Conditionally modify the context object instead of conditionally

returning early.

Author: Jeremy Phelps

pydruid/client.py

@@ -53,7 +53,7 @@ def _prepare_url_headers_and_body(self, query):
             username_password = \
                 b64encode(bytes('{}:{}'.format(self.username, self.password)))
             headers['Authorization'] = 'Basic {}'.format(username_password)
-            
+
         return headers, querystr, url
 
     def _post(self, query):
@@ -71,10 +71,8 @@ def _post(self, query):
 
     def topn(self, **kwargs):
         """
-        A TopN query returns a set of the values in a given dimension,
-        sorted by a specified metric. Conceptually, a topN can be
-        thought of as an approximate GroupByQuery over a single
-        dimension with an Ordering spec. TopNs are
+        A TopN query returns a set of the values in a given dimension, sorted by a specified metric. Conceptually, a
+        topN can be thought of as an approximate GroupByQuery over a single dimension with an Ordering spec. TopNs are
         faster and more resource efficient than GroupBy for this use case.
 
         Required key/value pairs:
@@ -83,8 +81,7 @@ def topn(self, **kwargs):
         :param str granularity: Aggregate data by hour, day, minute, etc.,
         :param intervals: ISO-8601 intervals of data to query
         :type intervals: str or list
-        :param dict aggregations: A map from aggregator name to one of
-          the pydruid.utils.aggregators e.g., doublesum
+        :param dict aggregations: A map from aggregator name to one of the pydruid.utils.aggregators e.g., doublesum
         :param str dimension: Dimension to run the query against
         :param str metric: Metric over which to sort the specified dimension by
         :param int threshold: How many of the top items to return
@@ -94,10 +91,8 @@ def topn(self, **kwargs):
 
         Optional key/value pairs:
 
-        :param pydruid.utils.filters.Filter filter: Indicates which rows
-          of data to include in the query
-        :param post_aggregations:   A dict with string key = 'post_aggregator_name',
-          and value pydruid.utils.PostAggregator
+        :param pydruid.utils.filters.Filter filter: Indicates which rows of data to include in the query
+        :param post_aggregations:   A dict with string key = 'post_aggregator_name', and value pydruid.utils.PostAggregator
         :param dict context: A dict of query context options
 
         Example:
@@ -117,35 +112,30 @@ def topn(self, **kwargs):
                             context={"timeout": 1000}
                         )
                 >>> print top
-                >>> [{'timestamp': '2013-06-14T00:00:00.000Z',
-                    'result': [{'count': 22.0, 'user': "cool_user"}}]}]
+                >>> [{'timestamp': '2013-06-14T00:00:00.000Z', 'result': [{'count': 22.0, 'user': "cool_user"}}]}]
         """
         query = self.query_builder.topn(kwargs)
         return self._post(query)
 
     def timeseries(self, **kwargs):
         """
-        A timeseries query returns the values of the requested metrics (in aggregate)
-        for each timestamp.
+        A timeseries query returns the values of the requested metrics (in aggregate) for each timestamp.
 
         Required key/value pairs:
 
         :param str datasource: Data source to query
         :param str granularity: Time bucket to aggregate data by hour, day, minute, etc.,
         :param intervals: ISO-8601 intervals for which to run the query on
         :type intervals: str or list
-        :param dict aggregations: A map from aggregator name to one of the
-          ``pydruid.utils.aggregators`` e.g., ``doublesum``
+        :param dict aggregations: A map from aggregator name to one of the pydruid.utils.aggregators e.g., doublesum
 
         :return: The query result
         :rtype: Query
 
         Optional key/value pairs:
 
-        :param pydruid.utils.filters.Filter filter: Indicates which rows of
-          data to include in the query
-        :param post_aggregations:   A dict with string key =
-          'post_aggregator_name', and value pydruid.utils.PostAggregator
+        :param pydruid.utils.filters.Filter filter: Indicates which rows of data to include in the query
+        :param post_aggregations:   A dict with string key = 'post_aggregator_name', and value pydruid.utils.PostAggregator
         :param dict context: A dict of query context options
 
