def getPath(day=None):
'''
return (p1,p2,p3) (调用链指标,平台指标,数据说明)
'''
prex_path = data_path.get_data_path(
) if not day else data_path.get_data_path(day)
p1 = os.path.join(prex_path, "调用链指标")
p2 = os.path.join(prex_path, "平台指标")
p3 = os.path.join(prex_path, "数据说明")
return p1, p2, p3
def getKpis(files=None, path=None,kpis = {}):
"""[获取KPI曲线数据,将所有指标数据读到内存]
Args:
files ([type], optional): [传入文件列表 如list(["dcos_docker.csv"])]. Defaults to None.\n
path: 目录，files 是path目录下的具体文件
Returns:
[type]: 字典{(cmdb_id,name,bomc_id,itemid):[row1,row2......]} 每一行 ['itemid', 'name', 'bomc_id', 'timestamp', 'value', 'cmdb_id']
"""
if not path:
path = os.path.join(data_path.get_data_path(), "平台指标")
files = os.listdir(path) if not files else files
for f in files:
# f = "dcos_docker.csv"
p = os.path.join(path, f)
if not os.path.isfile(p):
continue
data = readCSV(p)
# print(data[0])
for row in data[1:]:
#['itemid', 'name', 'bomc_id', 'timestamp', 'value', 'cmdb_id']
key = "%s,%s,%s,%s" % (row[5], row[1], row[2], row[0])
if kpis.get(key) == None:
kpis[key] = []
kpis[key].append(row)
# break
return kpis
def main():
path = os.path.join(data_path.get_data_path(), "平台指标")
print(path)
print("读取文件信息：")
kpis = getKpis(["dcos_docker.csv"])
filter_list = ["cpu"]
print("画图中：")
showKpiCurve(kpis, filter_list)
def getPath(day=None):
'''
return (p1,p2,p3) (调用链指标,平台指标,数据说明)
'''
prex_path = data_path.get_data_path()
p1 = os.path.join(prex_path, "调用链指标")
p2 = os.path.join(prex_path, "平台指标")
p3 = data_path.data_instruction_path
return p1, p2, p3
def start_build_trace(days=["2020_04_20"]):
indicators = fill_all_indicators(data_path.data_instruction_path)
time0 = time.time()
## 将平台指标的每一中指标拆分开，单独一个csv
for day in days:
plat_path = os.path.join(data_path.get_data_path(day), "平台指标")
if len(os.listdir(plat_path)) <= 5:
divide_file(plat_path)
#! 保存
saveJson(build_trace(days), data_path.data_save_path(),
"trace_data_" + days[0] + ".json")
print("程序运行完毕！花费: " + str(time.time() - time0) + " 秒")
def build_trace(days, res={}):
"""[summary]
Args:
path ([str]): trace调用链路径
Returns:
[type]: traces字典 格式{traceId:{ startTime:str,spans:{spanId}}}
"""
# todo 将所有文件合并成 trace
print("开始trace数据合并！")
merge_time = []
for day in days:
for traceName in traceNames:
# csvName = "trace_csf"
p = os.path.join(data_path.get_data_path(day), "调用链指标",
traceName + ".csv")
time1 = time.time()
print("正在读取文件 " + traceName)
temp = readCSV(p)
print("读取" + traceName + "完毕，开始生成trace")
for i in tqdm(range(len(temp) - 1),
desc=traceName,
ncols=100,
ascii=' =',
bar_format='{l_bar}{bar}|'):
# 0 callType,1 startTime,2 elapsedTime,3 success,4 traceId,5 id,6 pid,7 cmdb_id,8 serviceName
row = temp[i + 1]
if len(row) <= 3:
continue
# 通过row 构造span
span = generate_span(row)
# 将span放到相应的trace中
traceId, span_id = row[4], row[5]
if res.get(traceId) == None:
res[traceId] = {}
trace = res[traceId]
trace[span_id] = span
# break
time_spend = time.time() - time1
merge_time.append(time_spend)
print("文件" + traceName + "_" + day + ".csv " + "合并完毕,共花费 " +
