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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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|---|---|---|
| .. | ||
| bin | ||
| Perf-Trace-Util | ||
| arm-cs-trace-disasm.py | ||
| check-perf-trace.py | ||
| compaction-times.py | ||
| event_analyzing_sample.py | ||
| export-to-postgresql.py | ||
| export-to-sqlite.py | ||
| exported-sql-viewer.py | ||
| failed-syscalls-by-pid.py | ||
| flamegraph.py | ||
| futex-contention.py | ||
| intel-pt-events.py | ||
| libxed.py | ||
| mem-phys-addr.py | ||
| net_dropmonitor.py | ||
| netdev-times.py | ||
| powerpc-hcalls.py | ||
| sched-migration.py | ||
| sctop.py | ||
| stackcollapse.py | ||
| stat-cpi.py | ||
| syscall-counts-by-pid.py | ||
| syscall-counts.py | ||
| task-analyzer.py | ||