo apt install heavyai

If you need to install a specific version of HEAVY.AI, because you are upgrading from Omnisci or for different reasons, you must run the following command:

hai_version="6.0.0"
sudo apt install heavyai=$(apt-cache madison heavyai | grep $hai_version | cut -f 2 -d '|' | xargs)

Installing with a Tarball

First create the installation directory.

sudo mkdir /opt/heavyai && sudo chown $USER /opt/heavyai

Download the archive and install the software. A different archive is downloaded depending on the Edition (Enterprise, Free, or Open Source) and the device used for runtime (GPU or CPU).

curl \
https://releases.heavy.ai/ee/tar/heavyai-ee-latest-Linux-x86_64-render.tar.gz \
| sudo tar zxf - --strip-components=1 -C /opt/heavyai
curl \
https://releases.heavy.ai/ee/tar/heavyai-ee-latest-Linux-x86_64-cpu.tar.gz \
| sudo tar zxf - --strip-components=1 -C /opt/heavyai
curl \
https://releases.heavy.ai/os/tar/heavyai-os-latest-Linux-x86_64.tar.gz \
| sudo tar zxf - --strip-components=1 -C /opt/heavyai
curl \
https://releases.heavy.ai/os/tar/heavyai-os-latest-Linux-x86_64-cpu.tar.gz \
| sudo tar zxf - --strip-components=1 -C /opt/heavyai

Configuration

Follow these steps to prepare your HEAVY.AI environment.

Set Environment Variables

For convenience, you can update .bashrc with these environment variables

echo "# HEAVY.AI variable and paths
export HEAVYAI_PATH=/opt/heavyai
export HEAVYAI_BASE=/var/lib/heavyai
export HEAVYAI_LOG=$HEAVYAI_BASE/storage/log
export PATH=$HEAVYAI_PATH/bin:$PATH" \
>> ~/.bashrc
source ~/.bashrc

Although this step is optional, you will find references to the HEAVYAI_BASE and HEAVYAI_PATH variables. These variables contain respectively the paths where configuration, license, and data files are stored and where the software is installed. Setting them is strongly recommended.

Initialization

Run the systemd installer to create heavyai services, a minimal config file, and initialize the data storage.

cd $HEAVYAI_PATH/systemd
./install_heavy_systemd.sh

Accept the default values provided or make changes as needed.

The script creates a data directory in $HEAVYAI_BASE/storage (default /var/lib/heavyai/storage) with the directories catalogs, data, export and log.The import directory is created when you insert data the first time. If you are HEAVY.AI administrator, the log directory is of particular interest.

Activation

Heavy Immerse is not available in the OSS Edition, so if running the OSS Edition the systemctl command using the heavy_web_server has no effect.

Enable the automatic startup of the service at reboot and start the HEAVY.AI services.

sudo systemctl enable heavydb --now
sudo systemctl enable heavy_web_server --now
sudo systemctl enable heavydb --now

Configure Firewall ᴼᴾᵀᴵᴼᴺᴬᴸ

If a firewall is not already installed and you want to harden your system, install theufw.

sudo apt install ufw
sudo ufw allow ssh

To use Heavy Immerse or other third-party tools, you must prepare your host machine to accept incoming HTTP(S) connections. Configure your firewall for external access.

sudo ufw disable
sudo ufw allow 6273:6278/tcp
sudo ufw enable

Most cloud providers use a different mechanism for firewall configuration. The commands above might not run in cloud deployments.

Licensing HEAVY.AI ᵉᵉ⁻ᶠʳᵉᵉ ᵒⁿˡʸ

If you are using Enterprise or Free Edition, you need to validate your HEAVY.AI instance with your license key.

  1. Copy your license key of Enterprise or Free Edition from the registration email message. If you do not have a license and you want to evaluate HEAVI.AI in an unlimited

  2. Connect to Heavy Immerse using a web browser connected to your host machine on port 6273. For example, http://heavyai.mycompany.com:6273.

