README.md

Fabric8-Analytics Core library and services

Note on naming: The Fabric8-Analytics project has evolved from 2 different projects called "cucos" and "bayesian". We're currently in process of renaming the modules and updating documentation. Until that is completed, please consider "cucos" and "bayesian" to be synonyms of "Fabric8-Analytics".

This library provides basic infrastructure for development of services and concrete implemementation of services.

The following libraries are provided:

• Database abstraction
• Task Queue Worker/Node abstraction
• Utilities
  • File tree walker with filtering
  • One-to-many dictionary
  • Shell command wrapper with timeout support

See ./cucoslib/workers/README.md for a listing of the concrete services.

Contributing

See our contributing guidelines for more info.

Running worker environment with docker-compose

There are two sets of workers - API and ingestion. API workers serve requests that are passed from API endpoint. Ingestion workers are used for background data ingestion. To run them use:

$ docker-compose up worker-api worker-ingestion

Running the tests locally

Docker based API testing

Run the tests in a container using the helper script:

$ ./runtests.sh

(The above command assumes you have passwordless docker invocation configured - if you don't, then sudo will be necessary to enable docker invocation).

If you're changing dependencies rather than just editing source code locally, you will need images to be rebuilt when invoking runtest.sh. You can set environment variable REBUILD=1 to request image rebuilding.

If the offline virtualenv based tests have been run, then this may complain about mismatched locations in compiled files. Those can be deleted using:

$ find -name *.pyc -delete

NOTE: Running the container based tests is likely to cause any already running local Fabric8-Analytics instance launched via Docker Compose to fall over due to changes in the SELinux labels on mounted volumes, and may also cause spurious test failures.

Virtualenv based offline testing

Test cases marked with pytest.mark.offline may be executed without having a Docker daemon running locally.

To configure a virtualenv (called cucos-worker in the example) to run these tests:

(cucos-worker) $ python -m pip install -r requirements.txt
(cucos-worker) $ python -m pip install -r tests/requirements.txt

The marked offline tests can then be run as:

(cucos-worker) $ py.test -m offline tests/

If the Docker container based tests have been run, then this may complain about mismatched locations in compiled files. Those can be deleted using:

(cucos-worker) $ sudo find -name *.pyc -delete

Testing against not-yet-released worker dependencies

The Dockerfile.tests file is set up to install any cucos-license-check-unreleased.rpm file stored locally in this directory. In combination with the license-check-worker/make_rpm.sh script in the data-mining-tools repo, this feature can be used to run the worker tests against a version of cucos-license-check that is not yet released.