volumeClaimTemplate: spec: storageClassName: fast volumeMode: Filesystem resources: requests: storage: 40Gi $ oc -n openshift-user Alertmanager 有两个副本，所以您需要四个 PV 来支持整个监控堆栈。PV 可从 Local Storage Operator 获得，但如果您启用了动态置备存储，则不会使用它。 在配置持久性卷时，使用 Filesystem 作为 volumeMode 参数的存储类型值。 配置本地持久性存储。 注意 注意 如果将本地卷用于持久性存储，请不要使用原始块卷，这在 LocalVolume 对象中 的 volumeMode: emptyDir 卷，则 Suports 只是一个文件名，在这种 情况下，将保存在 /var/log/prometheus 的 emptyDir 卷中。不支持相对路 径，也不支持对 linux std 流的写操 作。默认："" remoteWrite array(remotewritespec) RemoteWrite 使用远程写入配置， 所有来自 url 的所有内容，授权进 行重新标记 资源

analysis toolkit, Release 1.2.0 (continued from previous page) Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

analysis toolkit, Release 1.2.3 (continued from previous page) Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

analysis toolkit, Release 1.3.2 (continued from previous page) Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

analysis toolkit, Release 1.3.3 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

analysis toolkit, Release 1.3.4 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

analysis toolkit, Release 1.4.2 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

analysis toolkit, Release 1.4.4 In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

numerical data of my data table In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 In [26]: grouped = df.groupby(["month", "week"]) In [27]: grouped["x"].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81

numerical data of my data table In [9]: df.describe() Out[9]: Age count 3.000000 mean 38.333333 std 18.230012 min 22.000000 25% 28.500000 50% 35.000000 75% 46.500000 max 58.000000 The describe() titanic[["Age", "Fare"]].describe() Out[6]: Age Fare count 714.000000 891.000000 mean 29.699118 32.204208 std 14.526497 49.693429 min 0.420000 0.000000 25% 20.125000 7.910400 50% 28.000000 14.454200 75% 38 toolkit, Release 1.1.1 (continued from previous page) In [27]: grouped['x'].agg([np.mean, np.std]) Out[27]: mean std month week 5 1 63.653367 40.601965 2 78.126605 53.342400 3 92.091886 57.630110 6 1 81