5812bd8429
As of this commit, to change the configuration using Banana, we need to make an HTTP POST request to `/banana` REST API. This API is temporary and is likely to be changed later. The implementation is done entirely in the `banana` module. Under this module there are: * `typeck` module contains the type checker, * `grammar` module contains the parser and the AST and, * `eval` module contains the interpreter. Additionally, a test framework has been created to ease the test of particular conditions of the language. Within the banana module, there is a README.md file for each associated sub-module explaining further the details of the language. Once this commit is merged, there's still a lot that can be improved: - All components should be tested in Banana. - The 'deadpathck' pass could be improved (see TODO) - We don't support generated JSON ingestors yet. - Imports will be key for reusability (not implemented). Change-Id: I1305bdfa0606f30619b31404afbe0acf111c029f
56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
#!/usr/bin/env python
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# Copyright (c) 2016 Hewlett Packard Enterprise Development Company, L.P.
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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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from monasca_analytics.ldp.monasca_combine import MonascaCombineLDP
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from monasca_analytics.parsing.api import create_fn_with_config
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from test.util_for_testing import gen_metric
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from test.util_for_testing import MonanasTestCase
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class TestMonascaAggregateLDP(MonanasTestCase):
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def setUp(self):
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super(TestMonascaAggregateLDP, self).setUp()
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self.all_metrics = [
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gen_metric("nb_cores", 1.2, 0, "h1"),
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gen_metric("nb_cores", 2.0, 1, "h1"),
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gen_metric("idl_perc", 0.2, 0, "h1"),
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gen_metric("idl_perc", 0.8, 1, "h1"),
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]
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def tearDown(self):
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super(TestMonascaAggregateLDP, self).tearDown()
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def test_combine_for_two_metric_product(self):
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fn = create_fn_with_config({"a": "nb_cores", "b": "idl_perc"}, "a * b")
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res = MonascaCombineLDP.combine(self.all_metrics, fn, "cpu_usage", 2)
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res = map(lambda m: m["metric"]["value"], res)
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self.assertEqual(res, [0.24, 1.6])
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def test_combine_for_two_metric_sum(self):
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fn = create_fn_with_config({"a": "nb_cores", "b": "idl_perc"},
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"b - a")
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res = MonascaCombineLDP.combine(self.all_metrics, fn, "cpu_usage", 2)
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res = map(lambda m: m["metric"]["value"], res)
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self.assertEqual(res, [-1.0, -1.2])
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def test_combine_for_two_metric_some_expr(self):
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fn = create_fn_with_config({"a": "nb_cores", "b": "idl_perc"},
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"a * b - a + b")
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res = MonascaCombineLDP.combine(self.all_metrics, fn, "cpu_usage", 2)
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res = map(lambda m: m["metric"]["value"], res)
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self.assertEqual(res, [-0.76, 0.40000000000000013])
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