Merge pull request #763 from LavredisG/main

adds karmadactl flags for proper display
karmada-io · Dec 16, 2024 · ce1468d · ce1468d
2 parents c824534 + c788a71
commit ce1468d
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diff --git a/docs/tutorials/autoscaling-with-custom-metrics.md b/docs/tutorials/autoscaling-with-custom-metrics.md
@@ -116,7 +116,7 @@ If you use the `hack/local-up-karmada.sh` script to deploy Karmada, `karmada-met
 
 ## Deploy workload in `member1` and `member2` cluster
 
-You need to deploy a sample deployment(1 replica) and service in `member1` and `member2`.
+You need to deploy a sample deployment (1 replica) and service in `member1` and `member2`.
 
 ```yaml
 apiVersion: apps/v1
@@ -190,12 +190,12 @@ spec:
             weight: 1
 ```
 
-After deploying, you can check the distribution of the pods and service:
+After deploying, you can check the distribution of the Pods and Service:
 ```sh
-$ karmadactl get pods
+$ karmadactl get pods --operation-scope members
 NAME                                  CLUSTER   READY   STATUS    RESTARTS   AGE
 sample-app-9b7d8c9f5-xrnfx            member1   1/1     Running   0          111s
-$ karmadactl get svc
+$ karmadactl get svc --operation-scope members
 NAME                 CLUSTER   TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE     ADOPTION
 sample-app           member1   ClusterIP   10.11.29.250    <none>        80/TCP    3m53s   Y
 ```
@@ -409,7 +409,7 @@ As mentioned before, you need a multi-cluster service to route the requests to t
 
 After deploying, you can check the multi-cluster service:
 ```sh
-$ karmadactl get svc
+$ karmadactl get svc --operation-scope members
 NAME                    CLUSTER   TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE   ADOPTION
 derived-sample-app      member1   ClusterIP   10.11.59.213    <none>        80/TCP    9h    Y
 ```
@@ -428,14 +428,14 @@ docker cp hey_linux_amd64 member1-control-plane:/usr/local/bin/hey
 
 * Check the pod distribution firstly.
   ```sh
-  $ karmadactl get pods
+  $ karmadactl get pods --operation-scope members
   NAME                                  CLUSTER   READY   STATUS    RESTARTS   AGE
   sample-app-9b7d8c9f5-xrnfx            member1   1/1     Running   0          111s
   ```
 
 * Check multi-cluster service ip.
   ```sh
-  $ karmadactl get svc
+  $ karmadactl get svc --operation-scope members
   NAME                    CLUSTER   TYPE        CLUSTER-IP        EXTERNAL-IP   PORT(S)   AGE   ADOPTION
   derived-sample-app      member1   ClusterIP   10.11.59.213      <none>        80/TCP    20m   Y
   ```
@@ -447,7 +447,7 @@ docker cp hey_linux_amd64 member1-control-plane:/usr/local/bin/hey
 
 * Wait 15s, the replicas will be scaled up, then you can check the pod distribution again.
   ```sh
-  $ karmadactl get po -l app=sample-app
+  $ karmadactl get pods --operation-scope members -l app=sample-app
   NAME                         CLUSTER   READY   STATUS    RESTARTS   AGE
   sample-app-9b7d8c9f5-454vz   member2   1/1     Running   0          84s
   sample-app-9b7d8c9f5-7fjhn   member2   1/1     Running   0          69s
@@ -465,7 +465,7 @@ docker cp hey_linux_amd64 member1-control-plane:/usr/local/bin/hey
 
 After 1 minute, the load testing tool will be stopped, then you can see the workload is scaled down across clusters.
 ```sh
-$ karmadactl get pods -l app=sample-app
+$ karmadactl get pods --operation-scope members -l app=sample-app
 NAME                         CLUSTER   READY   STATUS    RESTARTS   AGE
 sample-app-9b7d8c9f5-xrnfx   member1   1/1     Running   0          91m
 ```
diff --git a/docs/tutorials/autoscaling-with-resource-metrics.md b/docs/tutorials/autoscaling-with-resource-metrics.md
@@ -169,10 +169,10 @@ spec:
 
 After deploying, you can check the propagation of the Pods and Service:
 ```sh
-$ karmadactl get pods
+$ karmadactl get pods --operation-scope members
 NAME                     CLUSTER   READY   STATUS    RESTARTS   AGE
 nginx-777bc7b6d7-mbdn8   member1   1/1     Running   0          9h
-$ karmadactl get svc
+$ karmadactl get svc --operation-scope members
 NAME                    CLUSTER   TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE   ADOPTION
 nginx-service           member1   ClusterIP   10.11.216.215   <none>        80/TCP    9h    Y
 nginx-service           member2   ClusterIP   10.13.46.61     <none>        80/TCP    9h    Y
@@ -272,7 +272,7 @@ As mentioned before, we need a multi-cluster Service to route the requests to th
 
 After deploying, you can check the multi-cluster Service:
 ```sh
-$ karmadactl get svc
+$ karmadactl get svc --operation-scope members
 NAME                    CLUSTER   TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE   ADOPTION
 derived-nginx-service   member1   ClusterIP   10.11.59.213    <none>        80/TCP    9h    Y
 ```
@@ -291,14 +291,14 @@ docker cp hey_linux_amd64 member1-control-plane:/usr/local/bin/hey
 
 * Check the Pod propagation firstly.
   ```sh
-  $ karmadactl get pods
+  $ karmadactl get pods --operation-scope members
   NAME                     CLUSTER   READY   STATUS      RESTARTS   AGE
   nginx-777bc7b6d7-mbdn8   member1   1/1     Running     0          61m
   ```
 
 * Check multi-cluster Service IP.
   ```sh
-  $ karmadactl get svc
+  $ karmadactl get svc --operation-scope members
   NAME                    CLUSTER   TYPE        CLUSTER-IP        EXTERNAL-IP   PORT(S)   AGE   ADOPTION
   derived-nginx-service   member1   ClusterIP   10.11.59.213      <none>        80/TCP    20m   Y
   ```
@@ -310,7 +310,7 @@ docker cp hey_linux_amd64 member1-control-plane:/usr/local/bin/hey
 
 * Wait 15s, the replicas will be scaled up, then you can check the Pod propagation again.
   ```sh
-  $ karmadactl get pods -l app=nginx
+  $ karmadactl get pods --operation-scope members -l app=nginx
   NAME                     CLUSTER   READY   STATUS      RESTARTS   AGE
   nginx-777bc7b6d7-c2cfv   member1   1/1     Running     0          22s
   nginx-777bc7b6d7-mbdn8   member1   1/1     Running     0          62m
@@ -329,7 +329,7 @@ docker cp hey_linux_amd64 member1-control-plane:/usr/local/bin/hey
 
 After 1 minute, the load testing tool will be stopped, then you can see the workload is scaled down across clusters.
 ```sh
-$ karmadactl get pods -l app=nginx
+$ karmadactl get pods --operation-scope members -l app=nginx
 NAME                     CLUSTER   READY   STATUS    RESTARTS   AGE
 nginx-777bc7b6d7-mbdn8   member1   1/1     Running   0          64m
 ```