[Findit] Adding data model for capturing check flake analysis metadata

appengine/findit/model/flake/master_flake_analysis_data.py

Index: appengine/findit/model/flake/master_flake_analysis_data.py
diff --git a/appengine/findit/model/flake/master_flake_analysis_data.py b/appengine/findit/model/flake/master_flake_analysis_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..727dd4d3525e97a6ef2815a5d85c9dd800b4c80d
--- /dev/null
+++ b/appengine/findit/model/flake/master_flake_analysis_data.py
@@ -0,0 +1,92 @@
+# 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.
+
+import base64
+
+from google.appengine.ext import ndb
+
+from model.base_build_model import BaseBuildModel
+
+
+class CheckFlakeAnalysisData(BaseBuildModel):
+  """Represents a check flake task's metadata for a complete run."""
+  # The UTC timestamp the check flake task was requested.
+  created_time = ndb.DateTimeProperty(indexed=True)
+
+  # The UTC timestamp the check flake task came completed.
+  completed_time = ndb.DateTimeProperty(indexed=True)
+
+  # A dict containing information about each swarming rerun's results that can
+  # be used for metrics, such as number of cache hits, average run time, etc.
+  # Example dict:
+  # {
+  #     task_id_1: {
+  #         'request_time': 2016-09-06 (10:21:26.288) UTC
+  #         'start_time': 2016-09-06 (10:21:26.288) UTC,
+  #         'end_time': 2016-09-06 (10:21:26.288) UTC,
+  #         'build_number': 12345,
+  #         'cache_hit': True/False,
+  #         'number_of_iterations': 100,
+  #         'number_of_passes': 90,
+  #     },
+  #     task_id_2: {
+  #         ...
+  #     },
+  #     ...
+  # }
+  swarming_rerun_results = ndb.JsonProperty(indexed=False)
+
+  # Error code and message, if any.
+  error = ndb.JsonProperty(indexed=False)
+
+  # Integer representing the suspected build number that regressed.
+  regression_build_number = ndb.IntegerProperty(indexed=False)
+
+  # Boolean whether or not the suspected regression range/build is correct.
+  correct = ndb.BooleanProperty(indexed=False)
+
+  # The look back algorithm parameters that were used, as specified in Findit's
+  # configuration. For example,
+  # {
+  #     'iterations_to_rerun': 100,
+  #     'lower_flake_threshold': 0.02,
+  #     'max_build_numbers_to_look_back': 500,
+  #     'max_flake_in_a_row': 4,
+  #     'max_stable_in_a_row': 4,
+  #     'upper_flake_threshold': 0.98
+  # }
+  algorithm_parameters = ndb.JsonProperty(indexed=False)
+
+  @staticmethod
+  def _CreateKey(master_name, builder_name, build_number, step_name, test_name,
+                 version):
+    encoded_test_name = base64.urlsafe_b64encode(test_name)
+    key = '%s/%s/%s/%s/%s/%s' % (master_name, builder_name, build_number,
+                                 step_name, encoded_test_name, version)
+    return ndb.Key('CheckFlakeAnalysisData', key)
+
+  @staticmethod
+  def Create(master_name, builder_name, build_number, step_name, test_name,
+             version):
+    return CheckFlakeAnalysisData(key=CheckFlakeAnalysisData._CreateKey(
+        master_name, builder_name, build_number, step_name, test_name, version))
+
+  @staticmethod
+  def Get(master_name, builder_name, build_number, step_name, test_name,
+          version):
+    return CheckFlakeAnalysisData._CreateKey(
+        master_name, builder_name, build_number, step_name, test_name,
+        version).get()
+
+  @ndb.ComputedProperty
+  def step_name(self):
+    return self.key.pairs()[0][1].split('/')[3]
+
+  @ndb.ComputedProperty
+  def test_name(self):
+    return base64.urlsafe_b64decode(self.key.pairs()[0][1].split('/')[4])
+
+  @ndb.ComputedProperty
+  def version(self):
+    return self.key.pairs()[0][1].split('/')[5]