"should": []
}
}
}
},
"size": 100000
}
for iter_item in user_list:
query_body["query"]["filtered"]["filter"]["bool"]["should"].append(
{"term": {
"uid": iter_item
}})
text_result = es_text.search(index="sensitive_user_text",
doc_type="user",
body=query_body)["hits"]['hits']
bulk_action = []
count_c = 0
weibo_user_dict = dict()
for item in text_result:
iter_uid = item["_source"]["uid"]
if weibo_user_dict.has_key(iter_uid):
weibo_user_dict[iter_uid].append(item["_source"])
else:
weibo_user_dict[iter_uid] = [item["_source"]]
count_c += 1
print "weibo_user_dict: ", len(weibo_user_dict)
print "times: ", i
if weibo_user_dict:
compute_attribute(weibo_user_dict)
from text_attribute import compute_attribute
reload(sys)
sys.path.append('./../../')
from global_utils import R_RECOMMENTATION as r
from global_utils import es_sensitive_user_text as es_text
from time_utils import datetime2ts, ts2datetime
date = ts2datetime(time.time()-24*3600).replace('-', '')
temp = r.hget('compute_appoint', date)
if temp:
now_list = json.loads(temp)
uid_list = []
count = 0
for item in now_list:
uid_list.append(item[0])
user_weibo_dict = dict()
# extract user weibo text
compute_attribute(user_weibo_dict)
for i in range(now_list):
uid = now_list[i][0]
source = now_list[i][1]
if source == '1':
r.hset('identify_in_sensitive_'+str(date), uid, '3') # finish comoute
else:
r.hset('identify_in_influence_'+str(date), uid, '3')
r.hdel('compute_appoint', date)
sys.path.append('./../../')
from global_utils import R_RECOMMENTATION as r
from global_utils import es_sensitive_user_text as es_text
from time_utils import datetime2ts, ts2datetime
date = ts2datetime(time.time()).replace('-', '')
temp = r.hget('compute_now', date)
if temp:
now_list = json.loads(temp)
uid_list = []
count = 0
for item in now_list:
uid_list.append(item[0])
user_weibo_dict = dict()
# extract user weibo text
compute_attribute(user_weibo_dict)
for i in range(now_list):
uid = now_list[i][0]
source = now_list[i][1]
if source == '1':
r.hset('identify_in_sensitive_'+str(date), uid, '3') # finish comoute
else:
r.hset('identify_in_influence_'+str(date), uid, '3')
renow_list = json.loads(r.hget('compute_now', date))
revise_set = set(renow_list) - set(now_list)
if revise_set:
r.hset('compute_now', date)
else:
r.hdel('compute_now', date)
"filter": {
"bool": {
"should": [
]
}
}
}
},
"size": 100000
}
for iter_item in user_list:
query_body["query"]["filtered"]["filter"]["bool"]["should"].append({"term": {"uid": iter_item}})
text_result = es_text.search(index="sensitive_user_text", doc_type="user", body=query_body)["hits"]['hits']
bulk_action = []
count_c = 0
weibo_user_dict = dict()
for item in text_result:
iter_uid = item["_source"]["uid"]
if weibo_user_dict.has_key(iter_uid):
weibo_user_dict[iter_uid].append(item["_source"])
else:
weibo_user_dict[iter_uid] = [item["_source"]]
count_c += 1
print "weibo_user_dict: ", len(weibo_user_dict)
print "times: ", i
if weibo_user_dict:
compute_attribute(weibo_user_dict)
