我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用elasticsearch.VERSION。
def _build_search_filters_dwithin(self, dwithin): lng, lat = dwithin['point'].get_coords() # NB: the 1.0.0 release of elasticsearch introduce an # incompatible change on the distance filter formating if elasticsearch.VERSION >= (1, 0, 0): distance = "%(dist).6f%(unit)s" % { 'dist': dwithin['distance'].km, 'unit': "km" } else: distance = dwithin['distance'].km dwithin_filter = { "geo_distance": { "distance": distance, dwithin['field']: { "lat": lat, "lon": lng } } } return dwithin_filter
def setup(self): """ Defers loading until needed. """ # Get the existing mapping & cache it. We'll compare it # during the ``update`` & if it doesn't match, we'll put the new # mapping. try: self.existing_mapping = self.conn.indices.get_mapping(index=self.index_name) except NotFoundError: pass except Exception: if not self.silently_fail: raise unified_index = haystack.connections[self.connection_alias].get_unified_index() self.content_field_name, field_mapping = self.build_schema(unified_index.all_searchfields()) # fixing ES 1.x/2.x compatible `_boost` current_mapping = { 'modelresult': { 'properties': field_mapping, } } if elasticsearch.VERSION < (2, 0, 0): current_mapping['modelresult']['_boost'] = { 'name': 'boost', 'null_value': 1.0 } # end fixing ES 1.x/2.x compatible `_boost` if current_mapping != self.existing_mapping: try: # Make sure the index is there first. self.conn.indices.create(index=self.index_name, body=self.DEFAULT_SETTINGS, ignore=400) self.conn.indices.put_mapping(index=self.index_name, doc_type='modelresult', body=current_mapping) self.existing_mapping = current_mapping except Exception: if not self.silently_fail: raise self.setup_complete = True
def setUp(self): super(ElasticsearchSearchQuerySpatialBeforeReleaseTestCase, self).setUp() self.backend = connections['default'].get_backend() self._elasticsearch_version = elasticsearch.VERSION elasticsearch.VERSION = (0,9,9)
def tearDown(self): elasticsearch.VERSION = self._elasticsearch_version
def setUp(self): super(ElasticsearchSearchQuerySpatialAfterReleaseTestCase, self).setUp() self.backend = connections['default'].get_backend() self._elasticsearch_version = elasticsearch.VERSION elasticsearch.VERSION = (1,0,0)
def log_request_fail(self, method, full_url, path, body, duration, status_code=None, response=None, exception=None): if es.VERSION < (5, 0): super().log_request_fail(method, full_url, body, duration, status_code, response, exception) else: super().log_request_fail(method, full_url, path, body, duration, status_code, response, exception)
def test_query__in_empty_list(self): """Confirm that an empty list avoids a Elasticsearch exception""" sqs = SearchQuerySet(using='default').filter(id__in=[]) self.assertEqual(sqs.query.build_query(), u'id:(!*:*)') # class ElasticsearchSearchQuerySpatialBeforeReleaseTestCase(TestCase): # def setUp(self): # super(ElasticsearchSearchQuerySpatialBeforeReleaseTestCase, self).setUp() # self.backend = connections['default'].get_backend() # self._elasticsearch_version = elasticsearch.VERSION # elasticsearch.VERSION = (0,9,9) # def tearDown(self): # elasticsearch.VERSION = self._elasticsearch_version # def test_build_query_with_dwithin_range(self): # """ # Test build_search_kwargs with dwithin range for Elasticsearch versions < 1.0.0 # """ # search_kwargs = self.backend.build_search_kwargs('where', dwithin={ # 'field': "location_field", # 'point': Point(1.2345678, 2.3456789), # 'distance': D(m=500) # }) # self.assertEqual(search_kwargs['query']['filtered']['filter']['bool']['must'][1]['geo_distance'], {'distance': 0.5, 'location_field': {'lat': 2.3456789, 'lon': 1.2345678}}) # class ElasticsearchSearchQuerySpatialAfterReleaseTestCase(TestCase): # def setUp(self): # super(ElasticsearchSearchQuerySpatialAfterReleaseTestCase, self).setUp() # self.backend = connections['elasticsearch'].get_backend() # self._elasticsearch_version = elasticsearch.VERSION # elasticsearch.VERSION = (1,0,0) # def tearDown(self): # elasticsearch.VERSION = self._elasticsearch_version # def test_build_query_with_dwithin_range(self): # """ # Test build_search_kwargs with dwithin range for Elasticsearch versions >= 1.0.0 # """ # search_kwargs = self.backend.build_search_kwargs('where', dwithin={ # 'field': "location_field", # 'point': Point(1.2345678, 2.3456789), # 'distance': D(m=500) # }) # self.assertEqual(search_kwargs['query']['filtered']['filter']['bool']['must'][1]['geo_distance'], {'distance': "0.500000km", 'location_field': {'lat': 2.3456789, 'lon': 1.2345678}})
