def run(self, accessor):
result = {}
cluster = 0
for bucket, stats_info in stats_buffer.buckets.iteritems():
values = stats_info[accessor["scale"]][accessor["counter"]]
timestamps = values["timestamp"]
timestamps = [x - timestamps[0] for x in timestamps]
nodeStats = values["nodeStats"]
samplesCount = values["samplesCount"]
trend = []
total = 0
data = []
num_error = []
for node, vals in nodeStats.iteritems():
#a, b = util.linreg(timestamps, vals)
value = sum(vals) / samplesCount
total += value
if value > accessor["threshold"]:
num_error.append({"node":node, "value":value})
trend.append((node, util.pretty_float(value)))
data.append(value)
total /= len(nodeStats)
trend.append(("total", util.pretty_float(total)))
trend.append(("variance", util.two_pass_variance(data)))
if len(num_error) > 0:
trend.append(("error", num_error))
cluster += total
result[bucket] = trend
if len(stats_buffer.buckets) > 0:
result["cluster"] = util.pretty_float(cluster / len(stats_buffer.buckets))
return result
def run(self, accessor, threshold=None):
result = {}
cluster = 0
if threshold.has_key("ActiveReplicaResidentRatio"):
threshold_val = threshold["ActiveReplicaResidentRatio"][accessor["name"]]
else:
threshold_val = accessor["threshold"]
for bucket, stats_info in stats_buffer.buckets.iteritems():
values = stats_info[accessor["scale"]][accessor["counter"]]
timestamps = values["timestamp"]
timestamps = [x - timestamps[0] for x in timestamps]
nodeStats = values["nodeStats"]
samplesCount = values["samplesCount"]
trend = []
total = 0
data = []
num_error = []
for node, vals in nodeStats.iteritems():
#a, b = util.linreg(timestamps, vals)
if samplesCount > 0:
value = sum(vals) / samplesCount
else:
value = 0
total += value
if value > 0 and value < threshold_val:
symptom = accessor["symptom"].format(util.pretty_float(value) + "%", util.pretty_float(threshold_val) + "%")
num_error.append({"node":node, "value":symptom})
trend.append((node, util.pretty_float(value) + "%"))
data.append(value)
if len(nodeStats) > 0:
total /= len(nodeStats)
trend.append(("total", util.pretty_float(total) + "%"))
if total > 0 and total < threshold_val:
symptom = accessor["symptom"].format(util.pretty_float(total) + "%", util.pretty_float(threshold_val) + "%")
num_error.append({"node": "total", "value":symptom})
trend.append(("variance", util.two_pass_variance(data)))
if len(num_error) > 0:
trend.append(("error", num_error))
cluster += total
result[bucket] = trend
if len(stats_buffer.buckets) > 0:
result["cluster"] = util.pretty_float(cluster / len(stats_buffer.buckets)) + "%"
return result
def run(self, accessor):
result = {}
cluster = 0
for bucket, stats_info in stats_buffer.buckets.iteritems():
values = stats_info[accessor["scale"]][accessor["counter"]]
timestamps = values["timestamp"]
timestamps = [x - timestamps[0] for x in timestamps]
nodeStats = values["nodeStats"]
samplesCount = values["samplesCount"]
trend = []
total = 0
data = []
for node, vals in nodeStats.iteritems():
avg = sum(vals) / samplesCount
trend.append((node, util.size_label(avg)))
data.append(avg)
#print data
trend.append(("variance", util.two_pass_variance(data)))
result[bucket] = trend
return result
