iter8-analytics
iter8 enables statistically robust continuous experimentation of microservices in your CI/CD pipelines.
For in-depth information about how to use iter8, visit iter8.tools.
In this README:
Introduction
Use an iter8 experiment to safely expose competing versions of a service to application traffic, gather in-depth insights about key metrics for your microservice versions, and intelligently rollout the best version of your service.
Iter8’s expressive model of cloud experimentation supports a variety of CI/CD scenarios. Using an iter8 experiment, you can:
- Run a conformance test with a single version of a microservice.
- Perform a canary release with two versions, a baseline and a candidate. Iter8 will shift application traffic safely and gradually to the candidate, if it meets the criteria you specify in the experiment.
- Perform an A/B test with two versions – a baseline and a candidate. Iter8 will identify and shift application traffic safely and gradually to the winner, where the winning version is defined by the criteria you specify in the experiment.
- Perform an A/B/n test with multiple versions – a baseline and multiple candidates. Iter8 will identify and shift application traffic safely and gradually to the winner.
Under the hood, iter8 uses advanced Bayesian learning techniques coupled with multi-armed bandit approaches to compute a variety of statistical assessments for your microservice versions, and uses them to make robust traffic control and rollout decisions.
Repositories
The components of iter8 are divided across a few github repositories.
- iter8 The main iter8 repository containing the kubernetes controller that orchestrates iter8's experiments.
- iter8-analytics This repository containing the iter8-analytics component.
- iter8-trend The repository contains the iter8-trend component.
In addition,
- iter8's extensions to Kiali is contained in kiali, kiali-ui, and k-charted.
- iter8's extensions to Kui is contained in kui.
Developers
This section is for iter8 developers and contains documentation on running and testing iter8-analytics locally.
Running iter8-analytics v1.0.0 locally
The following instructions have been tested in a Python 3.7.4 virtual environment.
1. git clone [email protected]:iter8-tools/iter8-analytics.git
2. cd iter8-analytics
3. pip install -r requirements.txt
4. pip install -e .
5. export ITER8_ANALYTICS_METRICS_BACKEND_URL=<URL of your prometheus service>
6. cd iter8_analytics
7. python fastapi_app.py
Navigate to http://localhost:5555/docs on your browser. You can interact with the iter8-analytics service and read its API documentation here. When you POST a request to iter8-analytics, it interacts with Prometheus -- make sure your Prometheus URL in step 5 is accessible if you want the POST to work.
Running unit tests for iter8-analytics v1.0.0 locally
The following instructions have been tested in a Python 3.7.4 virtual environment.
1. git clone [email protected]:iter8-tools/iter8-analytics.git
2. cd iter8-analytics
3. pip install -r requirements.txt
4. pip install -r test-requirements.txt
5. pip install -e .
6. export ITER8_ANALYTICS_METRICS_BACKEND_URL=<URL of your prometheus service>
7. make test
You can see the coverage report by opening htmlcov/index.html on your browser. The prometheus URL in step 6 is a dummy URL since all Prometheus calls are mocked in unit tests.
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