This repository contains code that will deploy the ecosystem necessary to run Combine. There are two deployment methods explained below. Choose the one that meets your needs.
Please be aware that running this system requires not insignificant resources. Required is at least 12-16gb RAM and 2+ CPU cores for the fully built server. This is due in large part to the memory requirements of Apache Spark.
If you are looking to test-drive, install, or develop on Combine, you have arrived at the right place!
Once you do have an instance of the server up and running, you can find a QuickStart walkthrough here.
For releases, there will be an attempt to pin releases from this Combine Playbook repository to the Combine repository with the same tag. This is set in combine_git_branch
setting under group_vars/all.yml
.
To build a release, before any steps below, checkout the release/tag of this playbook you are interested in building and note the release number like v0.3.1
or v0.4
. For example, to build version v0.3.1
, do the following:
# clone playbook repo
git clone https://github.com/WSULib/combine-playbook
cd combine-playbook
# checkout v0.3.1 tag
git checkout v0.3.1
This will then attempt to build the v0.3.1
tagged release of Combine.
- This deployment assumes an Ubuntu 18.04 server
- For either installation, there are a host of variables that set default values. They are all found in the
all.yml
file inside thegroup_vars
folder.- If you are installing this system on a remote server, you MUST update the
ip_address
variable found inall.yml
. Change it to your remote server's ip address. - If you are installing the system locally with Vagrant, you don't need to do anything. Your server will be available at 192.168.45.10.
- If you are installing this system on a remote server, you MUST update the
-
If you are looking to run an instance of the Combine ecosystem on your own computer, you will use the Vagrant-based installation method. This method assumes that you have 4GB of RAM and 2 processor cores available to devote to this system. Double-check and make sure you have this available on your computer. This means you will need MORE than that in RAM and cores in order to not bring your computer to a complete halt. Default settings create a VM that uses 4GB of RAM and 2 cores. Local testing has been performed on iMacs running MacOS Sierra that have a total of 4 cores and 16 GB of RAM.
-
Install VirtualBox and Vagrant.
-
Clone this Github repository.
-
Navigate to the repository in your favorite terminal/shell/command line interface.
-
Build the system.
vagrant up
-
This installation will take a while. The command you just ran initializes the vagrant tool to manage the installation process. It will first download and install a copy of Ubuntu Linux (v.18.04) on your VirtualBox VM. Then, it will configure your networking to allow SSH access through an account called
vagrant
and make the server available only to your local computer at the IP address of 192.168.45.10. After that initial work, the vagrant tool will use ansible to provision (i.e. install all components and dependencies) to a VM on your computer. -
After completed, your server will be available at http://192.168.45.10. Navigating to http://192.168.45.10/admin will allow you to setup your system defaults (OAI endpoints, etc). Going to http://192.168.45.10/combine will take you to the heart of the application where you can ingest, transform, and analyze metadata. Login using the credentials the following credentials:
username: combine password: combine
-
Access via SSH is available through the accounts below. Both have sudo privileges. The combine password defaults to what is listed below. If you have edited
group_vars/all.yml
and changed the password listed there, please adjust accordingly.username: combine password: combine username: vagrant password: vagrant
-
If you have a remote server that you want to install the system upon, these installation instructions are for you. Your server should already be running Ubuntu 18.04. It needs to be remotely accessible through SSH from your client machine and have at least port 80 accessible. Also, it needs Python 2.7 installed on it. Your server will need at least 8GB of RAM and 2 cores, but more is better.
-
Install Ansible, Python, and Passlib on your client machine. This installation method has not been tested using Windows as client machine, and, therefore, we offer no support for running an installation using Windows as a client. For more information, please refer to these Windows-based instructions: http://docs.ansible.com/ansible/latest/intro_windows.html#using-a-windows-control-machine
- NB: when installing Passlib, you should be able to simply run
pip install passlib
if you have the pip tool installed. If you're not certain or if that command doesn't successfully run, see the following link for instructions on installing Pip: http://www.pythonforbeginners.com/basics/how-to-use-pip-and-pypi.
- NB: when installing Passlib, you should be able to simply run
-
Exchange ssh keys with your server.
-
Example command on MacOS
ssh-keygen -t rsa cat ~/.ssh/id_rsa.pub | ssh USERNAME@IP_ADDRESS_OR_FQDN "mkdir -p ~/.ssh && cat >> ~/.ssh/authorized_keys"
-
-
Point ansible to remote server.
- You do this by creating a file named
hosts
inside the following directory:/etc/ansible
. If you are using a Linux or MacOS machine, you should have anetc
directory, but you will probably have to create theansible
folder. Place your server's IP address or FQDN in thishosts
file. If the username you used to exchange keys with the server is anything other than root, you will have to addansible_user=YOUR_USERNAME
. Your hosts file could end up looking something like this:192.168.45.10 ansible_user=USERNAME
. For more information see: http://docs.ansible.com/ansible/latest/intro_getting_started.html#your-first-commands
- You do this by creating a file named
-
Check your target machine is accessible and ansible is configured by running the following command:
ansible all -m ping
- A successful response will look something similar to this. Substitute your IP for the one listed below in the example.
192.168.44.10 | SUCCESS => { "changed": false, "failed": false, "ping": "pong" }
- A successful response will look something similar to this. Substitute your IP for the one listed below in the example.
-
Clone this Github repository
-
Update
ip_address
ingroup_vars/all.yml
- Change the
ip_address
variable to your remote server's IP address.
- Change the
-
Install pre-requisites (NOTE: You may need to make sure the roles are being installed in the repository's
roles
directory with... -p ./roles ...
)ansible-galaxy install -f -c -r requirements.yml
-
Run ansible playbook
ansible-playbook -vvvvv playbook.yml
-
This installation will take a while. Ansible provisions the server with all of the necessary components and dependencies.
-
After the installation is complete, your server will be ready for you to use Combine's web-based interface. Go to your server's IP address. Navigating to
/admin
will allow you to setup your system defaults (OAI endpoints, etc). Going to/combine
will take you to the heart of the application where you can ingest, transform, and analyze metadata. Login using the following credentials:username: combine password: combine
-
Access via SSH is available through the account below. It has sudo privileges. The password below is correct unless you have changed it inside
group_vars/all.yml
.username: combine password: combine
If the Livy logs include errors like this:
19/08/19 11:43:30 ERROR rsc.RSCClient: Failed to connect to context.
check for an error like this:
19/08/19 11:21:25 INFO utils.LineBufferedStream: 2019-08-19 11:21:25 WARN RSCConf:146 - Your hostname, [blahblahblah], resolves to a loopback address, but we couldn't find any external IP address!
If you find that, add the hostname to /etc/hosts
:
127.0.0.1 localhost blahblahblah blahblah_ip
sudo systemctl restart elasticsearch.service
If your XSLT transforms are failing with an error like NoneType has no such method 'transform'
, double-check that any includes inside your transform XSLT use correct locations. This is particularly important to check if you have imported any transformations that used local includes.