Kinetica Installation with KAgent

​

System Requirements

​

Certified OS List

​

Minimum Hardware Requirements

​

GPU Driver Matrix

​

Active Directory

​

KAgent Installation

KAGENT_REPO=https://repo.kinetica.com/yum/7.2/CentOS/8/x86_64
KAGENT_PKG=$(wget -q -O - ${KAGENT_REPO} | sed 's/<[^>]*>//g' | grep -o "kagent.*ga.*rpm " | sort -V | tail -1)
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo yum install ./${KAGENT_PKG}

KAGENT_REPO=https://repo.kinetica.com/debian/7.2/Ubuntu/focal/binary-amd64
KAGENT_PKG=$(wget -q -O - ${KAGENT_REPO} | sed 's/<[^>]*>//g' | grep -o "kagent.*ga.*deb " | sort -V | tail -1)
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo apt install ./${KAGENT_PKG}

KAGENT_REPO=https://repo.kinetica.com/debian/7.2/Ubuntu/jammy/binary-amd64
KAGENT_PKG=$(wget -q -O - ${KAGENT_REPO} | sed 's/<[^>]*>//g' | grep -o "kagent.*ga.*deb " | sort -V | tail -1)
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo apt install ./${KAGENT_PKG}

KAGENT_REPO=https://repo.kinetica.com/yum/7.2/SUSE/15.3/x86_64
KAGENT_PKG=$(wget -q -O - ${KAGENT_REPO} | sed 's/<[^>]*>//g' | grep -o "kagent.*ga.*rpm " | sort -V | tail -1)
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo zypper install ./${KAGENT_PKG}

KAGENT_REPO=https://repo.kinetica.com/yum/7.2/CentOS/8/x86_64
KAGENT_PKG=kagent-7.2.0.4.20240326024429.ga-0.x86_64.el8.rpm
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo yum install ./${KAGENT_PKG}

KAGENT_REPO=https://repo.kinetica.com/debian/7.2/Ubuntu/focal/binary-amd64
KAGENT_PKG=kagent_7.2.0.4.20240326024429.ga-0_amd64.ubuntu20.04.deb
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo apt install ./${KAGENT_PKG}

KAGENT_REPO=https://repo.kinetica.com/debian/7.2/Ubuntu/jammy/binary-amd64
KAGENT_PKG=kagent_7.2.0.4.20240326024429.ga-0_amd64.ubuntu22.04.deb
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo apt install ./${KAGENT_PKG}

KAGENT_REPO=https://repo.kinetica.com/yum/7.2/SUSE/15.3/x86_64
KAGENT_PKG=kagent-7.2.0.4.20240326024429.ga-0.x86_64.sles15.3.rpm
wget ${KAGENT_REPO}/${KAGENT_PKG}
sudo zypper install ./${KAGENT_PKG}

​

Kinetica Installation

​

KAgent UI

1. Ensure the KAgent service is started:
   service kagent_ui status
   
2. Browse to the KAgent UI using IP or host name:
   http://<kagent-host>:8081/kagent
   
3. Optionally, if using custom rings, i.e. not the default ring, click Rings then click Clusters next to the desired ring. See High Availability Architecture for more information about rings and high availability (HA).
4. Click Add New or Existing Cluster.

​

Cluster

1. Enter a name for the cluster. The name cannot contain spaces or underscores.
2. Optionally, select one or more of the following packages:
   • Select Core if node(s) in the cluster should have the core database functionality installed on them.
     First-time setups should always have Core selected.
   • Select Graph if a node in the cluster should have the graph server installed on it. See Graphs & Solvers Concepts for more information.
   • Optionally, select to install KML (Kinetica Machine Learning) if a node should have KML installed on it. An existing Kubernetes cluster is required for KML processing. See Machine Learning for more information on KML features.
   • Optionally, select to install KAgent if a node should also have KAgent installed on it. See KAgent for more information.
   • Optionally, select to install RabbitMQ if setting up a ring for High Availability. Review High Availability Architecture and High Availability Configuration & Management for more information.
3. For the Install Mode, select either Online (install directly from the online Kinetica repository) or Offline (install from uploaded packages). If Offline is selected, click Upload Packages, then upload a package file for each component or driver desired for the installation.
   If performing an offline installation, all necessary dependencies will need to be installed prior to cluster setup.
4. For the Version, select either CUDA (GPU) or Intel (CPU-only) to determine the package variant to install.
5. If the Version is set to CUDA, ensure Automatically install Nvidia driver is selected. This will automatically configure the server(s) for an Nvidia GPU driver and install the most compatible driver.
6. Enter the license key.
7. If KML is selected to install, upload a configuration file for an already-existing Kubernetes installation. Note that KML requires Kubernetes; see Machine Learning for more information.
8. Click Next.

