def generate_statistic_graph(name_prefix, thread_count, x_axis_labels, min_times, max_times,
avg_times):
"""Generate statistic graph with min, average, and max times."""
title = "core API endpoint: min, max, and avg times for {t} concurrent threads".format(
t=thread_count)
name = "{p}_concurrent_{t}_threads_min_max_avg_times".format(p=name_prefix, t=thread_count)
graph.generate_timing_statistic_graph(title, name, x_axis_labels,
min_times, max_times, avg_times, 640, 480)
def generate_statistic_graph(name_prefix, thread_count, x_axis_labels, min_times, max_times,
avg_times):
"""Generate statistic graph with min, average, and max times."""
title = "core API endpoint: min, max, and avg times for {t} concurrent threads".format(
t=thread_count)
name = "{p}_concurrent_{t}_threads_min_max_avg_times".format(p=name_prefix, t=thread_count)
graph.generate_timing_statistic_graph(title, name, x_axis_labels,
min_times, max_times, avg_times, 640, 480)
def run_sequenced_benchmark(
api,
s3,
title_prefix,
name_prefix,
function,
pauses=None,
measurement_count=SEQUENCED_BENCHMARKS_DEFAULT_COUNT,
compute_stack_analysis_jobs_durations=False):
"""Start benchmarks by calling selected function sequentially."""
pauses = pauses or [10]
print("pauses: {p}".format(p=pauses))
print("measurement_count: {c}".format(c=measurement_count))
stack_analysis_jobs_durations = None
# for the stack analysis we are able to compute statistic for each job
if compute_stack_analysis_jobs_durations:
stack_analysis_jobs_durations = {}
stack_analysis_jobs_durations_min_times = {}
stack_analysis_jobs_durations_max_times = {}
stack_analysis_jobs_durations_avg_times = {}
for job_name in STACK_ANALYSIS_JOB_NAMES:
stack_analysis_jobs_durations[job_name] = []
stack_analysis_jobs_durations_min_times[job_name] = []
stack_analysis_jobs_durations_max_times[job_name] = []
stack_analysis_jobs_durations_avg_times[job_name] = []
measurements = []
min_times = []
max_times = []
avg_times = []
for pause in pauses:
if len(pauses) > 1:
title = "{t}, {s} seconds between calls".format(t=title_prefix,
s=pause)
name = "{n}_{s}_pause_time".format(n=name_prefix, s=pause)
else:
title = "{t}".format(t=title_prefix)
name = "{n}".format(n=name_prefix)
print(" " + title)
values, debug = function(api, s3, measurement_count, pause)
deltas = [value["delta"] for value in values]
graph.generate_wait_times_graph(title, name, deltas)
print("Breathe (statistic graph)...")
time.sleep(BREATHE_PAUSE)
min_times.append(min(deltas))
max_times.append(max(deltas))
avg_times.append(sum(deltas) / len(deltas))
measurements.extend(deltas)
if compute_stack_analysis_jobs_durations:
for job_name in STACK_ANALYSIS_JOB_NAMES:
durations = job_durations(job_name, debug)
# all durations for specific jobs need to be stored here
stack_analysis_jobs_durations[job_name].extend(durations)
# compute statistic
cnt = len(durations)
stack_analysis_jobs_durations_min_times[job_name].append(
min(durations))
stack_analysis_jobs_durations_max_times[job_name].append(
max(durations))
stack_analysis_jobs_durations_avg_times[job_name].append(
sum(durations) / cnt)
print(min_times)
print(max_times)
print(avg_times)
if compute_stack_analysis_jobs_durations:
print_job_durations(stack_analysis_jobs_durations,
stack_analysis_jobs_durations_min_times,
stack_analysis_jobs_durations_max_times,
stack_analysis_jobs_durations_avg_times)
title = "{t}: min. max. and avg times".format(t=title_prefix)
min_max_avg_name = "{n}_min_max_avg_times".format(n=name_prefix)
graph.generate_timing_statistic_graph(title, min_max_avg_name, pauses,
min_times, max_times, avg_times)
export_sequenced_benchmark_into_csv(name, measurements,
compute_stack_analysis_jobs_durations,
stack_analysis_jobs_durations)
def run_sequenced_benchmark(api,
s3,
title_prefix,
name_prefix,
function,
pauses=None,
measurement_count=10,
compute_stack_analysis_jobs_durations=False):
"""Start benchmarks by calling selected function sequentially."""
pauses = pauses or [10]
print("pauses:")
print(pauses)
# for the stack analysis we are able to compute statistic for each job
if compute_stack_analysis_jobs_durations:
stack_analysis_jobs_durations_min_times = {}
stack_analysis_jobs_durations_max_times = {}
stack_analysis_jobs_durations_avg_times = {}
for job_name in STACK_ANALYSIS_JOB_NAMES:
stack_analysis_jobs_durations_min_times[job_name] = []
stack_analysis_jobs_durations_max_times[job_name] = []
stack_analysis_jobs_durations_avg_times[job_name] = []
measurements = []
min_times = []
max_times = []
avg_times = []
for pause in pauses:
if len(pauses) > 1:
title = "{t}, {s} seconds between calls".format(t=title_prefix,
s=pause)
name = "{n}_{s}_pause_time".format(n=name_prefix, s=pause)
else:
title = "{t}".format(t=title_prefix)
name = "{n}".format(n=name_prefix)
print(" " + title)
values, debug = function(api, s3, measurement_count, pause)
graph.generate_wait_times_graph(title, name, values)
print("Breathe (statistic graph)...")
