def cal_text_work(item):
uid = item['uid']
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
text = item['text']
if isinstance(text, str):
text = text.decode('utf-8', 'ignore')
RE = re.compile(u'#([a-zA-Z-_⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+)#', re.UNICODE)
hashtag_list = RE.findall(text)
if hashtag_list:
# there all use unicode·
hashtag_dict = dict()
for hashtag in hashtag_list:
try:
hashtag_dict[hashtag] += 1
except:
hashtag_dict[hashtag] = 1
try:
hashtag_count_string = r_cluster.hget('hashtag_'+str(ts), str(uid))
hashtag_count_dict = json.loads(hashtag_count_string)
for hashtag in hashtag_dict:
count = hashtag_dict[hashtag]
try:
hashtag_count_dict[hashtag] += count
except:
hashtag_count_dict[hashtag] = count
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_count_dict))
except:
r_cluster.hset('hashtag_'+str(ts), str(uid), json.dumps(hashtag_dict))
def update_day_hashtag(uid_list):
results = {}
all_results = {}
now_ts = time.time()
#run_type
if RUN_TYPE == 1:
now_date_ts = datetime2ts(ts2datetime(now_ts))
else:
now_date_ts = test_ts
for i in range(WEEK,0,-1):
ts = now_date_ts - DAY*i
count = 0
hashtag_results = r_cluster_3.hmget('hashtag_'+str(ts), uid_list)
for uid in uid_list:
if uid not in results:
results[uid] = {}
hashtag_item = hashtag_results[count]
if hashtag_item:
hashtag_dict = json.loads(hashtag_item)
else:
hashtag_dict = {}
for hashtag in hashtag_dict:
try:
results[uid][hashtag] += 1
except:
results[uid][hashtag] = 1
count += 1
for uid in uid_list:
user_hashtag_dict = results[uid]
hashtag_string = '&'.join(user_hashtag_dict.keys())
all_results[uid] = {'hashtag': hashtag_string, 'hashtag_dict':json.dumps(user_hashtag_dict)}
return all_results
def scan_offline_task():
query = {
"query": {
"bool": {
"must": [{
"term": {
"status": 0
}
}]
}
},
"size": 1000
}
results = es_user_portrait.search(index=USER_RANK_KEYWORD_TASK_INDEX,
doc_type=USER_RANK_KEYWORD_TASK_TYPE,
body=query)['hits']['hits']
if results:
for item in results:
task_id = item['_id']
iter_item = item['_source']
search_type = iter_item['search_type']
pre = iter_item['pre']
during = iter_item['during']
start_time = iter_item['start_time']
keyword = json.loads(iter_item['keyword'])
search_key = iter_item['user_ts']
number = iter_item['number']
sort_norm = iter_item['sort_norm']
sort_scope = iter_item['sort_scope']
time = iter_item['time']
isall = iter_item['isall']
redis_task.lpush(
"task_user_rank",
json.dumps([
task_id, search_type, pre, during, start_time, keyword,
search_key, sort_norm, sort_scope, time, isall, number
]))
iter_item['status'] = -1
task_id = item['_id']
#print item
es_user_portrait.index(index=USER_RANK_KEYWORD_TASK_INDEX,
doc_type=USER_RANK_KEYWORD_TASK_TYPE,
id=task_id,
body=iter_item)
def update_day_sensitive(uid_list):
results = {}
all_results = {}
now_ts = time.time()
#run_type
if RUN_TYPE == 1:
now_date_ts = datetime2ts(ts2datetime(now_ts))
else:
now_date_ts = test_ts
today_sensitive_dict = {}
for i in range(WEEK, 0, -1):
ts = now_date_ts - DAY * i
count = 0
sensitive_results = r_cluster_3.hmget('sensitive_' + str(ts), uid_list)
#print 'sensitive_results:', sensitive_results
for uid in uid_list:
if uid not in results:
results[uid] = {}
sensitive_item = sensitive_results[count]
if uid not in today_sensitive_dict:
today_sensitive_dict[uid] = {}
if sensitive_item:
sensitive_dict = json.loads(sensitive_item)
else:
sensitive_dict = {}
for sensitive in sensitive_dict:
try:
results[uid][sensitive] += 1
except:
results[uid][sensitive] = 1
if ts == now_date_ts - DAY:
try:
today_sensitive_dict[uid][sensitive] += 1
except:
today_sensitive_dict[uid][sensitive] = 1
count += 1
#print 'results:', results
for uid in uid_list:
user_sensitive_dict = results[uid]
#print 'uid,sensitive:', uid, user_sensitive_dict
sensitive_score = 0
today_sensitive_dict_user = today_sensitive_dict[uid]
for item in today_sensitive_dict_user:
k = item
v = today_sensitive_dict_user[k]
tmp_stage = r_sensitive.hget('sensitive_words', k)
if tmp_stage:
sensitive_score += v * sensitive_score_dict[str(tmp_stage)]
#print 'sensitive_score:', sensitive_score
sensitive_string = '&'.join(user_sensitive_dict.keys())
