62008d4e5b
Some drivers/modules might raise NotImplementedError whereas we would expect things to work. Skipping test when such an error is raised would be a bad idea, we would know the test failed. So let's switch to a custom NotImplementedError and let's skip test only if this one is raised. Closes-Bug: #1369556 Change-Id: Ie88a281787218f9aba13b6e662eb4d49d3e685d0
141 lines
4.9 KiB
Python
141 lines
4.9 KiB
Python
# Author: John Tran <jhtran@att.com>
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# Julien Danjou <julien@danjou.info>
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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#
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import operator
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import types
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from sqlalchemy import and_
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from sqlalchemy import asc
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from sqlalchemy import desc
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from sqlalchemy import not_
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from sqlalchemy import or_
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from sqlalchemy.orm import aliased
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import ceilometer
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from ceilometer.storage.sqlalchemy import models
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META_TYPE_MAP = {bool: models.MetaBool,
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str: models.MetaText,
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unicode: models.MetaText,
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types.NoneType: models.MetaText,
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int: models.MetaBigInt,
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long: models.MetaBigInt,
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float: models.MetaFloat}
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class QueryTransformer(object):
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operators = {"=": operator.eq,
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"<": operator.lt,
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">": operator.gt,
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"<=": operator.le,
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"=<": operator.le,
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">=": operator.ge,
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"=>": operator.ge,
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"!=": operator.ne,
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"in": lambda field_name, values: field_name.in_(values)}
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complex_operators = {"or": or_,
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"and": and_,
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"not": not_}
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ordering_functions = {"asc": asc,
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"desc": desc}
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def __init__(self, table, query):
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self.table = table
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self.query = query
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def _handle_complex_op(self, complex_op, nodes):
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op = self.complex_operators[complex_op]
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if op == not_:
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nodes = [nodes]
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element_list = []
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for node in nodes:
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element = self._transform(node)
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element_list.append(element)
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return op(*element_list)
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def _handle_simple_op(self, simple_op, nodes):
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op = self.operators[simple_op]
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field_name = nodes.keys()[0]
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value = nodes.values()[0]
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if field_name.startswith('resource_metadata.'):
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return self._handle_metadata(op, field_name, value)
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else:
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return op(getattr(self.table, field_name), value)
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def _handle_metadata(self, op, field_name, value):
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if op == self.operators["in"]:
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raise ceilometer.NotImplementedError('Metadata query with in '
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'operator is not implemented')
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field_name = field_name[len('resource_metadata.'):]
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meta_table = META_TYPE_MAP[type(value)]
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meta_alias = aliased(meta_table)
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on_clause = and_(self.table.internal_id == meta_alias.id,
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meta_alias.meta_key == field_name)
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# outer join is needed to support metaquery
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# with or operator on non existent metadata field
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# see: test_query_non_existing_metadata_with_result
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# test case.
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self.query = self.query.outerjoin(meta_alias, on_clause)
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return op(meta_alias.value, value)
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def _transform(self, sub_tree):
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operator = sub_tree.keys()[0]
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nodes = sub_tree.values()[0]
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if operator in self.complex_operators:
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return self._handle_complex_op(operator, nodes)
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else:
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return self._handle_simple_op(operator, nodes)
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def apply_filter(self, expression_tree):
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condition = self._transform(expression_tree)
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self.query = self.query.filter(condition)
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def apply_options(self, orderby, limit):
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self._apply_order_by(orderby)
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if limit is not None:
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self.query = self.query.limit(limit)
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def _apply_order_by(self, orderby):
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if orderby is not None:
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for field in orderby:
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ordering_function = self.ordering_functions[field.values()[0]]
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self.query = self.query.order_by(ordering_function(
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getattr(self.table, field.keys()[0])))
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else:
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self.query = self.query.order_by(desc(self.table.timestamp))
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def get_query(self):
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return self.query
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trait_models_dict = {'string': models.Trait.t_string,
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'integer': models.Trait.t_int,
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'datetime': models.Trait.t_datetime,
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'float': models.Trait.t_float}
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def trait_op_condition(conditions, trait_type, value, op='eq'):
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trait_model = trait_models_dict[trait_type]
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op_dict = {'eq': (trait_model == value), 'lt': (trait_model < value),
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'le': (trait_model <= value), 'gt': (trait_model > value),
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'ge': (trait_model >= value), 'ne': (trait_model != value)}
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conditions.append(op_dict[op])
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return conditions
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