def do_search(self):
if self.is_evaluated:
return
body = self.make_search_body()
if self.facets_fields:
aggs = dict([
(field, {'terms':
{'field': field}})
for field in self.facets_fields
])
if self.facets_limit:
aggs[field]['terms']['size'] = self.facets_limit
if self.global_facets:
aggs = {'global_count': {'global': {}, 'aggs': aggs}}
body['aggs'] = aggs
if self.suggest_fields:
suggest = {}
for field_name in self.suggest_fields:
suggest[field_name] = {"text": self._query,
"term": {"field": field_name}}
if self.suggest_limit:
suggest[field_name]["term"]["size"] = self.suggest_limit
body['suggest'] = suggest
if self.ordering:
body['sort'] = [{f: "asc"} if f[0] != '-' else {f[1:]: "desc"}
for f in self.ordering] + ["_score"]
search_params = {
'index': self.index,
'doc_type': self.doc_type
}
if self._start:
search_params['from'] = self._start
if self._stop:
search_params['size'] = self._stop - self._start
if self.extra_body:
body.update(self.extra_body)
search_params['body'] = body
self._body = body
if self.mode == self.MODE_MLT:
# change include's defaults to False
search_params['include'] = self.mlt_kwargs.pop('include', False)
# update search params names
search_params.update(self.mlt_kwargs)
for param in ['type', 'indices', 'types', 'scroll', 'size', 'from']:
if param in search_params:
search_params['search_{0}'.format(param)] = search_params.pop(param)
r = es_client.mlt(**search_params)
else:
if 'from' in search_params:
search_params['from_'] = search_params.pop('from')
r = es_client.search(**search_params)
self._response = r
if self.facets_fields:
if self.global_facets:
self._facets = r['aggregations']['global_count']
else:
self._facets = r['aggregations']
self._suggestions = r.get('suggest')
if self._deserialize:
self._result_cache = [self.model.es.deserialize(e['_source'])
for e in r['hits']['hits']]
else:
self._result_cache = [e['_source'] for e in r['hits']['hits']]
self._max_score = r['hits']['max_score']
self._total = r['hits']['total']
return
def do_search(self):
if self.is_evaluated:
return
body = self.make_search_body()
if self.facets_fields:
aggs = dict([
(field, {'terms':
{'field': field}})
for field in self.facets_fields
])
if self.facets_limit:
aggs[field]['terms']['size'] = self.facets_limit
if self.global_facets:
aggs = {'global_count': {'global': {}, 'aggs': aggs}}
body['aggs'] = aggs
if self.suggest_fields:
suggest = {}
for field_name in self.suggest_fields:
suggest[field_name] = {"text": self._query,
"term": {"field": field_name}}
if self.suggest_limit:
suggest[field_name]["term"]["size"] = self.suggest_limit
body['suggest'] = suggest
if self.ordering:
body['sort'] = [{f: "asc"} if f[0] != '-' else {f[1:]: "desc"}
for f in self.ordering] + ["_score"]
search_params = {
'index': self.index,
'doc_type': self.doc_type
}
if self._start:
search_params['from'] = self._start
if self._stop:
search_params['size'] = self._stop - self._start
if self.extra_body:
body.update(self.extra_body)
search_params['body'] = body
self._body = body
if self.mode == self.MODE_MLT:
# change include's defaults to False
search_params['include'] = self.mlt_kwargs.pop('include', False)
# update search params names
search_params.update(self.mlt_kwargs)
for param in ['type', 'indices', 'types', 'scroll', 'size', 'from']:
if param in search_params:
search_params['search_{0}'.format(param)] = search_params.pop(param)
r = es_client.mlt(**search_params)
else:
if 'from' in search_params:
search_params['from_'] = search_params.pop('from')
r = es_client.search(**search_params)
self._response = r
if self.facets_fields:
if self.global_facets:
self._facets = r['aggregations']['global_count']
else:
self._facets = r['aggregations']
self._suggestions = r.get('suggest')
if self._deserialize:
self._result_cache = [self.model.es.deserialize(e['_source'])
for e in r['hits']['hits']]
else:
self._result_cache = [e['_source'] for e in r['hits']['hits']]
self._max_score = r['hits']['max_score']
self._total = r['hits']['total']
return
def do_search(self, extra_body=None):
if self.is_evaluated:
return self._result_cache
print "do_search"
body = self.make_search_body()
if self.facets_fields:
aggs = dict([(field, {
'terms': {
'field': field
}
}) for field in self.facets_fields])
if self.facets_limit:
aggs[field]['terms']['size'] = self.facets_limit
if self.global_facets:
aggs = {'global_count': {'global': {}, 'aggs': aggs}}
body['aggs'] = aggs
if self.suggest_fields:
suggest = {}
for field_name in self.suggest_fields:
suggest[field_name] = {
"text": self._query,
"term": {
"field": field_name
}
}
if self.suggest_limit:
suggest[field_name]["text"]["term"][
"size"] = self.suggest_limit
body['suggest'] = suggest
if self.ordering:
if not len(body.get('sort', [])):
body['sort'] = []
body['sort'].extend([{
f: "asc"
} if f[0] != '-' else {
f[1:]: "desc"
} for f in self.ordering] + ["_score"])
search_params = {'index': self.index, 'doc_type': self.doc_type}
if self._start:
search_params['from'] = self._start
if self._stop:
search_params['size'] = self._stop - self._start
search_params['body'] = body
self._body = body
# print search_params
# print body
if self.mode == self.MODE_MLT:
