system/scripts/python/libvirt-xml2netbox-csv_interfaces.py
Georg 46ab0ff4d6 Init libvirt-xml2netbox-csv_interfaces.py
Signed-off-by: Georg <georg@lysergic.dev>
2022-01-02 17:17:56 +01:00

52 lines
2.2 KiB
Python
Executable File

#!/usr/bin/python3
"""
Takes a directory with libvirt XML domain dumps and creates a NetBox importable CSV with VM interfaces. Intended to be used subsequently to libvirt-xml2netbox.py.
Note: we want the VM interfaces to be created with the interface name the operating system is seeing. Since libvirt only knows the host-side interface or network association, and since it was decided that integrating an automated interface name extraction process with a different source which merges with the libvirt data would not be economic, manual editing of the interface names in the resulting CSV - according to the data presented by the VM operating systems - is required.
Created and Last modified: 02/01/2022 by Georg Pfuetzenreuter <georg@lysergic.dev>
"""
import os
import xml.etree.ElementTree as xmlet
import pandas
cluster = "xxx"
indir = "xmls/" + cluster + "/domains"
outfile = cluster + "_interfaces.csv"
columns = [ "virtual_machine", "name", "enabled", "mac_address", "mtu", "description", "mode" ]
rows = []
for domainxml in os.listdir(indir):
xmlparse = xmlet.parse(indir + "/" + domainxml)
xmlroot = xmlparse.getroot()
domain = xmlroot.find("name").text
for interface in xmlroot.findall("devices/interface"):
interface_type = next(iter(interface.attrib.values()))
if interface_type == "network":
source = interface.find("source").attrib["network"]
if interface_type == "bridge":
source = interface.find("source").attrib["bridge"]
mac_address = interface.find("mac").attrib["address"]
model = interface.find("model").attrib["type"]
enabled = "true"
mtu = "1500"
mode = "access"
description = "Imported from libvirt. Model: " + model + "."
rows.append(
{
"virtual_machine": domain,
"name": source,
"enabled": enabled,
"mac_address": mac_address,
"mtu": mtu,
"description": description,
"mode": mode
}
)
convert = pandas.DataFrame(rows, columns=columns)
convert.to_csv(outfile, index=False)