使用rook编排ceph

sealyun

发布于 2019-07-25 17:31:01

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发布于 2019-07-25 17:31:01

文章被收录于专栏：sealyun

在k8s上编排ceph是容器生态存储方案的一个趋势，能非常简单快速的构建出存储集群，特别适合供有状态服务使用，计算存储分离将使应用的管理变简单，业务层与云操作系统层也能更好的解耦。

本文中需要用的yaml文件和Dockerfile等都放到这个仓库包含：rook operator ceph cluster storage class配置，mysql wordpress事例，性能测试fio Dockerfile与yaml等

安装

	git clone https://github.com/rook/rookcd cluster/examples/kubernetes/ceph kubectl create -f operator.yaml

查看operator是否成功：

[root@dev-86-201 ~]# kubectl get pod -n rook-ceph-system NAME READY STATUS RESTARTS AGE rook-ceph-agent-5z6p7 1/1 Running 0 88m rook-ceph-agent-6rj7l 1/1 Running 0 88m rook-ceph-agent-8qfpj 1/1 Running 0 88m rook-ceph-agent-xbhzh 1/1 Running 0 88m rook-ceph-operator-67f4b8f67d-tsnf2 1/1 Running 0 88m rook-discover-5wghx 1/1 Running 0 88m rook-discover-lhwvf 1/1 Running 0 88m rook-discover-nl5m2 1/1 Running 0 88m rook-discover-qmbx7 1/1 Running 0 88m

然后创建ceph集群：

1	kubectl create -f cluster.yaml

查看ceph集群：

[root@dev-86-201 ~]# kubectl get pod -n rook-ceph NAME READY STATUS RESTARTS AGE rook-ceph-mgr-a-8649f78d9b-jklbv 1/1 Running 0 64m rook-ceph-mon-a-5d7fcfb6ff-2wq9l 1/1 Running 0 81m rook-ceph-mon-b-7cfcd567d8-lkqff 1/1 Running 0 80m rook-ceph-mon-d-65cd79df44-66rgz 1/1 Running 0 79m rook-ceph-osd-0-56bd7545bd-5k9xk 1/1 Running 0 63m rook-ceph-osd-1-77f56cd549-7rm4l 1/1 Running 0 63m rook-ceph-osd-2-6cf58ddb6f-wkwp6 1/1 Running 0 63m rook-ceph-osd-3-6f8b78c647-8xjzv 1/1 Running 0 63m

参数说明：

apiVersion: ceph.rook.io/v1
kind: CephCluster
metadata:
  name: rook-ceph
  namespace: rook-ceph
spec:
  cephVersion:
    # For the latest ceph images, see https://hub.docker.com/r/ceph/ceph/tags
    image: ceph/ceph:v13.2.2-20181023
  dataDirHostPath: /var/lib/rook # 数据盘目录
  mon:
    count: 3
    allowMultiplePerNode: true
  dashboard:
    enabled: true
  storage:
    useAllNodes: true
    useAllDevices: false
    config:
      databaseSizeMB: "1024"
      journalSizeMB: "1024"

访问ceph dashboard:

[root@dev-86-201 ~]# kubectl get svc -n rook-ceph NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE rook-ceph-mgr ClusterIP 10.98.183.33 <none> 9283/TCP 66m rook-ceph-mgr-dashboard NodePort 10.103.84.48 <none> 8443:31631/TCP 66m # 把这个改成NodePort模式 rook-ceph-mon-a ClusterIP 10.99.71.227 <none> 6790/TCP 83m rook-ceph-mon-b ClusterIP 10.110.245.119 <none> 6790/TCP 82m rook-ceph-mon-d ClusterIP 10.101.79.159 <none> 6790/TCP 81m

然后访问https://10.1.86.201:31631 即可

管理账户admin,获取登录密码：

1	kubectl -n rook-ceph get secret rook-ceph-dashboard-password -o yaml \| grep "password:" \| awk '{print $2}' \| base64 --decode

