def summary(profile):
summary = profile.find_by_name('Summary')
execution_profile = profile.find_by_name('Execution Profile')
counter_map = summary.counter_map()
counter_map_execution_profile = execution_profile.counter_map()
host_list = models.host_by_metric(profile, 'PeakMemoryUsage', exprs=[max])
host_list = sorted(host_list, key=lambda x: x[1], reverse=True)
peak_memory = models.TCounter(
value=host_list[0][1], unit=3) if host_list else models.TCounter(
value=0, unit=3) # The value is not always present
return [{
'key': 'PlanningTime',
'value': counter_map['PlanningTime'].value,
'unit': counter_map['PlanningTime'].unit
}, {
'key': 'RemoteFragmentsStarted',
'value': counter_map['RemoteFragmentsStarted'].value,
'unit': counter_map['RemoteFragmentsStarted'].unit
}, {
'key': 'TotalTime',
'value': counter_map_execution_profile['TotalTime'].value,
'unit': counter_map_execution_profile['TotalTime'].unit
}, {
'key': 'PeakMemoryUsage',
'value': peak_memory.value,
'unit': peak_memory.unit
}]
def heatmap_by_host(profile, counter_name):
rows = models.host_by_metric(profile,
counter_name,
exprs=[max, sum])
# Modify the data to contain the relative data as well
sum_sum = 0
max_max = 0
if (rows):
sum_sum = float(sum([v[2] for v in rows]))
max_max = float(max([v[1] for v in rows]))
result = []
for r in rows:
result.append([r[0], float(r[1]), float(r[2]),
float(r[1]) / float(max_max) if float(max_max) != 0 else 0,
float(r[2]) / float(sum_sum) if float(sum_sum) != 0 else 0,
rows.unit])
return { 'data': result, 'max': max_max, 'unit': rows.unit }