#print 'uid, sensitive:', uid, sensitive_string, sensitive_score
all_results[uid] = {'sensitive_string': sensitive_string, 'sensitive_dict':json.dumps(user_sensitive_dict),\
'sensitive': sensitive_score}
#print 'all_results:', all_results
return all_results
def update_day_sensitive(uid_list):
results = {}
all_results = {}
now_ts = time.time()
#run_type
if RUN_TYPE == 1:
now_date_ts = datetime2ts(ts2datetime(now_ts))
else:
now_date_ts = test_ts
today_sensitive_dict = {}
for i in range(WEEK,0,-1):
ts = now_date_ts - DAY*i
count = 0
sensitive_results = r_cluster_3.hmget('sensitive_'+str(ts), uid_list)
#print 'sensitive_results:', sensitive_results
for uid in uid_list:
if uid not in results:
results[uid] = {}
sensitive_item = sensitive_results[count]
if uid not in today_sensitive_dict:
today_sensitive_dict[uid] = {}
if sensitive_item:
sensitive_dict = json.loads(sensitive_item)
else:
sensitive_dict = {}
for sensitive in sensitive_dict:
try:
results[uid][sensitive] += 1
except:
results[uid][sensitive] = 1
if ts == now_date_ts - DAY:
try:
today_sensitive_dict[uid][sensitive] += 1
except:
today_sensitive_dict[uid][sensitive] = 1
count += 1
#print 'results:', results
for uid in uid_list:
user_sensitive_dict = results[uid]
#print 'uid,sensitive:', uid, user_sensitive_dict
sensitive_score = 0
today_sensitive_dict_user = today_sensitive_dict[uid]
for item in today_sensitive_dict_user:
k = item
v = today_sensitive_dict_user[k]
tmp_stage = r_sensitive.hget('sensitive_words', k)
if tmp_stage:
sensitive_score += v * sensitive_score_dict[str(tmp_stage)]
#print 'sensitive_score:', sensitive_score
sensitive_string = '&'.join(user_sensitive_dict.keys())
#print 'uid, sensitive:', uid, sensitive_string, sensitive_score
all_results[uid] = {'sensitive_string': sensitive_string, 'sensitive_dict':json.dumps(user_sensitive_dict),\
'sensitive': sensitive_score}
#print 'all_results:', all_results
return all_results
def scan_offline_task():
query = {"query":{"bool":{"must":[{"term":{"status":0}}]}},"size":1000}
results = es_user_portrait.search(index = USER_RANK_KEYWORD_TASK_INDEX , doc_type = USER_RANK_KEYWORD_TASK_TYPE,body=query)['hits']['hits']
if results :
for item in results:
task_id = item['_id']
iter_item = item['_source']
search_type = iter_item['search_type']
pre = iter_item['pre']
during = iter_item['during']
start_time = iter_item['start_time']
keyword = json.loads(iter_item['keyword'])
search_key = iter_item['user_ts']
number = iter_item['number']
sort_norm = iter_item['sort_norm']
sort_scope = iter_item['sort_scope']
time = iter_item['time']
isall = iter_item['isall']
redis_task.lpush("task_user_rank", json.dumps([task_id, search_type , pre , during , start_time , keyword , search_key , sort_norm , sort_scope ,time , isall, number]))
iter_item['status'] = -1
task_id = item['_id']
#print item
es_user_portrait.index(index=USER_RANK_KEYWORD_TASK_INDEX, doc_type=USER_RANK_KEYWORD_TASK_TYPE, id=task_id, body=iter_item)
def update_day_hashtag(uid_list):
results = {}
all_results = {}
now_ts = time.time()
#run_type
if RUN_TYPE == 1:
now_date_ts = datetime2ts(ts2datetime(now_ts))
else:
now_date_ts = test_ts
for i in range(WEEK, 0, -1):
ts = now_date_ts - DAY * i
count = 0
hashtag_results = r_cluster_3.hmget('hashtag_' + str(ts), uid_list)
for uid in uid_list:
if uid not in results:
results[uid] = {}
hashtag_item = hashtag_results[count]
if hashtag_item:
hashtag_dict = json.loads(hashtag_item)
else:
hashtag_dict = {}
for hashtag in hashtag_dict:
try:
results[uid][hashtag] += 1
except:
results[uid][hashtag] = 1
count += 1
for uid in uid_list:
user_hashtag_dict = results[uid]
hashtag_string = '&'.join(user_hashtag_dict.keys())
all_results[uid] = {
'hashtag': hashtag_string,
'hashtag_dict': json.dumps(user_hashtag_dict)
}
return all_results
]))
iter_item['status'] = -1
task_id = item['_id']
#print item
es_user_portrait.index(index=USER_RANK_KEYWORD_TASK_INDEX,
doc_type=USER_RANK_KEYWORD_TASK_TYPE,
id=task_id,
body=iter_item)
def cron_task(data):
key_words_search(data[0], data[1], data[2], data[3], data[4], data[5],
data[6], data[7], data[8], data[9], data[10], data[11])
if __name__ == "__main__":
scan_offline_task()
while 1:
