Add scripts to plot performance

performance_analysis/plot_from_tapper_logs.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+import pathlib
+import re
+import sys
+import typing
+
+
+# Extract cpu and rss samples from log files and plot them
+# Input: List of log files
+
+def append_sample(name: str, line: str, samples: typing.List[float]):
+    pattern = name + r': (\d+\.\d+)'
+    maybe_sample = re.findall(pattern, line)
+    if len(maybe_sample) == 0:
+        return
+
+    sample = float(maybe_sample[0])
+    samples.append(sample)
+
+
+def extract_samples(f: typing.IO) -> typing.Tuple[pd.Series, pd.Series]:
+    cpu_samples = []
+    rss_samples = []
+    for line in f:
+        append_sample('cpu', line, cpu_samples)
+        append_sample('rss', line, rss_samples)
+
+    cpu_samples = pd.Series(cpu_samples)
+    rss_samples = pd.Series(rss_samples)
+
+    return cpu_samples, rss_samples
+
+if __name__ == '__main__':
+    filenames = sys.argv[1:]
+
+    fig = plt.figure(1)
+
+    for ii, filename in enumerate(filenames):
+        with open(filename, 'r') as f:
+            cpu_samples, rss_samples = extract_samples(f)
+
+        cpu_samples.name = pathlib.Path(filename).name
+        rss_samples.name = pathlib.Path(filename).name
+
+        plt.subplot(2, 1, 1)
+        cpu_samples.plot()
+
+        plt.subplot(2, 1, 2)
+        (rss_samples / 1024 / 1024).plot()
+        plt.title('rss')
+
+    plt.subplot(2, 1, 1)
+    plt.legend()
+    plt.title('cpu')
+    plt.xlabel('# sample')
+    plt.ylabel('cpu (%)')
+
+    plt.subplot(2, 1, 2)
+    plt.title('rss')
+    plt.legend()
+    plt.xlabel('# sample')
+    plt.ylabel('mem (MB)')
+
+    plt.show()

performance_analysis/requirements.txt

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+matplotlib
+pandas