[Findit] Adding script for custom try job queries

appengine/findit/util_scripts/remote_queries/try_job_data_metrics.py

Index: appengine/findit/util_scripts/remote_queries/try_job_data_metrics.py
diff --git a/appengine/findit/util_scripts/remote_queries/try_job_data_metrics.py b/appengine/findit/util_scripts/remote_queries/try_job_data_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..590d2b234689e3c729551b9ec2a2b8035d9feced
--- /dev/null
+++ b/appengine/findit/util_scripts/remote_queries/try_job_data_metrics.py
@@ -0,0 +1,399 @@
+# Copyright 2016 The Chromium Authors. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Pulls historical try job metadata from Findit and prints a report."""
+
+import argparse
+from collections import defaultdict
+import datetime
+import os
+import sys
+
+_REMOTE_API_DIR = os.path.join(os.path.dirname(__file__), os.path.pardir)
+sys.path.insert(1, _REMOTE_API_DIR)
+
+import remote_api
+
+from model.wf_config import FinditConfig
+from model.wf_try_job_data import WfTryJobData
+
+
+NOT_AVAILABLE = 'N/A'
+
+
+def _GetAverageOfNumbersInList(numbers):
+  """Returns a float average of numbers or NOT_AVAILABLE if numbers is empty."""
+  return (float(sum(numbers)) / len(numbers)) if numbers else NOT_AVAILABLE
+
+
+def _FormatDigits(number):
+  """Formats number into a 2-digit float, or NOT_AVAILABLE."""
+  if isinstance(number, float):
+    return float('%.2f' % number)
+  return NOT_AVAILABLE
+
+
+def _FormatSecondsAsHMS(seconds):
+  """Formats the number of seconds into hours, minutes, seconds."""
+  if seconds == NOT_AVAILABLE:
+    return NOT_AVAILABLE
+
+  minutes, seconds = divmod(seconds, 60)
+  hours, minutes = divmod(minutes, 60)
+  return '%d:%02d:%02d' % (hours, minutes, seconds)
+
+
+def _GetReportInformation(try_job_data_list, start_date, end_date):
+  """Computes and returns try job metadata.
+
+  Args:
+    try_job_data_list: A list of WfTryJobData entities.
+    start_date: The earliest request date to compute data.
+    end_date: The latest request date to compute data.
+
+  Returns:
+    A dict in the following format:
+    {
+        'try_jobs_per_day': The average number of jobs requested over the time
+            period specified,
+        'average_regression_range_size': The average number of revisions in the
+            regression range when the original failure was detected,
+        'average_execution_time': The average amount of time spent on each try
+            job not including in-queue time.
+        'average_time_in_queue': The average amount of time a try job spends
+            in-queue before it is picked up.
+        'average_commits_analyzed': The average number of revisions each try job
+            needed to run before coming to a conclusion,
+        'longest_execution_time': The length of time of the slowest try job,
+        'shortest_execution_time': The length of time of the fastest try job,
+        'number_of_try_jobs': The number of try jobs in this list,
+        'detection_rate': The number of try jobs that found any culprits at all
+            regardless of correctness over the total number of try jobs.
+        'error_rate': The number of try jobs that had an error / the total
+            number of try jobs in the list.
+        'time_per_revision': The average amount of execution time spent on each
+            revision.
+        'under_five_minutes_rate': The number of try jobs that finished under 5
+            minutes / total try jobs.
+        'under_fifteen_minutes_rate': The number of try jobs that finished in
+            under 15 minutes / total try jobs.
+        'under_thirty_minutes_rate': The number of try jobs that finished in
+            under 30 minutes / total try jobs.
+        'over_thirty_minutes_rate': The number of try jobs that finished in over
+            30 minutes / total try jobs.