使用

创建pool

apiVersion: ceph.rook.io/v1
kind: CephBlockPool
metadata:
  name: replicapool   # operator会监听并创建一个pool，执行完后界面上也能看到对应的pool
  namespace: rook-ceph
spec:
  failureDomain: host
  replicated:
    size: 3
---
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
   name: rook-ceph-block    # 这里创建一个storage class, 在pvc中指定这个storage class即可实现动态创建PV
provisioner: ceph.rook.io/block
parameters:
  blockPool: replicapool
  # The value of "clusterNamespace" MUST be the same as the one in which your rook cluster exist
  clusterNamespace: rook-ceph
  # Specify the filesystem type of the volume. If not specified, it will use `ext4`.
  fstype: xfs
# Optional, default reclaimPolicy is "Delete". Other options are: "Retain", "Recycle" as documented in https://kubernetes.io/docs/concepts/storage/storage-classes/
reclaimPolicy: Retain

创建pvc

在cluster/examples/kubernetes 目录下，官方给了个worldpress的例子，可以直接运行一下：

	kubectl create -f mysql.yaml kubectl create -f wordpress.yaml

查看PV PVC：

[root@dev-86-201 ~]# kubectl get pvc
NAME             STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS      AGE
mysql-pv-claim   Bound    pvc-a910f8c2-1ee9-11e9-84fc-becbfc415cde   20Gi       RWO            rook-ceph-block   144m
wp-pv-claim      Bound    pvc-af2dfbd4-1ee9-11e9-84fc-becbfc415cde   20Gi       RWO            rook-ceph-block   144m

[root@dev-86-201 ~]# kubectl get pv
NAME                                       CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS   CLAIM                    STORAGECLASS      REASON   AGE
pvc-a910f8c2-1ee9-11e9-84fc-becbfc415cde   20Gi       RWO            Retain           Bound    default/mysql-pv-claim   rook-ceph-block            145m
pvc-af2dfbd4-1ee9-11e9-84fc-becbfc415cde   20Gi       RWO            Retain           Bound    default/wp-pv-claim      rook-ceph-block

看下yaml文件：

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mysql-pv-claim
  labels:
    app: wordpress
spec:
  storageClassName: rook-ceph-block   # 指定storage class
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 20Gi  # 需要一个20G的盘

...

        volumeMounts:
        - name: mysql-persistent-storage
          mountPath: /var/lib/mysql
      volumes:
      - name: mysql-persistent-storage
        persistentVolumeClaim:
          claimName: mysql-pv-claim  # 指定上面定义的PVC

是不是非常简单。

要访问wordpress的话请把service改成NodePort类型，官方给的是loadbalance类型：

kubectl edit svc wordpress

[root@dev-86-201 kubernetes]# kubectl get svc
NAME              TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)        AGE
wordpress         NodePort    10.109.30.99   <none>        80:30130/TCP   148m

外部访问ceph集群

cluster.yaml里有配置，可配置成共享宿主机网络，这样外面可直接连接ceph集群：

network:
    # toggle to use hostNetwork
    hostNetwork: false

ceph集群监控

通过prometheus operator配合rook可以快速构建ceph集群的监控，sealyun安装包中已经自带了prometheus operator，所以直接干即可

启动ceph prometheus

注意这里是为ceph单独起了一个prometheus，这样做挺好，因为毕竟可以缓解prometheus单点的压力

	cd cluster/examples/kubernetes/ceph/monitoring kubectl create -f service-monitor.yaml kubectl create -f prometheus.yaml kubectl create -f prometheus-service.yaml

然后我们的grafana在30000端口，先在grafana上添加数据源

数据源要配置成：

1	http://rook-prometheus.rook-ceph.svc.cluster.local:9090

导入dashboard

还有几个别的dashboard可以导入： Ceph - Cluster Ceph - OSD Ceph - Pools

再次感叹生态之强大

增加节点,删除节点

1	kubectl edit cephcluster rook-ceph -n rook-ceph

把useAllNodes设置成false，然后在nodes列表里增加节点名即可，保存退出后会自动增加ceph节点

	nodes: - config: null name: izj6c3afuzdjhtkj156zt0z resources: {} - config: null name: izj6cg4wnagly61eny0hy9z resources: {} - config: null name: izj6cg4wnagly61eny0hyaz resources: {} useAllDevices: false