data = redis_task.rpop("task_user_rank")
print data
#"""
if data:
try:
cron_task(json.loads(data))
except Exception, e:
print e, '&error&', ts2date(time.time())
else:
break
#"""
uidlist = []
f = open("uid_list_0520.txt")
for line in f:
uid = line.strip()
uidlist.append(uid)
f.close()
data = []
dates = [
"2016-05-14", "2016-05-15", "2016-05-16", "2016-05-17", "2016-05-18",
"2016-05-19", "2016-05-20"
]
tss = [datetime2ts(d) for d in dates]
for ts in tss:
ns = "hashtag_" + str(ts)
hashtag_list = R_CLUSTER_FLOW3.hmget(ns, uidlist)
hashtag_list = [json.loads(h) if h else None for h in hashtag_list]
uhlist = zip(uidlist, hashtag_list)
uhtlist = []
for uh in uhlist:
uh = list(uh)
uh.append(ts)
uhtlist.append(uh)
data.extend(uhtlist)
with open("hashtag_0521.txt", "w") as fw:
for d in data:
if d[1] != None:
fw.write("%s\n" % json.dumps(d))
at_data = []
time = iter_item['time']
isall = iter_item['isall']
redis_task.lpush("task_user_rank", json.dumps([task_id, search_type , pre , during , start_time , keyword , search_key , sort_norm , sort_scope ,time , isall, number]))
iter_item['status'] = -1
task_id = item['_id']
#print item
es_user_portrait.index(index=USER_RANK_KEYWORD_TASK_INDEX, doc_type=USER_RANK_KEYWORD_TASK_TYPE, id=task_id, body=iter_item)
def cron_task(data):
key_words_search(data[0], data[1], data[2], data[3], data[4], data[5], data[6], data[7], data[8], data[9], data[10], data[11])
if __name__ == "__main__":
scan_offline_task()
while 1:
data = redis_task.rpop("task_user_rank")
print data
#"""
if data:
try:
cron_task(json.loads(data))
except Exception, e:
print e, '&error&', ts2date(time.time())
else:
break
#"""
statusnum = 0
else:
uname = uid
location = ''
try:
fansnum = bci_history_dict['fields']['user_fansnum'][0]
except:
fansnum = 0
try:
statusnum = bci_history_dict['fields']['weibo_month_sum'][0]
except:
statusnum = 0
if status == 'show_in':
if user_type == "sensitive":
tmp_ts = datetime2ts(date) - DAY
tmp_data = r_cluster.hget("sensitive_"+str(tmp_ts), uid)
if tmp_data:
sensitive_dict = json.loads(tmp_data)
sensitive_words = sensitive_dict.keys()
else:
sensitive_words = []
if sensitive_history_dict.get('fields',0):
#print sensitive_history_dict['fields'][sensitive_string][0]
#print top_sensitive
sensitive_value = math.log(sensitive_history_dict['fields'][sensitive_string][0]/float(top_sensitive)*9+1, 10)*100
#print "sensitive_value", sensitive_value
else:
sensitive_value = 0
results.append([uid, uname, location, fansnum, statusnum, influence, sensitive_words, sensitive_value])
else:
results.append([uid, uname, location, fansnum, statusnum, influence])
def main():
if RUN_TYPE:
now_ts = time.time()-DAY # 前一天
now_ts = datetime2ts('2016-03-27')
ts = str(datetime2ts(ts2datetime(now_ts)))
else:
ts = str(datetime2ts('2016-03-16'))
now_ts = int(ts)
print now_ts
sensitive_string = "sensitive_" + ts
date_string = ts
update_sensitive_key = "sensitive_score_" + ts # 更新的键
sensitive_dict_key = "sensitive_dict_" + ts
sensitive_string_key = "sensitive_string_" + ts
sensitive_day_change_key = "sensitive_" + ts +"_day_change"
del_month = datetime2ts(ts2datetime(now_ts - MONTH))
del_sensitive_key = "sensitive_score_"+str(del_month) # 要删除的键
former_ts = int(ts) - DAY
former_date = str(datetime2ts(ts2datetime(former_ts)))
former_sensitive_key = "sensitive_score_" + former_date
iter_count = 0
bulk_action = []
mappings(ES_SENSITIVE_INDEX)
total_number = r.hlen(sensitive_string)
scan_cursor = 0
print total_number
"""
while 1:
re_scan = r.hscan(sensitive_string, scan_cursor, count=1000)
scan_cursor = re_scan[0]
if len(re_scan[1]) != 0:
sensitive_info = re_scan[1] # 字典形式，uid：sensitive_words_dict
uid_list = sensitive_info.keys()
sensitive_results = es.mget(index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX, body={"ids":uid_list})['docs']
if sensitive_results:
for item in sensitive_results:
uid = item['_id']
sensitive_words_dict = json.loads(sensitive_info[uid]) # json.loads
current_sensitive_score = 0
for k,v in sensitive_words_dict.iteritems():
tmp_stage = r_sensitive.hget("sensitive_words", k)
if tmp_stage:
current_sensitive_score += v*sensitive_score_dict[str(tmp_stage)]
if item['found']: # 之前存在相关信息
revise_item = item["_source"]
