class ES_query(object):
def __init__(self):
self.es = Elasticsearch()
self.nlq = NLQuery()
#load schema and create an index
def create_index(self,index_name):
with open('sportsman_schema.txt','r') as schema:
sports_schema = json.load(schema)
if self.es.indices.exists(index_name):
self.es.indices.delete(index_name)
novel_index = self.es.indices.create(index = index_name, body = sports_schema)
#bulk load the data
def bulk_loading(self):
with open(r'data\onthesnowplace.json','r') as j:
json_text = json.load(j)
bulk_file = []
action = { "index": { "_index": "i_sportsman", "_type": "stadium" }}
for i in range(len(json_text)):
bulk_file.append(action)
bulk_file.append(json_text[i])
#return bulk_file
#call create_index function to create i_novel index
self.create_index("i_sportsman")
bulk_load = self.es.bulk(body = bulk_file)
self.es.indices.refresh(index = "i_sportsman")
return bulk_load
def q_mwf(self,address,type,lat,lon):
query_body = {"query" : {
"filtered" : {
"query": {
"bool" : {
"must": [{"match": {"address": {
"query":address, "operator": "and"}}},
{"match": {"activity_types": type}}],
#"should": {"match": {"text" : string2} },
"boost" : 1.0}},
"filter" : {
"geo_distance" : {
"distance" : "100km",
"geo_location" : {
"lat" : lat,
"lon" : lon
}
}}
}
}}
res = self.es.search(index = "i_sportsman", doc_type = "stadium", body = query_body,size = 10000)
self.prints(res)
return res
def q_nl(self,string,geolocation):
query_body = self.nlq.gen_query(string,geolocation)
res = self.es.search(index = "i_sportsman", doc_type = "stadium", body = query_body,size = 10000)
self.prints(res)
return res
#print the required results by order
def prints(self,res):
hits = res["hits"]["hits"]
print 'totle number of hits: ' + str(len(hits))
for i in range(min(10,len(hits))):
print '\n'
print 'name: ' + hits[i]["_source"]['name']
stadium = hits[i]["_source"]
print stadium
def __init__(self):
self.es = Elasticsearch()
self.nlq = NLQuery()
