CentOS/RHEL 7 EE CPU Installation With Tarball

Assumptions

These instructions assume the following:

• You are installing on a “clean” CentOS/RHEL 7 host machine with only the operating system installed.
• Your OmniSci host only runs the daemons and services required to support OmniSci.
• Your OmniSci host is connected to the Internet.

Preparation

Prepare your CentOS/RHEL 7 machine by updating the system, installing the Java Development Kit, creating a OmniSci user, and enabling access to HTTP port 9092 through the firewall.

sudo yum update
sudo reboot

sudo useradd -U mapd

sudo firewall-cmd --zone=public --add-port=9092/tcp --permanent
sudo firewall-cmd --reload

Installation

You install the OmniSci application itself by expanding a TAR file.

1. Create an installs directory in your home folder:

   cd ~
   sudo mkdir installs
   cd installs

2. Download the desired version of OmniSci from the URL provided you by your Account Executive.
3. Expand the OmniSci archive file in the installs directory with the following command: 

   sudo tar -xvf <file_name>.tar.gz

4. List the contents of the installs directory and copy the name of the directory created when expanding the archive. For example:

   mapd-ee-4.3.0-20181119-b7f85d00bd-Linux-x86_64-render`

5. Go to the opt folder and create a symbolic link to the directory you just copied:

   cd /opt
   ln -s ~/installs/<onmisci_directory> mapd

Configuration

These are the steps to prepare your OmniSci environment.

Initialization

Run the systemd installer. This script requires sudo access. You might be prompted for a password.

cd $MAPD_PATH/systemd
sudo ./install_mapd_systemd.sh

You will be prompted for two paths during install: MAPD_PATH and MAPD_STORAGE. MAPD_PATH must be the same as the location of the symbolic link you created in step 5 of the installation process and the environment variable you just created. In a standard installation, that path is /opt/mapd. MAPD_STORAGE defaults to /var/lib/mapd

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 of particular interest to OmniSci administrators.

Activation

Start and use OmniSci Core and Immerse.

1. Start OmniSci Core

   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 correctly, 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 the 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.