str(time_spend) + "S")
# break
# break
print("Trace 合并完毕！共花费 " + str(sum(merge_time)) + "S,分别是", merge_time)
return res
def getKPIs(timeStamp, cmd_id, bias=0, day="2020_04_22"):
if not cmd_id:
return {}
key = cmd_id.split('_')[0]
# 时间偏差不大，直接返回已经记录的
if kpis.get(cmd_id) != None and abs(kpis[cmd_id]["timestamp"] -
timeStamp) <= bias:
return kpis[cmd_id]["values"]
valueJson = {}
# 遍历所有指标名，闭关将相应的指标获取
for bomc_id, (sample_period, indicator_name) in indicators[key].items():
# csv 路径
plat_path = os.path.join(data_path.get_data_path(day), "平台指标")
file_path = os.path.join(plat_path, key, indicator_name + ".csv")
# 指标名，取样周期
valueJson.update(
get_kpis_for_an_indicator(timeStamp, cmd_id, bomc_id,
sample_period * 1000, file_path))
kpis[cmd_id] = {"timestamp": timeStamp, "values": valueJson}
return valueJson
def get_abnormal_interval(days, useGiven=True):
business_paths = [
os.path.join(data_path.get_data_path(day), "业务指标", "esb.csv")
for day in days
]
# 获取业务指标数据，去掉表头,np.array
data = None
for p in business_paths:
data = pd.concat([data, pd.read_csv(p)], ignore_index=True)
data = data.values
# 根据时间序列排序
data = data[np.argsort(data[:, 1])]
## !异常时间区间
interval_times, fault_ids = [], []
# 根据给出的异常文档找出异常时间段
if useGiven:
interval_times, fault_ids = anomaly_detection.fault_time(
bias=0 * 60 * 100, file_day=days[0], type=2)
else: ## 通过自己算法找出
# 异常数据
abnormal_data = anomaly_detection.find_abnormal_data(data)
# 异常时间序列
execption_times = abnormal_data[:, 1].astype(np.int64)
#! 异常时间区间
interval_times = anomaly_detection.to_interval(execption_times)
fault_ids = range(len(interval_times))
print(str(interval_times))
#! 对应时间区间是否是网络故障
is_net_error = [
] # anomaly_detection.is_net_error_func(interval_times,abnormal_data)
for i, j in zip(interval_times, is_net_error):
print(i, j)
# 画出找到的异常区间
anomaly_detection.draw_abnormal_period(data, interval_times)
return interval_times, is_net_error, fault_ids
# %%
import pandas as pd
import numpy as np
import os
from data_path import get_data_path
from sklearn.ensemble import IsolationForest
import show_Kpis
import anomaly_detection
# %%读取所有Kpis
day = '2020_04_22'
path = os.path.join(get_data_path(day), "平台指标")
kpis = show_Kpis.getKpis(["dcos_docker.csv"], path)
print("Get ALL KPIS")
# %% step1 获取一条具体的数据
def get_specific_kpi(kpis, filter_list):
"""[筛选出满足条件的具体指标]
Args:
kpis ([dict]): [指标字典]:
filter_list ([list]): [description]
Returns:[list]: [[(key,np.array),(),()]]
"""
res = []
for k, v in kpis.items():
if not list(filter(lambda x: x not in k, filter_list)):
res.append((k, np.array(v)))
return res
filter_list = ["docker_004", "cpu"]
from filecmp import cmp
from numpy import long
from tqdm import tqdm
import os
import csv
import re
from read_data import readCSV
import data_path
path = os.path.join(data_path.get_data_path(), "调用链指标")
def order(path):
files = os.listdir(path)
for file in files:
temp = readCSV(os.path.join(path, file))
row_first = temp[0]
temp = sorted(temp[1:], key=lambda x: x[1])
new_name = file.split(".")[0] + "_sorted"