         Example:
@@ -157,49 +147,39 @@ def timeseries(self, **kwargs):
                         datasource=twitterstream,
                         granularity='hour',
                         intervals='2013-06-14/pt1h',
-                        aggregations=\
-                            {"count": doublesum("count"), "rows": count("rows")},
-                        post_aggregations=\
-                            {'percent': (Field('count') / Field('rows')) * Const(100))},
+                        aggregations={"count": doublesum("count"), "rows": count("rows")},
+                        post_aggregations={'percent': (Field('count') / Field('rows')) * Const(100))},
                         context={"timeout": 1000}
                     )
                 >>> print counts
-                >>> [{'timestamp': '2013-06-14T00:00:00.000Z',
-                    'result': {'count': 9619.0, 'rows': 8007,
-                    'percent': 120.13238416385663}}]
+                >>> [{'timestamp': '2013-06-14T00:00:00.000Z', 'result': {'count': 9619.0, 'rows': 8007, 'percent': 120.13238416385663}}]
         """
         query = self.query_builder.timeseries(kwargs)
         return self._post(query)
 
     def groupby(self, **kwargs):
         """
-        A group-by query groups a results set (the requested aggregate
-        metrics) by the specified dimension(s).
+        A group-by query groups a results set (the requested aggregate metrics) by the specified dimension(s).
 
         Required key/value pairs:
 
         :param str datasource: Data source to query
         :param str granularity: Time bucket to aggregate data by hour, day, minute, etc.,
         :param intervals: ISO-8601 intervals for which to run the query on
         :type intervals: str or list
-        :param dict aggregations: A map from aggregator name to one of the
-          ``pydruid.utils.aggregators`` e.g., ``doublesum``
+        :param dict aggregations: A map from aggregator name to one of the pydruid.utils.aggregators e.g., doublesum
         :param list dimensions: The dimensions to group by
 
         :return: The query result
         :rtype: Query
 
         Optional key/value pairs:
 
-        :param pydruid.utils.filters.Filter filter: Indicates which rows of
-          data to include in the query
-        :param pydruid.utils.having.Having having: Indicates which groups
-          in results set of query to keep
-        :param post_aggregations:   A dict with string key = 'post_aggregator_name',
-          and value pydruid.utils.PostAggregator
+        :param pydruid.utils.filters.Filter filter: Indicates which rows of data to include in the query
+        :param pydruid.utils.having.Having having: Indicates which groups in results set of query to keep
+        :param post_aggregations:   A dict with string key = 'post_aggregator_name', and value pydruid.utils.PostAggregator
         :param dict context: A dict of query context options
-        :param dict limit_spec: A dict of parameters defining how to limit
-          the rows returned, as specified in the Druid api documentation
+        :param dict limit_spec: A dict of parameters defining how to limit the rows returned, as specified in the Druid api documentation
 
         Example:
 
@@ -222,25 +202,8 @@ def groupby(self, **kwargs):
                     )
                 >>> for k in range(2):
                     ...     print group[k]
-                >>> {
-                    'timestamp': '2013-10-04T00:00:00.000Z',
-                    'version': 'v1',
-                    'event': {
-                        'count': 1.0,
-                        'user_name': 'user_1',
-                        'reply_to_name': 'user_2',
-                    }
-                }
-                >>> {
-                    'timestamp': '2013-10-04T00:00:00.000Z',
-                    'version': 'v1',
-                    'event': {
-                        'count': 1.0,
-                        'user_name': 'user_2',
-                        'reply_to_name':
-                        'user_3',
-                    }
-                }
+                >>> {'timestamp': '2013-10-04T00:00:00.000Z', 'version': 'v1', 'event': {'count': 1.0, 'user_name': 'user_1', 'reply_to_name': 'user_2'}}
+                >>> {'timestamp': '2013-10-04T00:00:00.000Z', 'version': 'v1', 'event': {'count': 1.0, 'user_name': 'user_2', 'reply_to_name': 'user_3'}}
         """
         query = self.query_builder.groupby(kwargs)
         return self._post(query)
@@ -274,17 +237,11 @@ def segment_metadata(self, **kwargs):
         .. code-block:: python
             :linenos:
 