​

Deployment

1. Select the On Premise deployment method, and click Next.
   If clearing the Open Firewall Ports checkbox, the firewall then must be configured manually to allow the required ports listed in the default ports table. Consult Adjust Firewall for tips on configuring the firewall.

​

Security

1. Enter and confirm an Admin Password. It must meet the password strength requirements.
   This is the password used to access Reveal, Workbench, KAgent, and GAdmin as the default administrative user.
2. Select an SSL Mode:
   • Cert/key setup not required — Kinetica will not require SSL certificate/key creation/upload and SSL will not be enabled
   • User-provided cert/key per node — user must upload an SSL certificate and key for each node; Kinetica copies the cert/key pair to /opt/gpudb/certs, enables HTTPD, and configures HTTPD to use HTTPS
   • Generate self-signed cert/key per node — KAgent generates a self-signed certificate and key for each node and places it in /opt/gpudb/certs, enables HTTPD, and configures HTTPD to use HTTPS
3. Select an Authentication type and fill the fields as necessary:
   • None — no authentication or authorization
   • LDAP — configures Kinetica to authenticate via LDAP; requires authentication to connect to the database, enables authorization, enables external authentication, automatically creates users in the database for LDAP users, and automatically grants roles in the database to LDAP users
   • Active Directory — configures Kinetica to authenticate via Microsoft Active Directory; requires authentication to connect to the database, enables authorization, enables external authentication, automatically creates users in the database for Active Directory users, and automatically grants roles in the database to Active Directory users
   • Kerberos — configures Kinetica to authenticate via Kerberos; requires authentication to connect to the database, enables authorization, enables external authentication, automatically creates users in the database for Kerberos users, and automatically grants roles in the database to Kerberos users
   • OAuth — configures Kinetica to authenticate via an OAuth service; requires authentication to connect to the database, enables authorization, enables external authentication, automatically creates user in the database for OAuth users, and automatically grants roles in the database to OAuth users
   No SSL or authentication is not recommended! For more information on security configurations and settings as well as how to manually configure Kinetica for a secure setup, see Security Configuration.
4. Click Next.

​

Nodes

1. Click Add New Node until there are the desired number of nodes that will have Kinetica (and potentially other services) installed on them.
2. For each node, input a custom Label (hostname is suggested), the Internal IP, and the Public IP.
3. If the User-provided cert/key per node SSL Mode was selected in Security, an SSL column will be added to the configuration page—click the lock icon in the SSL column to open the SSL Certificate/Key window, where the SSL cert and key, along with an optional public hostname, can be provided. Repeat this for each node.
4. Optionally, select if each node should have the Core package installed. The Core package contains access to the database and its core components and functionality. Note that if the core package is not installed on a node, that node cannot be designated as the Head Node.
5. Select the desired node for the Head Node using the corresponding radio button. This server will receive user requests and parcel them out to the other worker nodes of the system. The head node of the cluster (or only node in a single-node system) will also be used for the administration of the cluster, and by default, the hosting of Reveal and GAdmin and as such, will require special handling during the installation process.
   All services and privileges (Head, Graph, KML, etc.) can exist on a single node if desired, assuming there are enough resources to handle it.
6. If the Graph package was selected for install in Cluster, select the desired node(s) to host the graph service using the corresponding radio button. The graph node does not need to have the Core package enabled. Consult Distributed Graph Servers for more information on leveraging multiple graph servers.
7. To reserve GPUs for KML, UDFs, or other external processes that may be running on the node, set the number under KML GPUs.
   Some UDFs and features of KML may require GPUs to work or have increased performance.
8. If the KAgent package was selected for install in Cluster, select the desired node to host the service. The KAgent node does not need to have the Core package enabled.
9. If the RabbitMQ package was selected for install in Cluster because a High Availability setup is required, select the desired node(s) to have RabbitMQ installed. Ensure at least one node will have RabbitMQ installed if enabling High Availability (HA) for the cluster; select additional nodes to have RabbitMQ installed for redundant queues. A node does not have to host any other services other than RabbitMQ if desired.
   In total, an odd number of nodes should be selected for RabbitMQ installation. Kinetica recommends installing RabbitMQ machines that will not have the Core package enabled.
10. Click Next.
11. Confirm which IP address KAgent should use to connect to the cluster: Internal or Public.