删除同理，直接edit删除即可，十分强大

性能测试

这里着重说明测试方法，给出在我的场景下的测试结果，用户应当根据自己的场景进行自己的测试。

测试环境

我这里使用阿里云服务器，挂载了一个1000G的磁盘，不过我实际测试时用的盘很少（盘大太慢）

	[root@izj6c3afuzdjhtkj156zt0z ~]# df -h\|grep dev devtmpfs 3.9G 0 3.9G 0% /dev tmpfs 3.9G 0 3.9G 0% /dev/shm /dev/vda1 40G 17G 21G 44% / /dev/vdb1 985G 41G 895G 5% /data /dev/rbd0 296G 2.1G 294G 1% /var/lib/kubelet/plugins/ceph.rook.io/rook-ceph-system/mounts/pvc-692e2be3-2434-11e9-aef7-00163e01e813

可看到一个本地盘和一个ceph挂过来的盘

先搞个fio的镜像

	[root@izj6c3afuzdjhtkj156zt0z fio]# cat Dockerfile FROM docker.io/centos:7.6.1810 RUN yum install -y fio

测试容器，稍微改一下mysql的yaml:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mysql-pv-claim
  labels:
    app: wordpress
spec:
  storageClassName: rook-ceph-block
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 300Gi
---
apiVersion: apps/v1beta1
kind: Deployment
metadata:
  name: wordpress-mysql
  labels:
    app: wordpress
spec:
  strategy:
    type: Recreate
  template:
    metadata:
      labels:
        app: wordpress
        tier: mysql
    spec:
      containers:
      - image: fio:latest
        imagePullPolicy: IfNotPresent
        command: ["sleep", "10000000"]
        name: mysql
        volumeMounts:
        - name: mysql-persistent-storage
          mountPath: /var/lib/mysql
      volumes:
      - name: mysql-persistent-storage
        persistentVolumeClaim:
          claimName: mysql-pv-claim

测试过程

进入到容器内跑测试工具：

$ kubectl exec -it wordpress-mysql-775c44887c-5vhx9 bash

# touch /var/lib/mysql/test # 创建测试文件
# fio -filename=/var/lib/mysql/test -direct=1 -iodepth=128 -rw=randrw -ioengine=libaio -bs=4k -size=200G -numjobs=8 -runtime=1

结果：

Rand_Write_Testing: (g=0): rw=randrw, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=libaio, iodepth=128 ... fio-3.1 Starting 2 processes Rand_Write_Testing: Laying out IO file (1 file / 2048MiB) Jobs: 2 (f=2): [m(2)][100.0%][r=16.6MiB/s,w=17.2MiB/s][r=4240,w=4415 IOPS][eta 00m:00s] Rand_Write_Testing: (groupid=0, jobs=2): err= 0: pid=34: Wed Jan 30 02:44:18 2019 read: IOPS=3693, BW=14.4MiB/s (15.1MB/s)(1443MiB/100013msec) slat (usec): min=2, max=203952, avg=262.47, stdev=2107.16 clat (msec): min=3, max=702, avg=30.85, stdev=30.97 lat (msec): min=3, max=702, avg=31.11, stdev=31.21 clat percentiles (msec): | 1.00th=[ 12], 5.00th=[ 14], 10.00th=[ 16], 20.00th=[ 18], | 30.00th=[ 20], 40.00th=[ 22], 50.00th=[ 24], 60.00th=[ 27], | 70.00th=[ 30], 80.00th=[ 36], 90.00th=[ 46], 95.00th=[ 64], | 99.00th=[ 194], 99.50th=[ 213], 99.90th=[ 334], 99.95th=[ 397], | 99.99th=[ 502] bw ( KiB/s): min= 440, max=12800, per=49.98%, avg=7383.83, stdev=3004.90, samples=400 iops : min= 110, max= 3200, avg=1845.92, stdev=751.22, samples=400 write: IOPS=3700, BW=14.5MiB/s (15.2MB/s)(1446MiB/100013msec) slat (usec): min=2, max=172409, avg=266.11, stdev=2004.53 clat (msec): min=7, max=711, avg=37.85, stdev=37.52 lat (msec): min=7, max=711, avg=38.12, stdev=37.72 clat percentiles (msec): | 1.00th=[ 16], 5.00th=[ 19], 10.00th=[ 21], 20.00th=[ 23], | 30.00th=[ 25], 40.00th=[ 27], 50.00th=[ 29], 60.00th=[ 32], | 70.00th=[ 36], 80.00th=[ 42], 90.00th=[ 53], 95.00th=[ 73], | 99.00th=[ 213], 99.50th=[ 292], 99.90th=[ 397], 99.95th=[ 472], | 99.99th=[ 600] bw ( KiB/s): min= 536, max=12800, per=49.98%, avg=7397.37, stdev=3000.10, samples=400 iops : min= 134, max= 3200, avg=1849.32, stdev=750.02, samples=400 lat (msec) : 4=0.01%, 10=0.22%, 20=22.18%, 50=68.05%, 100=5.90% lat (msec) : 250=3.09%, 500=0.54%, 750=0.02% cpu : usr=1.63%, sys=4.68%, ctx=311249, majf=0, minf=18 IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%, >=64=100.0% submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.1% issued rwt: total=369395,370084,0, short=0,0,0, dropped=0,0,0 latency : target=0, window=0, percentile=100.00%, depth=128Run status group 0 (all jobs): READ: bw=14.4MiB/s (15.1MB/s), 14.4MiB/s-14.4MiB/s (15.1MB/s-15.1MB/s), io=1443MiB (1513MB), run=100013-100013msec WRITE: bw=14.5MiB/s (15.2MB/s), 14.5MiB/s-14.5MiB/s (15.2MB/s-15.2MB/s), io=1446MiB (1516MB), run=100013-100013msecDisk stats (read/write): rbd0: ios=369133/369841, merge=0/35, ticks=4821508/7373587, in_queue=12172273, util=99.93%