+    }
+  """
+  try_jobs_per_day = NOT_AVAILABLE
+  average_regression_range_size = NOT_AVAILABLE
+  average_execution_time = NOT_AVAILABLE
+  average_time_in_queue = NOT_AVAILABLE
+  average_commits_analyzed = NOT_AVAILABLE
+  longest_execution_time = NOT_AVAILABLE
+  shortest_execution_time = NOT_AVAILABLE
+  detection_rate = NOT_AVAILABLE
+  error_rate = NOT_AVAILABLE
+  number_of_try_jobs = len(try_job_data_list) if try_job_data_list else 0
+  time_per_revision = NOT_AVAILABLE
+  under_five_minutes_rate = NOT_AVAILABLE
+  under_fifteen_minutes_rate = NOT_AVAILABLE
+  under_thirty_minutes_rate = NOT_AVAILABLE
+  over_thirty_minutes_rate = NOT_AVAILABLE
+
+  if try_job_data_list:
+    try_jobs_per_day = (
+        len(try_job_data_list) / float((end_date - start_date).days))
+    regression_range_sizes = []
+    execution_times_seconds = []
+    in_queue_times = []
+    commits_analyzed = []
+    culprits_detected = 0
+    errors_detected = 0
+    number_under_five_minutes = 0
+    number_under_fifteen_minutes = 0
+    number_under_thirty_minutes = 0
+    number_over_thirty_minutes = 0
+    total_number_of_try_jobs = len(try_job_data_list)
+
+    for try_job_data in try_job_data_list:
+      # Regression range size.
+      if try_job_data.regression_range_size:
+        regression_range_sizes.append(try_job_data.regression_range_size)
+
+      # Execution time.
+      if try_job_data.start_time and try_job_data.end_time:
+        execution_time_delta = (
+            try_job_data.end_time - try_job_data.start_time)
+        execution_time = execution_time_delta.total_seconds()
+        execution_times_seconds.append(execution_time)
+
+      # In-queue time.
+      if try_job_data.start_time and try_job_data.request_time:
+        in_queue_time_delta = (
+            try_job_data.start_time - try_job_data.request_time)
+        in_queue_time = in_queue_time_delta.total_seconds()
+        in_queue_times.append(in_queue_time)
+
+      # Total time end-to-end.
+      if try_job_data.request_time and try_job_data.end_time:
+        total_time_delta = try_job_data.end_time - try_job_data.start_time
+        total_time_seconds = total_time_delta.total_seconds()
+
+        if total_time_seconds < 300:  # Under 5 minutes.
+          number_under_five_minutes += 1
+        elif total_time_seconds < 900:  # Under 15 minutes.
+          number_under_fifteen_minutes += 1
+        elif total_time_seconds < 1800:  # Under 30 minutes.
+          number_under_thirty_minutes += 1
+        else:  # Over 30 minutes.
+          number_over_thirty_minutes += 1
+
+      # Number of commits analyzed.
+      if try_job_data.number_of_commits_analyzed:
+        commits_analyzed.append(try_job_data.number_of_commits_analyzed)
+
+      # Culprit detection rate.
+      if try_job_data.culprits:
+        culprits_detected += 1
+
+      if try_job_data.error:
+        errors_detected += 1
+
+    average_regression_range_size = _GetAverageOfNumbersInList(
+        regression_range_sizes)
+    average_execution_time = (_GetAverageOfNumbersInList(
+        execution_times_seconds) if execution_times_seconds else NOT_AVAILABLE)
+    average_time_in_queue = (
+        _GetAverageOfNumbersInList(in_queue_times) if in_queue_times else
+        NOT_AVAILABLE)
+    average_commits_analyzed = _GetAverageOfNumbersInList(
+        commits_analyzed)
+    longest_execution_time = (
+        str(datetime.timedelta(seconds=max(execution_times_seconds)))
+        if execution_times_seconds else NOT_AVAILABLE)
+    shortest_execution_time = (
+        str(datetime.timedelta(seconds=min(execution_times_seconds)))
+        if execution_times_seconds else NOT_AVAILABLE)
+    detection_rate = float(culprits_detected) / total_number_of_try_jobs
+    error_rate = float(errors_detected) / total_number_of_try_jobs
+    time_per_revision = (average_execution_time / average_commits_analyzed if (
+        average_execution_time != NOT_AVAILABLE and
+        average_commits_analyzed != NOT_AVAILABLE) else NOT_AVAILABLE)
+
+    under_five_minutes_rate = (
+        float(number_under_five_minutes) / total_number_of_try_jobs)
+    under_fifteen_minutes_rate = (
+        float(number_under_fifteen_minutes) / total_number_of_try_jobs)
+    under_thirty_minutes_rate = (
+        float(number_under_thirty_minutes) / total_number_of_try_jobs)