if del_sensitive_key in revise_item:
item.remove(del_sensitive_key)
revise_item['uid'] = uid
# 新更新的敏感度
revise_item[update_sensitive_key] = current_sensitive_score
# 新更新的敏感词
revise_item[sensitive_dict_key] = sensitive_info[uid]
# 新更新的string
revise_item[sensitive_string_key] = "&".join(sensitive_words_dict.keys())
# 当天和之前一天、一周和一月均值的差异
revise_item['sensitive_day_change'] = current_sensitive_score - revise_item.get(former_sensitive_key, 0)
revise_item['sensitive_week_change'] = current_sensitive_score - revise_item.get('sensitive_week_ave', 0)
revise_item['sensitive_month_change'] = current_sensitive_score - revise_item.get('sensitive_month_ave', 0)
# 更新后week、month的均值和方差
revise_item['sensitive_week_ave'], revise_item['sensitive_week_var'], revise_item['sensitive_week_sum'] = compute_week(revise_item, now_ts)
revise_item['senstiive_month_ave'], revise_item['sensitive_month_var'], revise_item['sensitive_month_sum'] = compute_month(revise_item, now_ts)
else:
revise_item = dict()
revise_item['uid'] = uid
revise_item[update_sensitive_key] = current_sensitive_score
revise_item[sensitive_dict_key] = sensitive_info[uid]
revise_item[sensitive_string_key] = "&".join(sensitive_words_dict.keys())
revise_item['sensitive_day_change'] = current_sensitive_score
revise_item['sensitive_week_change'] = current_sensitive_score
revise_item['sensitive_month_change'] = current_sensitive_score
revise_item['sensitive_week_ave'], revise_item['sensitive_week_var'], revise_item['sensitive_week_sum'] = compute_week(revise_item, now_ts)
revise_item['senstiive_month_ave'], revise_item['sensitive_month_var'], revise_item['sensitive_month_sum'] = compute_month(revise_item, now_ts)
action = {'index':{'_id': uid}}
bulk_action.extend([action, revise_item])
iter_count += 1
if iter_count % 1000 == 0:
es.bulk(bulk_action, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
bulk_action = []
print iter_count
if int(scan_cursor) == 0:
break
if bulk_action:
es.bulk(bulk_action, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
print iter_count
#######
#######更新尚未完成的用户
update_scan = scan(es, query={"query":{"filtered":{"filter":{"missing":{"field":update_sensitive_key}}}}}, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
iter_count = 0
bulk_action = []
"""
while 1:
try:
tmp = update_scan.next()
revise_item = tmp['_source']
if del_sensitive_key in revise_item:
revise_item.remove(del_sensitive_key)
uid = revise_item['uid']
# 新更新的敏感度
revise_item[update_sensitive_key] = 0
# 新更新的敏感词
revise_item[sensitive_dict_key] = json.dumps({})
# 新更新的string
revise_item[sensitive_string_key] = ""
# 当天和之前一天、一周和一月均值的差异
revise_item['sensitive_day_change'] = 0 - revise_item.get(former_sensitive_key, 0)
revise_item['sensitive_week_change'] = 0 - revise_item.get('sensitive_week_ave', 0)
revise_item['sensitive_month_change'] = 0 - revise_item.get('sensitive_month_ave', 0)
# 更新后week、month的均值和方差
revise_item['sensitive_week_ave'], revise_item['sensitive_week_var'], revise_item['sensitive_week_sum'] = compute_week(revise_item, now_ts)
revise_item['senstiive_month_ave'], revise_item['sensitive_month_var'], revise_item['sensitive_month_sum'] = compute_month(revise_item, now_ts)
action = {'index':{'_id': uid}}
bulk_action.extend([action, revise_item])
iter_count += 1
if iter_count % 1000 == 0:
es.bulk(bulk_action, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
bulk_action = []
print iter_count
except StopIteration:
print "all done"
if bulk_action:
es.bulk(bulk_action, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
except Exception, r:
print Exception, r
if bulk_action:
es.bulk(bulk_action, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
sensitive_words_dict = searchWord(text.encode('utf-8', 'ignore'),
DFA)
if sensitive_words_dict:
item['sensitive_words_string'] = "&".join(
sensitive_words_dict.keys())
item['sensitive_words_dict'] = json.dumps(sensitive_words_dict)
else:
item['sensitive_words_string'] = ""
item['sensitive_words_dict'] = json.dumps({})
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
if sensitive_words_dict:
print sensitive_words_dict.keys()[0]
sensitive_count_string = r_cluster.hget(
'sensitive_' + str(ts), str(uid))
