def evaluate(self, profile, plan_node_id):
"""
Evaluate the impact of this cause to the query. The return is a json string with
this format:
{
"impact": the amount of slow down (in ns),
"message" : the displayed "explanation" string
}
:return:
"""
impact = -1
if len(self.exprs):
assert len(self.metric_names) == 1
# metric_names can have multiple values create a dict for all of
# them
db_result = models.query_element_by_metric(profile, 'Summary', self.metric_names[0])
for k, g in groupby(db_result, lambda x: x.fid):
grouped = list(g)
# A list of pairs, with aggregated value and index at value for
# max / min like exprs
converted_exprs = self.check_exprs(grouped)
expr_vars = {
"vars": dict(zip(self.exprs, map(lambda x: x[0], converted_exprs))),
"idxs": dict(zip(self.exprs, map(lambda x: x[1], converted_exprs))),
}
expr_val = exprs.Expr.evaluate(self.rule["expr"], expr_vars)
if (impact is None or impact < expr_val):
impact = expr_val
else:
# For each of the metrics get the result
with Timer() as t:
# Get the metric values from the db grouped by metric name
db_result = [models.query_element_by_metric(profile, 'Summary', m) for m in self.metric_names]
# Assuming that for all metric names the same number of rows have been returned transpose the array
all_metrics = zip(*db_result)
for row in all_metrics:
# Convert to double values if unit is 6(double)
metric_values = map(lambda x: x.value if x.unit != 6 else to_double(x.value), row)
local_vars = {"vars": dict(zip(self.metric_names, metric_values))}
condition = True
if ("condition" in self.rule):
condition = exprs.Expr.evaluate(self.rule["condition"], local_vars)
if (condition):
expr_val = exprs.Expr.evaluate(self.rule["expr"], local_vars)
if (impact is None or impact < expr_val):
impact = expr_val
return {
"impact": impact,
"message": self.rule["message"],
"label": self.rule["label"]
}
def evaluate(self, profile, plan_node_id):
"""
Evaluate the impact of this cause to the query. The return is a json string with
this format:
{
"impact": the amount of slow down (in ns),
"message" : the displayed "explanation" string
}
:return:
"""
impact = -1
expr_data = ''
if len(self.exprs):
assert len(self.metric_names) == 1
# metric_names can have multiple values create a dict for all of
# them
db_result = models.query_node_by_id(profile, plan_node_id,
self.metric_names[0])
for k, g in groupby(db_result, lambda x: x.fid):
grouped = list(g)
# A list of pairs, with aggregated value and index at value for
# max / min like exprs
converted_exprs = self.check_exprs(grouped)
expr_vars = {
"vars":
dict(zip(self.exprs, map(lambda x: x[0],
converted_exprs))),
"idxs":
dict(zip(self.exprs, map(lambda x: x[1],
converted_exprs))),
}
expr_val = exprs.Expr.evaluate(self.rule["expr"], expr_vars)
if (impact is None or impact < expr_val):
impact = expr_val
else:
# For each of the metrics get the result
with Timer() as t:
# Get the metric values from the db grouped by metric name
db_result = [
models.query_node_by_id(profile, plan_node_id, m)
for m in self.metric_names
]
# Assuming that for all metric names the same number of rows have been returned transpose the array
all_metrics = zip(*db_result)
for row in all_metrics:
# Convert to double values if unit is 6(double)
metric_values = map(
lambda x: x.value
if x.unit != 6 else to_double(x.value), row)
surrogate_node = row[0].node
local_vars = {
"vars": dict(zip(self.metric_names, metric_values))
}
local_vars["vars"]["IOBound"] = self.isStorageBound(
surrogate_node)
local_vars["vars"]['InputRows'] = self.getNumInputRows(
surrogate_node)
condition = True
if ("condition" in self.rule):
condition = exprs.Expr.evaluate(self.rule["condition"],
local_vars)
if (condition):
expr_val = exprs.Expr.evaluate(self.rule["expr"],
local_vars)
if (impact is None or impact < expr_val):
impact = expr_val
if self.kwargs.get('info_names'):
db_result = [
models.query_element_by_info(profile, plan_node_id, m)
for m in self.kwargs['info_names']
]
all_metrics = zip(*db_result)
for row in all_metrics:
metric_values = map(lambda x: x.value, row)
local_vars['vars'].update(
dict(zip(self.kwargs['info_names'], metric_values)))
expr_data = exprs.Expr.evaluate(self.kwargs['fix']['data'],
local_vars)
msg = self.rule["label"] + ": " + self.rule["message"]
return {"impact": impact, "message": msg, "data": expr_data}
