def get_all_users():
today = datetime.date.today().strftime("%Y%m%d")
vpmn = get_vpmn_user() if get_vpmn_user() else 0
pyq = get_pyq_user() if get_pyq_user() else 0
hjh = get_hjh_user() if get_hjh_user() else 0
crbt = get_crbt_user() if get_crbt_user() else 0
vrbt = get_vrbt_user() if get_vrbt_user() else 0
ctx_only = get_ctxonly_user() if get_ctxonly_user() else 0
ctx_group = get_ctx_group() if get_ctxonly_user() else 0
# 转换格式，方便直接入库，key与数据库中表字段一致
users_db = {
'date': today,
'vpmn_volte': vpmn,
'crbt_volte': crbt,
'hjh_volte': hjh,
'pyq_volte': pyq,
'ctx_group': ctx_group,
'ctx_user': ctx_only,
'vrbt': vrbt
}
try:
db.insert('users',**users_db)
except Exception as e:
logging.error("用户数入库错误" + str(e))
return users_db
def create(i):
isint(i.userid)
isvarchar(i.name,1,50)
return db.insert('groups',
userid=i.userid,name=i.name,createdate=datetime.now(),
_test=is_debug)
def register(i):
isvarchar(i.email,1,50)
isvarchar(i.password,1,100)
return db.insert('users',
id=i.id,email=i.email,password=i.password,registerdate=datetime.now(),status=1,
_test=is_debug)
def create(uid,gid,content):
isbigint(uid)
isbigint(gid)
istext(gid)
summary = content[:150]
return db.insert('notes',
groupid=gid,userid=uid,notes=content,summary= summary,createdate=datetime.now(),status=1,
_test=is_debug)
def get_v_users(filename):
today = datetime.date.today().strftime("%Y%m%d")
all_users = get_data(filename)
user_db = {}
user_db['date'] = today
for x in all_users:
try:
x.remove('')
except ValueError:
pass
user_type = x[0].strip(' ')
if user_type in config.users_tilte.keys():
user_name = config.users_tilte.get(user_type)
user_value = int(x[1].strip(' '))
user_db[user_name] = user_value
crbt_temp = get_crbt_user()
crbt = crbt_temp if crbt_temp else 0
vrbt_temp = get_vrbt_user()
vrbt = vrbt_temp if vrbt_temp else 0
ctx_only_temp = get_ctxonly_user()
ctx_only = ctx_only_temp if ctx_only_temp else 0
ctx_group_temp = get_ctx_group()
ctx_group = ctx_group_temp if ctx_group_temp else 0
crbt23g = get_crbt23g_user()
newcy, ccp = get_cy_ccp()
scim = get_scim_user()
user_db.update({
'crbt_volte': crbt,
'vrbt': vrbt,
'ctx_user': ctx_only,
'ctx_group': ctx_group,
'crbt_23g': crbt23g,
'newcy': newcy,
'ccp': ccp,
'scim': scim
})
try:
db.insert('users',**user_db)
except Exception as e:
logging.error("用户数入库错误" + str(e))
return user_db
def get_vsstj(filename):
vss_db = {}
yesterday = (datetime.date.today() - datetime.timedelta(days=1))
vss_db['date'] = yesterday.strftime("%Y%m%d")
with open(filename) as file:
# 提取一周活性用户数
hx_user_temp = blocks(file, keywords="hx_user")
hx_user = float(hx_user_temp[-1].split(" ")[0])
# 提取短号短信总下发量
vss_data = blocks(file, keywords="---VSS")
vss_total = sum([float(x.split(" ")[-1]) for x in vss_data[1:]])
# 提取短号彩信总下发量
mms_data = blocks(file, keywords="---MMS")
