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Fabric8-Analytics Core API Documentation

The Fabric8-Analytics API is a microservice that is responsible for:

  • Serving generated analysis results to clients through API
  • Scheduling new analyses based on client requests

Contributing

See our contributing guidelines for more info.

API definition

The API details are provided as a RAML file.

A JSON schema definition is provided for the component analysis endpoint.

Core API Access Logs

Core API access logs have following format:

{remote hostname} {remote logname, default '-'} {username, default '-'} {access time} "{first line of request}" {response status} {response length} {referer, default '-'} "{user agent}"

In terms of httpd LogFormat option, this is %h %l %u %t \"%r\" %>s %b %{Referer}i \"%{User-agent}i\". See httpd documentation for details

For example:

172.19.0.1 - - [18/Mar/2016:07:59:17 +0000] "GET /static/patternfly/css/patternfly.min.css HTTP/1.1" 304 - http://localhost:32000/ "Mozilla/5.0 (X11; Fedora; Linux x86_64; rv:45.0) Gecko/20100101 Firefox/45.0"

Method of accessing logs can vary based on the way the API is deployed. See below for instructions on accessing logs.

Docker based API testing

From the top-level git directory, 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 core API 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.

For server testing, the virtualenv should be created using Python 3.4 or later

To configure a virtualenv (called bayesian in the example) to run these tests:

(bayesian) $ python -m pip install -e ../lib
(bayesian) $ python -m pip install -r requirements.txt
(bayesian) $ python -m pip install -r tests/requirements.txt

The marked offline tests can then be run as:

(bayesian) $ 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:

(bayesian) $ sudo find -name *.pyc -delete

Accessing operational data for stack analyses

It's possible to access operational data related to a particular stack analysis request:

/api/v1/stack-analyses/<request-id>/_debug

Note this endpoint is not part of the public API.