Learn how to elevate your Ansible update strategy by creating a dynamic, risk-based inventory using Excel 📊 and Python 🐍. This article walks you through replacing static hosts files with a flexible, easy-to-maintain setup that prioritizes updates based on risk levels ⚠️-keeping your patching process efficient and adaptable 🚀.
In this blog, we'll take a simple Ansible server update script and turn it into a Risk-Based Update System. Here, servers with the lowest risk get patched first, giving us a chance to test thoroughly before moving on to higher-priority systems.
The secret sauce? Setting up well-defined groups to make this flow seamlessly. But the real question is: can we pull this off without major changes to our Ansible script from last time? Let's find out!
Host File
The host file is at the heart of this change. In the last post, we used a static file grouped by server types. Now, we're adding a second layer of grouping by risk level-which does add some complexity to the host file.
But here's the twist: what if our host file could be dynamically generated from a more generic source? That would keep things flexible and save us from endless file editing!
Dynamic Host List
Ansible can work with dynamically created host files, which gives us a more flexible way to keep track of servers. In this example, we'll use an Excel file to organize our hosts.
Example hosts_data.xlsx
Structure:
Host Name | Server Environment | Ansible User | Server Type | DNS | Notes |
---|---|---|---|---|---|
mint | dev | richard | desktop | desktop.sebostech.LOCAL | Mint desk top |
ansible_node | dev | ansible_admin | Ansible | ansible_node.sebostech.local | Development server; Only updates monthly |
clone_master | dev | ansible_admin | clone | clone.dev.sebostech.local | Development server; Only updates monthly |
mele | staging | richard | nas | nas.stage.sebostech.local | Testing server; Used for application testing |
pbs | production | root | backup server | pbs.prod.sebostech.local | Testing server; Used for application testing |
pve | production | root | hypervisor | api.stage.sebostech.local | Testing server; Used for application testing |
samba | production | richard | nas | nas.prod.sebostech.local | Critical server; Requires daily backup |
firewall | production | richard | firewall | firewall.sebostech.local | Critical server; Requires daily backup |
Most IT departments already have a list of servers stashed in an Excel file, so why not put it to good use? This approach makes it easy to keep our Ansible hosts organized and up-to-date without constant manual updates.
But how does Ansible use the Excel file? Let's dive into how we can transform this data into a usable dynamic inventory!
## This will run agains all host
ansible-playbook -i dynamic_inventory.py playbook.yml
You can also use environment variables
option to target specific groups, based on Server Environment
, Server Type
, or even a combination of both:
## Just production
SERVER_ENVIRONMENT="production"
ansible-playbook -i dynamic_inventory.py playbook.yml --limit "high:web"
## Just nas
SERVER_TYPE="nas"
ansible-playbook -i dynamic_inventory.py playbook.yml --limit "high:web"
## production nas
SERVER_ENVIRONMENT="production"
SERVER_TYPE="nas"
ansible-playbook -i dynamic_inventory.py playbook.yml --limit "high:web"
Need new groups? Just update the Excel file and adjust the Python script accordingly-easy as that!
For a look at the Python code, see here.
Why Not Use a Hosts File?
When I first started using Ansible, the hosts file was my go-to. But as I added more servers, especially ones with dual roles, that file got more and more complex.
Could you use a traditional hosts file to achieve this? Sure-but there are a few drawbacks.
With a hosts file, you'd likely end up with duplicate entries or additional variables to capture all the structure you need. An Excel file, on the other hand, provides a clean, easy-to-maintain structure that keeps things organized.
In a corporate environment, there's a good chance there's already at least one Excel file with a server list, so why not take advantage of it?
If you'd like me to dive deeper into the Python code, just let me know!
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