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How to use current_fail_per_sec method in locust

Best Python code snippet using locust

graphs.py

Source:graphs.py

1from parsing import *2import matplotlib.pyplot as plt3import os4def make_graph(app, storage, test_name, test_size, dfs, y_axis_label, graph_type, \5    x_axis_label = 'Tempo desde inÃcio do benchmark (HH:MM:SS)', rotate_xlabel = True, close = True):6    fig, ax = plt.subplots(figsize = (16, 10))7    if type(dfs) == list:8        for df in dfs:9            df.plot(ax = ax, legend = len(dfs) > 1)10    elif callable(dfs):11        dfs(ax)12    ax.set_xlabel(x_axis_label, fontdict={'fontsize':12})13    ax.set_ylabel(y_axis_label, fontdict={'fontsize':12})14    if rotate_xlabel:15        ax.tick_params(axis = 'x', labelrotation = 45)16    for tick in ax.xaxis.get_major_ticks():17        tick.label1.set_fontsize(14)18        tick.label119    for tick in ax.yaxis.get_major_ticks():20        tick.label1.set_fontsize(14)21        tick.label122    filepath = f'{app.lower()}/{storage.replace("/", "_").lower()}/{test_name}_{test_size}'23    os.makedirs(filepath, exist_ok = True)24    fig.savefig(f'{filepath}/{test_name}_{test_size}_{graph_type}.png', transparent = True, bbox_inches = 'tight')25    if close:26        plt.close(fig)27    else:28        plt.show()29def make_system_graphs(app, storage, test_name, test_size, close = True):30    df = read_dfs_system(app, storage, test_name, test_size)[['cpu_%', 'mem_%', 'net_recv_mb', 'net_send_mb']]31    cpu_usage = df.reset_index() \32        .pivot(index = 'time', columns = 'machine', values = ['cpu_%'])['cpu_%'] \33        .median(axis = 1) \34        .rolling(2).mean() \35        .dropna()36    37    mem_usage = df.reset_index() \38        .pivot(index = 'time', columns = 'machine', values = ['mem_%'])['mem_%'] \39        .median(axis = 1) \40        .rolling(2).mean() \41        .dropna()42    make_graph(app, storage, test_name, test_size,43        [cpu_usage],44        y_axis_label = 'UtilizaÃ§Ã£o do CPU (%)',45        graph_type = 'cpu',46        close = close47    )48    make_graph(app, storage, test_name, test_size,49        [mem_usage],50        y_axis_label = 'UtilizaÃ§Ã£o da RAM (%)',51        graph_type = 'mem',52        close = close53    )54    usage_net_recv = df.reset_index() \55        .pivot(index = 'time', columns = 'machine', values = ['net_recv_mb'])['net_recv_mb'] \56        .sum(axis = 1) \57        .rolling(2).mean() \58        .dropna()59    usage_net_recv.name = 'Network RX'60    61    usage_net_send = df.reset_index() \62        .pivot(index = 'time', columns = 'machine', values = ['net_send_mb'])['net_send_mb'] \63        .sum(axis = 1) \64        .rolling(2).mean() \65        .dropna()66    usage_net_send.name = 'Network TX'67    make_graph(app, storage, test_name, test_size,68        [usage_net_recv, usage_net_send],69        y_axis_label = 'UtilizaÃ§Ã£o da rede (Mb/s)',70        graph_type = 'net',71        close = close72    )73def make_disk_nfs_graph(app, test_name, test_size, close = True):74    df = read_df_io(app, 'nfs', test_name, test_size, 'cloud108').loc['cloud108']75    read_series = df['read_mb'] \76        .rolling(2).mean() \77        .dropna()78    read_series.name = 'Leitura'79    write_series = df['write_mb'] \80        .rolling(2).mean() \81        .dropna()82    write_series.name = 'Escrita'83    make_graph(app, 'NFS', test_name, test_size,84        [read_series, write_series],85        y_axis_label = 'UtilizaÃ§Ã£o do disco (MB/s)',86        graph_type = 'disk',87        close = close88    )89def make_disk_ceph_graph(app, test_name, test_size, close = True):90    df = read_dfs_io(app, 'ceph', test_name, test_size)91    read_series = df.reset_index() \92        .pivot(index = 'time', columns = 'machine', values = ['read_mb'])['read_mb'] \93        .sum(axis = 1) \94        .rolling(2).mean() \95        .dropna()96    read_series.name = 'Leitura'97    write_series = df.reset_index() \98        .pivot(index = 'time', columns = 'machine', values = ['write_mb'])['write_mb'] \99        .sum(axis = 1) \100        .rolling(2).mean() \101        .dropna()102    write_series.name = 'Escrita'103    make_graph(app, 'Ceph', test_name, test_size,104        [read_series, write_series],105        y_axis_label = 'UtilizaÃ§Ã£o do disco (MB/s)',106        graph_type = 'disk',107        close = close108    )109def make_disk_comparation_graph(app, test_name, test_size, close = True, type = 'write'):110    df_nfs = read_df_io(app, 'nfs', test_name, test_size, 'cloud108').loc['cloud108']111    