def get_sample(self, **kwargs):
search_string = None
search_type = None
if 'search_string' in kwargs and 'search_type' in kwargs:
search_string = kwargs['search_string']
search_type = kwargs['search_type']
meter_name = kwargs['meter_name']
unit_type = kwargs['unit_type']
if search_string is None or search_type is None:
expr = metrics_helper.get_metrics_with_labels(
meter_name, None, None)
else:
expr = metrics_helper.get_metrics_with_labels(
meter_name, search_type, search_string)
if 'function_type' in meter_name and 'function_time' in meter_name:
expr = meter_name['function_type'] + "(" + expr + "[" + meter_name[
'function_time'] + "]" + ")"
if 'aggregation_op' in meter_name:
expr = meter_name['aggregation_op'] + "(" + expr + ")"
if 'aggregation_over_time' in meter_name and 'aggregation_over_time_value' in meter_name:
expr = meter_name[
'aggregation_over_time'] + "_over_time" + "(" + expr + "[" + meter_name[
'aggregation_over_time_value'] + "]" + ")"
if 'aggregation_paramop' in meter_name and 'aggregation_paramval' in meter_name:
expr = meter_name['aggregation_paramop'] + "(" + meter_name[
'aggregation_paramval'] + "," + "(" + expr + ")" + ")"
if 'group_by' in meter_name:
expr = expr + ' by ' + "(" + meter_name['group_by'] + ")"
if 'not_group_by' in meter_name:
expr = expr + ' without ' + "(" + meter_name['not_group_by'] + ")"
log.info("Expression %s", expr)
instance_value_list = prometheus_helper.get_metrics(expr, unit_type)
kwargs['result_list'] = instance_value_list
return kwargs
def get_sample(self, **kwargs):
search_string = None
search_type = None
if 'search_string' in kwargs and 'search_type' in kwargs:
search_string = kwargs['search_string']
search_type = kwargs['search_type']
meter_name = kwargs['meter_name']
unit_type = kwargs['unit_type']
if search_string is None or search_type is None:
expr = metrics_helper.get_metrics_with_labels(
json.loads(json.dumps(meter_name)), None, None)
else:
expr = metrics_helper.get_metrics_with_labels(
json.loads(json.dumps(meter_name)), search_type, search_string)
log.info("Expression %s", expr)
instance_value_list = prometheus_helper.get_metrics(expr, unit_type)
kwargs['result_list'] = instance_value_list
return kwargs
def get_sample(self, **kwargs):
search_string = None
search_type = None
if 'search_string' in kwargs and 'search_type' in kwargs:
search_string = kwargs['search_string']
search_type = kwargs['search_type']
meter_name = kwargs['meter_name']
unit_type = kwargs['unit_type']
if search_string is None or search_type is None:
expr = metrics_helper.get_metrics_with_labels(meter_name, None, None)
else:
expr = metrics_helper.get_metrics_with_labels(meter_name, search_type, search_string)
if 'function_type' in meter_name and 'function_time' in meter_name:
expr = meter_name['function_type']+"("+expr+"["+meter_name['function_time']+"]"+")"
if 'aggregation_op' in meter_name:
expr = meter_name['aggregation_op']+"("+expr+")"
if 'aggregation_over_time' in meter_name and 'aggregation_over_time_value' in meter_name:
expr = meter_name['aggregation_over_time']+"_over_time"+"("+expr+"["+meter_name['aggregation_over_time_value']+"]"+")"
if 'aggregation_paramop' in meter_name and 'aggregation_paramval' in meter_name:
expr = meter_name['aggregation_paramop']+"("+meter_name['aggregation_paramval']+","+"("+expr+")"+")"
if 'group_by' in meter_name:
expr = expr+' by '+"("+meter_name['group_by']+")"
if 'not_group_by' in meter_name:
expr = expr + ' without '+"("+meter_name['not_group_by']+")"
log.info("Expression %s", expr)
instance_value_list = prometheus_helper.get_metrics(expr, unit_type)
kwargs['result_list'] = instance_value_list
return kwargs
def create_json(name, unit_list, metrics_list, unit_type):
"""
Creates json for promdash
:param name: Name of the dashboard
:param unit_list: Name list of the units
:param metrics_list: List of metrics
:return:
"""
widgets_type = "graph"
widgets_interpolation_method = "linear"
widgets_resolution = 4
show_legend = "always"
axes_orientation = "left"
axes_renderer = "line"
axes_scale = "linear"
axes_format = "kmbt"
base_file = os.path.join(os.path.dirname(template_data.__file__),
"promdash.json")
base_json = open(base_file).read()
data = json.loads(base_json)
data['name'] = name
for i in unit_list:
expressions_id = 0
expressions_server_id = 1
expressions_axis_id = 1
axes_id = 1
legend_id = 1
expressions = []
legendFormatStrings = []
axes = []
widget = {}
widget["title"] = i
widget["type"] = widgets_type
widget["showLegend"] = show_legend
widget["interpolationMethod"] = widgets_interpolation_method
widget["resolution"] = widgets_resolution
widget["endTime"] = None
for j in metrics_list:
expression = {}
legendFormatString = {}
axe = {}
if axes_id < 3:
if axes_id > 1:
axe["orientation"] = "right"
axe['yMin'] = "1"
axe['yMax'] = "1000000000"
axe["scale"] = "log"
else:
axe["orientation"] = axes_orientation
axe["renderer"] = axes_renderer
axe["scale"] = axes_scale
axe["format"] = axes_format
axe["id"] = axes_id
axes.append(axe)
expression["id"] = expressions_id
expression["serverID"] = expressions_server_id
if unit_type == 'docker':
expression[
"expression"] = metrics_helper.get_metrics_with_labels(
j, "name", i)
elif unit_type == 'node':
expression[
"expression"] = metrics_helper.get_metrics_with_labels(
j, "instance", i)
elif unit_type == 'jmx':
expression[
"expression"] = metrics_helper.get_metrics_with_labels(
j, "instance", i)
expression["legendID"] = legend_id
expression["axisID"] = 1
legendFormatString["id"] = legend_id
legendFormatString["name"] = j['name']
axes_id += 1
expressions.append(expression)
legendFormatStrings.append(legendFormatString)
expressions_id += 1
expressions_axis_id += 1
legend_id += 1
widget["expressions"] = expressions
widget["legendFormatStrings"] = legendFormatStrings
widget["axes"] = axes
data["dashboard_json"]["widgets"].append(widget)
return json.dumps(data)