目标：根據ceph叢集的mon日志，以其中的時間為橫坐标，對象遷移速度為縱坐标，利用python的matplotlib生成圖表
# 日志格式
2021-04-22 14:00:20.687685 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31413: 3578 pgs: 1795 active+clean, 1518 active+recovery_wait+degraded, 265 active+recovering+degraded; 13709 GB data, 37462 GB used, 216 TB / 253 TB avail; 25963 B/s rd, 101 MB/s wr, 40 op/s; 3635096/10530407 objects degraded (34.520%); 231 MB/s, 57 objects/s recovering
2021-04-22 14:00:21.404638 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1226651' entity='client.admin' cmd=[{"prefix": "status"}]: dispatch
2021-04-22 14:00:22.064304 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31414: 3578 pgs: 1795 active+clean, 1518 active+recovery_wait+degraded, 265 active+recovering+degraded; 13710 GB data, 37465 GB used, 216 TB / 253 TB avail; 202 MB/s wr, 66 op/s; 3634943/10530710 objects degraded (34.518%); 491 MB/s, 122 objects/s recovering
2021-04-22 14:00:22.899451 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1226758' entity='client.admin' cmd=[{"prefix": "status"}]: dispatch
2021-04-22 14:00:23.117601 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31415: 3578 pgs: 1795 active+clean, 1518 active+recovery_wait+degraded, 265 active+recovering+degraded; 13710 GB data, 37466 GB used, 216 TB / 253 TB avail; 278 MB/s wr, 89 op/s; 3634877/10530920 objects degraded (34.516%); 656 MB/s, 164 objects/s recovering
2021-04-22 14:00:24.168534 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31416: 3578 pgs: 1795 active+clean, 1518 active+recovery_wait+degraded, 265 active+recovering+degraded; 13710 GB data, 37467 GB used, 216 TB / 253 TB avail; 46501 B/s rd, 156 MB/s wr, 54 op/s; 3634844/10530971 objects degraded (34.516%); 351 MB/s, 87 objects/s recovering
2021-04-22 14:00:24.310858 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1226862' entity='client.admin' cmd=[{"prefix": "df"}]: dispatch
2021-04-22 14:00:25.182928 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31417: 3578 pgs: 1796 active+clean, 1518 active+recovery_wait+degraded, 264 active+recovering+degraded; 13710 GB data, 37467 GB used, 216 TB / 253 TB avail; 50826 B/s rd, 73402 kB/s wr, 28 op/s; 3633653/10531025 objects degraded (34.504%); 272 MB/s, 68 objects/s recovering
2021-04-22 14:00:25.706503 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1227016' entity='client.admin' cmd=[{"prefix": "df"}]: dispatch
2021-04-22 14:00:26.211983 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31418: 3578 pgs: 1796 active+clean, 1518 active+recovery_wait+degraded, 264 active+recovering+degraded; 13710 GB data, 37468 GB used, 216 TB / 253 TB avail; 95139 kB/s wr, 30 op/s; 3633573/10531100 objects degraded (34.503%); 381 MB/s, 95 objects/s recovering
2021-04-22 14:00:27.264925 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31419: 3578 pgs: 1796 active+clean, 1518 active+recovery_wait+degraded, 264 active+recovering+degraded; 13711 GB data, 37471 GB used, 216 TB / 253 TB avail; 326 MB/s wr, 103 op/s; 3633320/10531484 objects degraded (34.500%); 947 MB/s, 236 objects/s recovering
2021-04-22 14:00:27.524231 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1227171' entity='client.admin' cmd=[{"prefix": "osd perf"}]: dispatch
2021-04-22 14:00:27.635542 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1227155' entity='client.admin' cmd=[{"prefix": "status"}]: dispatch
2021-04-22 14:00:27.957661 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.2:0/4594454' entity='client.admin' cmd=[{"prefix": "health"}]: dispatch
2021-04-22 14:00:28.162358 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.2:0/4594460' entity='client.admin' cmd=[{"prefix": "health"}]: dispatch
2021-04-22 14:00:28.292117 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31420: 3578 pgs: 1797 active+clean, 1518 active+recovery_wait+degraded, 263 active+recovering+degraded; 13711 GB data, 37471 GB used, 216 TB / 253 TB avail; 320 MB/s wr, 101 op/s; 3632295/10531565 objects degraded (34.490%); 803 MB/s, 200 objects/s recovering
2021-04-22 14:00:29.490974 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31421: 3578 pgs: 1797 active+clean, 1518 active+recovery_wait+degraded, 263 active+recovering+degraded; 13711 GB data, 37472 GB used, 216 TB / 253 TB avail; 58531 B/s rd, 107 MB/s wr, 50 op/s; 3632272/10531658 objects degraded (34.489%); 165 MB/s, 41 objects/s recovering
2021-04-22 14:00:30.560615 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31422: 3578 pgs: 1797 active+clean, 1517 active+recovery_wait+degraded, 264 active+recovering+degraded; 13711 GB data, 37472 GB used, 216 TB / 253 TB avail; 57007 B/s rd, 86775 kB/s wr, 42 op/s; 3632246/10531712 objects degraded (34.489%); 166 MB/s, 41 objects/s recovering
2021-04-22 14:00:30.935797 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1227362' entity='client.admin' cmd=[{"prefix": "df"}]: dispatch
2021-04-22 14:00:31.620008 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1227476' entity='client.admin' cmd=[{"prefix": "health"}]: dispatch
2021-04-22 14:00:31.627203 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1227460' entity='client.admin' cmd=[{"prefix": "health"}]: dispatch
2021-04-22 14:00:31.671392 7f97234a6700 0 log_channel(cluster) log [INF] : pgmap v31423: 3578 pgs: 1799 active+clean, 1517 active+recovery_wait+degraded, 262 active+recovering+degraded; 13711 GB data, 37474 GB used, 216 TB / 253 TB avail; 138 MB/s wr, 42 op/s; 3629994/10531892 objects degraded (34.467%); 337 MB/s, 84 objects/s recovering
2021-04-22 14:00:32.125331 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.3:0/4429248' entity='client.admin' cmd=[{"prefix": "health"}]: dispatch
2021-04-22 14:00:32.229556 7f971d7fe700 0 log_channel(audit) log [INF] : from='client.? 10.10.10.1:0/1227374' entity='client.admin' cmd=[{"prefix": "health"}]: dispatch 複制
# python代碼實作
import pylab
import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator
def get_number():
y1 = []
x1 = []
Syslog = "C:/Users/summer/Desktop/ceph-mon.log"
with open(Syslog,'r')as f:
for i in f.readlines():
try:
if ';' not in i or 'kB/s' in i:
continue
else:
info = i.split(';')
x1.append(info[0][11:19])
y1.append(float(i[i.rfind("MB/s"):][5:9]))
# print(x1 + y1)
except:
print('erro')
pylab.figure(figsize=(21, 7))
pylab.plot(x1,y1,'r')
plt.xticks(rotation=90,fontsize=11)
x_major_locator=MultipleLocator(50)
ax=plt.gca()
ax.xaxis.set_major_locator(x_major_locator)
# plt.tight_layout()
pylab.show()
if __name__ == '__main__':
get_number() 複制
# 生成的圖表展示