if sensitive_count_string: #redis取空
sensitive_count_dict = json.loads(sensitive_count_string)
for word in sensitive_words_dict.keys():
if sensitive_count_dict.has_key(word):
sensitive_count_dict[word] += sensitive_words_dict[
word]
else:
sensitive_count_dict[word] = sensitive_words_dict[
word]
r_cluster.hset('sensitive_' + str(ts), str(uid),
json.dumps(sensitive_count_dict))
else:
r_cluster.hset('sensitive_' + str(ts), str(uid),
json.dumps(sensitive_words_dict))
def main():
if RUN_TYPE:
now_ts = time.time() - DAY # 前一天
now_ts = datetime2ts('2016-03-27')
ts = str(datetime2ts(ts2datetime(now_ts)))
else:
ts = str(datetime2ts('2016-03-16'))
now_ts = int(ts)
print now_ts
sensitive_string = "sensitive_" + ts
date_string = ts
update_sensitive_key = "sensitive_score_" + ts # 更新的键
sensitive_dict_key = "sensitive_dict_" + ts
sensitive_string_key = "sensitive_string_" + ts
sensitive_day_change_key = "sensitive_" + ts + "_day_change"
del_month = datetime2ts(ts2datetime(now_ts - MONTH))
del_sensitive_key = "sensitive_score_" + str(del_month) # 要删除的键
former_ts = int(ts) - DAY
former_date = str(datetime2ts(ts2datetime(former_ts)))
former_sensitive_key = "sensitive_score_" + former_date
iter_count = 0
bulk_action = []
mappings(ES_SENSITIVE_INDEX)
total_number = r.hlen(sensitive_string)
scan_cursor = 0
print total_number
"""
while 1:
re_scan = r.hscan(sensitive_string, scan_cursor, count=1000)
scan_cursor = re_scan[0]
if len(re_scan[1]) != 0:
sensitive_info = re_scan[1] # 字典形式，uid：sensitive_words_dict
uid_list = sensitive_info.keys()
sensitive_results = es.mget(index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX, body={"ids":uid_list})['docs']
if sensitive_results:
for item in sensitive_results:
uid = item['_id']
sensitive_words_dict = json.loads(sensitive_info[uid]) # json.loads
current_sensitive_score = 0
for k,v in sensitive_words_dict.iteritems():
tmp_stage = r_sensitive.hget("sensitive_words", k)
if tmp_stage:
current_sensitive_score += v*sensitive_score_dict[str(tmp_stage)]
if item['found']: # 之前存在相关信息
revise_item = item["_source"]
if del_sensitive_key in revise_item:
item.remove(del_sensitive_key)
revise_item['uid'] = uid
# 新更新的敏感度
revise_item[update_sensitive_key] = current_sensitive_score
# 新更新的敏感词
revise_item[sensitive_dict_key] = sensitive_info[uid]
# 新更新的string
revise_item[sensitive_string_key] = "&".join(sensitive_words_dict.keys())
# 当天和之前一天、一周和一月均值的差异
revise_item['sensitive_day_change'] = current_sensitive_score - revise_item.get(former_sensitive_key, 0)
revise_item['sensitive_week_change'] = current_sensitive_score - revise_item.get('sensitive_week_ave', 0)
revise_item['sensitive_month_change'] = current_sensitive_score - revise_item.get('sensitive_month_ave', 0)
# 更新后week、month的均值和方差
revise_item['sensitive_week_ave'], revise_item['sensitive_week_var'], revise_item['sensitive_week_sum'] = compute_week(revise_item, now_ts)
revise_item['senstiive_month_ave'], revise_item['sensitive_month_var'], revise_item['sensitive_month_sum'] = compute_month(revise_item, now_ts)
else:
revise_item = dict()
revise_item['uid'] = uid
revise_item[update_sensitive_key] = current_sensitive_score
revise_item[sensitive_dict_key] = sensitive_info[uid]
revise_item[sensitive_string_key] = "&".join(sensitive_words_dict.keys())
revise_item['sensitive_day_change'] = current_sensitive_score
revise_item['sensitive_week_change'] = current_sensitive_score
revise_item['sensitive_month_change'] = current_sensitive_score
revise_item['sensitive_week_ave'], revise_item['sensitive_week_var'], revise_item['sensitive_week_sum'] = compute_week(revise_item, now_ts)
revise_item['senstiive_month_ave'], revise_item['sensitive_month_var'], revise_item['sensitive_month_sum'] = compute_month(revise_item, now_ts)
action = {'index':{'_id': uid}}
bulk_action.extend([action, revise_item])
iter_count += 1
if iter_count % 1000 == 0:
es.bulk(bulk_action, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
bulk_action = []
print iter_count
if int(scan_cursor) == 0:
break
if bulk_action:
es.bulk(bulk_action, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
print iter_count
#######
#######更新尚未完成的用户
