def parse_xml_jtl(self, granularity):
    """
    Parse Jmeter workload output in XML format and extract overall and per transaction data and key statistics

    :param string granularity: The time period over which to aggregate and average the raw data. Valid values are 'hour', 'minute' or 'second'
    :return: status of the metric parse
    """
    data = defaultdict(list)
    processed_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
    for input_file in self.infile_list:
      logger.info('Processing : %s', input_file)
      timestamp_format = None
      tree = ElementTree.parse(input_file)
      samples = tree.findall('./httpSample') + tree.findall('./sample')
      for sample in samples:
        if not timestamp_format or timestamp_format == 'unknown':
          timestamp_format = naarad.utils.detect_timestamp_format(sample.get('ts'))
        if timestamp_format == 'unknown':
          continue
        ts = naarad.utils.get_standardized_timestamp(sample.get('ts'), timestamp_format)
        if ts == -1:
          continue
        ts = naarad.utils.reconcile_timezones(ts, self.timezone, self.graph_timezone)
        aggregate_timestamp, averaging_factor = self.get_aggregation_timestamp(ts, granularity)
        self.aggregate_count_over_time(processed_data, sample, [self._sanitize_label(sample.get('lb')), 'Overall_Summary'], aggregate_timestamp)
        self.aggregate_values_over_time(processed_data, sample, [self._sanitize_label(sample.get('lb')), 'Overall_Summary'], ['t', 'by'], aggregate_timestamp)
        logger.info('Finished parsing : %s', input_file)
    logger.info('Processing metrics for output to csv')
    self.average_values_for_plot(processed_data, data, averaging_factor)
    logger.info('Writing time series csv')
    for csv in data.keys():
      self.csv_files.append(csv)
      with open(csv, 'w') as csvf:
        csvf.write('\n'.join(sorted(data[csv])))
    logger.info('Processing raw data for stats')
    self.calculate_key_stats(processed_data)
    return True
def parse_xml_jtl(self, granularity):
    """
    Parse Jmeter workload output in XML format and extract overall and per transaction data and key statistics

    :param string granularity: The time period over which to aggregate and average the raw data. Valid values are 'hour', 'minute' or 'second'
    :return: status of the metric parse
    """
    data = defaultdict(list)
    processed_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
    for input_file in self.infile_list:
      logger.info('Processing : %s', input_file)
      timestamp_format = None
      tree = ElementTree.parse(input_file)
      samples = tree.findall('./httpSample') + tree.findall('./sample')
      for sample in samples:
        if not timestamp_format or timestamp_format == 'unknown':
          timestamp_format = naarad.utils.detect_timestamp_format(sample.get('ts'))
        if timestamp_format == 'unknown':
          continue
        ts = naarad.utils.get_standardized_timestamp(sample.get('ts'), timestamp_format)
        if ts == -1:
          continue
        ts = naarad.utils.reconcile_timezones(ts, self.timezone, self.graph_timezone)
        aggregate_timestamp, averaging_factor = self.get_aggregation_timestamp(ts, granularity)
        self.aggregate_count_over_time(processed_data, sample, [self._sanitize_label(sample.get('lb')), 'Overall_Summary'], aggregate_timestamp)
        self.aggregate_values_over_time(processed_data, sample, [self._sanitize_label(sample.get('lb')), 'Overall_Summary'], ['t', 'by'], aggregate_timestamp)
        logger.info('Finished parsing : %s', input_file)
    logger.info('Processing metrics for output to csv')
    self.average_values_for_plot(processed_data, data, averaging_factor)
    logger.info('Writing time series csv')
    for csv in data.keys():
      self.csv_files.append(csv)
      with open(csv, 'w') as csvf:
        csvf.write('\n'.join(sorted(data[csv])))
    logger.info('Processing raw data for stats')
    self.calculate_key_stats(processed_data)
    return True
def parse_xml_jtl ( self , granularity ) : data = defaultdict ( list ) processed_data = defaultdict ( lambda : defaultdict ( lambda : defaultdict ( list ) ) ) for input_file in self . infile_list : logger . info ( 'Processing : %s' , input_file ) timestamp_format = None tree = ElementTree . parse ( input_file ) samples = tree . findall ( './httpSample' ) + tree . findall ( './sample' ) for sample in samples : if not timestamp_format or timestamp_format == 'unknown' : timestamp_format = naarad . utils . detect_timestamp_format ( sample . get ( 'ts' ) ) if timestamp_format == 'unknown' : continue ts = naarad . utils . get_standardized_timestamp ( sample . get ( 'ts' ) , timestamp_format ) if ts == - 1 : continue ts = naarad . utils . reconcile_timezones ( ts , self . timezone , self . graph_timezone ) aggregate_timestamp , averaging_factor = self . get_aggregation_timestamp ( ts , granularity ) self . aggregate_count_over_time ( processed_data , sample , [ self . _sanitize_label ( sample . get ( 'lb' ) ) , 'Overall_Summary' ] , aggregate_timestamp ) self . aggregate_values_over_time ( processed_data , sample , [ self . _sanitize_label ( sample . get ( 'lb' ) ) , 'Overall_Summary' ] , [ 't' , 'by' ] , aggregate_timestamp ) logger . info ( 'Finished parsing : %s' , input_file ) logger . info ( 'Processing metrics for output to csv' ) self . average_values_for_plot ( processed_data , data , averaging_factor ) logger . info ( 'Writing time series csv' ) for csv in data . keys ( ) : self . csv_files . append ( csv ) with open ( csv , 'w' ) as csvf : csvf . write ( '\n' . join ( sorted ( data [ csv ] ) ) ) logger . info ( 'Processing raw data for stats' ) self . calculate_key_stats ( processed_data ) return True
Parse Jmeter workload output in XML format and extract overall and per transaction data and key statistics

    :param string granularity: The time period over which to aggregate and average the raw data. Valid values are 'hour', 'minute' or 'second'
    :return: status of the metric parse
Parse Jmeter workload output in XML format and extract overall and per transaction data and key statistics