def main(
job_instance_ids, bins, statistics_names, stats_with_suffixes, labels,
titles, use_legend, legend, pickle, cumulative, filenames):
file_ext = 'pickle' if pickle else 'png'
plot = _Plot.plot_cumulative_histogram if cumulative else _Plot.plot_histogram
legends = iter(legend)
statistics = Statistics.from_default_collector()
with tempfile.TemporaryDirectory(prefix='openbach-histogram-') as root:
for fields, label, title, filename in itertools.zip_longest(statistics_names, labels, titles, filenames):
figure, axis = plt.subplots()
data = statistics.fetch_all(
job_instances=job_instance_ids,
suffix = None if stats_with_suffixes else '',
fields=fields, columns=legends)
plot(data, axis, label, bins, use_legend)
if title is not None:
axis.set_title(title)
if filename is not None:
filename = '{}.{}'.format(filename, file_ext)
elif fields is None:
filename = 'histogram.{}'.format(file_ext)
else:
filename = 'histogram_{}.{}'.format('_'.join(fields), file_ext)
filepath = os.path.join(root, filename)
save(figure, filepath, pickle)
collect_agent.store_files(now(), figure=filepath)
def main(src_ip, dst_ip, src_port, dst_port, proto, interface, capture_file,
duration):
"""Capture packets on a live network interface. Only consider packets matching the specified fields."""
capture_filter = build_capture_filter(src_ip, dst_ip, src_port, dst_port,
proto)
copy = False
if capture_file == "":
capture_file = "/tmp/tcpdump_capture.pcap"
copy = True
signal_handler_partial = partial(save_pcap, capture_file, copy,
os.getpid())
original_sigint_handler = signal.getsignal(signal.SIGINT)
original_sigterm_handler = signal.getsignal(signal.SIGTERM)
signal.signal(signal.SIGTERM, signal_handler_partial)
signal.signal(signal.SIGINT, signal_handler_partial)
try:
parent = pathlib.Path(capture_file).parent
pathlib.Path(parent).mkdir(parents=True, exist_ok=True)
subprocess.run(["rm", capture_file])
cmd = [
'tcpdump', '-i', interface, capture_filter, '-w', capture_file,
'-Z', 'root'
]
if duration:
cmd += ['-G', str(duration), '-W', str(1)]
p = subprocess.run(cmd)
if p.returncode != 0:
message = 'ERROR when lauching tcpdump: {}'.format(p.stderr)
collect_agent.send_log(syslog.LOG_ERR, message)
sys.exit(message)
except Exception as ex:
message = 'ERROR when capturing: {}'.format(ex)
collect_agent.send_log(syslog.LOG_ERR, message)
sys.exit(message)
collect_agent.store_files(int(time.time() * 1000),
pcap_file=capture_file,
copy=copy)
signal.signal(signal.SIGTERM, original_sigint_handler)
signal.signal(signal.SIGINT, original_sigterm_handler)
def main(job_instance_ids, statistics_names, stats_with_suffixes, ylabels, titles, xlabel_job_instance_names, pickle):
file_ext = 'pickle' if pickle else 'png'
xlabel_instance_names = iter(xlabel_job_instance_names)
statistics = Statistics.from_default_collector()
with tempfile.TemporaryDirectory(prefix='openbach-comparison-') as root:
for fields, ylabel, title in itertools.zip_longest(statistics_names, ylabels, titles):
figure, axis = plt.subplots()
data = statistics.fetch_all(
job_instances=job_instance_ids,
suffix = None if stats_with_suffixes else '',
fields=fields, columns=xlabel_instance_names)
data.plot_comparison(axis, ylabel, False)
if title is not None:
axis.set_title(title)
if statistics_names is None:
filename = 'comparison.{}'.format(file_ext)
else:
filename = 'comparison_{}.{}'.format('_'.join(fields), file_ext)
filepath = os.path.join(root, filename)
save(figure, filepath, pickle)
collect_agent.store_files(now(), figure=filepath)
def save_pcap(capture_file, copy, parent_pid, signum, frame):
collect_agent.store_files(int(time.time() * 1000),
pcap_file=capture_file,
copy=copy)
def main(job_instance_ids, statistics_names, aggregations_periods, bins_sizes,
offset, maximum, stats_with_suffixes, axis_labels, figures_titles,
legends_titles, use_legend, add_global, pickle):
file_ext = 'pickle' if pickle else 'png'
statistics = Statistics.from_default_collector()
statistics.origin = 0
with tempfile.TemporaryDirectory(
prefix='openbach-temporal-binning-histogram-') as root:
for job, fields, aggregations, bin_sizes, labels, titles, legend_titles in itertools.zip_longest(
job_instance_ids,
statistics_names,
aggregations_periods,
bins_sizes,
axis_labels,
figures_titles,
legends_titles,
fillvalue=[]):
data_collection = statistics.fetch(
job_instances=job,
suffix=None if stats_with_suffixes else '',
fields=fields)
# Drop multi-index columns to easily concatenate dataframes from their statistic names
df = pd.concat([
plot.dataframe.set_axis(
plot.dataframe.columns.get_level_values('statistic'),
axis=1,
inplace=False) for plot in data_collection
])
# Recreate a multi-indexed columns so the plot can function properly
df.columns = pd.MultiIndex.from_tuples(
[('', '', '', '', stat) for stat in df.columns],
names=['job', 'scenario', 'agent', 'suffix', 'statistic'])
plot = _Plot(df)
if not fields:
fields = list(df.columns.get_level_values('statistic'))
metadata = itertools.zip_longest(fields, labels, bin_sizes,
aggregations, legend_titles,
titles)
for field, label, bin_size, aggregation, legend, title in metadata:
if field not in df.columns.get_level_values('statistic'):
message = 'job instances {} did not produce the statistic {}'.format(
job, field)
collect_agent.send_log(syslog.LOG_WARNING, message)
print(message)
continue
if label is None:
collect_agent.send_log(
syslog.LOG_WARNING,
'no y-axis label provided for the {} statistic of job '
'instances {}: using the empty string instead'.format(
field, job))
label = ''
if aggregation is None:
collect_agent.send_log(
syslog.LOG_WARNING,
'invalid aggregation value of {} for the {} '
'statistic of job instances {}: choose from {}, using '
'"hour" instead'.format(aggregation, field, job,
TIME_OPTIONS))
aggregation = 'hour'
if legend is None and use_legend:
collect_agent.send_log(
syslog.LOG_WARNING,
'no legend title provided for the {} statistic of job '
'instances {}: using the empty string instead'.format(
field, job))
legend = ''
if bin_size is None:
collect_agent.send_log(
syslog.LOG_WARNING,
'no bin size provided for the {} statistic of job '
'instances {}: using the default value 100 instead'.
format(field, job))
bin_size = 100
figure, axis = plt.subplots()
axis = plot.plot_temporal_binning_histogram(
axis, label, field, None, bin_size, offset, maximum,
aggregation, add_global, use_legend, legend)
if title is not None:
axis.set_title(title)
filepath = os.path.join(
root,
'temporal_binning_histogram_{}.{}'.format(field, file_ext))
save(figure, filepath, pickle, False)
collect_agent.store_files(now(), figure=filepath)