update_scan = scan(es, query={"query":{"filtered":{"filter":{"missing":{"field":update_sensitive_key}}}}}, index=ES_SENSITIVE_INDEX, doc_type=DOCTYPE_SENSITIVE_INDEX)
iter_count = 0
bulk_action = []
"""
while 1:
try:
tmp = update_scan.next()
revise_item = tmp['_source']
if del_sensitive_key in revise_item:
revise_item.remove(del_sensitive_key)
uid = revise_item['uid']
# 新更新的敏感度
revise_item[update_sensitive_key] = 0
# 新更新的敏感词
revise_item[sensitive_dict_key] = json.dumps({})
# 新更新的string
revise_item[sensitive_string_key] = ""
# 当天和之前一天、一周和一月均值的差异
revise_item['sensitive_day_change'] = 0 - revise_item.get(
former_sensitive_key, 0)
revise_item['sensitive_week_change'] = 0 - revise_item.get(
'sensitive_week_ave', 0)
revise_item['sensitive_month_change'] = 0 - revise_item.get(
'sensitive_month_ave', 0)
# 更新后week、month的均值和方差
revise_item['sensitive_week_ave'], revise_item[
'sensitive_week_var'], revise_item[
'sensitive_week_sum'] = compute_week(revise_item, now_ts)
revise_item['senstiive_month_ave'], revise_item[
'sensitive_month_var'], revise_item[
'sensitive_month_sum'] = compute_month(
revise_item, now_ts)
action = {'index': {'_id': uid}}
bulk_action.extend([action, revise_item])
iter_count += 1
if iter_count % 1000 == 0:
es.bulk(bulk_action,
index=ES_SENSITIVE_INDEX,
doc_type=DOCTYPE_SENSITIVE_INDEX)
bulk_action = []
print iter_count
except StopIteration:
print "all done"
if bulk_action:
es.bulk(bulk_action,
index=ES_SENSITIVE_INDEX,
doc_type=DOCTYPE_SENSITIVE_INDEX)
except Exception, r:
print Exception, r
if bulk_action:
es.bulk(bulk_action,
index=ES_SENSITIVE_INDEX,
doc_type=DOCTYPE_SENSITIVE_INDEX)
def get_flow_information(uid_list):
results = {}
#results = {uid:{'hashtag_dict':{},'hashtag':'', 'keywords_dict':{}, 'keywords_string':'', 'activity_geo':'', 'activity_geo_dict':dict}}
iter_results = {} # iter_results = {uid:{'hashtag': hashtag_dict, 'geo':geo_dict, 'keywords':keywords_dict}}
now_ts = time.time()
now_date_ts = datetime2ts(ts2datetime(now_ts))
#test
now_date_ts = test_ts
for i in range(7,0,-1):
ts = now_date_ts - DAY*i
iter_date = ts2datetime(ts)
flow_text_index_name = flow_text_index_name_pre + iter_date
uid_day_geo = {}
#compute hashtag and geo
hashtag_results = r_cluster_3.hmget('hashtag_'+str(ts), uid_list)
ip_results = r_cluster.hmget('new_ip_'+str(ts), uid_list)
#compute sensitive_words
sensitive_results = r_cluster_3.hmget('sensitive_'+str(ts), uid_list)
count = 0
for uid in uid_list:
#init iter_results[uid]
if uid not in iter_results:
iter_results[uid] = {'hashtag':{}, 'geo':{},'geo_track':[],'keywords':{}, 'sensitive':{}}
#compute hashtag
hashtag_item = hashtag_results[count]
if hashtag_item:
uid_hashtag_dict = json.loads(hashtag_item)
else:
uid_hashtag_dict = {}
for hashtag in uid_hashtag_dict:
try:
iter_results[uid]['hashtag'][hashtag] += uid_hashtag_dict[hashtag]
except:
iter_results[uid]['hashtag'][hashtag] = uid_hashtag_dict[hashtag]
#compute sensitive
sensitive_item = sensitive_results[count]
if sensitive_item:
uid_sensitive_dict = json.loads(sensitive_item)
else:
uid_sensitive_dict = {}
for sensitive_word in uid_sensitive_dict:
try:
iter_results[uid]['sensitive'][sensitive_word] += uid_sensitive_dict[sensitive_word]
except:
iter_results[uid]['sensitive'][sensitive_word] = uid_sensitive_dict[sensitive_word]
#compute geo
uid_day_geo[uid] = {}
ip_item = ip_results[count]
if ip_item:
uid_ip_dict = json.loads(ip_item)
else:
uid_ip_dict = {}
for ip in uid_ip_dict:
ip_count = len(uid_ip_dict[ip].split('&'))
geo = ip2city(ip)
if geo:
#print 'geo:', geo
try:
iter_results[uid]['geo'][geo] += ip_count
except:
iter_results[uid]['geo'][geo] = ip_count
try:
uid_day_geo[uid][geo] += ip_count
except:
uid_day_geo[uid][geo] = ip_count
iter_results[uid]['geo_track'].append(uid_day_geo[uid])
count += 1
#compute keywords:
try:
text_results = es_flow_text.search(index=flow_text_index_name, doc_type=flow_text_index_type, \
body={'query':{'filtered':{'filter':{'terms':{'uid': uid_list}}}}, 'size':MAX_VALUE}, _source=True, fields=['uid', 'keywords_dict'])['hits']['hits']
except:
text_results = {}
for item in text_results:
#print 'keywords item:', item
uid = item['fields']['uid'][0]
uid_keywords_dict = json.loads(item['fields']['keywords_dict'][0])
for keywords in uid_keywords_dict:
try:
iter_results[uid]['keywords'][keywords] += uid_keywords_dict[keywords]
except:
iter_results[uid]['keywords'][keywords] = uid_keywords_dict[keywords]
#get keywords top
for uid in uid_list:
results[uid] = {}
hashtag_dict = iter_results[uid]['hashtag']
results[uid]['hashtag_dict'] = json.dumps(hashtag_dict)
results[uid]['hashtag'] = '&'.join(hashtag_dict.keys())
#sensitive words
sensitive_word_dict = iter_results[uid]['sensitive']
results[uid]['sensitive_dict'] = json.dumps(sensitive_word_dict)
results[uid]['sensitive_string'] = '&'.join(sensitive_word_dict.keys())
sensitive_score = 0
for item in sensitive_word_dict:
k = item
v = sensitive_word_dict[k]
tmp_stage = r_sensitive.hget('sensitive_words', k)
if tmp_stage:
sensitive_score += v * sensitive_score_dict[str(tmp_stage)]
results[uid]['sensitive'] = sensitive_score
#print 'sensitive_dict:', results[uid]['sensitive_dict']
#print 'sensitive_string:', results[uid]['sensitive_string']
#print 'sensitive:', results[uid]['sensitive']
#geo
geo_dict = iter_results[uid]['geo']
geo_track_list = iter_results[uid]['geo_track']
results[uid]['activity_geo_dict'] = json.dumps(geo_track_list)
geo_dict_keys = geo_dict.keys()
#print 'geo_dict_keys:', geo_dict_keys
results[uid]['activity_geo'] = '&'.join(['&'.join(item.split('\t')) for item in geo_dict_keys])
#print 'activity_geo:', results[uid]['activity_geo']
keywords_dict = iter_results[uid]['keywords']
keywords_top50 = sorted(keywords_dict.items(), key=lambda x:x[1], reverse=True)[:50]
keywords_top50_string = '&'.join([keyword_item[0] for keyword_item in keywords_top50])
results[uid]['keywords'] = json.dumps(keywords_top50)
results[uid]['keywords_string'] = keywords_top50_string
return results
def get_flow_information_v2(uid_list, all_user_keywords_dict):
results = {}
#results = {uid:{'hashtag_dict':{},'hashtag':'', 'keywords_dict':{}, 'keywords_string':'', 'activity_geo':'', 'activity_geo_dict':dict, 'activity_geo_aggs':''}}
iter_results = {} # iter_results = {uid:{'hashtag': hashtag_dict, 'geo':geo_dict, 'keywords':keywords_dict}}
now_ts = time.time()
#run_type
today_sensitive_results = {}
if RUN_TYPE == 1:
now_date_ts = datetime2ts(ts2datetime(now_ts))
else:
now_date_ts = test_ts
for i in range(WEEK,0,-1):
ts = now_date_ts - DAY*i
uid_day_geo = {}
#compute hashtag and geo
hashtag_results = r_cluster_3.hmget('hashtag_'+str(ts), uid_list)
ip_results = r_cluster.hmget('new_ip_'+str(ts), uid_list)
#compute sensitive_words
sensitive_results = r_cluster_3.hmget('sensitive_'+str(ts), uid_list)
count = 0
for uid in uid_list:
#init iter_results[uid]
if uid not in iter_results:
iter_results[uid] = {'hashtag':{}, 'geo':{},'geo_track':[],'keywords':{}, 'sensitive':{}, 'school':{}}
if uid not in today_sensitive_results:
today_sensitive_results[uid] = {}
#compute hashtag
hashtag_item = hashtag_results[count]
if hashtag_item:
uid_hashtag_dict = json.loads(hashtag_item)
else:
uid_hashtag_dict = {}
for hashtag in uid_hashtag_dict:
try:
iter_results[uid]['hashtag'][hashtag] += uid_hashtag_dict[hashtag]
except:
iter_results[uid]['hashtag'][hashtag] = uid_hashtag_dict[hashtag]
#compute sensitive
sensitive_item = sensitive_results[count]
if sensitive_item:
uid_sensitive_dict = json.loads(sensitive_item)
else:
uid_sensitive_dict = {}
for sensitive_word in uid_sensitive_dict:
try:
iter_results[uid]['sensitive'][sensitive_word] += uid_sensitive_dict[sensitive_word]
except:
iter_results[uid]['sensitive'][sensitive_word] = uid_sensitive_dict[sensitive_word]
if ts == now_date_ts - DAY:
try:
today_sensitive_results[uid][sensitive_word] += uid_sensitive_dict[sensitive_word]
except:
today_sensitive_results[uid][sensitive_word] = uid_sensitive_dict[sensitive_word]
#compute geo
uid_day_geo[uid] = {}
ip_item = ip_results[count]
if ip_item:
uid_ip_dict = json.loads(ip_item)
else:
uid_ip_dict = {}
for ip in uid_ip_dict:
ip_count = len(uid_ip_dict[ip].split('&'))
geo, school = ip2city(ip)
if geo:
try:
iter_results[uid]['geo'][geo] += ip_count
except:
iter_results[uid]['geo'][geo] = ip_count
try:
uid_day_geo[uid][geo] += ip_count
except:
uid_day_geo[uid][geo] = ip_count
if school:
try:
iter_results[uid]['school'][school] += ip_count