在宿主机上测试：

	$ touch /data/test $ fio -filename=/data/test -direct=1 -iodepth=128 -rw=randrw -ioengine=libaio -bs=4k -size=2G -numjobs=2 -runtime=100 -group_reporting -name=Rand_Write_Testing

Rand_Write_Testing: (g=0): rw=randrw, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=libaio, iodepth=128 ... fio-3.1 Starting 2 processes Rand_Write_Testing: Laying out IO file (1 file / 2048MiB) Jobs: 2 (f=2): [m(2)][100.0%][r=19.7MiB/s,w=19.8MiB/s][r=5043,w=5056 IOPS][eta 00m:00s] Rand_Write_Testing: (groupid=0, jobs=2): err= 0: pid=13588: Wed Jan 30 10:41:25 2019 read: IOPS=5024, BW=19.6MiB/s (20.6MB/s)(1963MiB/100019msec) slat (usec): min=2, max=80213, avg=191.32, stdev=2491.48 clat (usec): min=1022, max=176786, avg=19281.58, stdev=23666.08 lat (usec): min=1031, max=176791, avg=19473.50, stdev=23757.08 clat percentiles (msec): | 1.00th=[ 3], 5.00th=[ 4], 10.00th=[ 5], 20.00th=[ 6], | 30.00th=[ 7], 40.00th=[ 8], 50.00th=[ 9], 60.00th=[ 10], | 70.00th=[ 12], 80.00th=[ 25], 90.00th=[ 67], 95.00th=[ 73], | 99.00th=[ 81], 99.50th=[ 85], 99.90th=[ 93], 99.95th=[ 96], | 99.99th=[ 104] bw ( KiB/s): min= 9304, max=10706, per=49.99%, avg=10046.66, stdev=243.04, samples=400 iops : min= 2326, max= 2676, avg=2511.62, stdev=60.73, samples=400 write: IOPS=5035, BW=19.7MiB/s (20.6MB/s)(1967MiB/100019msec) slat (usec): min=3, max=76025, avg=197.61, stdev=2504.40 clat (msec): min=2, max=155, avg=31.21, stdev=27.12 lat (msec): min=2, max=155, avg=31.40, stdev=27.16 clat percentiles (msec) | 1.00th=[ 7], 5.00th=[ 9], 10.00th=[ 10], 20.00th=[ 11], | 30.00th=[ 13], 40.00th=[ 14], 50.00th=[ 15], 60.00th=[ 18], | 70.00th=[ 48], 80.00th=[ 68], 90.00th=[ 75], 95.00th=[ 80], | 99.00th=[ 88], 99.50th=[ 93], 99.90th=[ 102], 99.95th=[ 104], | 99.99th=[ 112] bw ( KiB/s): min= 9208, max=10784, per=49.98%, avg=10066.14, stdev=214.26, samples=400 iops : min= 2302, max= 2696, avg=2516.50, stdev=53.54, samples=400 lat (msec) : 2=0.04%, 4=3.09%, 10=34.76%, 20=33.31%, 50=5.05% lat (msec) : 100=23.67%, 250=0.08% cpu : usr=1.54%, sys=5.80%, ctx=286367, majf=0, minf=20 IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%, >=64=100.0% submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.1% issued rwt: total=502523,503598,0, short=0,0,0, dropped=0,0,0 latency : target=0, window=0, percentile=100.00%, depth=128Run status group 0 (all jobs): READ: bw=19.6MiB/s (20.6MB/s), 19.6MiB/s-19.6MiB/s (20.6MB/s-20.6MB/s), io=1963MiB (2058MB), run=100019-100019msec WRITE: bw=19.7MiB/s (20.6MB/s), 19.7MiB/s-19.7MiB/s (20.6MB/s-20.6MB/s), io=1967MiB (2063MB), run=100019-100019msecDisk stats (read/write): vdb: ios=502513/504275, merge=0/658, ticks=4271124/7962372, in_queue=11283349, util=92.48%