-                >>> meta = client.segment_metadata(
-                    datasource='twitterstream', intervals = '2013-10-04/pt1h')
+                >>> meta = client.segment_metadata(datasource='twitterstream', intervals = '2013-10-04/pt1h')
                 >>> print meta[0].keys()
                 >>> ['intervals', 'id', 'columns', 'size']
                 >>> print meta[0]['columns']['tweet_length']
-                >>> {
-                    'errorMessage': None,
-                    'cardinality': None,
-                    'type': 'FLOAT',
-                    'size': 30908008,
-                }
+                >>> {'errorMessage': None, 'cardinality': None, 'type': 'FLOAT', 'size': 30908008}
 
         """
         query = self.query_builder.segment_metadata(kwargs)
@@ -312,13 +269,7 @@ def time_boundary(self, **kwargs):
 
                 >>> bound = client.time_boundary(datasource='twitterstream')
                 >>> print bound
-                >>> [{
-                    'timestamp': '2011-09-14T15:00:00.000Z',
-                    'result': {
-                        'minTime': '2011-09-14T15:00:00.000Z',
-                        'maxTime': '2014-03-04T23:44:00.000Z',
-                    }
-                }]
+                >>> [{'timestamp': '2011-09-14T15:00:00.000Z', 'result': {'minTime': '2011-09-14T15:00:00.000Z', 'maxTime': '2014-03-04T23:44:00.000Z'}}]
         """
         query = self.query_builder.time_boundary(kwargs)
         return self._post(query)
@@ -337,12 +288,9 @@ def select(self, **kwargs):
 
         Optional key/value pairs:
 
-        :param pydruid.utils.filters.Filter filter: Indicates which rows of
-          data to include in the query
-        :param list dimensions: The list of dimensions to select. If left
-          empty, all dimensions are returned
-        :param list metrics: The list of metrics to select. If left empty,
-          all metrics are returned
+        :param pydruid.utils.filters.Filter filter: Indicates which rows of data to include in the query
+        :param list dimensions: The list of dimensions to select. If left empty, all dimensions are returned
+        :param list metrics: The list of metrics to select. If left empty, all metrics are returned
         :param dict context: A dict of query context options
 
         :return: The query result
@@ -360,22 +308,8 @@ def select(self, **kwargs):
                         paging_spec={'pagingIdentifies': {}, 'threshold': 1},
                         context={"timeout": 1000}
                     )
-                >>> print(raw_data)
-                >>> [{
-                    'timestamp': '2013-06-14T00:00:00.000Z',
-                    'result': {
-                        'pagingIdentifiers': {
-                            'twitterstream_...08:00:00.000Z_v1': 1,
-                            'events': [{
-                                'segmentId': 'twitterstr...000Z_v1',
-                                'offset': 0,
-                                'event': {
-                                    'timestamp': '2013-06-14T00:00:00.000Z',
-                                    'dim': 'value',
-                                }
-                            }]
-                        }
-                }]
+                >>> print raw_data
+                >>> [{'timestamp': '2013-06-14T00:00:00.000Z', 'result': {'pagingIdentifiers': {'twitterstream_2013-06-14T00:00:00.000Z_2013-06-15T00:00:00.000Z_2013-06-15T08:00:00.000Z_v1': 1, 'events': [{'segmentId': 'twitterstream_2013-06-14T00:00:00.000Z_2013-06-15T00:00:00.000Z_2013-06-15T08:00:00.000Z_v1', 'offset': 0, 'event': {'timestamp': '2013-06-14T00:00:00.000Z', 'dim': 'value'}}]}}]
         """
         query = self.query_builder.select(kwargs)
         return self._post(query)
@@ -388,8 +322,7 @@ def export_tsv(self, dest_path):
             Use Query.export_tsv() method instead.
         """
         if self.query_builder.last_query is None:
-            raise AttributeError(
-                "There was no query executed by this client yet. Can't export!")
+            raise AttributeError("There was no query executed by this client yet. Can't export!")
         else:
             return self.query_builder.last_query.export_tsv(dest_path)
 
