def __init__(self, connection_alias, **connection_options):
super(ElasticsearchSearchBackend, self).__init__(connection_alias, **connection_options)
# Update DEFAULT_SETTINGS with custom settings
self.DEFAULT_SETTINGS.update(**merge_dict(self.DEFAULT_SETTINGS, es_settings.INDEX_SETTINGS))
# Update FIELD_MAPPINGS with custom field mappings
FIELD_MAPPINGS.update(**merge_dict(FIELD_MAPPINGS, es_settings.FIELD_MAPPINGS))
def build_schema(self, fields, language):
"""
Build the index schema for the given field. New argument language.
:param fields:
:param language: the language code
:return: a dictionary wit the field name (string) and the
mapping configuration (dictionary)
"""
content_field_name = ''
mapping = {
DJANGO_CT: {'type': 'string', 'index': 'not_analyzed', 'include_in_all': False},
DJANGO_ID: {'type': 'string', 'index': 'not_analyzed', 'include_in_all': False},
}
for field_name, field_class in fields.items():
field_mapping = FIELD_MAPPINGS.get(
field_class.field_type, DEFAULT_FIELD_MAPPING).copy()
if field_class.boost != 1.0:
field_mapping['boost'] = field_class.boost
if field_class.document is True:
content_field_name = field_class.index_fieldname
# Do this last to override `text` fields.
if field_mapping['type'] == 'string':
# Use the language analyzer for text fields.
if field_mapping['analyzer'] == DEFAULT_FIELD_MAPPING['analyzer']:
field_mapping['analyzer'] = get_analyzer_for(language)
if field_class.indexed is False or hasattr(field_class, 'facet_for'):
field_mapping['index'] = 'not_analyzed'
del field_mapping['analyzer']
mapping[field_class.index_fieldname] = field_mapping
return content_field_name, mapping
def build_schema(self, fields):
content_field_name = ''
mapping = {
DJANGO_CT: {
'type': 'keyword',
'index': 'not_analyzed',
'include_in_all': False
},
DJANGO_ID: {
'type': 'keyword',
'index': 'not_analyzed',
'include_in_all': False
},
}
for field_name, field_class in fields.items():
field_mapping = FIELD_MAPPINGS.get(field_class.field_type,
DEFAULT_FIELD_MAPPING).copy()
if field_class.boost != 1.0:
field_mapping['boost'] = field_class.boost
if field_class.document is True:
content_field_name = field_class.index_fieldname
# Do this last to override `text` fields.
if field_mapping['type'] == 'string':
if field_class.indexed is False or hasattr(
field_class, 'facet_for'):
field_mapping['index'] = 'not_analyzed'
del field_mapping['analyzer']
mapping[field_class.index_fieldname] = field_mapping
return (content_field_name, mapping)
def build_schema(self, fields):
content_field_name = ''
mapping = {
DJANGO_CT: {'type': 'string', 'index': 'not_analyzed', 'include_in_all': False},
DJANGO_ID: {'type': 'string', 'index': 'not_analyzed', 'include_in_all': False},
}
for field_name, field_class in fields.items():
field_mapping = FIELD_MAPPINGS.get(
field_class.field_type, self.DEFAULT_FIELD_MAPPING).copy()
if field_class.boost != 1.0:
field_mapping['boost'] = field_class.boost
if field_class.document is True:
content_field_name = field_class.index_fieldname
# Do this last to override `text` fields.
if field_mapping['type'] == 'string':
if field_class.indexed is False or hasattr(field_class, 'facet_for'):
field_mapping['index'] = 'not_analyzed'
del field_mapping['analyzer']
mapping[field_class.index_fieldname] = field_mapping
return (content_field_name, mapping)
def build_schema(self, indexes):
"""Build Elasticsearch schema.
:param indexes: Dictionary of model -> index.
:type indexes: dict
:return: Schema.
:rtype: dict
"""
schema = {}
for model, index in indexes.iteritems():
mapping_properties = {
DJANGO_CT: {'type': 'string', 'index': 'not_analyzed', 'include_in_all': False},
DJANGO_ID: {'type': 'string', 'index': 'not_analyzed', 'include_in_all': False},
}
for field_name, field_class in index.fields.items():
field_mapping = FIELD_MAPPINGS.get(field_class.field_type, DEFAULT_FIELD_MAPPING).copy()
if field_class.boost != 1.0:
field_mapping['boost'] = field_class.boost
if field_class.stored is True:
field_mapping['store'] = True
# Do this last to override `text` fields.
if field_mapping['type'] == 'string':
if field_class.indexed is False or hasattr(field_class, 'facet_for') or getattr(field_class, 'is_multivalued', False):
field_mapping['index'] = 'not_analyzed'
try:
del field_mapping['analyzer']
del field_mapping['term_vector']
except:
pass
elif field_class.field_type not in ('ngram', 'edge_ngram'):
# Check analyzer attribute
if not hasattr(field_class, 'analyzer') or field_class.analyzer is None:
logger.warning("Set default analyzer for field {}".format(field_name))
field_mapping['index'] = 'analyzed'
field_mapping['analyzer'] = getattr(field_class, 'analyzer', self.DEFAULT_ANALYZER)
# Check term_vector attribute
if hasattr(field_class, 'term_vector') and field_class.term_vector is not None:
field_mapping['term_vector'] = field_class.term_vector
mapping_properties[field_class.index_fieldname] = field_mapping
mapping_type = {
'properties': mapping_properties,
'_boost': {'name': 'boost', 'null_value': 1.0},
}
schema[get_model_ct(model)] = mapping_type
return schema
try:
import elasticsearch
if not ((5, 0, 0) <= elasticsearch.__version__ < (6, 0, 0)):
raise ImportError
from elasticsearch.helpers import bulk, scan
except ImportError:
raise MissingDependency("The 'elasticsearch5' backend requires the \
installation of 'elasticsearch>=5.0.0,<6.0.0'. \
Please refer to the documentation.")
DEFAULT_FIELD_MAPPING = {'type': 'text', 'analyzer': 'snowball'}
FIELD_MAPPINGS.update({
'text': {
'type': 'text',
'analyzer': 'snowball'
},
'keyword': {
'type': 'keyword'
}
})
class Elasticsearch5SearchBackend(ElasticsearchSearchBackend):
def __init__(self, connection_alias, **connection_options):
super(Elasticsearch5SearchBackend,
self).__init__(connection_alias, **connection_options)
self.content_field_name = None
def clear(self, models=None, commit=True):
"""
Clears the backend of all documents/objects for a collection of models.
