[Findit] Adding metadata for manually vs automatically triggered flake analyses

appengine/findit/model/flake/master_flake_analysis.py

 # 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 import analysis_status
 from model.base_analysis import BaseAnalysis
 from model.base_build_model import BaseBuildModel
 from model.flake.flake_swarming_task import FlakeSwarmingTaskData
 from model.versioned_model import VersionedModel
+from waterfall.flake import triggering_sources
 
 
 class DataPoint(ndb.Model):
   build_number = ndb.IntegerProperty(indexed=False)
   pass_rate = ndb.FloatProperty(indexed=False)
 
 
 class MasterFlakeAnalysis(BaseAnalysis, BaseBuildModel, VersionedModel):
   """Represents an analysis of a flaky test on a Waterfall test cycle."""
 
@@ skipping 81 matching lines @@
   #     'upper_flake_threshold': 0.98
   # }
   algorithm_parameters = ndb.JsonProperty(indexed=False)
 
   # The suspected build number to have introduced the flakiness.
   suspected_flake_build_number = ndb.IntegerProperty()
 
   # The data points used to plot the flakiness graph build over build.
   data_points = ndb.LocalStructuredProperty(
       DataPoint, repeated=True, compressed=True)
+
+  # Whether the analysis was triggered by a manual request through check flake,
+  # Findit's automatic analysis upon detection, or Findit API.
+  triggering_source = ndb.IntegerProperty(
+      default=triggering_sources.FINDIT, indexed=True)

[stgao, 2016/10/13 06:14:38]

The default value is not true for legacy data. Maybe we could just leave it as
None by default.

[lijeffrey, 2016/10/14 23:45:21]

Done.

+
+  # Who triggered the analysis. Used for differentiating between manual and
+  # automatic runs, and determining the most active users to gather feedback.
+  triggering_user_email = ndb.StringProperty(default=None, indexed=False)