This tool reads a configuration file, checks it for syntax and semantic correctness, and then reconciles a running VPP daemon with its configuration. It is meant to be re-entrant and stateless. The tool connects to the VPP API and creates/removes all of the configuration in a minimally intrusive way.

NOTE This code is under development, and probably won't work well until this note is removed. If you're interested in helping, reach out to <pim at ipng dot ch> to discuss options.

Building

This program expects Python3 and PIP to be installed. It's known to work on OpenBSD and Debian.

sudo pip3 install argparse
sudo pip3 install yamale
sudo pip3 install pyyaml
sudo pip3 install pyinstaller

## Ensure all unittests pass.
./tests.py -t unittest/*.yaml

## Build the tool
pyinstaller vppcfg  --onefile

Running

dist/vppcfg -h
usage: vppcfg [-h] -c CONFIG [-s SCHEMA] [-d]

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG, --config CONFIG
                        YAML configuration file for VPP
  -s SCHEMA, --schema SCHEMA
                        YAML schema validation file
  -d, --debug           Enable debug, default False

Design

YAML Configuration

The main file that is handled by this program is the Configuration File.

Validation

There are three types of validation: schema which ensures that the input YAML has the correct fields of well known types, semantic which ensures that the configuration doesn't violate semantic constraints and runtime which ensures that the configuration can be applied to the VPP daemon.

Schema Validators

First the configuration file is held against a structural validator, provided by Yamale. Based on a validation schema in schema.yaml, the input file is checked for syntax correctness. For example, a dot1q field must be an integer between 1 and 4095, wile an lcp string must match a certain regular expression. After this first pass of syntax validation, I'm certain that if a field is set, it is of the right type (ie. string, int, enum).

Semantic Validators

A set of semantic validators, each with a unique name, ensure that the semantics of the YAML are correct. For example, a physical interface cannot have an LCP, addresses or sub-interfaces, if it is to be a member of a BondEthernet.

Validators are expected to return a tuple of (bool,[string]) where the boolean signals success (False meaning the validator rejected the configuration file, True meaning it is known to be correct), and a list of zero or more strings which contain messages meant for human consumption.

Runtime Validators

After the configuration file is considered syntax and semanticly valid, there is one more set of checks to perform -- runtime validators ensure that the configuration elements such as physical network devices (ie. HundredGigabitEthernet12/0/0 or plugin lcpng are present on the system. It does this by connecting to VPP and querying the runtime state to ensure that what is modeled in the configuration file is able to be committed.

Unit Tests

It is incredibly important that changes to this codebase, particularly the validators, are well tested. Unit tests are provided in the unittests/ directory with a Python test runner in tests.py. Besides regular unittests provided by the Python framework, a YAMLTest is a test which reads a two-document YAML file, with the first document describing test metadata, and the second document being a candidate configuration to test, and it then runs all syntax and semantic validators and reports back.

The format of the YAMLTest is as follows:

test:
  description: str()
  errors:
    expected: list(str())
    count: int()
---
<some YAML config contents>

Fields:

description: A string describing the behavior that is being tested against. Any failure of the unittest will print this description in the error logs.
errors.expected: A list of regular expressions, that will be expected to be in the error log of the validator. This field can be empty or omitted, in which case no errors will be expected.
errors.count: An integer of the total amount of errors that is to be expected. Sometimes an error is repeated N times, and it's good practice to precisely establish how many errors should be expected. That said, this field can be empty or omitted.