def run_api_concurrent_benchmark(core_api, function_to_call, name_prefix): """Call given API endpoint concurrently.""" measurement_count = 1 min_thread_count = 1 max_thread_count = 2 pauses = [2.0, 1.5, 1.0, 0.5, 0] # 2, 1, 0.5, 0] pauses = [ 0.0, ] summary_min_times = [] summary_max_times = [] summary_avg_times = [] for thread_count in range(min_thread_count, 1 + max_thread_count): min_times = [] max_times = [] avg_times = [] for pause in pauses: threads = [] q = queue.Queue() for thread_id in range(0, thread_count): t = threading.Thread(target=function_to_call, args=(core_api, measurement_count, pause, q, thread_id)) t.start() threads.append(t) wait_for_all_threads(threads) values = sum([q.get() for t in threads], []) title = "core API endpoint, {t} concurrent threads, {s} seconds between calls".format( t=thread_count, s=pause) name = "{p}_concurrent_{t}_threads_{s}_pause_time".format( p=name_prefix, t=thread_count, s=pause) graph.generate_wait_times_graph(title, name, values) min_times.append(min(values)) max_times.append(max(values)) avg_times.append(sum(values) / len(values)) print("Breathe...") time.sleep(BREATHE_PAUSE) print(min_times) print(max_times) print(avg_times) summary_min_times.append(min(values)) summary_max_times.append(max(values)) summary_avg_times.append(sum(values) / len(values)) generate_statistic_graph(name_prefix, thread_count, pauses, min_times, max_times, avg_times) print("Breathe (statistic graph)...") time.sleep(BREATHE_PAUSE) print(summary_min_times) print(summary_max_times) print(summary_avg_times) t = range(min_thread_count, 1 + thread_count) graph.generate_timing_threads_statistic_graph( "Duration for concurrent API calls", "{p}_{t}".format(p=name_prefix, t=thread_count), t, summary_min_times, summary_max_times, summary_avg_times)
def run_component_analysis_concurrent_calls_benchmark(jobs_api, s3): """Call component analysis in more threads and collect results.""" print("Component analysis concurrent benchmark") measurement_count = 1 min_thread_count = 1 max_thread_count = 100 summary_min_times = [] summary_max_times = [] summary_avg_times = [] for thread_count in range(min_thread_count, 1 + max_thread_count): min_times = [] max_times = [] avg_times = [] threads = [] q = queue.Queue() for thread_id in range(0, thread_count): t = threading.Thread( target=lambda api, s3, measurement_count, pause_time, q, thread_id: benchmarks.component_analysis_thread( api, s3, measurement_count, pause_time, q, thread_id), args=(jobs_api, s3, measurement_count, 10, q, thread_id)) t.start() threads.append(t) print("---------------------------------") print("Waiting for all threads to finish") wait_for_all_threads(threads) print("Done") values = sum([q.get() for t in threads], []) print("values") print(len(values)) print(values) print("----") title = "Component analysis, {t} concurrent threads".format( t=thread_count) name = "jobs_flow_scheduling_{t}_threads".format(t=thread_count) graph.generate_wait_times_graph(title, name, values) min_times.append(min(values)) max_times.append(max(values)) avg_times.append(sum(values) / len(values)) print("min_times:", min_times) print("max_times:", max_times) print("avg_times:", avg_times) summary_min_times.append(min(values)) summary_max_times.append(max(values)) summary_avg_times.append(sum(values) / len(values)) generate_statistic_graph("component_analysis", thread_count, [10], min_times, max_times, avg_times) print("Breathe (statistic graph)...") time.sleep(BREATHE_PAUSE) print(summary_min_times) print(summary_max_times) print(summary_avg_times) t = range(min_thread_count, 1 + thread_count) graph.generate_timing_threads_statistic_graph( "Duration for concurrent analysis", "durations_{i}".format(i=thread_count), t, summary_min_times, summary_max_times, summary_avg_times)
