Ubuntu EE GPU Installation With Apt

Installation

To install OmniSci:

1. In a web browser, download the OmniSci list file https://releases.mapd.com/ee/mapd-ee-cuda.list. You might be prompted for the user name and password provided by your OmniSci sales representative.
2. Move and rename the file to /etc/apt/sources.list.d/mapd.list.
3. Edit the mapd.list file, replacing user:pass with the name and password provided by your OmniSci sales representative:

   deb https://user:pass@releases.mapd.com/ee/apt/ stable cuda
   

4. Download and add a GPG key to apt:

   curl https://releases.mapd.com/GPG-KEY-mapd | sudo apt-key add -
   

5. Use update to locate the new installation options, and then install OmniSci:

   sudo apt update
   sudo apt install mapd

Configuration

Follow these steps to prepare your OmniSci environment.

You can also create a configuration file with optional settings. See Configuration.

Initialization

This step initializes the database and prepares systemd commands for OmniSci.

1. Run the systemd installer. This script requires sudo access. You might be prompted for a password. Accept the values provided (based on your environment variables) or make changes as needed. The script creates a data directory in $MAPD_STORAGE with the directories mapd_catalogs, mapd_data, and mapd_export. mapd_import and mapd_log directories are created when you insert data the first time. The mapd_log directory is the one of most interest to a OmniSci administrator.

   cd $MAPD_PATH/systemd
   sudo ./install_mapd_systemd.sh

Activation

Start and use OmniSci Core and Immerse.

1. Start OmniSci Core

   cd $MAPD_PATH
   sudo systemctl start mapd_server
   sudo systemctl start mapd_web_server

2. Enable OmniSci Core to start when the system reboots.

   sudo systemctl enable mapd_server
   sudo systemctl enable mapd_web_server

Checkpoint

To verify that everything is working, load some sample data, perform a mapdql query, and generate a pointmap using Immerse.

OmniSci ships with two sample datasets of airline flight information collected in 2008. To install the sample data, run the following command.

cd $MAPD_PATH
./insert_sample_data

When prompted, choose whether to insert dataset 1 (7 million rows) or dataset 2 (10 thousand rows).

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

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

$MAPD_PATH/bin/mapdql
password: ••••••••••••••••

Enter a SQL query such as the following, based on dataset 2 above:

mapdql> 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;
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

Connect to Immerse using a web browser connected to your host machine on port 9092. For example, http://omnisci.mycompany.com:9092.

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.

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