metrics-utility

metrics-utility deals with collecting, analyzing and reporting metrics from Ansible Automation Platform (AAP) Controller instances.

It provides two interfaces - a CLI and a python library.

Also see below for dev setup, and other docs.

CLI

A metrics-utility CLI tool for collecting and reporting metrics from Controller, allowing users to:

• Collect Controller usage data from the database, settings, and prometheus
• Analyze the data and generate .xlsx reports
• Support multiple storage adapters for data persistence (local directory, S3)
• Push metrics data to console.redhat.com

It can run either standalone (against a specified postgres instance), or inside the Controller's python virtual environment. The controller mode allows the config collector to collect more settings and takes DB connection details from there.

It provides two subcommands:

• gather_automation_controller_billing_data
  • collects data from controller, saves daily tarballs with .csv / .json inside
  • saves tarballs in specified storage
  • optionally sends to console
• build_report
  • builds a .xlsx report
    • 3 report types - CCSP, CCSPv2, RENEWAL_GUIDANCE
    • the ccsp* reports use the collected tarballs as the source
    • the renewal* report reads from controller db

Example invocation:

pip install metrics-utility

# common
export METRICS_UTILITY_SHIP_PATH="./out"
export METRICS_UTILITY_SHIP_TARGET="directory"

# gather data
metrics-utility gather_automation_controller_billing_data --ship --until=10m
ls out/data/`date +%Y/%m/%d`/ # data/<year>/<month>/<day>/<uuid>-<since>-<until>-<index>-<collection>.tar.gz

# build report
export METRICS_UTILITY_REPORT_TYPE="CCSPv2"

metrics-utility build_report --month=`date +%Y-%m` # year-month
ls out/reports/`date +%Y/%m`/ # reports/<year>/<month>/<type>-<year>-<month>.xlsx

See docs/cli.md and docs/old-readme.md for details on the usage,
See docs/environment.md for a full list of environment variables,
See docs/awx.md for more on running against an awx dev env.

Python library

The metrics_utility.library library provides a lower-level python API exposing the same functionality using these abstractions:

• collectors - functions that collect specific data, from database to a .csv, or from elsewhere into a python dict
• packagers - packages multiple related .csv & .json into .tar.gz daily tarballs
• extractors - extracts these tarballs, loading specific data into dicts or Pandas dataframe
• rollups - group and aggregate dataframes, compute stats and optionally save them
• reports - builds a xlsx report from a set of dataframes
• storage - unified storage backend for filesystem, s3, segment, crc and db
• instants - associated datetime-related helpers
• tempdir & db locking helpers

The library uses no env variables, and doesn't rely on Controller environment. The CLI is expected to use the library where possible, but is not limited to it.

Example use:

from metrics_utility.library.collectors.controller import config, main_jobevent
from metrics_utility.library.instants import last_day, this_day
from metrics_utility.library import lock, storage

db = ... # django.db.connection / psycopg 3

dir_storage = storage.StorageDirectory(base_path='./out')

with lock(db=db, key='my-unique-key'):
    # dict, will be converted to json
    config_dict = config(db=db).gather()

    # list of .csv filenames; since is included, until is excluded
    job_csvs = main_jobevent(db=db, since=last_day(), until=this_day()).gather()

# save in storage
dir_storage.put('config.json', dict=config_dict)
for index, file in enumerate(job_csvs):
    dir_storage.put(f'main_jobevent.{index}.csv', filename=file)
    os.remove(file)

See library README for details.
See workers/ for more library usage examples.

Developer setup

Prerequisites

• Python 3.12 or later
• uv
• Docker compose
• make, git

Dependencies are managed via pyproject.toml (& uv.lock). There is also setup.cfg with dependencies but those are only used for the controller mode.

The Docker compose environment is used to provide a quick postgres & minio instances on ports 5432 and 9000/9001, but they can be replaced with local setup. See docker-compose.yaml for details of the mc setup (substitute the minio hostname for localhost), and tools/docker/*.sql for users & data to import in postgres (start with roles.sql and latest.sql). (Or don't, and use docker.)

uv is also not required as long as you can manage your own python venv and install dependencies from pyproject.toml.

Optionally, uvx pre-commit install to run ruff checks from a pre-commit hook, defined in .pre-commit-config.yaml. Or you can run make lint / make fix manually.

Installation

# Clone the repository
git clone https://github.com/ansible/metrics-utility.git
cd metrics-utility

# Install dependencies using uv
uv sync

Run

cd metrics-utility
make compose

cd metrics-utility
uv run ./manage.py --help
uv run ./manage.py gather_automation_controller_billing_data --help
uv run ./manage.py build_report --help

make clean resets the docker environment, make lint & make fix run the linters & formatters, make psql runs psql in the postgres container.

Tests

Some tests depend on a running postgres & minio instance - run make compose to get one.

make test runs the full test suite, make coverage produces a coverage report.

Use uv run pytest -s -v for running tests with verbose output, also accepts test filenames.

See docs/tests-compose.md to run the tests inside the docker compose environment.

Documentation

More documentation is available in docs/, and elsewhere:

• CHANGELOG.md - changes between tagged releases
• LICENSE.md - the Apache-2.0 license
• README.md - this README
• docs/CONTRIBUTING.md - Contributor's guide
• docs/awx.md - running against awx dev env
• docs/cli.md - CLI docs
• docs/environment.md - Environment variables
• docs/old-readme.md - pre-0.5 README, with more examples
• docs/tests-compose.md - running tests inside docker compose
• docs/vcpu.md - docs for the total workers vcpu collector
• metrics_utility/library/ - library documentation
• tools/anonymized_db_perf_data/ - perf test data for anonymization
• tools/collections/ - scripts for pulling list of collections from galaxy & automation hub
• tools/docker/ - docker compose environment & mock awx data
• tools/perf/ - perf test data generator and scripts for build report
• tools/testathon/ - data generator for testing

Please follow our Contributor's Guide for details on submitting changes and documentation standards.