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How to use on_aggregated_data method in yandex-tank

Best Python code snippet using yandex-tank

tank_aggregator.py

Source:tank_aggregator.py

...9from netort.data_processing import Drain, Chopper, get_nowait_from_queue10logger = logging.getLogger(__name__)11class LoggingListener(AggregateResultListener):12    """ Log aggregated results """13    def on_aggregated_data(self, data, stats):14        logger.info("Got aggregated sample:\n%s", json.dumps(data, indent=2))15        logger.info("Stats:\n%s", json.dumps(stats, indent=2))16class TankAggregator(object):17    """18    Plugin that manages aggregation and stats collection19    """20    SECTION = 'aggregator'21    @staticmethod22    def get_key():23        return __file__24    def __init__(self, generator):25        # AbstractPlugin.__init__(self, core, cfg)26        """27        :type generator: GeneratorPlugin28        """29        self.generator = generator30        self.listeners = []  # [LoggingListener()]31        self.results = q.Queue()32        self.stats_results = q.Queue()33        self.data_cache = {}34        self.stat_cache = {}35        self.reader = None36        self.stats_reader = None37        self.drain = None38        self.stats_drain = None39    @staticmethod40    def load_config():41        return json.loads(resource_string(__name__, 'config/phout.json').decode('utf8'))42    def start_test(self, poll_period=1):43        self.reader = self.generator.get_reader()44        self.stats_reader = self.generator.get_stats_reader()45        aggregator_config = self.load_config()46        verbose_histogram = True47        if verbose_histogram:48            logger.info("using verbose histogram")49        if self.reader and self.stats_reader:50            pipeline = Aggregator(51                TimeChopper(52                    DataPoller(source=self.reader, poll_period=poll_period),53                    cache_size=3),54                aggregator_config,55                verbose_histogram)56            self.drain = Drain(pipeline, self.results)57            self.drain.start()58            self.stats_drain = Drain(59                Chopper(DataPoller(60                    source=self.stats_reader, poll_period=poll_period)),61                self.stats_results)62            self.stats_drain.start()63        else:64            logger.warning("Generator not found. Generator must provide a reader and a stats_reader interface")65    def _collect_data(self, end=False):66        """67        Collect data, cache it and send to listeners68        """69        data = get_nowait_from_queue(self.results)70        stats = get_nowait_from_queue(self.stats_results)71        logger.debug("Data timestamps: %s" % [d.get('ts') for d in data])72        logger.debug("Stats timestamps: %s" % [d.get('ts') for d in stats])73        for item in data:74            ts = item['ts']75            if ts in self.stat_cache:76                # send items77                data_item = item78                stat_item = self.stat_cache.pop(ts)79                self.__notify_listeners(data_item, stat_item)80            else:81                self.data_cache[ts] = item82        for item in stats:83            ts = item['ts']84            if ts in self.data_cache:85                # send items86                data_item = self.data_cache.pop(ts)87                stat_item = item88                self.__notify_listeners(data_item, stat_item)89            else:90                self.stat_cache[ts] = item91        if end and len(self.data_cache) > 0:92            logger.info('Timestamps without stats:')93            for ts, data_item in sorted(self.data_cache.items(), key=lambda i: i[0]):94                logger.info(ts)95                self.__notify_listeners(data_item, StatsReader.stats_item(ts, 0, 0))96    def is_aggr_finished(self):97        return self.drain._finished.is_set() and self.stats_drain._finished.is_set()98    def is_test_finished(self):99        self._collect_data()100        return -1101    def end_test(self, retcode):102        retcode = self.generator.end_test(retcode)103        if self.stats_reader:104            logger.info('Closing stats reader')105            self.stats_reader.close()106        if self.drain:107            logger.info('Waiting for gun drain to finish')108            self.drain.join()109            logger.info('Waiting for stats drain to finish')110            self.stats_drain.join()111        logger.info('Collecting remaining data')112        self._collect_data(end=True)113        return retcode114    def add_result_listener(self, listener):115        self.listeners.append(listener)116    def __notify_listeners(self, data, stats):117        """ notify all listeners about aggregate data and stats """118        for listener in self.listeners:...

