blob: da3912908265fa3e1960f109f2af3f8567ec9a4c (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
|
##############################################################################
# Copyright (c) 2017 ZTE Corp and others.
#
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the Apache License, Version 2.0
# which accompanies this distribution, and is available at
# http://www.apache.org/licenses/LICENSE-2.0
##############################################################################
import json
from os import path
import re
import yaml
RESOURCE_ROOT = path.join(path.dirname(__file__), '..', 'resources')
def normalize(score, base=2048):
""" Use 2048 as base score if the performance equals baseline """
return int(base * score)
def storperf(report_file, qpi_spec=None, baseline_file=None):
if qpi_spec is None:
qpi_spec = path.join(RESOURCE_ROOT, 'QPI', 'storage.yaml')
with open(qpi_spec) as f:
# load QPI spec as base template for report
qpi_report = yaml.safe_load(f.read())
if baseline_file is None:
baseline_file = path.join(RESOURCE_ROOT, 'baselines', 'storage.json')
with open(baseline_file) as f:
baseline_report = json.load(f)
baseline_metrics = baseline_report['report']['metrics']
with open(report_file) as f:
storperf_report = json.load(f)
reported_metrics = storperf_report['report']['metrics']
sections = qpi_report['sections']
for section in sections:
section_regex = re.compile(section['regex'])
ignored_regex = re.compile('^_') # ignore metrics starting with '_"
valid_metrics = [k for k in reported_metrics
if section_regex.search(k) and not ignored_regex.search(k) and k in baseline_metrics and
reported_metrics[k] != 0 and baseline_metrics[k] != 0]
if len(valid_metrics) == 0:
raise Exception('No valid metrics found')
section['score'] = sum([reported_metrics[k] / baseline_metrics[k]
if not section.get('use_reciprocal', False)
else baseline_metrics[k] / reported_metrics[k]
for k in valid_metrics]) / len(valid_metrics)
qpi_report['score'] = normalize(sum([section['score'] for section in sections]) / len(sections))
return qpi_report
|