with open(path + new_name + ".csv", 'w', newline="") as fd:
writer = csv.writer(fd)
writer.writerow(row_first)
for row in tqdm(temp,
desc=file,
ncols=100,
ascii=' =',
bar_format='{l_bar}{bar}|'):
writer.writerow(row)
print("文件" + file + "排序完成")
#! 将平台指标的每一中指标拆分开，单独一个csv
import numpy as np
import csv
import os
import matplotlib.pyplot as plt
import data_path
from read_data import readCSV
path = os.path.join(data_path.get_data_path(), "调用链指标", "Order_by_cmdid")
def draw(value, title):
plt.plot(value, color='r')
plt.title(title)
plt.show()
if __name__ == '__main__':
#value=[1,3,7,4,8,9,2,5,6,7,8]
fileName = "docker_003.csv"
filepath = os.path.join(path, fileName)
res = np.array(readCSV(filepath))
print(res.shape)
print(1)
value = res[:, 2].astype(np.float)
draw(value, title=fileName.split(".")[0] + "_" + "csf_001")
res = list(reader)
return res
def readCsvWithPandas(p):
'''
读取CSV文件， 通过pandas
'''
# todo 文件不存在直接返回
if not os.path.exists(p):
return []
# todo 读取文件，并以列表的形式返回
res = []
res = pd.read_csv(p,engine="python").values
return res
def read_json(p):
if not os.path.exists(p):
return []
res=[]
with open(p,'r')as f:
json_data=json.load(f)
return json_data
if __name__ == '__main__':
print("daf")
day = "2020_05_22"
path = os.path.join(data_path.get_data_path(day), "数据说明", "0故障说明.xlsx")
table = read_xlrd(path)
print(table)
#! 对应时间区间是否是网络故障
is_net_error = [
] # anomaly_detection.is_net_error_func(interval_times,abnormal_data)
for i, j in zip(interval_times, is_net_error):
print(i, j)
# 画出找到的异常区间
anomaly_detection.draw_abnormal_period(data, interval_times)
return interval_times, is_net_error, fault_ids
# %%
# 调用链指标,平台指标,数据说明
# importlib.reload(anomaly_detection)
plat_paths = [
os.path.join(data_path.get_data_path(day), "平台指标") for day in days
]
interval_times, is_net_error, fault_ids = get_abnormal_interval(days)
# print(fault_ids)
# %%
days = ['2020_05_22']
# ,'2020_05_23','2020_05_24','2020_05_25','2020_05_26'
# ,'2020_05_27','2020_05_28','2020_05_29','2020_05_30','2020_05_31']
# todo step2 获取所有trace
traces = {}
for day in days:
prex_path = data_path.get_data_path(day)
trace_p = os.path.join(prex_path, "调用链指标")
data_cleaning.build_trace(trace_p, traces)
# %%
# # %%
# pred = iforest(data,["avg_time","succee_rate"])
# #%%
# timestamps = data[pred==-1]["startTime"].values
# interval_times = to_period_time(timestamps)
# print(len(interval_times))
# for t in interval_times:
# print(t)
# print(fault_time())
# %%
if __name__ == "__main__":
day = "2020_05_22"
# 业务指标
business_path = os.path.join(data_path.get_data_path(day), "业务指标",
"esb.csv")
# 获取业务指标数据，去掉表头,np.array
data = readCsvWithPandas(business_path)
# 根据时间序列排序
data = data[np.argsort(data[:, 1])]
# todo step1 异常时间序列
# 异常数据
abnormal_data = find_abnormal_data(data)
# 异常时间序列
execption_times = abnormal_data[:, 1].astype(np.int64)
# 异常时间区间
interval_times = to_interval(execption_times)
is_net_error = is_net_error_func(interval_times, abnormal_data)
print(len(interval_times))
