Tuesday, July 30, 2019

missing required virtualport type on RHEL

If you define vm with virtualinterfaces, that don't have virtualport, then cloning of such vms will fail with below error


*** libvirtError: XML error: missing required virtualport type



Eg xml for the vm, where you'd see the failure while cloning

38     type='bridge'>
39       address='52:54:00:8c:30:e1'/>
40       bridge='br0'/>
41       type='virtio'/>
42       
type='pci' domain='0x0000' bus='0x00' slot='0x03' function='0x0'/>
43     
44     type='bridge'>
45       address='52:54:00:e6:70:f8'/>
46       bridge='nsx-managed'/>
47       type='openvswitch'>
48         interfaceid='df55d7eb-5f25-43db-8bf8-f67b259bf51b'/>
49       
50       type='virtio'/>
51       
type='pci' domain='0x0000' bus='0x00' slot='0x05' function='0x0'/>
52     

A bad work-around is to comment out the line which does this validation


/usr/share/virt-manager/virtinst/cloner.py
     def start_duplicate(self, meter=None):
         """
         Actually perform the duplication: cloning disks if needed and defining
         the new clone xml.
         """
         logging.debug("Starting duplicate.")
         meter = util.ensure_meter(meter)

         dom = None
         try:
             # Replace orig VM if required
             Guest.check_vm_collision(self.conn, self.clone_name,
                                      do_remove=self.replace)

             # Define domain early to catch any xml errors before duping storage
             # The below statement was commented out for systest. While cloning a VM
             # with no virtual port defined, it will error out
             #dom = self.conn.defineXML(self.clone_xml)
             dom = None
             if self.preserve:
                 for dst_dev in self.clone_disks:
                     dst_dev.setup(meter=meter)
                 if self._nvram_disk:
                     self._nvram_disk.setup(meter=meter)
         except Exception as e:
             logging.debug("Duplicate failed: %s", str(e))
             if dom:
                 dom.undefine()

             raise


posted by Chirag Radhakrishnan @ July 30, 2019   0 Comments

Plotting performance results of kubetest

kubetest is a open source test suite to test various aspects of Kubernetes cluster. One of the areas that of interest for a scaled environment is the Performance of cluster.

Configuring/Running kubetest is beyond scope of this blog - what I intend to share is how you can plot the results of these tests

API Responsiveness:



import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import json

data = json.load(open('PM1_APIResponsiveness_load_2019-06-19T15:11:48Z.json'))['dataItems']
perc99_data = []
for item in data:
  myl = []
  myl.append(item['labels']['Verb'] + "-" +item['labels']['Scope'] + '-' + item['labels']['Resource'])
  myl.append(item['data']['Perc50'])
  myl.append(item['data']['Perc90'])
  myl.append(item['data']['Perc99'])
  perc99_data.append(myl)
df = pd.DataFrame(perc99_data, columns = ['API', 'LatencyP50', 'LatencyP90', 'LatencyP99'])
df.plot.bar()
index = np.arange(len(perc99_data))
plt.bar(index, df['LatencyP50'])
plt.xlabel("APIs")
plt.ylabel("Latency (ms)")
plt.xticks(index, df['API'], fontsize=5, rotation=30, ha='right')
plt.show()



cradhakrish-a01:chirag cradhakrishnan$ cat PM1_APIResponsiveness_load_2019-06-19T15\:11\:48Z.json 
{
  "version": "v1",
  "dataItems": [
    {
      "data": {
        "Perc50": 5.484,
        "Perc90": 25.443,
        "Perc99": 114.011
      },
      "unit": "ms",
      "labels": {
        "Count": "2358",
        "Resource": "pods",
        "Scope": "namespace",
        "Subresource": "",
        "Verb": "DELETE"
      }
    },
    {
      "data": {
        "Perc50": 32.052,
        "Perc90": 51.073,
        "Perc99": 74.26
      },
      "unit": "ms",
      "labels": {
        "Count": "68",
        "Resource": "services",
        "Scope": "namespace",
        "Subresource": "",
        "Verb": "DELETE"
      }
.
.
.

    {
      "data": {
        "Perc50": 0.115,
        "Perc90": 0.178,
        "Perc99": 0.273
      },
      "unit": "ms",
      "labels": {
        "Count": "38",
        "Resource": "secrets",
        "Scope": "namespace",
        "Subresource": "",
        "Verb": "LIST"
      }
    }
  ]
}



Pod Responsiveness


import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import json

data = json.load(open('PM1_PodStartupLatency_density_2019-06-20T10:32:57Z.json'))['dataItems']
perc99_data = []
for item in data:
  myl = []
  myl.append(item['labels']['Metric'])
  myl.append(item['data']['Perc50'])
  myl.append(item['data']['Perc90'])
  myl.append(item['data']['Perc99'])
  myl.append(item['data']['Perc100'])
  perc99_data.append(myl)
df = pd.DataFrame(perc99_data, columns = ['Metric', 'LatencyP50', 'LatencyP90', 'LatencyP99', 'LatencyP100'])
df.plot.bar()
index = np.arange(len(perc99_data))
plt.bar(index, df['LatencyP50'])
plt.xlabel("Metric")
plt.ylabel("Latency (ms)")
plt.xticks(index, df['Metric'], fontsize=5, rotation=30, ha='right')
plt.show()

cradhakrish-a01:chirag cradhakrishnan$ cat PM1_PodStartupLatency_density_2019-06-20T10:32:57Z.json
{
  "version": "v1",
  "dataItems": [
    {
      "data": {
        "Perc100": 0,
        "Perc50": 0,
        "Perc90": 0,
        "Perc99": 0
      },
      "unit": "ms",
      "labels": {
        "Metric": "create_to_schedule"
      }
    },
    {
      "data": {
        "Perc100": 28000,
        "Perc50": 9000,
        "Perc90": 14000,
        "Perc99": 26000
      },
      "unit": "ms",
      "labels": {
        "Metric": "schedule_to_run"
      }
    },
    {
      "data": {
        "Perc100": -5327.442072,
        "Perc50": -6894.455178,
        "Perc90": -6155.301798,
        "Perc99": -5537.035922
      },
      "unit": "ms",
      "labels": {
        "Metric": "run_to_watch"
      }
    },
    {
      "data": {
        "Perc100": 20665.27986,
        "Perc50": 1753.811386,
        "Perc90": 6665.07306,
        "Perc99": 18600.530946
      },
      "unit": "ms",
      "labels": {
        "Metric": "schedule_to_watch"
      }
    },
    {
      "data": {
        "Perc100": 20665.27986,
        "Perc50": 1753.811386,
        "Perc90": 6665.07306,
        "Perc99": 18600.530946
      },
      "unit": "ms",
      "labels": {
        "Metric": "pod_startup"
      }
    }
  ]
}

posted by Chirag Radhakrishnan @ July 30, 2019   0 Comments