mms_total = sum([float(x.split(" ")[-1]) for x in mms_data[1:]])
# 数据入库
vss_db['hx_user'] = hx_user
vss_db['vss_total'] = vss_total
vss_db['mms_total'] = mms_total
try:
db.insert(table="vss", **vss_db)
except Exception as e:
logging.error("短号短信数据入库失败： " + str(e))
def get_caps(filename):
yesterday = (datetime.date.today() -
datetime.timedelta(days=1)).strftime("%Y%m%d")
caps_data = []
caps_db = {}
record_data = get_data(filename)
for value in record_data:
caps_name = config.caps_tilte.get(value[0])
try:
caps_value = float(value[1])
except ValueError:
# 跳过数据为空
caps_value = ""
caps_data.append(
[caps_name, caps_name.split('_')[0].upper(), caps_value])
caps_db[caps_name] = caps_value
caps_db['date'] = yesterday
try:
db.insert("caps", **caps_db)
except Exception as e:
logging.error("CAPS数据入库错误" + str(e))
return caps_data
def create(uid, gid, content):
isbigint(uid)
isbigint(gid)
istext(gid)
summary = content[:150]
return db.insert('notes',
groupid=gid,
userid=uid,
notes=content,
summary=summary,
createdate=datetime.now(),
status=1,
_test=is_debug)
def upload():
file = request.files['file']
if file and allowed_file(file.filename):
filename = randFileName(file.filename)
fullPath = os.path.join(savePath(), filename)
file.save(fullPath)
key = ''
while True:
key = encode(int("{}{}".format(curNum(), ranNum())))
if not db.select(key):
break
db.insert({
'id': key,
'fileName': file.filename,
'realPath': fullPath,
'mime': file.content_type,
'fileSize': os.stat(fullPath).st_size
})
return key
'''TEST DB'''
dados = {
"airline": "azul",
"airport_origin": "CWB",
"airport_destination": "VCP",
"date_departure": "2014-01-01",
"date_return": "2014-01-10",
"weekday": "Monday",
"min": 100,
"avg": 150,
"max": 200
}
db.insert("RangeSearch", dados)
db.selectAll("RangeSearch", ["*"])
test_cases = {
(2013, 12, 12): calendar.date_split("2013-12-12"),
(2013, 12, 12): calendar.date_split("2013/12/12", separator="/"),
(0000, 00, 00): calendar.date_split("0000-00-00"),
(0000, 0, 120): calendar.date_split("0000#0#120", separator="#"),
1: calendar.datecmp("2013-10-30", "2012-10-10"),
-1: calendar.datecmp("2011-10-10", "2012-10-10"),
0: calendar.datecmp("2013-10-10", "2013-10-10"),
"2013-11-30": calendar.date_to_str(datetime.datetime(2013, 11, 30)),
"2013-11-10": calendar.date_to_str(datetime.datetime(2013, 11, 10)),
"Saturday": calendar.date_to_weekday("2013-11-30"),
"Sunday": calendar.date_to_weekday("2013-12-01"),
def cpu_analyse(cpu_file):
title = ['网元', '主机', 'CPU', '内存', 'IO']
data = TxtParse(cpu_file, sep='|', titles=title)
df = data.get_df()
# 增加对应的网元类型
df["网元类型"] = list(map(lambda x: get_cluster_type(x), df["网元"]))
# 数据分割，当天，前一天，前一周
num = df.index[df['网元'].isin(["全网昨天CPU&内存数据获取"]) == True].tolist()[0]
num1 = df.index[df['网元'].isin(["全网前天CPU&内存数据"]) == True].tolist()[0]