df_ceph = read_dfs_io(app, 'ceph', test_name, test_size)112    series_nfs = df_nfs[type + '_mb'] \113        .rolling(2).mean() \114        .dropna()115    series_nfs.name = 'Leitura NFS' if type == 'read' else 'Escrita NFS'116    series_ceph = df_ceph.reset_index() \117        .pivot(index = 'time', columns = 'machine', values = [type + '_mb'])[type + '_mb'] \118        .sum(axis = 1) \119        .rolling(2).mean() \120        .dropna()121    series_ceph.name = 'Leitura Ceph' if type == 'read' else 'Escrita Ceph'122    make_graph(app, 'NFS/Ceph', test_name, test_size,123        [series_nfs, series_ceph],124        y_axis_label = 'UtilizaÃ§Ã£o do disco (MB/s)',125        graph_type = 'disk',126        close = close127    )128def make_locust_report_graphs(app, storage, test_name, test_size, close = True):129    df = read_df_reporthtml(app, storage, test_name, test_size)[['current_rps', 'current_fail_per_sec', 'response_time_percentile_50']]130    rps_series = df['current_rps'].rolling(2).mean().dropna()131    rps_series.name = 'Pedidos por segundo'132    133    fps_series = df['current_fail_per_sec'].rolling(2).mean().dropna()134    fps_series.name = 'Falhas por segundo'135    responsetimes_series = df['response_time_percentile_50'].rolling(2).mean().dropna()136    make_graph(app, storage, test_name, test_size,137        [rps_series, fps_series],138        y_axis_label = 'Throughput (nÂº pedidos/s)',139        graph_type = 'rpsfps',140        close = close141    )142    make_graph(app, storage, test_name, test_size,143        [responsetimes_series],144        y_axis_label = 'Tempo de resposta (ms)',145        graph_type = 'responsetimes',146        close = close147    )148def make_locust_csv_graphs(app, storage, test_name, close = True):149    df = read_df_locustcsv(app, storage, test_name)150    def draw(ax):151        df.plot(ax = ax, marker = 'o')152    153    make_graph(app, storage, test_name, 'all',154        draw,155        x_axis_label = 'Tempo de resposta (s)',156        y_axis_label = 'Throughput (nÂº pedidos/s)',157        graph_type = 'rps_of_responsetimes',158        close = close,159        rotate_xlabel = False160    )161def make_reqfails_comparation_graph(app, test_name, test_size, close = True):162    df_nfs = read_df_reporthtml(app, 'NFS', test_name, test_size)[['current_rps', 'current_fail_per_sec']]163    df_ceph = read_df_reporthtml(app, 'Ceph', test_name, test_size)[['current_rps', 'current_fail_per_sec']]164    rps_series_nfs = df_nfs['current_rps'] # .rolling(2).mean().dropna()165    rps_series_nfs.name = 'NFS'166    # rps_series_nfs.name = 'Pedidos por segundo (NFS)'167    168    # fps_series_nfs = df_nfs['current_fail_per_sec'] # .rolling(2).mean().dropna()169    # fps_series_nfs.name = 'Falhas por segundo (NFS)'170    rps_series_ceph = df_ceph['current_rps'] # .rolling(2).mean().dropna()171    rps_series_ceph.name = 'Ceph'172    # rps_series_ceph.name = 'Pedidos por segundo (Ceph)'173    174    # fps_series_ceph = df_ceph['current_fail_per_sec'] # .rolling(2).mean().dropna()175    # fps_series_ceph.name = 'Falhas por segundo (Ceph)'176    def draw(ax):177        rps_series_nfs.plot(ax = ax, linestyle = '-', color = 'blue', legend = True)178        # fps_series_nfs.plot(ax = ax, linestyle = '-', color = 'blue', legend = True)179        rps_series_ceph.plot(ax = ax, linestyle = '-', color = 'red', legend = True)180        # fps_series_ceph.plot(ax = ax, linestyle = '-', color = 'red', legend = True)181    make_graph(app, 'NFS/Ceph', test_name, test_size,182        draw,183        y_axis_label = 'Throughput (nÂº pedidos/s)',184        graph_type = 'rpsfps',185        close = close186    )187def make_responsetimes_comparation_graph(app, test_name, test_size, close = True):188    df_nfs = read_df_reporthtml(app, 'NFS', test_name, test_size)[['response_time_percentile_50']]189    df_ceph = read_df_reporthtml(app, 'Ceph', test_name, test_size)[['response_time_percentile_50']]190    responsetimes_series_nfs = df_nfs['response_time_percentile_50']191    responsetimes_series_nfs.name = 'NFS'192    responsetimes_series_ceph = df_ceph['response_time_percentile_50']193    responsetimes_series_ceph.name = 'Ceph'194    def draw(ax):195        responsetimes_series_nfs.plot(ax = ax, linestyle = '-', color = 'blue', legend = True)196        responsetimes_series_ceph.plot(ax = ax, linestyle = '-', color = 'red', legend = True)197    198    make_graph(app, 'NFS/Ceph', test_name, test_size,199        draw,200        y_axis_label = 'Tempo de resposta (ms)',201        graph_type = 'responsetimes',202        close = close...

