mirror of
https://github.com/kubeshark/kubeshark.git
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99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import pathlib
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import re
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import sys
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import typing
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COLORMAP = plt.get_cmap('turbo')
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# Extract cpu and rss samples from log files and plot them
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# Input: List of log files
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def append_sample(name: str, line: str, samples: typing.List[float]):
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pattern = name + r': ?(\d+(\.\d+)?)'
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maybe_sample = re.findall(pattern, line)
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if len(maybe_sample) == 0:
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return
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sample = float(maybe_sample[0][0])
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samples.append(sample)
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def extract_samples(f: typing.IO) -> typing.Tuple[pd.Series, pd.Series, pd.Series]:
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cpu_samples = []
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rss_samples = []
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count_samples = []
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for line in f:
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append_sample('cpu', line, cpu_samples)
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append_sample('rss', line, rss_samples)
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append_sample('"packetsCount"', line, count_samples)
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cpu_samples = pd.Series(cpu_samples)
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rss_samples = pd.Series(rss_samples)
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count_samples = pd.Series(count_samples)
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return cpu_samples, rss_samples, count_samples
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def plot(df: pd.DataFrame, title: str, xlabel: str, ylabel: str, group_pattern: typing.Optional[str]):
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if group_pattern:
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color = get_group_color(df.columns, group_pattern)
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df.plot(color=color, ax=ax)
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else:
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df.plot(cmap=COLORMAP, ax=ax)
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plt.title(title)
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plt.legend()
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plt.xlabel(xlabel)
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plt.ylabel(ylabel)
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def get_group_color(names, pattern):
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props = [int(re.findall(pattern, pathlib.Path(name).name)[0]) for name in names]
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key = dict(zip(sorted(list(set(props))), range(len(set(props)))))
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n_colors = len(key)
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color_options = plt.get_cmap('jet')(np.linspace(0, 1, n_colors))
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groups = [key[prop] for prop in props]
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color = color_options[groups] # type: ignore
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return color
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if __name__ == '__main__':
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filenames = sys.argv[1:]
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cpu_samples_all_files = []
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rss_samples_all_files = []
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count_samples_all_files = []
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for ii, filename in enumerate(filenames):
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with open(filename, 'r') as f:
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cpu_samples, rss_samples, count_samples = extract_samples(f)
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cpu_samples.name = pathlib.Path(filename).name
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rss_samples.name = pathlib.Path(filename).name
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count_samples.name = pathlib.Path(filename).name
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cpu_samples_all_files.append(cpu_samples)
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rss_samples_all_files.append(rss_samples)
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count_samples_all_files.append(count_samples)
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cpu_samples_df = pd.concat(cpu_samples_all_files, axis=1)
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rss_samples_df = pd.concat(rss_samples_all_files, axis=1)
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count_samples_df = pd.concat(count_samples_all_files, axis=1)
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group_pattern = r'^\d+'
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ax = plt.subplot(3, 1, 1)
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plot(cpu_samples_df, 'cpu', '# sample', 'cpu (%)', group_pattern)
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ax = plt.subplot(3, 1, 2)
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plot((rss_samples_df / 1024 / 1024), 'rss', '# sample', 'mem (MB)', group_pattern)
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ax = plt.subplot(3, 1, 3)
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plot(count_samples_df, 'packetsCount', '# sample', 'packetsCount', group_pattern)
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plt.show()
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