+    over_thirty_minutes_rate = (
+        float(number_over_thirty_minutes) / total_number_of_try_jobs)
+
+  return {
+      'try_jobs_per_day': _FormatDigits(try_jobs_per_day),
+      'average_regression_range_size': _FormatDigits(
+          average_regression_range_size),
+      'average_execution_time': _FormatSecondsAsHMS(
+          _FormatDigits(average_execution_time)),
+      'average_time_in_queue': _FormatSecondsAsHMS(
+          _FormatDigits(average_time_in_queue)),
+      'average_commits_analyzed': _FormatDigits(average_commits_analyzed),
+      'longest_execution_time': longest_execution_time,
+      'shortest_execution_time': shortest_execution_time,
+      'number_of_try_jobs': number_of_try_jobs,
+      'detection_rate': _FormatDigits(detection_rate),
+      'error_rate': _FormatDigits(error_rate),
+      'time_per_revision': _FormatSecondsAsHMS(
+          _FormatDigits(time_per_revision)),
+      'under_five_minutes_rate': _FormatDigits(under_five_minutes_rate),
+      'under_fifteen_minutes_rate': _FormatDigits(under_fifteen_minutes_rate),
+      'under_thirty_minutes_rate': _FormatDigits(under_thirty_minutes_rate),
+      'over_thirty_minutes_rate': _FormatDigits(over_thirty_minutes_rate)
+  }
+
+
+def PrintCommonStats(try_job_data_list, start_date, end_date, indent):
+  """Takes a list of WfTryJobData entities and prints their stats."""
+  spaces = ''
+  for _ in range(indent):
+    spaces += ' '
+
+  report_info = _GetReportInformation(try_job_data_list, start_date, end_date)
+  for key, value in report_info.iteritems():
+    print '%s%s: %s' % (spaces, key, value)
+
+
+def PrettyPrint(grouped_data, start_date, end_date, indent=0):
+  if not grouped_data:
+    return
+  if isinstance(grouped_data, list):
+    # Print the stats about the list.
+    PrintCommonStats(grouped_data, start_date, end_date, indent)
+  elif isinstance(grouped_data, dict):
+    spaces = ''
+    for _ in range(indent):
+      spaces += ' '
+
+    for field, data in grouped_data.iteritems():
+      print spaces + field
+      PrettyPrint(data, start_date, end_date, indent + 2)
+  else:
+    raise Exception('grouped_data dict should only contain dicts or lists.')
+
+
+def _SplitListByTryJobType(try_job_data_list):
+  categorized_data_dict = {
+      'compile': [],
+      'test': []
+  }
+  for try_job_data in try_job_data_list:
+    if try_job_data.try_job_type.lower() == 'compile':
+      categorized_data_dict['compile'].append(try_job_data)
+    elif try_job_data.try_job_type.lower() == 'test':
+      categorized_data_dict['test'].append(try_job_data)
+
+  return categorized_data_dict
+
+
+def _SplitListByMaster(try_job_data_list):
+  categorized_data_dict = defaultdict(list)
+
+  for try_job_data in try_job_data_list:
+    master_name = try_job_data.master_name
+
+    if not master_name:
+      continue
+
+    categorized_data_dict[master_name].append(try_job_data)
+
+  return categorized_data_dict
+
+
+def _SplitListByBuilder(try_job_data_list):
+  categorized_data_dict = defaultdict(list)
+
+  for try_job_data in try_job_data_list:
+    builder_name = try_job_data.builder_name
+
+    if not builder_name:
+      continue
+
+    categorized_data_dict[builder_name].append(try_job_data)
+
+  return categorized_data_dict
+
+
+def _SplitListByHeuristicResults(try_job_data_list):
+  categorized_data_dict = {
+      'with heuristic guidance': [],
+      'without heuristic guidance': []
+  }
+  for try_job_data in try_job_data_list:
+    if try_job_data.has_heuristic_results:
+      categorized_data_dict['with heuristic guidance'].append(try_job_data)
+    else:
+      categorized_data_dict['without heuristic guidance'].append(try_job_data)
+  return categorized_data_dict
+
+
+def _SplitListByCompileTargets(try_job_data_list):
+  categorized_data_dict = {
+      'with compile targets': [],
+      'without compile targets': []
+  }
+  for try_job_data in try_job_data_list:
+    if try_job_data.has_compile_targets:
+      categorized_data_dict['with compile targets'].append(try_job_data)
+    else:
+      categorized_data_dict['without compile targets'].append(try_job_data)
+  return categorized_data_dict
+
+
+def SplitListByOption(try_job_data_list, option):
+  """Takes a WfTryJobData list and separates it into a dict based on arg.
