def parse_res_file(path):
"""Read data from file ends with ".res".
Args:
path (str): The position of the res file.
Returns:
incremental_metrics (list, json array): The throughput at different time.
"""
with open(path) as csvfile:
reader = csv.DictReader(csvfile, delimiter=',')
incremental_metrics = []
for row in reader:
metrics_instance = {
"time": float(get_value_by_pattern(row, 'time', None)),
"throughput": float(get_value_by_pattern(row, 'throughput', None))
}
latency = {}
for key, pattern in LATENCY_ATTRIBUTE_MAPPING:
value = get_value_by_pattern(row, pattern, None)
latency[key] = float("{:.4}".format(value)) if value else value
metrics_instance['latency'] = latency
incremental_metrics.append(metrics_instance)
return incremental_metrics
def parse_latency_data(latency_dict):
"""
Parses the latency data from the format it is stored in the summary
file to the format we need for the API request.
Args:
latency_dict (dict): The "Latency Distribution" json object in the OLTPBench
summary file.
Returns:
latency (dict): The latency dict required to send to the performance storage
service.
"""
latency = {}
for key, pattern in LATENCY_ATTRIBUTE_MAPPING:
value = get_value_by_pattern(latency_dict, pattern, None)
latency[key] = float("{:.4}".format(value)) if value else value
return latency
def parse_metrics(summary):
""" Collect the OLTPBench results metrics from the summary file data """
return {
'throughput': get_value_by_pattern(summary, 'throughput', '-1.0'),
'latency': parse_latency_data(summary.get('Latency Distribution', {}))
}
def parse_timestamp(summary):
""" Get the timestamp in milliseconds from the summary file data """
return int(get_value_by_pattern(summary, 'timestamp', str(time())))