def run_analysis_concurrent_benchmark(api, s3, message, name_prefix, function_to_call, thread_counts=None): """Universal function to call any callback function in more threads and collect results.""" thread_counts = thread_counts or [1, 2, 3, 4] print(message + " concurrent benchmark") measurement_count = 1 summary_min_times = [] summary_max_times = [] summary_avg_times = [] for thread_count in thread_counts: print("Concurrent threads: {c}".format(c=thread_count)) min_times = [] max_times = [] avg_times = [] threads = [] q = queue.Queue() for thread_id in range(0, thread_count): t = threading.Thread( target=lambda api, s3, measurement_count, pause_time, q, thread_id: function_to_call( api, s3, measurement_count, pause_time, q, thread_id), args=(api, s3, measurement_count, 0, q, thread_id)) t.start() threads.append(t) print("---------------------------------") print("Waiting for all threads to finish") wait_for_all_threads(threads) print("Done") values = [q.get()[0][0]["delta"] for t in threads] print("values") print(len(values)) print(values) print("----") title = "{n}, {t} concurrent threads".format(n=message, t=thread_count) name = "{n}_{t}_threads".format(n=name_prefix, t=thread_count) graph.generate_wait_times_graph(title, name, values) min_times.append(min(values)) max_times.append(max(values)) avg_times.append(sum(values) / len(values)) print("min_times:", min_times) print("max_times:", max_times) print("avg_times:", avg_times) summary_min_times.append(min(values)) summary_max_times.append(max(values)) summary_avg_times.append(sum(values) / len(values)) generate_statistic_graph(name, thread_count, ["min/avg/max"], min_times, max_times, avg_times) print("Breathe (statistic graph)...") time.sleep(BREATHE_PAUSE) print(summary_min_times) print(summary_max_times) print(summary_avg_times) t = thread_counts graph.generate_timing_threads_statistic_graph("Duration for " + message, "{p}".format(p=name_prefix), t, summary_min_times, summary_max_times, summary_avg_times) with open(name_prefix + ".csv", "w") as csvfile: csv_writer = csv.writer(csvfile) for i in range(0, len(thread_counts)): csv_writer.writerow([ i, thread_counts[i], summary_min_times[i], summary_max_times[i], summary_avg_times[i] ])
def run_api_concurrent_benchmark(core_api, function_to_call, name_prefix): """Call given API endpoint concurrently.""" measurement_count = 1 min_thread_count = 1 max_thread_count = 2 pauses = [2.0, 1.5, 1.0, 0.5, 0] # 2, 1, 0.5, 0] pauses = [0.0, ] summary_min_times = [] summary_max_times = [] summary_avg_times = [] for thread_count in range(min_thread_count, 1 + max_thread_count): min_times = [] max_times = [] avg_times = [] for pause in pauses: threads = [] q = queue.Queue() for thread_id in range(0, thread_count): t = threading.Thread(target=function_to_call, args=(core_api, measurement_count, pause, q, thread_id)) t.start() threads.append(t) wait_for_all_threads(threads) values = sum([q.get() for t in threads], []) title = "core API endpoint, {t} concurrent threads, {s} seconds between calls".format( t=thread_count, s=pause) name = "{p}_concurrent_{t}_threads_{s}_pause_time".format(p=name_prefix, t=thread_count, s=pause) graph.generate_wait_times_graph(title, name, values) min_times.append(min(values)) max_times.append(max(values)) avg_times.append(sum(values) / len(values)) print("Breathe...") time.sleep(BREATHE_PAUSE) print(min_times) print(max_times) print(avg_times) summary_min_times.append(min(values)) summary_max_times.append(max(values)) summary_avg_times.append(sum(values) / len(values)) generate_statistic_graph(name_prefix, thread_count, pauses, min_times, max_times, avg_times) print("Breathe (statistic graph)...") time.sleep(BREATHE_PAUSE) print(summary_min_times) print(summary_max_times) print(summary_avg_times) t = range(min_thread_count, 1 + thread_count) graph.generate_timing_threads_statistic_graph("Duration for concurrent API calls", "{p}_{t}".format(p=name_prefix, t=thread_count), t, summary_min_times, summary_max_times, summary_avg_times)