time.sleep(20)
min_times.append(min(values))
max_times.append(max(values))
avg_times.append(sum(values) / len(values))
measurements.extend(values)
if compute_stack_analysis_jobs_durations:
for job_name in STACK_ANALYSIS_JOB_NAMES:
durations = job_durations(job_name, debug)
cnt = len(durations)
stack_analysis_jobs_durations_min_times[job_name].append(
min(durations))
stack_analysis_jobs_durations_max_times[job_name].append(
max(durations))
stack_analysis_jobs_durations_avg_times[job_name].append(
sum(durations) / cnt)
print(min_times)
print(max_times)
print(avg_times)
if compute_stack_analysis_jobs_durations:
print("stack analysis jobs")
for job_name in STACK_ANALYSIS_JOB_NAMES:
print(job_name)
print(stack_analysis_jobs_durations_min_times[job_name])
print(stack_analysis_jobs_durations_max_times[job_name])
print(stack_analysis_jobs_durations_avg_times[job_name])
title = "{t}: min. max. and avg times".format(t=title_prefix)
min_max_avg_name = "{n}_min_max_avg_times".format(n=name_prefix)
graph.generate_timing_statistic_graph(title, min_max_avg_name, pauses,
min_times, max_times, avg_times)
with open(name + ".csv", "w") as csvfile:
csv_writer = csv.writer(csvfile)
for m in measurements:
csv_writer.writerow([m])
def run_sequenced_benchmark(api, s3, title_prefix, name_prefix, function,
pauses=None, measurement_count=SEQUENCED_BENCHMARKS_DEFAULT_COUNT,
compute_stack_analysis_jobs_durations=False):
"""Start benchmarks by calling selected function sequentially."""
pauses = pauses or [10]
print("pauses: {p}".format(p=pauses))
print("measurement_count: {c}".format(c=measurement_count))
stack_analysis_jobs_durations = None
# for the stack analysis we are able to compute statistic for each job
if compute_stack_analysis_jobs_durations:
stack_analysis_jobs_durations = {}
stack_analysis_jobs_durations_min_times = {}
stack_analysis_jobs_durations_max_times = {}
stack_analysis_jobs_durations_avg_times = {}
for job_name in STACK_ANALYSIS_JOB_NAMES:
stack_analysis_jobs_durations[job_name] = []
stack_analysis_jobs_durations_min_times[job_name] = []
stack_analysis_jobs_durations_max_times[job_name] = []
stack_analysis_jobs_durations_avg_times[job_name] = []
measurements = []
min_times = []
max_times = []
avg_times = []
for pause in pauses:
if len(pauses) > 1:
title = "{t}, {s} seconds between calls".format(t=title_prefix, s=pause)
name = "{n}_{s}_pause_time".format(n=name_prefix, s=pause)
else:
title = "{t}".format(t=title_prefix)
name = "{n}".format(n=name_prefix)
print(" " + title)
values, debug = function(api, s3, measurement_count, pause)
deltas = [value["delta"] for value in values]
graph.generate_wait_times_graph(title, name, deltas)
print("Breathe (statistic graph)...")
time.sleep(BREATHE_PAUSE)
min_times.append(min(deltas))
max_times.append(max(deltas))
avg_times.append(sum(deltas) / len(deltas))
measurements.extend(deltas)
if compute_stack_analysis_jobs_durations:
for job_name in STACK_ANALYSIS_JOB_NAMES:
durations = job_durations(job_name, debug)
# all durations for specific jobs need to be stored here
stack_analysis_jobs_durations[job_name].extend(durations)
# compute statistic
cnt = len(durations)
stack_analysis_jobs_durations_min_times[job_name].append(min(durations))
stack_analysis_jobs_durations_max_times[job_name].append(max(durations))
stack_analysis_jobs_durations_avg_times[job_name].append(sum(durations) / cnt)
print(min_times)
print(max_times)
print(avg_times)
if compute_stack_analysis_jobs_durations:
print_job_durations(stack_analysis_jobs_durations, stack_analysis_jobs_durations_min_times,
stack_analysis_jobs_durations_max_times,
stack_analysis_jobs_durations_avg_times)
title = "{t}: min. max. and avg times".format(t=title_prefix)
min_max_avg_name = "{n}_min_max_avg_times".format(n=name_prefix)
graph.generate_timing_statistic_graph(title, min_max_avg_name,
pauses, min_times, max_times, avg_times)
export_sequenced_benchmark_into_csv(name, measurements,
compute_stack_analysis_jobs_durations,
stack_analysis_jobs_durations)