这里看到随机读写性能损失约 27%多，这个结论并没有太多参考意义，用户应当根据自己实际场景进行测试

改用ceph共享宿主机网络模式进行测试，结果差不多，并无性能提升

要想排除在容器内测试因素的影响，也可以直接在宿主机上对块设备进行测试，做法很简单，可以把块挂到别的目录上如：

	umount /dev/rbd0 mkdir /data1 mount /dev/rbd0 /data1 touch /data1/test # 然后对这个文件测试，我这边测试结果与容器内差不多

bluestore方式

直接使用裸盘而不使用分区或者文件系统的方式部署ceph

storage:
  useAllNodes: false
  useAllDevices: false
  deviceFilter:
  location:
  config:
    storeType: bluestore
  nodes:
  - name: "ke-dev1-worker1"
    devices:
    - name: "vde"
  - name: "ke-dev1-worker3"
    devices:
    - name: "vde"
  - name: "ke-dev1-worker4"
    devices:
    - name: "vdf"

bluestore模式能显著提升ceph性能，我这边测试随机读写性能提升了8%左右

Rand_Write_Testing: (g=0): rw=randrw, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=libaio, iodepth=128 ... fio-3.1 Starting 2 processes Rand_Write_Testing: Laying out IO file (1 file / 2048MiB) Jobs: 2 (f=2): [m(2)][100.0%][r=16.4MiB/s,w=16.7MiB/s][r=4189,w=4284 IOPS][eta 00m:00s]] Rand_Write_Testing: (groupid=0, jobs=2): err= 0: pid=25: Thu Jan 31 11:37:39 2019 read: IOPS=3990, BW=15.6MiB/s (16.3MB/s)(1566MiB/100464msec) slat (usec): min=2, max=246625, avg=239.16, stdev=1067.33 clat (msec): min=2, max=493, avg=27.68, stdev=16.71 lat (msec): min=2, max=493, avg=27.92, stdev=16.75 clat percentiles (msec): | 1.00th=[ 12], 5.00th=[ 15], 10.00th=[ 16], 20.00th=[ 18], | 30.00th=[ 20], 40.00th=[ 22], 50.00th=[ 24], 60.00th=[ 27], | 70.00th=[ 30], 80.00th=[ 35], 90.00th=[ 45], 95.00th=[ 54], | 99.00th=[ 74], 99.50th=[ 84], 99.90th=[ 199], 99.95th=[ 334], | 99.99th=[ 485] bw ( KiB/s): min= 2118, max=10456, per=50.20%, avg=8013.40, stdev=1255.78, samples=400 iops : min= 529, max= 2614, avg=2003.31, stdev=313.95, samples=400 write: IOPS=3997, BW=15.6MiB/s (16.4MB/s)(1569MiB/100464msec) slat (usec): min=3, max=273211, avg=246.87, stdev=1026.98 clat (msec): min=11, max=499, avg=35.90, stdev=18.04 lat (msec): min=12, max=506, avg=36.15, stdev=18.08 clat percentiles (msec): | 1.00th=[ 19], 5.00th=[ 22], 10.00th=[ 23], 20.00th=[ 26], | 30.00th=[ 28], 40.00th=[ 30], 50.00th=[ 33], 60.00th=[ 35], | 70.00th=[ 39], 80.00th=[ 44], 90.00th=[ 54], 95.00th=[ 63], | 99.00th=[ 85], 99.50th=[ 95], 99.90th=[ 309], 99.95th=[ 351], | 99.99th=[ 489] bw ( KiB/s): min= 2141, max=10163, per=50.20%, avg=8026.23, stdev=1251.78, samples=400 iops : min= 535, max= 2540, avg=2006.51, stdev=312.94, samples=400 lat (msec) : 4=0.01%, 10=0.10%, 20=16.63%, 50=73.71%, 100=9.25% lat (msec) : 250=0.22%, 500=0.09% cpu : usr=1.85%, sys=5.60%, ctx=366744, majf=0, minf=17 IO depths : 1=0.1%, 2=0.1%, 4=0.1%, 8=0.1%, 16=0.1%, 32=0.1%, >=64=100.0% submit : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0% complete : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.1% issued rwt: total=400928,401597,0, short=0,0,0, dropped=0,0,0 latency : target=0, window=0, percentile=100.00%, depth=128Run status group 0 (all jobs): READ: bw=15.6MiB/s (16.3MB/s), 15.6MiB/s-15.6MiB/s (16.3MB/s-16.3MB/s), io=1566MiB (1642MB), run=100464-100464msec WRITE: bw=15.6MiB/s (16.4MB/s), 15.6MiB/s-15.6MiB/s (16.4MB/s-16.4MB/s), io=1569MiB (1645MB), run=100464-100464msecDisk stats (read/write): rbd0: ios=400921/401817, merge=0/50, ticks=4341605/7883816, in_queue=12217335, util=99.96%

