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![]() This patch adds the possibility to write the trace and the summary as csv files to a user specified file. A format as such simplifies further data processing. This is achieved by having ";" as separators instead of spaces and solely one header per file. Additional parameters are being considered, like in the normal usage of the script. Colors are turned off in the case of a csv output, thus the highlight option is also being ignored. Usage: Write standard task to csv file: $ perf script report tasks-analyzer --csv <file> write limited output to csv file in nanoseconds: $ perf script report tasks-analyzer --csv <file> --ns --limit-to-tasks 1337 Write summary to a csv file: $ perf script report tasks-analyzer --csv-summary <file> Write summary to csv file with additional schedule information: $ perf script report tasks-analyzer --csv-summary <file> --summary-extended Write both summary and standard task to a csv file: $ perf script report tasks-analyzer --csv --csv-summary The following examples illustrate what is possible with the CSV output. The first command sequence will record all scheduler switch events for 10 seconds, the task-analyzer calculates task information like runtimes as CSV. A small python snippet using pandas and matplotlib will visualize the most frequent task (e.g. kworker/1:1) runtimes - each runtime as a bar in a bar chart: $ perf record -e sched:sched_switch -a -- sleep 10 $ perf script report tasks-analyzer --ns --csv tasks.csv $ cat << EOF > /tmp/freq-comm-runtimes-bar.py import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv("tasks.csv", sep=';') most_freq_comm = df["COMM"].value_counts().idxmax() most_freq_runtimes = df[df["COMM"]==most_freq_comm]["Runtime"] plt.title(f"Runtimes for Task {most_freq_comm} in Nanoseconds") plt.bar(range(len(most_freq_runtimes)), most_freq_runtimes) plt.show() $ python3 /tmp/freq-comm-runtimes-bar.py As a seconds example, the subsequent script generates a pie chart of all accumulated tasks runtimes for 10 seconds of system recordings: $ perf record -e sched:sched_switch -a -- sleep 10 $ perf script report tasks-analyzer --csv-summary task-summary.csv $ cat << EOF > /tmp/accumulated-task-pie.py import pandas as pd from matplotlib.pyplot import pie, axis, show df = pd.read_csv("task-summary.csv", sep=';') sums = df.groupby(df["Comm"])["Accumulated"].sum() axis("equal") pie(sums, labels=sums.index); show() EOF $ python3 /tmp/accumulated-task-pie.py A variety of other visualizations are possible in matplotlib and other environments. Of course, pandas, numpy and co. also allow easy statistical analysis of the data! Signed-off-by: Petar Gligoric <petar.gligoric@rohde-schwarz.com> Cc: Andi Kleen <ak@linux.intel.com> Cc: Ian Rogers <irogers@google.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Link: https://lore.kernel.org/r/20221206154406.41941-3-petar.gligor@gmail.com Signed-off-by: Hagen Paul Pfeifer <hagen@jauu.net> Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com> |
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