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)