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"

      }

    }

  ]

}