# Testing Placement

Most of the handler code in the placement API is tested using gabbi. Some utility code is tested with unit tests found in `placement/tests/unit`. The back-end objects are tested with a combination of unit and functional tests found in `placement/tests/unit/objects` and `placement/tests/functional/db`.

When writing tests for handler code (that is, the code found in `placement/handlers`) a good rule of thumb is that if you feel like there needs to be a unit test for some of the code in the handler, that is a good sign that the piece of code should be extracted to a separate method. That method should be independent of the handler method itself (the one decorated by the `wsgify` method) and testable as a unit, without mocks if possible. If the extracted method is useful for multiple resources consider putting it in the `util` package.

As a general guide, handler code should be relatively short and where there are conditionals and branching, they should be reachable via the gabbi functional tests. This is merely a design goal, not a strict constraint.

## Using Gabbi

Gabbi was developed in the telemetry project to provide a declarative way to test HTTP APIs that preserves visibility of both the request and response of the HTTP interaction. Tests are written in YAML files where each file is an ordered suite of tests. Fixtures (such as a database) are set up and torn down at the beginning and end of each file, not each test. JSON response bodies can be evaluated with JSONPath. The placement WSGI application is run via wsgi-intercept, meaning that real HTTP requests are being made over a file handle that appears to Python to be a socket.

In the placement API the YAML files (aka “gabbits”) can be found in `placement/tests/functional/gabbits`. Fixture definitions are in `placement/tests/functional/fixtures/gabbits.py`. Tests are frequently grouped by handler name (e.g., `resource-provider.yaml` and `inventory.yaml`). This is not a requirement and as we increase the number of tests it makes sense to have more YAML files with fewer tests, divided up by the arc of API interaction that they test.

The gabbi tests are integrated into the functional tox target, loaded via `placement/tests/functional/test_api.py`. If you want to run just the gabbi tests one way to do so is:

    tox -efunctional test_api

If you want to run just one yaml file (in this example `inventory.yaml`):

    tox -efunctional api.inventory

It is also possible to run just one test from within one file. When you do this every test prior to the one you asked for will also be run. This is because the YAML represents a sequence of dependent requests. Select the test by using the name in the yaml file, replacing space with `_`:

    tox -efunctional api.inventory_post_new_ipv4_address_inventory

Note

`tox.ini` in the placement repository is configured by a `group_regex` so that each gabbi YAML is considered a group. Thus, all tests in the file will be run in the same process when running stestr concurrently (the default).

## Writing More Gabbi Tests

The docs for gabbi try to be complete and explain the syntax in some depth. Where something is missing or confusing, please log a bug.

While it is possible to test all aspects of a response (all the response headers, the status code, every attribute in a JSON structure) in one single test, doing so will likely make the test harder to read and will certainly make debugging more challenging. If there are multiple things that need to be asserted, making multiple requests is reasonable. Since database set up is only happening once per file (instead of once per test) and since there is no TCP overhead, the tests run quickly.

While fixtures can be used to establish entities that are required for tests, creating those entities via the HTTP API results in tests which are more descriptive. For example the `inventory.yaml` file creates the resource provider to which it will then add inventory. This makes it easy to explore a sequence of interactions and a variety of responses with the tests:

- create a resource provider

- confirm it has empty inventory

- add inventory to the resource provider (in a few different ways)

- confirm the resource provider now has inventory

- modify the inventory

- delete the inventory

- confirm the resource provider now has empty inventory

Nothing special is required to add a new set of tests: create a YAML file with a unique name in the same directory as the others. The other files can provide examples. Gabbi can provide a useful way of doing test driven development of a new handler: create a YAML file that describes the desired URLs and behavior and write the code to make it pass.

It’s also possible to use gabbi against a running placement service, for example in devstack. See gabbi-run to get started. If you don’t want to go to the trouble of using devstack, but do want a live server see Quick Placement Development.

## Profiling

If you wish to profile requests to the placement service, to get an idea of which methods are consuming the most CPU or are being used repeatedly, it is possible to enable a ProfilerMiddleware to output per-request python profiling dumps. The environment (Quick Placement Development is a good place to start) in which the service is running will need to have Werkzeug added.

- If the service is already running, stop it.

- Install Werkzeug.

- Set an environment variable, `OS_WSGI_PROFILER`, to a directory where profile results will be written.

- Make sure the directory exists.

- Start the service, ensuring the environment variable is passed to it.

- Make an HTTP request that exercises the code you wish to profile.

The profiling results will be in the directory named by `OS_WSGI_PROFILER`. There are many ways to analyze the files. See Profiling WSGI Apps for an example.

## Profiling with OSProfiler

To use OSProfiler with placement:

- Add a [profiler] section to the placement.conf:

      [profiler]
      connection_string = mysql+pymysql://root:admin@127.0.0.1/osprofiler?charset=utf8
      hmac_keys = my-secret-key
      enabled = True

- Include the hmac_keys in your API request:

      $ openstack resource provider list --os-profile my-secret-key

  The openstack client will return the trace id:

      Trace ID: 67428cdd-bfaa-496f-b430-507165729246

- Extract the trace in html format:

      $ osprofiler trace show --html 67428cdd-bfaa-496f-b430-507165729246 \
        --connection-string mysql+pymysql://root:admin@127.0.0.1/osprofiler?charset=utf8

this page last updated: 2026-04-01 21:43:44

Except where otherwise noted, this document is licensed under Creative Commons Attribution 3.0 License. See all OpenStack Legal Documents.

found an error? report a bug