def operate_data_send(operate_data):
send_data = []
for key in operate_data.keys():
describe = operate_data.get(key)
if describe is not None:
datetime = dt.datetime.strftime(dt.datetime.now(), '%Y-%m-%d %H:%M:%S')
oper_num = describe.get('OperNum')
json_describe = json.dumps(describe, ensure_ascii=False)
send_data.append([datetime,json_describe,key,oper_num])
pg = Postgres()
conn, cr = pg.db_conn()
for data in send_data:
# print(data)
cr.execute(sql_insert, data)
conn.commit()
pg.db_close()
def call_pg_data(sql,pg_inf=None):
if pg_inf:
db = Postgres(pg_inf)
else:
db = Postgres()
pg_conn, cr = db.db_conn()
if pg_conn:
cr.execute(sql)
index = cr.description
result = cr.fetchall()
db.db_close()
if result:
fresult = tuple2frame(result, index)
return fresult
else:
return pd.DataFrame({})
class OperateStatistics():
def __init__(self, operate_data=pd.DataFrame({})):
self.operate_data = operate_data
self.count_dict = {}
for key in KEYWORD:
self.count_dict[key] = 0
def operate_type_percent(self):
operate_data = self.operate_data
for i in range(len(operate_data)):
oper_type = operate_data.iloc[i][6]
for type in oper_type:
count = self.count_dict.get(type)
if count is not None:
self.count_dict[type] += 1
return self.count_dict
def user_operate_sum(self):
operate_data = self.operate_data
grouped = operate_data.groupby('userid')
result = {}
# operate_data.drop_duplicates()
for index, group in grouped:
operater = Operater()
sum_num = len(group['opertime'].drop_duplicates().tolist())
result[index] = {'OperNum':sum_num}
for i in range(len(group)):
oper_type = group.iloc[i][6]
for type in oper_type:
count = operater.count_dict.get(type)
if count is not None:
operater.count_dict[type] += 1
result[index]['TypeCount'] = operater.count_dict
return result
def alarm_operate_match(self, s_datetime, e_datetime):
pg_inf = pg_inf_46
self.pg = Postgres(pg_inf)
host = pg_inf.get('host')
conn,cr = self.pg.db_conn()
if cr:
cr.execute(sql_alarm_operate_match.format(host,s_datetime,e_datetime,MATCH_LIMITTIME))
conn.commit()