+
+  Args:
+    try_job_data_list: A list of WfTryJobData entities.
+    option: An option with which to split the data by.
+
+  Returns:
+    A dict where the keys are how the data is separated based on arg and the
+    values are the corresponding lists of data.
+  """
+
+  if option == 't':  # Try job type.
+    return _SplitListByTryJobType(try_job_data_list)
+  elif option == 'm':  # Main waterfall master.
+    return _SplitListByMaster(try_job_data_list)
+  elif option == 'b':  # Main waterfall builder.
+    return _SplitListByBuilder(try_job_data_list)
+  elif option == 'r':  # Whether or not heuristic results are included.
+    return _SplitListByHeuristicResults(try_job_data_list)
+  elif option == 'c':  # Whether or not compile targets are included.
+    return _SplitListByCompileTargets(try_job_data_list)
+  # TODO(lijeffrey): Add support for splitting by platform.
+
+  # Unsupported flag, bail out without modification.
+  return try_job_data_list
+
+
+def SplitStructByOption(try_job_data_struct, option):
+  if isinstance(try_job_data_struct, list):
+    try_job_data_struct = SplitListByOption(try_job_data_struct, option)
+  elif isinstance(try_job_data_struct, dict):
+    for key, struct in try_job_data_struct.iteritems():
+      try_job_data_struct[key] = SplitStructByOption(struct, option)
+  else:
+    raise Exception('try job data dict must only contain lists or dicts.')
+
+  return try_job_data_struct
+
+
+def GetArgsInOrder():
+  command_line_args = sys.argv[1:]
+
+  parser = argparse.ArgumentParser()
+  parser.add_argument('-t', action='store_true',
+                      help='group try job data by type (compile, test)')
+  parser.add_argument('-m', action='store_true',
+                      help='group try job data by master')
+  parser.add_argument('-b', action='store_true',
+                      help='group try job data by builder')
+  parser.add_argument('-r', action='store_true',
+                      help=('group try job data by those with and without '
+                            'heuristic results'))
+  parser.add_argument('-c', action='store_true',
+                      help=('group try job data by those with and without '
+                            'compile targets'))
+
+  args_dict = vars(parser.parse_args())
+
+  # Preserve order from original command.
+  ordered_args = []
+
+  for original_arg in command_line_args:
+    parsed_arg = original_arg[1:]
+    if args_dict[parsed_arg]:
+      ordered_args.append(parsed_arg)
+
+  return ordered_args
+
+
+if __name__ == '__main__':
+  # Set up the Remote API to use services on the live App Engine.
+  remote_api.EnableRemoteApi(app_id='findit-for-me')
+
+  START_DATE = datetime.datetime(2016, 5, 1)
+  END_DATE = datetime.datetime(2016, 6, 17)
+
+  try_job_data_query = WfTryJobData.query(
+      WfTryJobData.request_time >= START_DATE,
+      WfTryJobData.request_time < END_DATE)
+  categorized_data = try_job_data_query.fetch()
+
+  args = GetArgsInOrder()
+  for arg in args:
+    categorized_data = SplitStructByOption(categorized_data, arg)
+
+  # TODO(lijeffrey): Display data in an html page instead of printing.
+  PrettyPrint(categorized_data, START_DATE, END_DATE)