CentOS/RHEL 7 CE GPU Installation With Yum

Configuration

These are the steps to prepare your OmniSci environment.

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 everthing is working, load some sample data, perform a mapdql query, and generate a pointmap using Immerse.

1. 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
   sudo ./insert_sample_data

2. 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

3. 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: ••••••••••••••••

4. 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

5. Connect to Immerse using a web browser connected to your host machine on port 9092. For example, http://omnisci.mycompany.com:9092.
6. 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 or flights_2008_7M table as the data source, depending on which dataset you selected for ingest.
   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.