def search_sensitive_weibo(index_name):
# sensitive weibo user recommend
query_body = {
"query": {
"filtered": {
"filter": {
"bool": {
"should": [{
"range": {
"s_retweeted_weibo_number": {
"gt": 0
}
}
}, {
"range": {
"s_origin_weibo_number": {
"gt": 0
}
}
}]
}
}
}
},
"size": 10000000
}
result = es_cluster.search(index=index_name,
doc_type="bci",
body=query_body)['hits']['hits']
sensitive_uid = []
for item in result:
sensitive_uid.append(item['_source']['uid'])
return sensitive_uid
def search_rank(index_name, start_point, size, index_type="bci"):
query_body={
"query": {
"match_all": {}
},
"sort": [{"user_index": {"order": "desc"}}],
"from": start_point,
"size": size
}
result = es.search(index=index_name, doc_type=index_type, body=query_body, _source=False)['hits']['hits']
return result
def search_sensitive_weibo(index_name):
query_body={
"query":{
"match_all":{}
},
"sort":{'s_origin_weibo_comment_top_number':{"order": "desc"}},
"size":2000
}
result = es_cluster.search(index=index_name, doc_type="bci", body=query_body)['hits']['hits']
sensitive_uid = []
for item in result:
sensitive_uid.append(item['_source']['uid'])
return sensitive_uid
def search_from_es(date):
index_time = 'bci_' + date.replace('-', '')
index_type = 'bci'
print index_time
query_body = {
'query':{
'match_all':{}
},
'size':k,
'sort':[{'user_index':{'order':'desc'}}]
}
result = es_bci.search(index=index_time, doc_type=index_type, body=query_body, _source=False, fields=['user_index'])['hits']['hits']
user_set = []
user_set = [user_dict['_id'] for user_dict in result]
return set(user_set), result
def search_top_k(index_name, top_k):
# top_k recommend
query_body={
"query":{
"match_all":{}
},
"size":top_k,
"sort": [{"user_index": {"order": "desc"}}]
}
result = es_cluster.search(index=index_name,doc_type="bci", body=query_body)["hits"]["hits"]
sensitive_uid = []
for item in result:
sensitive_uid.append(item['_source']['uid'])
return sensitive_uid
def get_evaluate_max(index_name):
max_result = {}
index_type = 'bci'
evaluate_index = ['user_index']
for evaluate in evaluate_index:
query_body = {
'query':{
'match_all':{}
},
'size':1,
'sort':[{evaluate: {'order': 'desc'}}]
}
try:
result = es_cluster.search(index=index_name, doc_type=index_type, body=query_body)['hits']['hits']
except Exception, e:
raise e
max_evaluate = result[0]['_source'][evaluate]
max_result[evaluate] = max_evaluate
def search_rank(index_name, start_point, size, index_type="bci"):
query_body = {
"query": {
"match_all": {}
},
"sort": [{
"user_index": {
"order": "desc"
}
}],
"from": start_point,
"size": size
}
result = es.search(index=index_name,
doc_type=index_type,
body=query_body,
_source=False)['hits']['hits']
return result
def search_top_k(index_name, top_k):
# top_k recommend
query_body = {
"query": {
"match_all": {}
},
"size": top_k,
"sort": [{
"user_index": {
"order": "desc"
}
}]
}
result = es_cluster.search(index=index_name,
doc_type="bci",
body=query_body)["hits"]["hits"]
sensitive_uid = []
for item in result:
sensitive_uid.append(item['_source']['uid'])
return sensitive_uid
def search_from_es(date):
index_time = 'bci_' + date.replace('-', '')
index_type = 'bci'
print index_time
query_body = {
'query': {
'match_all': {}
},
'size': k,
'sort': [{
'user_index': {
'order': 'desc'
}
}]
}
result = es_bci.search(index=index_time,
doc_type=index_type,
body=query_body,
_source=False,
fields=['user_index'])['hits']['hits']
user_set = []
user_set = [user_dict['_id'] for user_dict in result]
return set(user_set), result
def search_sensitive_weibo(index_name):
# sensitive weibo user recommend
query_body={
"query":{
"filtered":{
"filter":{
"bool":{
"should":[
{"range":{"s_retweeted_weibo_number":{"gt":0}}},
{"range":{"s_origin_weibo_number":{"gt":0}}}
]
}
}
}
},
"size":10000000
}
result = es_cluster.search(index=index_name, doc_type="bci", body=query_body)['hits']['hits']
sensitive_uid = []
for item in result:
sensitive_uid.append(item['_source']['uid'])
return sensitive_uid