def run_component_analysis_concurrent_calls_benchmark(jobs_api, s3): """Call component analysis in more threads and collect results.""" print("Component analysis concurrent benchmark") measurement_count = 1 min_thread_count = 1 max_thread_count = 100 summary_min_times = [] summary_max_times = [] summary_avg_times = [] for thread_count in range(min_thread_count, 1 + max_thread_count): min_times = [] max_times = [] avg_times = [] threads = [] q = queue.Queue() for thread_id in range(0, thread_count): t = threading.Thread(target=lambda api, s3, measurement_count, pause_time, q, thread_id: benchmarks.component_analysis_thread(api, s3, measurement_count, pause_time, q, thread_id), args=(jobs_api, s3, measurement_count, 10, q, thread_id)) t.start() threads.append(t) print("---------------------------------") print("Waiting for all threads to finish") wait_for_all_threads(threads) print("Done") values = sum([q.get() for t in threads], []) print("values") print(len(values)) print(values) print("----") title = "Component analysis, {t} concurrent threads".format( t=thread_count) name = "jobs_flow_scheduling_{t}_threads".format(t=thread_count) graph.generate_wait_times_graph(title, name, values) min_times.append(min(values)) max_times.append(max(values)) avg_times.append(sum(values) / len(values)) print("min_times:", min_times) print("max_times:", max_times) print("avg_times:", avg_times) summary_min_times.append(min(values)) summary_max_times.append(max(values)) summary_avg_times.append(sum(values) / len(values)) generate_statistic_graph("component_analysis", thread_count, [10], min_times, max_times, avg_times) print("Breathe (statistic graph)...") time.sleep(BREATHE_PAUSE) print(summary_min_times) print(summary_max_times) print(summary_avg_times) t = range(min_thread_count, 1 + thread_count) graph.generate_timing_threads_statistic_graph("Duration for concurrent analysis", "durations_{i}".format(i=thread_count), t, summary_min_times, summary_max_times, summary_avg_times)
def run_analysis_concurrent_benchmark(api, s3, message, name_prefix, function_to_call, thread_counts=None): """Universal function to call any callback function in more threads and collect results.""" thread_counts = thread_counts or [1, 2, 3, 4] print(message + " concurrent benchmark") measurement_count = 1 summary_min_times = [] summary_max_times = [] summary_avg_times = [] for thread_count in thread_counts: print("Concurrent threads: {c}".format(c=thread_count)) min_times = [] max_times = [] avg_times = [] threads = [] q = queue.Queue() for thread_id in range(0, thread_count): t = threading.Thread(target=lambda api, s3, measurement_count, pause_time, q, thread_id: function_to_call(api, s3, measurement_count, pause_time, q, thread_id), args=(api, s3, measurement_count, 0, q, thread_id)) t.start() threads.append(t) print("---------------------------------") print("Waiting for all threads to finish") wait_for_all_threads(threads) print("Done") queue_size = q.qsize() check_number_of_results(queue_size, thread_count) # read all really stored results from the queue values = [q.get()[0][0]["delta"] for i in range(queue_size)] print("values") print("count: {cnt}".format(cnt=len(values))) print(values) print("----") title = "{n}, {t} concurrent threads".format(n=message, t=thread_count) name = "{n}_{t}_threads".format(n=name_prefix, t=thread_count) graph.generate_wait_times_graph(title, name, values) min_times.append(min(values)) max_times.append(max(values)) avg_times.append(sum(values) / len(values)) print("min_times:", min_times) print("max_times:", max_times) print("avg_times:", avg_times) summary_min_times.append(min(values)) summary_max_times.append(max(values)) summary_avg_times.append(sum(values) / len(values)) generate_statistic_graph(name, thread_count, ["min/avg/max"], min_times, max_times, avg_times) print("Breathe (statistic graph)...") time.sleep(BREATHE_PAUSE) print(summary_min_times) print(summary_max_times) print(summary_avg_times) t = thread_counts graph.generate_timing_threads_statistic_graph("Duration for " + message, "{p}".format(p=name_prefix), t, summary_min_times, summary_max_times, summary_avg_times) with open(name_prefix + ".csv", "w") as csvfile: csv_writer = csv.writer(csvfile) for i in range(0, len(thread_counts)): csv_writer.writerow([i, thread_counts[i], summary_min_times[i], summary_max_times[i], summary_avg_times[i]])