def search(self, index=None, doc_type=None, body=None, params=None):
searchable_indexes = self._normalize_index_to_list(index)
matches = []
for searchable_index in searchable_indexes:
for document in self.__documents_dict[searchable_index]:
if doc_type:
if isinstance(
doc_type,
list) and document.get('_type') not in doc_type:
continue
if isinstance(doc_type,
str) and document.get('_type') != doc_type:
continue
matches.append(document)
result = {
'hits': {
'total': len(matches),
'max_score': 1.0
},
'_shards': {
# Simulate indexes with 1 shard each
'successful': len(searchable_indexes),
'failed': 0,
'total': len(searchable_indexes)
},
'took': 1,
'timed_out': False
}
hits = []
for match in matches:
match['_score'] = 1.0
hits.append(match)
if 'scroll' in params:
result['_scroll_id'] = str(get_random_scroll_id())
params['size'] = int(
params.get('size') if 'size' in params else 10)
params['from'] = int(
params.get('from') +
params.get('size') if 'from' in params else 0)
self.__scrolls[result.get('_scroll_id')] = {
'index': index,
'doc_type': doc_type,
'body': body,
'params': params
}
hits = hits[params.get('from'):params.get('from') +
params.get('size')]
result['hits']['hits'] = hits
return result
def search(self, index=None, doc_type=None, body=None, params=None):
searchable_indexes = self._normalize_index_to_list(index)
matches = []
for searchable_index in searchable_indexes:
for document in self.__documents_dict[searchable_index]:
if doc_type:
if isinstance(
doc_type,
list) and document.get('_type') not in doc_type:
continue
if isinstance(doc_type,
str) and document.get('_type') != doc_type:
continue
matches.append(document)
result = {
'hits': {
'total': len(matches),
'max_score': 1.0
},
'_shards': {
# Simulate indexes with 1 shard each
'successful': len(searchable_indexes),
'failed': 0,
'total': len(searchable_indexes)
},
'took': 1,
'timed_out': False
}
hits = []
for match in matches:
match['_score'] = 1.0
hits.append(match)
# build aggregations
if body is not None and 'aggs' in body:
aggregations = {}
for aggregation, definition in body['aggs'].items():
aggregations[aggregation] = {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": []
}
if aggregations:
result['aggregations'] = aggregations
if 'scroll' in params:
result['_scroll_id'] = str(get_random_scroll_id())
params['size'] = int(
params.get('size') if 'size' in params else 10)
params['from'] = int(
params.get('from') +
params.get('size') if 'from' in params else 0)
self.__scrolls[result.get('_scroll_id')] = {
'index': index,
'doc_type': doc_type,
'body': body,
'params': params
}
hits = hits[params.get('from'):params.get('from') +
params.get('size')]
result['hits']['hits'] = hits
return result
def search(self,
index=None,
doc_type=None,
body=None,
params=None,
headers=None):
searchable_indexes = self._normalize_index_to_list(index)
matches = []
conditions = []
if body and 'query' in body:
query = body['query']
for query_type_str, condition in query.items():
conditions.append(
self._get_fake_query_condition(query_type_str, condition))
for searchable_index in searchable_indexes:
for document in self.__documents_dict[searchable_index]:
if doc_type:
if isinstance(
doc_type,
list) and document.get('_type') not in doc_type:
continue
if isinstance(doc_type,
str) and document.get('_type') != doc_type:
continue
if conditions:
for condition in conditions:
if condition.evaluate(document):
matches.append(document)
break
else:
matches.append(document)
for match in matches:
self._find_and_convert_data_types(match['_source'])
result = {
'hits': {
'total': {
'value': len(matches),
'relation': 'eq'
},
'max_score': 1.0
},
'_shards': {
# Simulate indexes with 1 shard each
'successful': len(searchable_indexes),
'skipped': 0,
'failed': 0,
'total': len(searchable_indexes)
},
'took': 1,
'timed_out': False
}
hits = []
for match in matches:
match['_score'] = 1.0
hits.append(match)
# build aggregations
if body is not None and 'aggs' in body:
aggregations = {}
for aggregation, definition in body['aggs'].items():
aggregations[aggregation] = {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets":
self.make_aggregation_buckets(definition, matches)
}
if aggregations:
result['aggregations'] = aggregations
if body is not None and 'sort' in body:
for key, value in body['sort'][0].items():
if body['sort'][0][key]['order'] == 'desc':
hits = sorted(hits,
key=lambda k: k['_source'][key],
reverse=True)
else:
hits = sorted(hits, key=lambda k: k['_source'][key])
if 'from' in body and 'size' in body and body['from'] + body[
'size'] > 0:
hits = hits[body['from']:body['from'] + body['size']]
if 'scroll' in params:
result['_scroll_id'] = str(get_random_scroll_id())
params['size'] = int(params.get('size', 10))
params['from'] = int(
params.get('from') +
params.get('size') if 'from' in params else 0)
self.__scrolls[result.get('_scroll_id')] = {
'index': index,
'doc_type': doc_type,
'body': body,
'params': params
}
hits = hits[params.get('from'):params.get('from') +
params.get('size')]
elif 'size' in params:
hits = hits[:int(params['size'])]
result['hits']['hits'] = hits
return result