  3. When prompted, paste your license key in the text box and click Apply.

  4. Log into Heavy Immerse by entering the default username (admin) and password (HyperInteractive), and then click Connect.

    .

Final Checks

Load Sample Data and Run a Simple Query

HEAVY.AI ships with two sample datasets of airline flight information collected in 2008, and a census of New York City trees. To install sample data, run the following command.

cd $HEAVYAI_PATH
sudo ./insert_sample_data --data /var/lib/heavyai/storage
#     Enter dataset number to download, or 'q' to quit:
Dataset           Rows    Table Name          File Name
1)    Flights (2008)    7M      flights_2008_7M     flights_2008_7M.tar.gz
2)    Flights (2008)    10k     flights_2008_10k    flights_2008_10k.tar.gz
3)    NYC Tree Census (2015)    683k    nyc_trees_2015_683k    nyc_trees_2015_683k.tar.gz

Connect to HeavyDB by entering the following command in a terminal on the host machine (default password is HyperInteractive):

$HEAVYAI_PATH/bin/heavysql
password: ••••••••••••••••

Enter a SQL query such as the following

SELECT origin_city AS "Origin", 
dest_city AS "Destination", 
AVG(airtime) AS "Average Airtime" 
FROM flights_2008_10k WHERE distance < 175 
GROUP BY origin_city, dest_city;

The results should be similar to the results below.

Origin|Destination|Average Airtime
Austin|Houston|33.055556
Norfolk|Baltimore|36.071429
Ft. Myers|Orlando|28.666667
Orlando|Ft. Myers|32.583333
Houston|Austin|29.611111
Baltimore|Norfolk|31.714286

After installing Enterprise or Free Edition, check if Heavy Immerse is running as intended.

  1. Connect to Heavy Immerse using a web browser connected to your host machine on port 6273. For example, http://heavyai.mycompany.com:6273.

  2. Log into Heavy Immerse by entering the default username (admin) and password (HyperInteractive), and then click Connect.

Create a new dashboard and a Scatter Plot to verify that backend rendering is working.

  1. Click New Dashboard.

  2. Click Add Chart.

  3. Click SCATTER.

  4. Click Add Data Source.

  5. Choose the flights_2008_10k table as the data source.

  6. Click X Axis +Add Measure.

  7. Choose depdelay.

  8. Click Y Axis +Add Measure.

  9. Choose arrdelay.

  10. Click Size +Add Measure.

  11. Choose airtime.

  12. Click Color +Add Measure.

  13. Choose dest_state.

The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.

Create a new dashboard and a Table chart to verify that Heavy Immerse is working.

  1. Click New Dashboard.

  2. Click Add Chart.

  3. Click Bubble.

  4. Click Select Data Source.

  5. Choose the flights_2008_10k table as the data source.

  6. Click Add Dimension.

  7. Choose carrier_name.

  8. Click Add Measure.

  9. Choose depdelay.

  10. Click Add Measure.

  11. Choose arrdelay.

  12. Click Add Measure.

  13. Choose #Records.

The resulting chart shows, unsurprisingly, that also the average departure delay is correlated to the average of arrival delay, while there is quite a difference between Carriers.

¹ In the OS Edition, Heavy Immerse is unavailable.

² The OS Edition does not require a license key.

PreviousInstalling on UbuntuNextInstall NVIDIA Drivers and Vulkan on Ubuntu

Last updated 11 months ago

Start and use HeavyDB and Heavy Immerse.

For more information, see .

Skip this section if you are on Open Source Edition

enterprise environment, contact your Sales Representative or register for your 30-day trial of Enterprise Edition . If you need a Free License you can get one .

To verify that everything is working, load some sample data, perform a heavysql query, and generate a Pointmap using Heavy Immerse

Create a Dashboard Using Heavy Immerse ᵉᵉ⁻ᶠʳᵉᵉ ᵒⁿˡʸ

https://help.ubuntu.com/lts/serverguide/firewall.html
here
here
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²
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Gpu Drawed Scatterplot
Cpu Drawed Bubble chart