总结

分布式存储在容器集群中充当非常重要的角色，使用容器集群一个非常重要的理念就是把集群当成一个整体使用，如果你在使用中还关心单个主机，比如调度到某个节点，

挂载某个节点目录等，必然会导致不能把云的威力百分之百发挥出来。一旦计算存储分离后，就可真正实现随意漂移，对集群维护来说是个极大的福音。

比如集群机器过保了需要下架，那么我们云化的架构因为所有东西无单点，所以只需要简单驱逐改节点，然后下架即可，不用关心上面跑的是什么业务，不管是有状态还是无

状态的都可以自动修复。不过目前面临最大的挑战可能还是分布式存储的性能问题。在性能要求不苛刻的场景下我是极推荐这种计算存储分离架构的。

常见问题

注意主机时间一定要同步

1	ntpdate 0.asia.pool.ntp.org

ceph cluster无法启动

报这个错误

$ kubectl logs rook-ceph-mon-a-c5f54799f-rd7s4 -n rook-ceph 2019-01-27 11:04:59.985 7f0a34a4f140 -1 rocksdb: Invalid argument: /var/lib/rook/mon-a/data/store.db: does not exist (create_if_missing is false) 2019-01-27 11:04:59.985 7f0a34a4f140 -1 rocksdb: Invalid argument: /var/lib/rook/mon-a/data/store.db: does not exist (create_if_missing is false) 2019-01-27 11:04:59.985 7f0a34a4f140 -1 error opening mon data directory at '/var/lib/rook/mon-a/data': (22) Invalid argument 2019-01-27 11:04:59.985 7f0a34a4f140 -1 error opening mon data directory at '/var/lib/rook/mon-a/data': (22) Invalid argument

需要把宿主机store.db文件删掉,然后delete pod即可, 主意别指错目录如果自己改了目录的话

1	rm -rf /var/lib/rook/mon-a/data/store.db

rook删除PVC PV依然存在

因为storageclass回收参数：

1	reclaimPolicy: Retain # 设置成Delete即可

rook-ceph namespace无法删除 cephclusters.ceph.rook.io CRD无法删除

	[root@izj6c3afuzdjhtkj156zt0z ~]# kubectl get ns NAME STATUS AGE default Active 18h kube-public Active 18h kube-system Active 18h monitoring Active 18h rook-ceph Terminating 17h

把CRD metadata finalizers下面的内容删了，CRD就会自动删除，然后只要rook-ceph namespace里没有东西就会自动清理掉

1	$ kubectl edit crd cephclusters.ceph.rook.io

使用宿主机网络时集群无法正常启动

集群中单节点时把mon设置成1即可

  mon:
    count: 1
    allowMultiplePerNode: true

  network:
    # toggle to use hostNetwork
    hostNetwork: true

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原始发表：2019-02-18，如有侵权请联系 cloudcommunity@tencent.com 删除

分布式

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分布式

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