Moved model-based dataset generator tools to common
* Adds datasets.py in cloudcafe/common * Adds methods for helping create dataset generators from model lists. * Adds compute dataset generators for images, flavors, and images_by_flavor Change-Id: I6480f0c61b04183514e49081fe96abbfbc57f82c
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Jose Idar
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106
cloudcafe/common/datasets.py
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106
cloudcafe/common/datasets.py
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"""
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Copyright 2014 Rackspace
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from random import shuffle
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from cafe.drivers.unittest.datasets import DatasetList
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class DatasetGeneratorError(Exception):
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pass
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class ModelBasedDatasetToolkit(object):
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"""Collection of dataset generators and helper methods for
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developing data driven tests
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"""
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INCLUSION_MODE = 'inclusion'
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EXCLUSION_MODE = 'exclusion'
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@classmethod
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def _get_model_list(
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cls, get_model_list_method, model_type_name,
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*get_method_args, **get_method_kwargs):
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"""Gets list of all models in the environment."""
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resp = get_model_list_method(*get_method_args, **get_method_kwargs)
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if not resp.ok:
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raise DatasetGeneratorError(
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"Request for list of {0} during data-driven-test setup failed "
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"with an HTTP {1} ERROR".format(
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model_type_name, resp.status_code))
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if resp.entity is None:
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raise DatasetGeneratorError(
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"Unable to deserialize list of {0} during data-driven-test "
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"setup. API responded with an HTTP {1}".format(
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model_type_name, resp.status_code))
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return resp.entity
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@classmethod
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def _filter_model_list(
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cls, model_list, model_filter=None, filter_mode=None):
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"""Filters should be dictionaries with model attributes as keys and
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lists of attributes as key values.
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example: {"id": ["12345", "42"]}
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Include only those models who match at least one criteria in the
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model_filter dictionary.
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filter_mode can be 'inclusion' or 'exclusion'.
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inclusion mode will include models that match any attributes in
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the model_filter in the final model_list.
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exclusion mode will exclude any models that match attributes in
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the model-filer from the final model_list.
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"""
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if not model_filter:
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return model_list
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if filter_mode not in [cls.INCLUSION_MODE, cls.EXCLUSION_MODE]:
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raise Exception(
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"Invalid filter_mode {0}. _filter_model_list must be called "
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"with a mode set to either 'inclusion' or 'exclusion'.".format(
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filter_mode))
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final_list = []
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for model in model_list:
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excluded = False
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for k in model_filter:
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if filter_mode == cls.INCLUSION_MODE:
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if str(getattr(model, k)) in model_filter[k]:
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final_list.append(model)
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break
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elif filter_mode == cls.EXCLUSION_MODE:
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model_value = str(getattr(model, k))
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filter_value = model_filter[k]
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if not excluded and model_value not in filter_value:
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final_list.append(model)
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break
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else:
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excluded = True
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return final_list
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@classmethod
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def _modify_dataset_list(
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cls, dataset_list, max_datasets=None, randomize=False):
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"""Aggregates common modifiers for dataset lists"""
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if randomize:
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shuffle(dataset_list)
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if max_datasets:
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dataset_list = DatasetList(dataset_list[:max_datasets])
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return dataset_list
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126
cloudcafe/compute/datasets.py
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126
cloudcafe/compute/datasets.py
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"""
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Copyright 2014 Rackspace
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from cafe.drivers.unittest.datasets import DatasetList
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from cafe.drivers.unittest.decorators import memoized
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from cloudcafe.common.datasets import ModelBasedDatasetToolkit
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from cloudcafe.compute.composites import \
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_ComputeAuthComposite, ImagesComposite, FlavorsComposite
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class ComputeDatasets(ModelBasedDatasetToolkit):
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"""Collection of dataset generators for compute and compute-integration
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data driven tests
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"""
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_images = ImagesComposite(_ComputeAuthComposite())
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_flavors = FlavorsComposite(_ComputeAuthComposite())
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@classmethod
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@memoized
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def _get_images(cls):
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"""Gets list of all Images in the environment, and caches it for
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future calls"""
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return cls._get_model_list(
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cls._images.client.list_images_with_detail, 'images')
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@classmethod
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@memoized
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def _get_flavors(cls):
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"""Gets list of all Flavors in the environment, and caches it for
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future calls"""
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return cls._get_model_list(
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cls._flavors.client.list_flavors_with_detail, 'flavors')
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@classmethod
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def images(
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cls, max_datasets=None, randomize=False, model_filter=None,
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filter_mode=ModelBasedDatasetToolkit.INCLUSION_MODE):
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"""Returns a DatasetList of all Images.
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Filters should be dictionaries with model attributes as keys and
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lists of attributes as key values
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"""
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image_list = cls._get_images()
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image_list = cls._filter_model_list(
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image_list, model_filter=model_filter, filter_mode=filter_mode)
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dataset_list = DatasetList()
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for img in image_list:
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data = {'image': img}
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dataset_list.append_new_dataset(
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str(img.name).replace(" ", "_"), data)
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# Apply modifiers
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return cls._modify_dataset_list(
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dataset_list, max_datasets=max_datasets, randomize=randomize)
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@classmethod
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def flavors(
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cls, max_datasets=None, randomize=False, model_filter=None,
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filter_mode=ModelBasedDatasetToolkit.INCLUSION_MODE):
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"""Returns a DatasetList of all Flavors
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Filters should be dictionaries with model attributes as keys and
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lists of attributes as key values
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"""
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flavor_list = cls._get_flavors()
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flavor_list = cls._filter_model_list(
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flavor_list, model_filter=model_filter, filter_mode=filter_mode)
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dataset_list = DatasetList()
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for flavor in flavor_list:
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data = {'flavor': flavor}
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dataset_list.append_new_dataset(
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str(flavor.name).replace(" ", "_"), data)
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# Apply modifiers
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return cls._modify_dataset_list(
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dataset_list, max_datasets=max_datasets, randomize=randomize)
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@classmethod
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def images_by_flavor(
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cls, max_datasets=None, randomize=False,
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image_filter=None, flavor_filter=None,
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image_filter_mode=ModelBasedDatasetToolkit.INCLUSION_MODE,
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flavor_filter_mode=ModelBasedDatasetToolkit.INCLUSION_MODE):
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"""Returns a DatasetList of all combinations of Flavors and Images.
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Filters should be dictionaries with model attributes as keys and
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lists of attributes as key values
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"""
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image_list = cls._get_images()
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image_list = cls._filter_model_list(
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image_list, model_filter=image_filter,
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filter_mode=image_filter_mode)
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flavor_list = cls._get_flavors()
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flavor_list = cls._filter_model_list(
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flavor_list, model_filter=flavor_filter,
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filter_mode=flavor_filter_mode)
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dataset_list = DatasetList()
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for image in image_list:
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for flavor in flavor_list:
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data = {'flavor': flavor,
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'image': image}
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testname = \
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"image_{0}_and_flavor_{1}".format(
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str(image.name).replace(" ", "_"),
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str(flavor.name).replace(" ", "_"))
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dataset_list.append_new_dataset(testname, data)
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# Apply modifiers
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return cls._modify_dataset_list(
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dataset_list, max_datasets=max_datasets, randomize=randomize)
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