@@ -401,17 +334,15 @@ def export_pandas(self):
             Use Query.export_pandas() method instead
         """
         if self.query_builder.last_query is None:
-            raise AttributeError(
-                "There was no query executed by this client yet. Can't export!")
+            raise AttributeError("There was no query executed by this client yet. Can't export!")
         else:
             return self.query_builder.last_query.export_pandas()
 
 
 class PyDruid(BaseDruidClient):
     """
-    PyDruid contains the functions for creating and executing Druid queries.
-    Returns Query objects that can be used for exporting query results
-    into TSV files or pandas.DataFrame objects for subsequent analysis.
+    PyDruid contains the functions for creating and executing Druid queries. Returns Query objects that can be used
+    for exporting query results into TSV files or pandas.DataFrame objects for subsequent analysis.
 
     :param str url: URL of Broker node in the Druid cluster
     :param str endpoint: Endpoint that Broker listens for queries on
@@ -460,18 +391,8 @@ class PyDruid(BaseDruidClient):
                 }
 
             >>> print top.result
-            >>> [{
-                'timestamp': '2013-10-04T00:00:00.000Z',
-                'result': [
-                    {
-                        'count': 7.0,
-                        'user_name': 'user_1',
-                    },
-                    {
-                        'count': 6.0,
-                        'user_name': 'user_2',
-                    },
-                ]}]
+            >>> [{'timestamp': '2013-10-04T00:00:00.000Z',
+                'result': [{'count': 7.0, 'user_name': 'user_1'}, {'count': 6.0, 'user_name': 'user_2'}]}]
 
             >>> df = top.export_pandas()
             >>> print df
@@ -484,10 +405,9 @@ def __init__(self, url, endpoint):
 
     def ssl_context(self):
         ctx = ssl.create_default_context()
-        if not self.ignore_certificate_errors:
-            return ctx
-        ctx.check_hostname = False
-        ctx.verify_mode = ssl.CERT_NONE
+        if self.ignore_certificate_errors:
+            ctx.check_hostname = False
+            ctx.verify_mode = ssl.CERT_NONE
         return ctx
 
     def _post(self, query):
@@ -512,52 +432,3 @@ def _post(self, query):
         else:
             query.parse(data)
             return query
-
-    def scan(self, **kwargs):
-        """
-        A scan query returns raw Druid rows
-
-        Required key/value pairs:
-
-        :param str datasource: Data source to query
-        :param str granularity: Time bucket to aggregate data by hour, day, minute, etc.
-        :param int limit: The maximum number of rows to return
-        :param intervals: ISO-8601 intervals for which to run the query on
-        :type intervals: str or list
-
-        Optional key/value pairs:
-
-        :param pydruid.utils.filters.Filter filter: Indicates which rows of
-          data to include in the query
-        :param list dimensions: The list of dimensions to select. If left
-          empty, all dimensions are returned
-        :param list metrics: The list of metrics to select. If left empty,
-          all metrics are returned
-        :param dict context: A dict of query context options
-
-        :return: The query result
-        :rtype: Query
-
-        Example:
-
-        .. code-block:: python
-            :linenos:
-
-                >>> raw_data = client.scan(
-                        datasource=twitterstream,
-                        granularity='all',
-                        intervals='2013-06-14/pt1h',
-                        limit=1,
-                        context={"timeout": 1000}
-                    )
-                >>> print raw_data
-                >>> [{
-                    u'segmentId': u'zzzz',
-                    u'columns': [u'__time', 'status', 'region'],
-                    'events': [{
-                        u'status': u'ok', 'region': u'SF', u'__time': 1509494400000,
-                    }]
-                }]
-        """
-        query = self.query_builder.scan(kwargs)
-        return self._post(query)