plugin.py

Source:plugin.py

...11from ...common.util import Drain, Chopper12logger = logging.getLogger(__name__)13class LoggingListener(AggregateResultListener):14    """ Log aggregated results """15    def on_aggregated_data(self, data, stats):16        logger.info("Got aggregated sample:\n%s", json.dumps(data, indent=2))17        logger.info("Stats:\n%s", json.dumps(stats, indent=2))18def get_from_queue(queue):19    data = []20    for _ in range(queue.qsize()):21        try:22            data.append(queue.get_nowait())23        except q.Empty:24            break25    return data26class Plugin(AbstractPlugin):27    """28    Plugin that manages aggregation and stats collection29    """30    SECTION = 'aggregator'31    @staticmethod32    def get_key():33        return __file__34    def __init__(self, core, config_section):35        AbstractPlugin.__init__(self, core, config_section)36        self.listeners = []  # [LoggingListener()]37        self.reader = None38        self.stats_reader = None39        self.results = q.Queue()40        self.stats = q.Queue()41        self.verbose_histogram = False42        self.data_cache = {}43        self.stat_cache = {}44    def get_available_options(self):45        return ["verbose_histogram"]46    def configure(self):47        self.aggregator_config = json.loads(48            resource_string(__name__, 'config/phout.json').decode('utf8'))49        verbose_histogram_option = self.get_option("verbose_histogram", "0")50        self.verbose_histogram = (51            verbose_histogram_option.lower() == "true") or (52                verbose_histogram_option.lower() == "1")53        if self.verbose_histogram:54            logger.info("using verbose histogram")55    def start_test(self):56        if self.reader and self.stats_reader:57            pipeline = Aggregator(58                TimeChopper(59                    DataPoller(60                        source=self.reader, poll_period=1), cache_size=3),61                self.aggregator_config,62                self.verbose_histogram)63            self.drain = Drain(pipeline, self.results)64            self.drain.start()65            self.stats_drain = Drain(66                Chopper(DataPoller(67                    source=self.stats_reader, poll_period=1)),68                self.stats)69            self.stats_drain.start()70        else:71            raise PluginImplementationError(72                "Generator must pass a Reader and a StatsReader"73                " to Aggregator before starting test")74    def _collect_data(self):75        """76        Collect data, cache it and send to listeners77        """78        data = get_from_queue(self.results)79        stats = get_from_queue(self.stats)80        logger.debug("Data timestamps:\n%s" % [d.get('ts') for d in data])81        logger.debug("Stats timestamps:\n%s" % [d.get('ts') for d in stats])82        for item in data:83            ts = item['ts']84            if ts in self.stat_cache:85                # send items86                data_item = item87                stat_item = self.stat_cache.pop(ts)88                self.__notify_listeners(data_item, stat_item)89            else:90                self.data_cache[ts] = item91        for item in stats:92            ts = item['ts']93            if ts in self.data_cache:94                # send items95                data_item = self.data_cache.pop(ts)96                stat_item = item97                self.__notify_listeners(data_item, stat_item)98            else:99                self.stat_cache[ts] = item100    def is_test_finished(self):101        self._collect_data()102        return -1103    def end_test(self, retcode):104        if self.reader:105            self.reader.close()106        if self.drain:107            self.drain.join()108        if self.stats_reader:109            self.stats_reader.close()110        if self.stats_drain:111            self.stats_drain.join()112        self._collect_data()113        return retcode114    def add_result_listener(self, listener):115        self.listeners.append(listener)116    def __notify_listeners(self, data, stats):117        """ notify all listeners about aggregate data and stats """118        for listener in self.listeners:...

scanner.py

Source:scanner.py

...68        if self._inflight_data and self._inflight_data[-1].index[0] != data.index[0]:69            # a new row is for a new time period70            aggregated_data = pd.concat(self._inflight_data, axis=1)71            self._inflight_data = []72            self.events.on_aggregated_data(aggregated_data)73        self._inflight_data.append(data)74    def _analyze_data(self, data):75        """76        Compute rolling statistics and select frequencies with the strongest signal.77        """78        def merge(mask, row):79            to_merge = []80            idxs = []81            for i, x in enumerate(mask.iloc[1:], 1):82                if mask.iloc[i-1] and mask.iloc[i]:83                    if idxs:84                        idxs.append(row.index[i])85                    else:86                        idxs.extend([row.index[i-1], row.index[i]])...

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