diff --git a/superset/connectors/druid/models.py b/superset/connectors/druid/models.py
index acb1951c98ba2..a666253f26c07 100644
--- a/superset/connectors/druid/models.py
+++ b/superset/connectors/druid/models.py
@@ -908,6 +908,9 @@ def values_for_column(self,
                           column_name,
                           limit=10000):
         """Retrieve some values for the given column"""
+        logging.info(
+            'Getting values for columns [{}] limited to [{}]'
+            .format(column_name, limit))
         # TODO: Use Lexicographic TopNMetricSpec once supported by PyDruid
         if self.fetch_values_from:
             from_dttm = utils.parse_human_datetime(self.fetch_values_from)
@@ -954,6 +957,37 @@ def _add_filter_from_pre_query_data(self, df, dimensions, dim_filter):
                     ret = Filter(type='and', fields=[ff, dim_filter])
         return ret
 
+    def get_aggregations(self, all_metrics):
+        aggregations = OrderedDict()
+        for m in self.metrics:
+            if m.metric_name in all_metrics:
+                aggregations[m.metric_name] = m.json_obj
+        return aggregations
+
+    def check_restricted_metrics(self, aggregations):
+        rejected_metrics = [
+            m.metric_name for m in self.metrics
+            if m.is_restricted and
+            m.metric_name in aggregations.keys() and
+            not sm.has_access('metric_access', m.perm)
+        ]
+        if rejected_metrics:
+            raise MetricPermException(
+                'Access to the metrics denied: ' + ', '.join(rejected_metrics),
+            )
+
+    def get_dimensions(self, groupby, columns_dict):
+        dimensions = []
+        groupby = [gb for gb in groupby if gb in columns_dict]
+        for column_name in groupby:
+            col = columns_dict.get(column_name)
+            dim_spec = col.dimension_spec if col else None
+            if dim_spec:
+                dimensions.append(dim_spec)
+            else:
+                dimensions.append(column_name)
+        return dimensions
+
     def run_query(  # noqa / druid
             self,
             groupby, metrics,
@@ -987,40 +1021,17 @@ def run_query(  # noqa / druid
 
         query_str = ''
         metrics_dict = {m.metric_name: m for m in self.metrics}
-
         columns_dict = {c.column_name: c for c in self.columns}
 
         all_metrics, post_aggs = DruidDatasource.metrics_and_post_aggs(
             metrics,
             metrics_dict)
 
-        aggregations = OrderedDict()
-        for m in self.metrics:
-            if m.metric_name in all_metrics:
-                aggregations[m.metric_name] = m.json_obj
-
-        rejected_metrics = [
-            m.metric_name for m in self.metrics
-            if m.is_restricted and
-            m.metric_name in aggregations.keys() and
-            not sm.has_access('metric_access', m.perm)
-        ]
-
-        if rejected_metrics:
-            raise MetricPermException(
-                'Access to the metrics denied: ' + ', '.join(rejected_metrics),
-            )
+        aggregations = self.get_aggregations(all_metrics)
+        self.check_restricted_metrics(aggregations)
 
         # the dimensions list with dimensionSpecs expanded
-        dimensions = []
-        groupby = [gb for gb in groupby if gb in columns_dict]
-        for column_name in groupby:
-            col = columns_dict.get(column_name)
-            dim_spec = col.dimension_spec
-            if dim_spec:
-                dimensions.append(dim_spec)
-            else:
-                dimensions.append(column_name)
+        dimensions = self.get_dimensions(groupby, columns_dict)
         extras = extras or {}
         qry = dict(
             datasource=self.datasource_name,
@@ -1042,17 +1053,20 @@ def run_query(  # noqa / druid
         having_filters = self.get_having_filters(extras.get('having_druid'))
         if having_filters:
             qry['having'] = having_filters
+
         order_direction = 'descending' if order_desc else 'ascending'
+
         if len(groupby) == 0 and not having_filters:
+            logging.info('Running timeseries query for no groupby values')
             del qry['dimensions']
             client.timeseries(**qry)
         elif (
             not having_filters and
             len(groupby) == 1 and
-            order_desc and
-            not isinstance(list(qry.get('dimensions'))[0], dict)
+            order_desc
         ):
             dim = list(qry.get('dimensions'))[0]
+            logging.info('Running two-phase topn query for dimension [{}]'.format(dim))
             if timeseries_limit_metric:
                 order_by = timeseries_limit_metric
             else:
@@ -1063,9 +1077,14 @@ def run_query(  # noqa / druid
             pre_qry['threshold'] = min(row_limit,
                                        timeseries_limit or row_limit)
             pre_qry['metric'] = order_by
-            pre_qry['dimension'] = dim
+            if isinstance(dim, dict):
+                if 'dimension' in dim:
+                    pre_qry['dimension'] = dim['dimension']
+            else:
+                pre_qry['dimension'] = dim
             del pre_qry['dimensions']
             client.topn(**pre_qry)
+            logging.info('Phase 1 Complete')
             query_str += '// Two phase query\n// Phase 1\n'
             query_str += json.dumps(
                 client.query_builder.last_query.query_dict, indent=2)
@@ -1077,19 +1096,22 @@ def run_query(  # noqa / druid
             df = client.export_pandas()
             qry['filter'] = self._add_filter_from_pre_query_data(
                 df,
-                qry['dimensions'], filters)
+                [pre_qry['dimension']],
+                filters)
             qry['threshold'] = timeseries_limit or 1000
             if row_limit and granularity == 'all':
                 qry['threshold'] = row_limit
-            qry['dimension'] = list(qry.get('dimensions'))[0]
             qry['dimension'] = dim
             del qry['dimensions']
             qry['metric'] = list(qry['aggregations'].keys())[0]
             client.topn(**qry)
+            logging.info('Phase 2 Complete')
         elif len(groupby) > 0:
             # If grouping on multiple fields or using a having filter
             # we have to force a groupby query
+            logging.info('Running groupby query for dimensions [{}]'.format(dimensions))
             if timeseries_limit and is_timeseries:
+                logging.info('Running two-phase query for timeseries')
                 order_by = metrics[0] if metrics else self.metrics[0]
                 if timeseries_limit_metric:
                     order_by = timeseries_limit_metric
@@ -1107,7 +1129,18 @@ def run_query(  # noqa / druid
                         'direction': order_direction,
                     }],
                 }
+                pre_qry_dims = []
+                # Replace dimensions specs with their `dimension`
+                # values, and ignore those without
+                for dim in qry['dimensions']:
+                    if isinstance(dim, dict):
+                        if 'dimension' in dim:
+                            pre_qry_dims.append(dim['dimension'])
+                    else:
+                        pre_qry_dims.append(dim)
+                pre_qry['dimensions'] = list(set(pre_qry_dims))
                 client.groupby(**pre_qry)
+                logging.info('Phase 1 Complete')
                 query_str += '// Two phase query\n// Phase 1\n'
                 query_str += json.dumps(
                     client.query_builder.last_query.query_dict, indent=2)
@@ -1119,7 +1152,7 @@ def run_query(  # noqa / druid
                 df = client.export_pandas()
                 qry['filter'] = self._add_filter_from_pre_query_data(
                     df,
-                    qry['dimensions'],
+                    pre_qry['dimensions'],
                     filters,
                 )
                 qry['limit_spec'] = None
@@ -1134,6 +1167,7 @@ def run_query(  # noqa / druid
                     }],
                 }
             client.groupby(**qry)
+            logging.info('Query Complete')
         query_str += json.dumps(
             client.query_builder.last_query.query_dict, indent=2)
         return query_str
diff --git a/superset/connectors/druid/views.py b/superset/connectors/druid/views.py
index ad3664b58aba9..66b3bc50e51e8 100644
--- a/superset/connectors/druid/views.py
+++ b/superset/connectors/druid/views.py
@@ -1,4 +1,5 @@
 from datetime import datetime
+import json
 import logging
 
