Merge pull request #212 from bskiba/metric_parser

Add script for parsing metrics from CA e2e tests

hack/scripts/ca_metrics_parser.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python
+
+# Copyright 2017 The Kubernetes Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+'''
+This script parses metrics from Cluster Autoscaler e2e tests.
+'''
+from __future__ import division
+from __future__ import print_function
+import argparse
+import json
+
+
+class CAMetric(object):
+
+  def __init__(self, function_name):
+    self.function_name = function_name
+    self.sum = 0.0
+    self.average = 0.0
+    self.buckets = []
+    self.count = 0
+    self.upper_bound = 0.0
+
+  def print(self):
+    print(self.function_name, '\t', self.sum, '\t', self.count,'\t',  self.avg,
+      '\t',  self.upper_bound)
+    print(self.buckets)
+
+
+def print_summary(summary):
+  print('function_name\t sum\t count\t avg\t upper_bound')
+  print('buckets')
+  for metric in summary.values():
+    metric.print()
+
+
+def function_name(sample):
+  return sample['metric']['function']
+
+
+def metric_value(sample):
+  return sample['value'][1]
+
+
+def upper_bound(buckets):
+  '''
+  Going from the rightmost bucket, find the first one that has some samples
+  and return its upper bound.
+  '''
+  for i in xrange(len(buckets) - 1, -1, -1):
+    le, count = buckets[i]
+    if i == 0:
+      return le
+    else:
+      le_prev, count_prev = buckets[i-1]
+      if count_prev < count:
+        return le
+
+
+def parse_metrics_file(metrics_file):
+  '''
+  Return interesting metrics for all Cluster Autoscaler functions.
+
+  Merics are stored in a map keyed by function name and are expressed in 
+  seconds. They include
+  * sum of all samples
+  * count of sumples
+  * average value of samples
+  * upper bound - all collected samples were smaller than this value
+  * buckets - list of tuples (# of samples, bucket upper bound)
+  '''
+  summary = {}
+  with open(metrics_file) as metrics_file:
+    summary = {}
+    metrics = json.load(metrics_file)
+    ca_metrics = metrics['ClusterAutoscalerMetrics']
+
+    total_sum = ca_metrics['cluster_autoscaler_function_duration_seconds_sum']
+    for sample in total_sum:
+      function = function_name(sample)
+      summary[function] = CAMetric(function)
+      summary[function].sum = float(metric_value(sample))
+
+    count = ca_metrics['cluster_autoscaler_function_duration_seconds_count']
+    for sample in count:
+      function = function_name(sample)
+      summary[function].count = int(metric_value(sample))
+      summary[function].avg = summary[function].sum / summary[function].count
+
+    buckets = ca_metrics['cluster_autoscaler_function_duration_seconds_bucket']
+    for sample in buckets:
+      function = function_name(sample)
+      summary[function].buckets.append(
+        (float(sample['metric']['le']), int(metric_value(sample))))
+
+    for value in summary.values():
+      value.upper_bound = upper_bound(value.buckets)
+  return summary
+
+
+def main():
+  parser = argparse.ArgumentParser(description='Parse metrics from Cluster Autoscaler e2e test')
+  parser.add_argument('metrics_file', help='File to read metrics from')
+  args = parser.parse_args()
+
+  summary = parse_metrics_file(args.metrics_file)  
+  print_summary(summary)
+
+
+if __name__ == '__main__':
+  main()
+