num2 = df.index[df['网元'].isin(["全网前一周CPU&内存数据"]) == True].tolist()[0]
# 昨天AS主机性能数据
df_td = df.loc[:num - 1]
# 全网昨天性能数据
df_ytd = df.loc[num + 1:num1 - 1]
# 全网前天性能数据
df_b4ytd = df.loc[num1 + 1:num2 - 1]
# 全网前一周性能数据
df_wk = df.loc[num2 + 1:]
# 将数据进行分组处理,并转成list,方便生成HTML
# list_td_grp, list_td_grp_html = get_max_cpu_host(df_td, col="网元")
list_td_grp_html = get_max_cpu_host(df_td, col="网元")
# 数据入库
yesterday = datetime.date.today() - datetime.timedelta(days=1)
yesterday = yesterday.strftime("%Y%m%d")
for values in list_td_grp_html[1:]:
# 数值使用float转换，否则插入数据库Mysql会报错
db.insert("as_pfmc", date=yesterday, cluste=values[0], max_cpu=float(values[1]), max_cpu_host=values[2],
max_mem=float(values[3]), max_mem_host=values[4], max_io=float(values[5]), max_io_host=values[6])
list_ytd_grp = get_max_cpu(df_ytd, col="网元类型")
list_b4ytd_grp = get_max_cpu(df_b4ytd, col="网元类型")
list_wk_grp = get_max_cpu(df_wk, col="网元类型")
# 转为DataFrame
df_ytd_grp = pd.DataFrame(list_ytd_grp[1:], columns=["网元类型", "CPU", "MEM", "IO"])
df_b4ytd_grp = pd.DataFrame(list_b4ytd_grp[1:], columns=["网元类型", "CPU", "MEM", "IO"])
df_wk_grp = pd.DataFrame(list_wk_grp[1:], columns=["网元类型", "CPU", "MEM", "IO"])
# 昨天、前天、上周数据合并为一张表
df_temp = pd.merge(df_b4ytd_grp, df_wk_grp, how="left", on="网元类型")
df_merge = pd.merge(df_ytd_grp, df_temp, how="left", on="网元类型")
# 计算同比增长（前一周比较）
df_merge["t_cpu_growth"] = (df_merge["CPU"] - df_merge["CPU_y"]) / df_merge["CPU_y"]
df_merge["t_mem_growth"] = (df_merge["MEM"] - df_merge["MEM_y"]) / df_merge["MEM_y"]
# 计算环比增长（前一天比较）
df_merge["h_cpu_growth"] = (df_merge["CPU"] - df_merge["CPU_x"]) / df_merge["CPU_x"]
df_merge["h_mem_growth"] = (df_merge["MEM"] - df_merge["MEM_x"]) / df_merge["MEM_x"]
# 将小数转换成百分数
for col in ["t_cpu_growth", "t_mem_growth", "h_cpu_growth", "h_mem_growth"]:
df_merge[col] = df_merge[col].apply(lambda x: format(x, ".2%"))
# 将CPU MEM统一保留2位小数
for col in ["CPU", "MEM"]:
df_merge[col] = df_merge[col].apply(lambda x: format(x, ".2f"))
# 输出表格
output_data = [["网元类型", "性能指标分析"]]
for i in range(len(df_merge.index)):
str = "CPU占用: 最高 {}% ,同比(前一周同一天的数据对比){} ,环比(前一天数据对比){}, 内存占用: 最高 {}% ,同比{} ,环比\
{}".format(df_merge.ix[i]["CPU"],check(df_merge.ix[i]["t_cpu_growth"]),check(df_merge.ix[i]["h_cpu_growth"]),df_merge.ix[i]["MEM"],check(df_merge.ix[i]["t_mem_growth"]),check(df_merge.ix[i]["h_mem_growth"]))
output_data.append([config.cluters_map.get(df_merge.ix[i]["网元类型"]), str])
t_start = '<table class="tableizer-table" cellspacing=0 width="60%";>\n'
t_end = '</table>\n'
zyjh_html = parseHtml(output_data, "作业计划指标")
cpu_today_html = parseHtml(list_td_grp_html, "AS主机昨日性能指标")
cpu_yes_html = parseHtml(list_ytd_grp, "全网主机昨日性能指标")
cpu_html = cpu_today_html + t_end + "<br></br>" + t_start + cpu_yes_html
return zyjh_html, cpu_html