 from flask import flash, Markup, redirect
@@ -61,9 +62,28 @@ class DruidColumnInlineView(CompactCRUDMixin, SupersetModelView):  # noqa
             True),
     }
 
+    def pre_update(self, col):
+        # If a dimension spec JSON is given, ensure that it is
+        # valid JSON and that `outputName` is specified
+        if col.dimension_spec_json:
+            try:
+                dimension_spec = json.loads(col.dimension_spec_json)
+            except ValueError as e:
+                raise ValueError('Invalid Dimension Spec JSON: ' + str(e))
+            if not isinstance(dimension_spec, dict):
+                raise ValueError('Dimension Spec must be a JSON object')
+            if 'outputName' not in dimension_spec:
+                raise ValueError('Dimension Spec does not contain `outputName`')
+            if 'dimension' not in dimension_spec:
+                raise ValueError('Dimension Spec is missing `dimension`')
+            # `outputName` should be the same as the `column_name`
+            if dimension_spec['outputName'] != col.column_name:
+                raise ValueError(
+                    '`outputName` [{}] unequal to `column_name` [{}]'
+                    .format(dimension_spec['outputName'], col.column_name))
+
     def post_update(self, col):
         col.generate_metrics()
-        utils.validate_json(col.dimension_spec_json)
 
     def post_add(self, col):
         self.post_update(col)
diff --git a/tests/druid_func_tests.py b/tests/druid_func_tests.py
index 4c047dff1930b..74da48665f581 100644
--- a/tests/druid_func_tests.py
+++ b/tests/druid_func_tests.py
@@ -226,7 +226,8 @@ def test_run_query_single_groupby(self):
         self.assertIn('dimensions', client.groupby.call_args_list[0][1])
         self.assertEqual(['col1'], client.groupby.call_args_list[0][1]['dimensions'])
         # order_desc but timeseries and dimension spec
-        spec = {'spec': 1}
+        # calls topn with single dimension spec 'dimension'
+        spec = {'outputName': 'hello', 'dimension': 'matcho'}
         spec_json = json.dumps(spec)
         col3 = DruidColumn(column_name='col3', dimension_spec_json=spec_json)
         ds.columns.append(col3)
@@ -238,13 +239,14 @@ def test_run_query_single_groupby(self):
             client=client, order_desc=True, timeseries_limit=5,
             filter=[], row_limit=100,
         )
-        self.assertEqual(0, len(client.topn.call_args_list))
-        self.assertEqual(2, len(client.groupby.call_args_list))
+        self.assertEqual(2, len(client.topn.call_args_list))
+        self.assertEqual(0, len(client.groupby.call_args_list))
         self.assertEqual(0, len(client.timeseries.call_args_list))
-        self.assertIn('dimensions', client.groupby.call_args_list[0][1])
-        self.assertIn('dimensions', client.groupby.call_args_list[1][1])
-        self.assertEqual([spec], client.groupby.call_args_list[0][1]['dimensions'])
-        self.assertEqual([spec], client.groupby.call_args_list[1][1]['dimensions'])
+        self.assertIn('dimension', client.topn.call_args_list[0][1])
+        self.assertIn('dimension', client.topn.call_args_list[1][1])
+        # uses dimension for pre query and full spec for final query
+        self.assertEqual('matcho', client.topn.call_args_list[0][1]['dimension'])
+        self.assertEqual(spec, client.topn.call_args_list[1][1]['dimension'])
 
     def test_run_query_multiple_groupby(self):
         client = Mock()