prometheus_exporter.py

Source:prometheus_exporter.py

1# coding: utf82import six3from itertools import chain4from flask import request, Response5from locust import stats as locust_stats, runners as locust_runners6from locust import User, task, events7from prometheus_client import Metric, REGISTRY, exposition8# This locustfile adds an external web endpoint to the locust master, and makes it serve as a prometheus exporter.9# Runs it as a normal locustfile, then points prometheus to it.10# locust -f prometheus_exporter.py --master11# Lots of code taken from [mbolek's locust_exporter](https://github.com/mbolek/locust_exporter), thx mbolek!12class LocustCollector(object):13    registry = REGISTRY14    def __init__(self, environment, runner):15        self.environment = environment16        self.runner = runner17    def collect(self):18        # collect metrics only when locust runner is hatching or running.19        runner = self.runner20        if runner and runner.state in (locust_runners.STATE_HATCHING, locust_runners.STATE_RUNNING):21            stats = []22            for s in chain(locust_stats.sort_stats(runner.stats.entries), [runner.stats.total]):23                stats.append({24                    "method": s.method,25                    "name": s.name,26                    "num_requests": s.num_requests,27                    "num_failures": s.num_failures,28                    "avg_response_time": s.avg_response_time,29                    "min_response_time": s.min_response_time or 0,30                    "max_response_time": s.max_response_time,31                    "current_rps": s.current_rps,32                    "median_response_time": s.median_response_time,33                    "ninetieth_response_time": s.get_response_time_percentile(0.9),34                    # only total stats can use current_response_time, so sad.35                    #"current_response_time_percentile_95": s.get_current_response_time_percentile(0.95),36                    "avg_content_length": s.avg_content_length,37                    "current_fail_per_sec": s.current_fail_per_sec38                })39            # perhaps StatsError.parse_error in e.to_dict only works in python slave, take notices!40            errors = [e.to_dict() for e in six.itervalues(runner.stats.errors)]41            metric = Metric('locust_user_count', 'Swarmed users', 'gauge')42            metric.add_sample('locust_user_count', value=runner.user_count, labels={})43            yield metric44            45            metric = Metric('locust_errors', 'Locust requests errors', 'gauge')46            for err in errors:47                metric.add_sample('locust_errors', value=err['occurrences'],48                                  labels={'path': err['name'], 'method': err['method'],49                                          'error': err['error']})50            yield metric51            is_distributed = isinstance(runner, locust_runners.MasterRunner)52            if is_distributed:53                metric = Metric('locust_slave_count', 'Locust number of slaves', 'gauge')54                metric.add_sample('locust_slave_count', value=len(runner.clients.values()), labels={})55                yield metric56            metric = Metric('locust_fail_ratio', 'Locust failure ratio', 'gauge')57            metric.add_sample('locust_fail_ratio', value=runner.stats.total.fail_ratio, labels={})58            yield metric59            metric = Metric('locust_state', 'State of the locust swarm', 'gauge')60            metric.add_sample('locust_state', value=1, labels={'state': runner.state})61            yield metric62            stats_metrics = ['avg_content_length', 'avg_response_time', 'current_rps', 'current_fail_per_sec',63                             'max_response_time', 'ninetieth_response_time', 'median_response_time', 'min_response_time',64                             'num_failures', 'num_requests']65            for mtr in stats_metrics:66                mtype = 'gauge'67                if mtr in ['num_requests', 'num_failures']:68                    mtype = 'counter'69                metric = Metric('locust_stats_' + mtr, 'Locust stats ' + mtr, mtype)70                for stat in stats:71                    # Aggregated stat's method label is None, so name it as Aggregated72                    # locust has changed name Total to Aggregated since 0.12.173                    if 'Aggregated' != stat['name']:74                        metric.add_sample('locust_stats_' + mtr, value=stat[mtr],75                                          labels={'path': stat['name'], 'method': stat['method']})76                    else:77                        metric.add_sample('locust_stats_' + mtr, value=stat[mtr],78                                          labels={'path': stat['name'], 'method': 'Aggregated'})79                yield metric80@events.init.add_listener81def locust_init(environment, runner, **kwargs):82    print("locust init event received")83    if environment.web_ui and runner:84        @environment.web_ui.app.route("/export/prometheus")85        def prometheus_exporter():86            registry = REGISTRY87            encoder, content_type = exposition.choose_encoder(request.headers.get('Accept'))88            if 'name[]' in request.args:89                registry = REGISTRY.restricted_registry(request.args.get('name[]'))90            body = encoder(registry)91            return Response(body, content_type=content_type)92        REGISTRY.register(LocustCollector(environment, runner))93class Dummy(User):94    @task(20)95    def hello(self):...

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