I've been managing a fleet of Fireboxes for a few clients, and one of the recurring headaches was keeping track of configuration changes and having a quick rollback point. Manually exporting `.xml` files and naming them with dates was error-prone and never versioned properly.
So, I built a simple Python script that uses the Firebox's REST API to pull the configuration and automatically commit it to a private Git repository. This gives me full history, branch-based testing for major changes (like policy overhauls), and a single source of truth. Here's the core of it:
```python
#!/usr/bin/env python3
import requests
from datetime import datetime
import git
import os
import sys
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
# Config
firebox_ip = "10.0.10.1"
username = "api-user"
password = "YOUR_STRONG_PASSWORD"
repo_path = "/opt/backups/firebox-configs"
config_name = "firebox-a"
# Fetch config from Firebox
api_url = f"https://{firebox_ip}:8080/ws/config/backup"
response = requests.get(api_url, auth=(username, password), verify=False)
response.raise_for_status()
# Write to file with timestamp
timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
filename = f"{config_name}-{timestamp}.xml"
filepath = os.path.join(repo_path, filename)
with open(filepath, 'wb') as f:
f.write(response.content)
# Git operations
repo = git.Repo(repo_path)
repo.index.add([filename])
repo.index.commit(f"Backup config {config_name} at {timestamp}")
origin = repo.remote(name='origin')
origin.push()
print(f"Backup successful: {filename}")
```
The key parts:
* It uses the `/ws/config/backup` endpoint, which requires a user with "Backup Administrator" role.
* The script is designed to run from a cron job or a CI/CD scheduler (like a GitHub Action).
* Git operations are basic; you could extend it to check for diffs and only commit on changes.
I've found this invaluable after a bad policy push last month—I could instantly see what changed and revert to the known-good config from two days prior.
Has anyone else built something similar? I'm curious about handling SSH-based backups for older models or how you might integrate this into a broader network device config management system like Oxidized or Unimus. Also, any thoughts on storing these XML files securely, given they contain the full device config?
Build fast. Fail fast. Fix fast.
That's a solid approach to a real operations problem, and I appreciate the pragmatism of automating the capture of config state. Where this concept really pays dividends in the marketing technology space is when you extend it beyond simple backup and start tying config changes directly to performance metrics.
A script like this creates an immutable change log. If you mapped each of those commits to a corresponding period in your attribution platform, you could quantitatively measure the impact of a specific configuration update - like a change in lead scoring thresholds or an adjustment to an engagement program - on pipeline velocity or conversion rates. The branch-based testing you mentioned is key; you could hypothetically run an A/B test on two different nurture stream configurations by deploying each to a segment and comparing the performance data, with every detail of the setup versioned.
The caveat is that the config file is only one part of the operational state. For a true rollback, you'd also need to snapshot the associated data schema changes in your CRM or the audience definitions in your CDP, as they're often interdependent. Without that, restoring an old Marketo program config might break if a custom object field it references is missing.
Totally agree about the config file being just one piece. It's that exact interdependence that's bitten me before. A rollback script might revert your automation workflow in HubSpot, but if the connected Salesforce custom objects or page layouts were also changed manually, the whole thing falls apart.
The idea of tying commits to performance metrics is really interesting, though. I've tried a lighter version by tagging releases in GitHub and then looking at dashboard changes in Looker around that time. It's messy, but even a loose correlation can tell you a lot, like when a new lead status actually slowed down the sales cycle.
Your last point about the CDP audience definitions is key, that's a hidden layer of config that never gets committed. Makes me think the next step for these scripts is a package of snapshots, not just one file.