Also error error count to hit plot.

So apdex score impact of errors vs. slowness can be easily estimated.

apachedex/__init__.py

@@ -142,30 +142,47 @@ def getClassForStatusHit(hit, status):
     return 'problem'
   return ''
 
-def getDataPoints(apdex_dict):
+def getDataPoints(apdex_dict, status_period_dict={}):
+  period_error_dict = defaultdict(int)
+  for status, period_dict in status_period_dict.iteritems():
+    if statusIsError(status):
+      for period, hit in period_dict.iteritems():
+        period_error_dict[period] += hit
+  try:
+    # If there was an error, there was a hit, and apdex_dict must contain it
+    # (at same date).
+    assert len(set(period_error_dict) - set(apdex_dict)) == 0
+  except:
+    import pdb; pdb.set_trace()
+    raise
   return [
-    (value_date, apdex.getApdex() * 100, apdex.hit) for value_date, apdex
-    in sorted(apdex_dict.iteritems(), key=ITEMGETTER0)]
+    (
+      value_date,
+      apdex.getApdex() * 100,
+      apdex.hit,
+      period_error_dict.get(value_date, 0),
+    ) for value_date, apdex in sorted(apdex_dict.iteritems(), key=ITEMGETTER0)
+  ]
 
 def prepareDataForGraph(daily_data, date_format, placeholder_delta,
     coefficient_callback, x_min=None, x_max=None):
   current_date = datetime.strptime(x_min or daily_data[0][0], date_format)
   new_daily_data = []
   append = new_daily_data.append
-  for (measure_date_string, apdex, hit) in daily_data:
+  for (measure_date_string, apdex, hit, error_hit) in daily_data:
     measure_date = datetime.strptime(measure_date_string, date_format)
     if current_date < measure_date:
-      append((current_date.strftime(date_format), 100, 0))
+      append((current_date.strftime(date_format), 100, 0, 0))
       placeholder_end_date = measure_date - placeholder_delta
       if placeholder_end_date > current_date:
-        append((placeholder_end_date.strftime(date_format), 100, 0))
-    append((measure_date_string, apdex,
-      hit * coefficient_callback(measure_date)))
+        append((placeholder_end_date.strftime(date_format), 100, 0, 0))
+    coef = coefficient_callback(measure_date)
+    append((measure_date_string, apdex, hit * coef, error_hit * coef))
     current_date = measure_date + placeholder_delta
   if x_max is not None and current_date < datetime.strptime(x_max,
       date_format):
-    append((current_date.strftime(date_format), 100, 0))
-    append((x_max, 100, 0))
+    append((current_date.strftime(date_format), 100, 0, 0))
+    append((x_max, 100, 0, 0))
   return new_daily_data
 
 def graphPair(daily_data, date_format, graph_period, apdex_y_min=None,
@@ -200,7 +217,17 @@ def graphPair(daily_data, date_format, graph_period, apdex_y_min=None,
       },
     },
   ) + graph('Hits (per %s)' % graph_period,
-    [zip(date_list, (x[2] for x in daily_data))],
+    [
+      {
+        'label': 'Errors',
+        'data': zip(date_list, (x[3] for x in daily_data)),
+        'color': 'red',
+      },
+      {
+        'label': 'Hits',
+        'data': zip(date_list, (x[2] for x in daily_data)),
+      },
+    ],
     {
       'xaxis': {
         'mode': 'time',
@@ -221,6 +248,7 @@ def graphPair(daily_data, date_format, graph_period, apdex_y_min=None,
     },
   )
 
+
 def graph(title, data, options={}):
   result = []
   append = result.append
@@ -369,7 +397,7 @@ class GenericSiteStats(object):
     self.user_agent_counter[match.group('agent')] += 1
 
   def getApdexData(self):
-    return getDataPoints(self.apdex)
+    return getDataPoints(self.apdex, self.status)
 
   def asHTML(self, date_format, placeholder_delta, graph_period,
       graph_coefficient, encoding, stat_filter=lambda x: x,
@@ -1043,7 +1071,7 @@ def asHTML(out, encoding, per_site, args, default_site, period_parameter_dict,
     if apdex_data_list:
       x_min = min(x_min, apdex_data_list[0][0])
       x_max = max(x_max, apdex_data_list[-1][0])
-      for hit_date, _, hit in apdex_data_list:
+      for hit_date, _, hit, _ in apdex_data_list:
         hit_per_day[decimator(hit_date)] += hit
   if x_min == LARGER_THAN_INTEGER_STR:
     x_min = None