# change include's defaults to False
# search_params['include'] = self.mlt_kwargs.pop('include', False)
# update search params names
for param in [
'type', 'indices', 'types', 'scroll', 'size', 'from'
]:
if param in search_params:
search_params['search_{0}'.format(
param)] = search_params.pop(param)
search_params.update(self.mlt_kwargs)
r = es_client.mlt(**search_params)
else:
if 'from' in search_params:
search_params['from_'] = search_params.pop('from')
r = es_client.search(**search_params)
self._response = r
if self.facets_fields:
if self.global_facets:
try:
self._facets = r['aggregations']['global_count']
except KeyError:
self._facets = {}
else:
self._facets = r['aggregations']
self._suggestions = r.get('suggest')
if self.lat is not None and self.lng is not None:
for e in r['hits']['hits']:
e['_source']['distance'] = haversine(self.lng, self.lat,
e['_source']['lng'],
e['_source']['lat'])
self._result_cache = [e['_source'] for e in r['hits']['hits']]
self._max_score = r['hits']['max_score']
self._total = r['hits']['total']
return self
def do_search(self, extra_body=None):
if self.is_evaluated:
return self._result_cache
body = self.make_search_body()
if self.facets_fields:
aggs = dict([(field, {"terms": {"field": field}}) for field in self.facets_fields])
if self.facets_limit:
aggs[field]["terms"]["size"] = self.facets_limit
if self.global_facets:
aggs = {"global_count": {"global": {}, "aggs": aggs}}
body["aggs"] = aggs
if self.suggest_fields:
suggest = {}
for field_name in self.suggest_fields:
suggest[field_name] = {"text": self._query, "term": {"field": field_name}}
if self.suggest_limit:
suggest[field_name]["term"]["size"] = self.suggest_limit
body["suggest"] = suggest
if self.ordering:
body["sort"] = [{f: "asc"} if f[0] != "-" else {f[1:]: "desc"} for f in self.ordering] + ["_score"]
search_params = {"index": self.index, "doc_type": self.doc_type}
if self._start:
search_params["from"] = self._start
if self._stop:
search_params["size"] = self._stop - self._start
search_params["body"] = body
self._body = body
if self.mode == self.MODE_MLT:
# change include's defaults to False
search_params["include"] = self.mlt_kwargs.pop("include", False)
# update search params names
search_params.update(self.mlt_kwargs)
for param in ["type", "indices", "types", "scroll", "size", "from"]:
if param in search_params:
search_params["search_{0}".format(param)] = search_params.pop(param)
r = es_client.mlt(**search_params)
else:
if "from" in search_params:
search_params["from_"] = search_params.pop("from")
r = es_client.search(**search_params)
self._response = r
if self.facets_fields:
if self.global_facets:
self._facets = r["aggregations"]["global_count"]
else:
self._facets = r["aggregations"]
self._suggestions = r.get("suggest")
if self._deserialize:
self._result_cache = [self.model.es.deserialize(e["_source"]) for e in r["hits"]["hits"]]
else:
self._result_cache = [e["_source"] for e in r["hits"]["hits"]]
self._max_score = r["hits"]["max_score"]
self._total = r["hits"]["total"]
return self
def do_search(self, extra_body=None):
if self.is_evaluated:
return self._result_cache
print "do_search"
body = self.make_search_body()
if self.facets_fields:
aggs = dict([
(field, {'terms':
{'field': field}})
for field in self.facets_fields
])
if self.facets_limit:
aggs[field]['terms']['size'] = self.facets_limit
if self.global_facets:
aggs = {'global_count': {'global': {}, 'aggs': aggs}}
body['aggs'] = aggs
if self.suggest_fields:
suggest = {}
for field_name in self.suggest_fields:
suggest[field_name] = {"text": self._query,
"term": {"field": field_name}}
if self.suggest_limit:
suggest[field_name]["text"]["term"]["size"] = self.suggest_limit
body['suggest'] = suggest
if self.ordering:
if not len(body.get('sort', [])):
body['sort'] = []
body['sort'].extend([{f: "asc"} if f[0] != '-' else {f[1:]: "desc"}
for f in self.ordering] + ["_score"])
search_params = {
'index': self.index,
'doc_type': self.doc_type
}
if self._start:
search_params['from'] = self._start
if self._stop:
search_params['size'] = self._stop - self._start
search_params['body'] = body
self._body = body
# print search_params
# print body
if self.mode == self.MODE_MLT:
# change include's defaults to False
# search_params['include'] = self.mlt_kwargs.pop('include', False)
# update search params names
for param in ['type', 'indices', 'types', 'scroll', 'size', 'from']:
if param in search_params:
search_params['search_{0}'.format(param)] = search_params.pop(param)
search_params.update(self.mlt_kwargs)
r = es_client.mlt(**search_params)
else:
if 'from' in search_params:
search_params['from_'] = search_params.pop('from')
r = es_client.search(**search_params)
self._response = r
if self.facets_fields:
if self.global_facets:
try:
self._facets = r['aggregations']['global_count']
except KeyError:
self._facets = {}
else:
self._facets = r['aggregations']
self._suggestions = r.get('suggest')
if self.lat is not None and self.lng is not None:
for e in r['hits']['hits']:
e['_source']['distance'] = haversine(self.lng, self.lat,
e['_source']['lng'], e['_source']['lat'])
self._result_cache = [e['_source'] for e in r['hits']['hits']]
self._max_score = r['hits']['max_score']
self._total = r['hits']['total']
return self
