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metrics_fetch.py
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metrics_fetch.py
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#coding=utf-8
import urllib.parse
import requests
import os
import datetime
import time
import pandas as pd
import numpy as np
template = {
"vCPU": "sum(rate(container_cpu_usage_seconds_total{{namespace='{0}',container='{1}'}}[{2}]))",
"cpu":"sum(irate(container_cpu_usage_seconds_total{{container=~'{1}',namespace=~'{0}'}}[1m]))/sum(container_spec_cpu_quota{{container=~'{1}',namespace=~'{0}'}}/container_spec_cpu_period{{container=~'{1}',namespace=~'{0}'}})",
"mem": "sum(container_memory_usage_bytes{{namespace='{0}',container='{1}'}}) / sum(container_spec_memory_limit_bytes{{namespace='{0}',container='{1}'}})",
"mem_": "sum(container_memory_usage_bytes{{namespace='{0}',container='{1}'}})",
"res": "sum(rate(istio_request_duration_milliseconds_sum{{reporter='destination',destination_workload_namespace='{0}',destination_workload='{1}'}}[{2}]))/sum(rate(istio_request_duration_milliseconds_count{{reporter='destination',destination_workload_namespace='{0}',destination_workload='{1}'}}[{2}]))/1000",
"req": "sum(rate(istio_requests_total{{destination_workload_namespace='{0}',destination_workload='{1}'}}[{2}]))",
"pod": "count(container_spec_cpu_period{{namespace='{0}',container='{1}'}})"
}
prefix_api = "http://localhost:30090/api/v1/query?query="
namespace = 'boutiquessj'
interval = 120
services = ["adservice", "cartservice", "checkoutservice","currencyservice","emailservice","frontend","paymentservice","productcatalogservice","recommendationservice","shippingservice"]
metrics = ['cpu','res','req','pod']
training_root_dir = ''
def fetch_cpu_usage(svc_name, namespace=namespace, interval=30):
cpu_api = template["cpu"].format(namespace, svc_name, str(interval)+'s')
url = prefix_api + urllib.parse.quote_plus(cpu_api)
res = requests.get(url).json()["data"]
v = 0
if "result" in res and len(res["result"]) > 0 and "value" in res["result"][0]:
v = res["result"][0]["value"][1]
return float(v)
def fetch_mem_usage(svc_name, namespace=namespace):
mem_api = template["mem"].format(namespace, svc_name)
url = prefix_api + urllib.parse.quote_plus(mem_api)
res = requests.get(url).json()["data"]
v = 0
if "result" in res and len(res["result"]) > 0 and "value" in res["result"][0]:
v = res["result"][0]["value"][1]
return float(v)
def fetch_res_time(svc_name, namespace=namespace, interval=30):
res_api = template["res"].format(namespace, svc_name, str(interval)+'s')
url = prefix_api + urllib.parse.quote_plus(res_api)
res = requests.get(url).json()["data"]
if "result" in res and len(res["result"]) > 0 and "value" in res["result"][0]:
v = res["result"][0]["value"]
if v[1] != 'NaN':
return float(v[1])
return 0
def fetch_req(svc_name, namespace=namespace, interval=30):
req_api = template["req"].format(namespace, svc_name, str(interval)+'s')
url = prefix_api + urllib.parse.quote_plus(req_api)
req = requests.get(url).json()["data"]
if "result" in req and len(req["result"]) > 0 and "value" in req["result"][0]:
v = req["result"][0]["value"]
if v[1] != 'NaN':
return int(float(v[1]))
return 0
def fetch_prior_req(svc_name, namespace=namespace, interval=30, delta=30):
req_api = template["req"].format(namespace, svc_name, str(interval)+'s')
url = prefix_api + urllib.parse.quote_plus(req_api) + "&time=" + str(time.time() - delta)
req = requests.get(url).json()["data"]
if "result" in req and len(req["result"]) > 0 and "value" in req["result"][0]:
v = req["result"][0]["value"]
if v[1] != 'NaN':
return int(float(v[1]))
return 0
def fetch_pods(svc_name, namespace=namespace):
pod_api = template["pod"].format(namespace, svc_name)
url = prefix_api + urllib.parse.quote_plus(pod_api)
print(url)
#pod = requests.get(url).json()
pod = requests.get(url).json()["data"]
if "result" in pod and len(pod["result"]) > 0 and "value" in pod["result"][0]:
v = pod["result"][0]["value"]
if v[1] != 'NaN':
return int(float(v[1]))
return 0
def save_fetch_data(svc_name, mode, start_time, latsted_time, interval, save_file):
api_str = template[mode].format(namespace, svc_name, str(interval)+'s')#mode为pod，
# start_time = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')
with open(save_file, 'w') as f:
for i in range(0, latsted_time, int(interval)):#以interval为间隔
t = start_time + datetime.timedelta(seconds=i)#加时间
unixtime = time.mktime(t.timetuple())#返回用秒数来表示时间的浮点数。
url = prefix_api + urllib.parse.quote_plus(api_str) + "&time=" + str(unixtime)
res = requests.get(url).json()["data"]
if "result" in res and len(res["result"]) > 0 and "value" in res["result"][0]:
v = res["result"][0]["value"]
if v[1] == 'NaN':
print("0", file=f)
else:
print(str(v[1]), file=f)
else:
print("0", file=f)
def save_all_fetch_data(times=[], start_iter=1, root_dir='/home/boutiquessj/pythonForK6/data/', interval=interval, services=services, metrics=metrics):
if not os.path.exists(root_dir):#若不存在则创建一个
os.makedirs(root_dir)
for i, (start_time, end_or_lasted) in enumerate(times):
start_time = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') #+ datetime.timedelta(hours=8)
if type(end_or_lasted) == str:
end_time = datetime.datetime.strptime(end_or_lasted, '%Y-%m-%d %H:%M:%S') #+ datetime.timedelta(hours=8)#######因为时区问题要加8
lasted_time = int((end_time-start_time).total_seconds())#有多少秒
else:
lasted_time = int(end_or_lasted)
if os.path.exists(root_dir+'{}_productpage_res.log'.format(start_iter+i)):
print("Iternum needs update")
return
for svc in services:
for m in metrics:
save_fetch_data(svc, m, start_time, lasted_time, interval, root_dir+"{}_{}_{}.log".format(start_iter+i, svc, m))
print("saved file:"+root_dir+"{}_{}_{}.log".format(start_iter+i, svc, m))
def load_fetch_data(root_dir, start_iter=1, end_iter=None, services=services, metrics=metrics) -> pd.DataFrame:
if not end_iter:
end_iter = start_iter
data = {}
for svc in services:
data[svc] = {}
for m in metrics:
data[svc][m] = []
for svc in services:
for m in metrics:
for iternum in range(start_iter, end_iter+1):
path = root_dir + '{}_{}_{}.log'.format(iternum, svc, m)
with open(path, 'r') as f:
lines = f.readlines()
data[svc][m] += list(map(lambda x:float(x), lines))
D = [data[svc][m] for svc in services for m in metrics]
data_df = pd.DataFrame(np.array(D).T, columns=[svc+"_"+m for svc in services for m in metrics])
return data_df
def load_processed_fetch_data(iternums=[1, 2], root_dir=training_root_dir, metrics=metrics):
data_df = load_fetch_data(iternums, root_dir, metrics)
D, l = [], len(metrics)
for r in data_df.values:
D.append([1]+list(r[:l]))
D.append([2]+list(r[l:2*l]))
D.append([3]+list(r[2*l:3*l]))
D.append([4]+list(r[3*l:]))
data_df = pd.DataFrame(D, columns=['svc']+metrics)
data_df['pod'] = data_df['pod'].astype(int)
return data_df
if __name__ == '__main__':
times = [
('2023-03-04 04:09:01', '2023-03-04 05:25:15')
]#
save_all_fetch_data(times, 1, root_dir='/ssj/ssj/boutiquessj/pyboutique/newData/slohpa/', interval=30, services=services)#interval 间隔
print("ok")