r_cluster.delete('activity_'+str(delete_ts))
#delete hashtag
r_cluster.delete('hashtag_'+str(delete_ts))
#delete sensitive words
r_cluster.delete('sensitive_'+str(delete_ts))
#delete recommendation
r.delete('recomment_'+str(delete_date))
if __name__ == "__main__":
now_ts = time.time()
current_path = os.getcwd()
file_path_redis = os.path.join(current_path, 'delete_redis.py')
print_log = "&".join([file_path_redis, "start", ts2datetime(now_ts)])
print print_log
now_datetime = datetime2ts(ts2datetime(now_ts))
new_ip_number = r_cluster.hlen('new_ip_'+str(now_datetime))
new_hashtag_number = r_cluster.hlen('hashtag_'+str(now_datetime))
#if new_ip_number and new_hashtag_number: # flow2/flow4写入新数据,可以清楚8天前数据
# main()
now_ts = time.time()
print_log = "&".join([file_path_redis, "end", ts2datetime(now_ts)])
print print_log
def main():
if RUN_TYPE:
now_ts = time.time() - DAY # 前一天
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
def main():
if RUN_TYPE:
now_ts = time.time()-DAY # 前一天
ts = str(datetime2ts(ts2datetime(now_ts)))
else:
ts = str(datetime2ts('2013-09-07'))
now_ts = int(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.pop(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)