​

Credentials

1. For the Server SSH Credentials, enter the SSH username and password or upload the SSH private key that will be used to access the node(s).
2. Optionally, enter the sudo password.
3. Click Verify.

​

Installation

1. Review the Installation Summary to ensure there are no validation errors in the information. The highlighted IP address will be the one KAgent uses to connect to the cluster.
   Click CLI Commands to view and/or copy the KAgent command line interface commands that will be run in the background (order is from top to bottom).
2. Click Install. KAgent will open a window displaying the progress of the installation.
   Click Details next to a step to see stdout and stderr for that step. Click to copy the displayed text.

​

Validation

​

Curl Test

$ curl localhost:9191
Kinetica is running!

​

API Test

$ /opt/gpudb/bin/gpudb_python /opt/gpudb/kitools/gadmin_sim.py -u <username> -p <password> --table --summary
+-----------------+--------------------------------+----------------------+----------------------+-------+
|     Schema      |           Table/View           |       Records        |       Type ID        |  TTL  |
+=================+================================+======================+======================+=======+
| SYSTEM          | <ALL TABLES/VIEWS>             |                    1 |                      |       |
| SYSTEM          | ITER                           |                    1 |        UNSET_TYPE_ID |    -1 |
+-----------------+--------------------------------+----------------------+----------------------+-------+

+---------------------------+----------------------+
|        Object Type        |        Count         |
+===========================+======================+
| Schemas                   |                    1 |
| Tables & Views            |                    1 |
| Records                   |                    1 |
| Records + Track Elements  |                    1 |
+---------------------------+----------------------+

​

GAdmin Status Test

​

Ingest/Read Test

1. Navigate to the Demo tab on the Cluster page.
2. Click Load Sample Data under the NYC Taxi section, then click Load to confirm.
3. Once the data is finished loading, click View Loaded Data. The data should be available in the nyctaxi table located in the demo schema.

1. Navigate to:
   http://<head-node-ip-address>:8088/
   
2. Log into Reveal and change the administration account’s default password.
3. Click NYC Taxi under Dashboards. The default NYC Taxi dashboard should load.

​

Core Utilities

​

Environment Configuration and Tools

​

Helper Scripts

​

Logging

​

Additional Configuration

• Enabling auditing
• Changing the persist directory
  The directory should meet the following criteria:

  • Available disk space that is at least 4x memory
  • Writable by the gpudb user
  • Consist of raided SSDs
  • Not be part of a network share or NFS mount
• Enabling UDFs (procs)
• Adjusting storage tiers and resource groups

1. Log into GAdmin
   1. Enter admin for the Username
   2. Enter the Admin Password provided to KAgent for the Password (refer to KAgent UI for more information)
   3. Click Log In
2. Stop the system.
3. Navigate to Cluster —> Config
4. Edit the file in the text window.
5. Click Update, then click Start Service.

1. Stop the system.
2. Open /opt/gpudb/core/etc/gpudb.conf in the desired text editor.
3. Edit and save the file.
4. Start the system.

​

Uninstallation

1. Stop the system
2. Remove the KAgent and Kinetica packages from your machine:
   RHEL

   Ubuntu

   SUSE

   sudo yum remove kagent
   sudo yum remove gpudb-<gpuhardware>-<licensetype>
   

   sudo apt remove kagent
   sudo apt remove gpudb-<gpuhardware>-<licensetype>
   

   sudo zypper remove kagent
   sudo zypper remove gpudb-<gpuhardware>-<licensetype>
   
3. Optionally, remove the Kinetica Machine Learning (KML) package from your machine:
   RHEL

   Ubuntu

   SUSE

   sudo yum remove kinetica-ml
   

   sudo apt remove kinetica-ml
   

   sudo zypper remove kinetica-ml
   
4. Remove any user-defined persist directories (these directories are set in /opt/gpudb/core/etc/gpudb.conf)
5. Clean-up all Kinetica artifacts:
   sudo rm -rf /opt/gpudb
   
6. Remove the gpudb & gpudb_proc users from the machine:
   RHEL

   Ubuntu

   SUSE

   sudo userdel -r gpudb
   sudo userdel -r gpudb_proc
   

   sudo deluser --remove-home gpudb
   sudo deluser --remove-home gpudb_proc
   

   sudo userdel -r gpudb
   sudo userdel -r gpudb_proc
   
7. Remove the gpudb group from the machine:
   groupdel gpudb