except:
iter_results[uid]['school'][school] = ip_count
iter_results[uid]['geo_track'].append(uid_day_geo[uid])
count += 1
#get keywords top
for uid in uid_list:
results[uid] = {}
#hashtag
hashtag_dict = iter_results[uid]['hashtag']
results[uid]['hashtag_dict'] = json.dumps(hashtag_dict)
results[uid]['hashtag'] = '&'.join(hashtag_dict.keys())
#sensitive words
sensitive_word_dict = iter_results[uid]['sensitive']
results[uid]['sensitive_dict'] = json.dumps(sensitive_word_dict)
results[uid]['sensitive_string'] = '&'.join(sensitive_word_dict.keys())
sensitive_score = 0
today_sensitive_results_user = today_sensitive_results[uid]
for sensitive_item in today_sensitive_results_user:
k = sensitive_item
v = today_sensitive_results_user[sensitive_item]
tmp_stage = r_sensitive.hget('sensitive_words', k)
if tmp_stage:
sensitive_score += v * sensitive_score_dict[str(tmp_stage)]
results[uid]['sensitive'] = sensitive_score
#print 'sensitive_dict:', results[uid]['sensitive_dict']
#print 'sensitive_string:', results[uid]['sensitive_string']
#print 'sensitive:', results[uid]['sensitive']
#geo
geo_dict = iter_results[uid]['geo']
geo_track_list = iter_results[uid]['geo_track']
results[uid]['activity_geo_dict'] = json.dumps(geo_track_list)
geo_dict_keys = geo_dict.keys()
results[uid]['activity_geo'] = '&'.join(['&'.join(item.split('\t')) for item in geo_dict_keys])
try:
results[uid]['activity_geo_aggs'] = '&'.join([item.split('\t')[-1] for item in geo_dict_keys])
except:
results[uid]['activity_geo_aggs'] = ''
#keywords
keywords_dict = all_user_keywords_dict[uid]
keywords_top50 = sorted(keywords_dict.items(), key=lambda x:x[1], reverse=True)[:50]
keywords_top50_string = '&'.join([keyword_item[0] for keyword_item in keywords_top50])
results[uid]['keywords'] = json.dumps(keywords_top50)
results[uid]['keywords_string'] = keywords_top50_string
#school dict
school_dict = iter_results[uid]['school']
school_string = '&'.join(school_dict.keys())
if school_dict != {}:
is_school = '1'
else:
is_school = '0'
results[uid]['is_school'] = is_school
results[uid]['school_string'] = school_string
results[uid]['school_dict'] = json.dumps(school_dict)
return results
item['keywords_string'] = keywords_string # use to search
sensitive_words_dict = searchWord(text.encode('utf-8', 'ignore'), DFA)
if sensitive_words_dict:
item['sensitive_words_string'] = "&".join(sensitive_words_dict.keys())
item['sensitive_words_dict'] = json.dumps(sensitive_words_dict)
else:
item['sensitive_words_string'] = ""
item['sensitive_words_dict'] = json.dumps({})
timestamp = item['timestamp']
date = ts2datetime(timestamp)
ts = datetime2ts(date)
if sensitive_words_dict:
print sensitive_words_dict.keys()[0]
sensitive_count_string = r_cluster.hget('sensitive_'+str(ts), str(uid))
if sensitive_count_string: #redis取空
sensitive_count_dict = json.loads(sensitive_count_string)
for word in sensitive_words_dict.keys():
if sensitive_count_dict.has_key(word):
sensitive_count_dict[word] += sensitive_words_dict[word]
else:
sensitive_count_dict[word] = sensitive_words_dict[word]
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_count_dict))
else:
r_cluster.hset('sensitive_'+str(ts), str(uid), json.dumps(sensitive_words_dict))
#identify whether to mapping new es
weibo_timestamp = item['timestamp']
should_index_name_date = ts2datetime(weibo_timestamp)
if should_index_name_date != now_index_name_date:
from time_utils import datetime2ts,ts2datetime
from global_utils import R_CLUSTER_FLOW3, R_CLUSTER_FLOW2, R_CLUSTER_FLOW1
uidlist = []
f = open("uid_list_0520.txt")
for line in f:
uid = line.strip()
uidlist.append(uid)
f.close()
data = []
dates = ["2016-05-14", "2016-05-15", "2016-05-16", "2016-05-17", "2016-05-18", "2016-05-19", "2016-05-20"]
tss = [datetime2ts(d) for d in dates]
for ts in tss:
ns = "hashtag_" + str(ts)
hashtag_list = R_CLUSTER_FLOW3.hmget(ns, uidlist)
hashtag_list = [json.loads(h) if h else None for h in hashtag_list]
uhlist = zip(uidlist, hashtag_list)
uhtlist = []
for uh in uhlist:
uh = list(uh)
uh.append(ts)
uhtlist.append(uh)
data.extend(uhtlist)
with open("hashtag_0521.txt", "w") as fw:
for d in data:
if d[1] != None:
fw.write("%s\n" % json.dumps(d))
at_data = []