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123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
#!/usr/bin/env python
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# Copyright (c) 2020 Proton Technologies AG
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#
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# This file is part of ProtonMail Bridge.
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#
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# ProtonMail Bridge is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# ProtonMail Bridge is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with ProtonMail Bridge. If not, see <https://www.gnu.org/licenses/>.
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import glob
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import pandas as pd
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import re
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def print_header(report_file):
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print('\n======== %s ========' %
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(report_file.replace("./bench-", "").replace(".log", "")))
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rx_line = {
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'exists': re.compile(r'.*Res[A-Za-z]?: [*] (?P<mails>\d+) EXISTS.*\n'),
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'bench': re.compile(r'Benchmark(?P<name>[^ \t]+)[ \t]+(?P<rpts>\d+)[ \t]+(?P<ns>\d+) ns/op.*\n'),
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# 'total' : re.compile(r'ok[ \t]+(?P<pkg>[^ \t]+)[ \t]+(?P<time>[^ \t\n]+)[ \t]*\n'),
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}
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def parse_line(line):
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for key, rx in rx_line.items():
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match = rx.search(line)
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if match:
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return key, match
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# if there are no matches
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return None, None
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rx_count = re.compile(r'Fetch/1:(?P<count>\d+)-')
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def parse_file(filepath):
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data = [] # create an empty list to collect the data
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# open the file and read through it line by line
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with open(filepath, 'r') as file_object:
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line = file_object.readline()
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last_count = 0
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mails = 1
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while line:
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# at each line check for a match with a regex
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key, match = parse_line(line)
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# print(line, key, match)
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if key != None:
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row = match.groupdict()
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if key == 'exists':
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mails = int(row['mails'])
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last_count = 0
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if key == 'bench':
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match = rx_count.search(row['name'])
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row['mails'] = mails - last_count
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if match:
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count = int(match.group('count'))
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if count < mails:
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row['mails'] = count - last_count
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last_count = count
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row['rpts'] = int(row['rpts'])
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row['ns'] = int(row['ns'])
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row['time/op'] = human_duration(row['ns'])
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if row['mails'] > 0:
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row['time/mails'] = human_duration(
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row['ns']/row['mails']
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)
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data.append(row)
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if key == 'total':
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row['name'] = key
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data.append(row)
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line = file_object.readline()
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return data
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def human_duration(duration_ns):
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unit = 'ns'
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factor = 1.
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unit_factors = [
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('us', 1.e3),
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('ms', 1.e3),
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('s ', 1.e3),
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('m ', 60.),
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('h ', 60.),
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('d ', 24.),
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('w ', 7.),
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('m ', 30./7.),
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('y ', 12.),
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]
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for unit_factor in unit_factors:
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if (abs(duration_ns) / factor / unit_factor[1]) < 1.0:
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break
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unit = unit_factor[0]
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factor *= unit_factor[1]
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return "%4.2f%s" % (duration_ns/factor, unit)
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def print_table(data):
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data = pd.DataFrame(data)
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data.set_index('name', inplace=True)
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print(data)
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if __name__ == "__main__":
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# for d in [ 0.5, 1, 2, 5, 1e3, 5e3, 1e4, 1e5, 1e6, 1e9, 2e9, 1e10, 1e11, 1e12, ]: print(human_duration(int(d)))
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for report_file in glob.glob("./*.log"):
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print_header(report_file)
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print_table(parse_file(report_file))
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