Reliably extension for the Chaos Toolkit
Project description
Chaos Toolkit extension for Reliably
Reliably support for the Chaos Toolkit.
Install
To be used from your experiment, this package must be installed in the Python environment where chaostoolkit already lives.
$ pip install chaostoolkit-reliably
Authentication
To use this package, you must create have registered with Reliably services through their CLI.
You have two ways to pass on the credentials information.
The first one by specifying the path to the Reliably's configuration file,
which defaults to $HOME/.config/reliably/config.yaml. The simplest way to
achieve this is by running $ reliably login as this will generate the
appropriate file.
{
"configuration": {
"reliably_config_path": "~/.config/reliably/config.yaml"
}
}
Because we use the default path, you may omit this configuration's entry altogether unless you need a specific different path.
The second one is by setting some environment variables as secrets. This is for specific use case and usually not required.
RELIABLY_TOKEN: the token to authenticate against Reliably's APIRELIABLY_ORG: the Reliably organisation to useRELIABLY_HOST:: the hostname to connect to, default toreliably.com
{
"secrets": {
"reliably": {
"token": {
"type": "env",
"key": "RELIABLY_TOKEN"
},
"org": {
"type": "env",
"key": "RELIABLY_ORG"
},
"host": {
"type": "env",
"key": "RELIABLY_HOST",
"default": "reliably.com"
}
}
}
}
Usage
As Steady Steate Hypothesis
You can use Reliably's SLO as a mechanism to determine if your system has deviated during a Chaos Toolkit experiment. Here is a simple example:
"steady-state-hypothesis": {
"title": "We do not consume all of our error budgets during the experiment",
"probes": [
{
"name": "Our 'Must be good' SLO results must be OK",
"type": "probe",
"tolerance": true,
"provider": {
"type": "python",
"module": "chaosreliably.slo.probes",
"func": "slo_is_met",
"arguments": {
"labels": {"name": "must-be-good", "service": "must-be-good-service"},
"limit": 5
}
},
}
]
}
This above example will get the last 5 Objective Results for our Must be good SLO and determine if they were all okay or whether we've spent our error budget
they are allowed.
As controls
You can use controls provided by chaostoolkit-reliably to track your experiments
within Reliably.
A full example of using the controls is below:
"controls": [
{
"name": "chaosreliably",
"provider": {
"type": "python",
"module": "chaosreliably.controls.experiment",
"arguments": {
"experiment_ref": "a81c7b966a673190"
}
}
}
],
The experiment_ref argument is a random string that is used by reliably to
reference this particular experiment. It can be any string but should as
unique as possible to prevent collision. In such case, the experiment would
be overwritten in your organisation.
Once set, you should not change this value or Reliably will not be able to attach runs to that experiment.
As Safeguards
Safeguards, provided by the Chaos Toolkit addons extension gives you a nice way to interrupt an experiment as soon as error budgets have been consumed. This is orthogonal to the steady-state hypothesis as it is a mechanism to protect your system from being harmed too harshly by an experiment.
"controls": [
{
"name": "safeguard",
"provider": {
"type": "python",
"module": "chaosaddons.controls.safeguards",
"arguments": {
"probes": [
{
"name": "we-do-not-have-enough-error-budget-left-to-carry-on",
"type": "probe",
"frequency": 5,
"provider": {
"type": "python",
"module": "chaosreliably.slo.probes",
"func": "slo_is_met",
"arguments": {
"labels": {"name": "must-be-good", "service": "must-be-good-service"},
"limit": 5
}
},
"tolerance": true
}
]
}
}
}
]
As you can notice it is the same construct as for the steady-state, it's merely used with a different purpose. Here these probes will be executed every 5s during the experiment (this frequence is for demo purposes, you would usually only run it once every minute or less).
Contribute
From a code perspective, if you wish to contribute, you will need to run a
Python 3.6+ environment. Please, fork this project, write unit tests to cover
the proposed changes, implement the changes, ensure they meet the formatting
standards set out by black, flake8, isort, and mypy, add an entry into
CHANGELOG.md, and then raise a PR to the repository for review
Please refer to the formatting section for more information on the formatting standards.
The Chaos Toolkit projects require all contributors must sign a Developer Certificate of Origin on each commit they would like to merge into the master branch of the repository. Please, make sure you can abide by the rules of the DCO before submitting a PR.
Develop
If you wish to develop on this project, make sure to install the development dependencies. But first, create a virtual environment and then install those dependencies.
$ make install-dev
Test
To run the tests for the project execute the following:
$ make tests
Formatting and Linting
We use a combination of black, flake8, isort,
and mypy to both lint and format this repositories code.
Before raising a Pull Request, we recommend you run formatting against your code with:
$ make format
This will automatically format any code that doesn't adhere to the formatting standards.
As some things are not picked up by the formatting, we also recommend you run:
$ make lint
To ensure that any unused import statements/strings that are too long, etc.
are also picked up. It will also provide you with any errors mypy picks up.
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