def needed_elements_of_expression(expression):
if expression[0] in ["rrd", "scalar"]:
yield tuple(expression[1:])
elif expression[0] in ["operator", "transformation"]:
for operand in expression[2]:
yield from needed_elements_of_expression(operand)
elif expression[0] == "combined" and not cmk_version.is_raw_edition():
# Suppression is needed to silence pylint in CRE environment
from cmk.gui.cee.plugins.metrics.graphs import ( # pylint: disable=no-name-in-module # isort: skip
resolve_combined_single_metric_spec,)
metrics = resolve_combined_single_metric_spec(expression[1])
for out in (needed_elements_of_expression(m['expression']) for m in metrics):
yield from out
def evaluate_time_series_expression(expression, rrd_data) -> List[TimeSeries]:
if rrd_data:
sample_data = next(iter(rrd_data.values()))
num_points = len(sample_data)
twindow = sample_data.twindow
else:
# no data, default clean graph, use for pure scalars on custom graphs
num_points = 1
twindow = (0, 60, 60)
if expression[0] == "operator":
operator_id, operands = expression[1:]
operands_evaluated_l = [evaluate_time_series_expression(a, rrd_data) for a in operands]
operands_evaluated = [item for lists in operands_evaluated_l for item in lists]
return [time_series_math(operator_id, operands_evaluated)]
if expression[0] == "transformation":
(transform, conf), operands = expression[1:]
operands_evaluated = evaluate_time_series_expression(operands[0], rrd_data)
return evaluate_timeseries_transformation(transform, conf, operands_evaluated)
if expression[0] == "rrd":
key = tuple(expression[1:])
if key in rrd_data:
return [rrd_data[key]]
return [TimeSeries([None] * num_points, twindow)]
if expression[0] == "constant":
return [TimeSeries([expression[1]] * num_points, twindow)]
if expression[0] == "combined":
metrics = resolve_combined_single_metric_spec(expression[1])
curves = []
for m in metrics:
for curve in evaluate_time_series_expression(m['expression'], rrd_data):
curve.metadata = {k: m[k] for k in m if k in ['line_type', 'title']}
curves.append(curve)
return curves
raise NotImplementedError()
def evaluate_time_series_expression(expression, rrd_data):
if rrd_data:
sample_data = next(iter(rrd_data.values()))
num_points = len(sample_data)
twindow = sample_data.twindow
else:
# no data, default clean graph, use for pure scalars on custom graphs
num_points = 1
twindow = (0, 60, 60)
if expression[0] == "operator":
operator_id, operands = expression[1:]
operands_evaluated = [evaluate_time_series_expression(a, rrd_data) for a in operands]
return time_series_math(operator_id, operands_evaluated)
if expression[0] == "transformation":
(transform, conf), operands = expression[1:]
operands_evaluated = evaluate_time_series_expression(operands[0], rrd_data)
return evaluate_timeseries_transformation(transform, conf, operands_evaluated)
if expression[0] == "rrd":
key = tuple(expression[1:])
if key in rrd_data:
return rrd_data[key]
return TimeSeries([None] * num_points, twindow)
if expression[0] == "constant":
return TimeSeries([expression[1]] * num_points, twindow)
if expression[0] == "combined":
metrics = resolve_combined_single_metric_spec(expression[1])
return [(m["line_type"], m["color"], m['title'],
evaluate_time_series_expression(m['expression'], rrd_data)) for m in metrics]
raise NotImplementedError()
def evaluate_time_series_expression(expression, rrd_data):
if rrd_data:
num_points = len(next(iter(rrd_data.values())))
else:
num_points = 1
if expression[0] == "operator":
operator_id, operands = expression[1:]
operands_evaluated = [
evaluate_time_series_expression(a, rrd_data) for a in operands
]
return time_series_math(operator_id, operands_evaluated)
if expression[0] == "transformation":
(transform, conf), operands = expression[1:]
operands_evaluated = evaluate_time_series_expression(
operands[0], rrd_data)
if transform == 'percentile':
return time_series_operator_perc(operands_evaluated, conf)
if transform == 'filter_top':
if isinstance(operands_evaluated, TimeSeries):
return operands_evaluated
return operands_evaluated[:conf["amount"]]
if transform == 'value_sort':
if isinstance(operands_evaluated, TimeSeries):
return operands_evaluated
aggr_func = {
"min": lambda x: min(x or [0]),
"max": lambda x: max(x or [0]),
"average": lambda x: sum(x) / float(len(x) or 1),
}[conf['aggregation']]
orderlist = sorted(operands_evaluated,
key=lambda metric: aggr_func(
clean_time_series_point(metric[3])),
reverse=conf["reverse"])
# fix multi-line stack line styling
if orderlist[0][0] == 'stack':
line_types = ['area'] + ['stack'] * (len(orderlist) - 1)
orderlist = [(lt, ) + metric[1:]
for lt, metric in zip(line_types, orderlist)]
return orderlist
if transform == 'forecast':
if cmk_version.is_raw_edition():
raise MKGeneralException(
_("Forecast calculations are only available with the "
"Checkmk Enterprise Editions"))
# Suppression is needed to silence pylint in CRE environment
from cmk.gui.cee.plugins.metrics.forecasts import time_series_transform_forecast # pylint: disable=no-name-in-module
return time_series_transform_forecast(
TimeSeries(operands_evaluated, rrd_data['__range']), conf)
if expression[0] == "rrd":
key = tuple(expression[1:])
if key in rrd_data:
return rrd_data[key]
return [None] * num_points
if expression[0] == "constant":
return [expression[1]] * num_points
if expression[0] == "combined" and not cmk_version.is_raw_edition():
# Suppression is needed to silence pylint in CRE environment
from cmk.gui.cee.plugins.metrics.graphs import resolve_combined_single_metric_spec # pylint: disable=no-name-in-module
metrics = resolve_combined_single_metric_spec(expression[1])
return [(m["line_type"], m["color"], m['title'],
evaluate_time_series_expression(m['expression'], rrd_data))
for m in metrics]
raise NotImplementedError()
if expression[0] == "transformation":
(transform, conf), operands = expression[1:]
operands_evaluated = evaluate_time_series_expression(operands[0], rrd_data)
return evaluate_timeseries_transformation(transform, conf, operands_evaluated)
if expression[0] == "rrd":
key = tuple(expression[1:])
if key in rrd_data:
return [rrd_data[key]]
return [TimeSeries([None] * num_points, twindow)]
if expression[0] == "constant":
return [TimeSeries([expression[1]] * num_points, twindow)]
if expression[0] == "combined":
metrics = resolve_combined_single_metric_spec(expression[1])
curves = []
for m in metrics:
for curve in evaluate_time_series_expression(m['expression'], rrd_data):
curve.metadata = {k: m[k] for k in m if k in ['line_type', 'title']}
curves.append(curve)
return curves
raise NotImplementedError()
def time_series_math(operator_id: Literal["+", "*", "-", "/", "MAX", "MIN", "AVERAGE", "MERGE"],
operands_evaluated: List[TimeSeries]) -> Optional[TimeSeries]:
operators = time_series_operators()
if operator_id not in operators: