CentOS/RHEL 7 CE CPU Installation With Yum

Note MapD has been rebranded to OmniSci.

This is an end-to-end recipe for installing OmniSci Community Edition on a CentOS/RHEL 7 machine running without GPUs using a tarball. This install has all of the functionality of OmniSci, except for backend rendering (Pointmap, Scatterplot, and other charts might not be available).

Here is a quick video overview of the installation process.

The installation phases are:
Important 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” 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 host machine by updating your system, creating the OmniSci user, and enabling a firewall.

Update and Reboot

Update the entire system and reboot to activate the latest kernel.

sudo yum update
sudo reboot

Create the OmniSci User

Create a group called mapd and a user named mapd, who will be the owner of the OmniSci database. You can create both the group and user with the useradd command and the -U switch.

sudo useradd -U mapd

Firewall

To use Immerse, you must prepare your host machine to accept HTTP connections. You can configure your firewall for external access.

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

For more information, see https://fedoraproject.org/wiki/Firewalld?rd=FirewallD.

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

Installation

Use curl to download the OmniSci repository file to the yum repository directory.

curl https://releases.mapd.com/ce/mapd-ce-cpu.repo | sudo tee /etc/yum.repos.d/mapd.repo

Use yum to install OmniSci .

sudo yum install mapd

Configuration

These are the steps to prepare your OmniSci environment.

Set Environment Variables

For convenience, you can update .bashrc with the required environment variables.

  1. Open a terminal window.
  2. Enter cd ~/ to go to your home directory.
  3. Open .bashrc in a text editor. For example, sudo gedit .bashrc.
  4. Edit the .bashrc file. Add the following export commands under “User specific aliases and functions.” 
    # User specific aliases and functions
export MAPD_USER=mapd
export MAPD_GROUP=mapd
export MAPD_STORAGE=/var/lib/mapd
export MAPD_PATH=/opt/mapd
export MAPD_LOG=/var/lib/mapd/data/mapd_log
  5. Save the .bashrc file.
  6. Open a new terminal window to use your changes.

The $MAPD_STORAGE directory must be dedicated to OmniSci: do not set it to a directory shared by other packages.

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 correctly, load some sample data, perform a mapdql query, and generate a Table chart 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). The examples below use the smaller 10 thousand row dataset. 
    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 (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;
    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
  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 Table chart:
    1. Click New Dashboard.
    2. Click Add Chart. Table is the default chart type.
    3. Click Add Data Source.
    4. Choose the flights_2008_10k or the flights_2008_7M table as the datasource, depending on which dataset you chose for ingest.
    5. Click Add Measure.
    6. Choose depdelay.
    7. Click Add Measure.
    8. Choose arrdelay.

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

    4_firstTable.png