calculation/ObsSizingTemplateGivenTimeSeriesCount-Rev1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as img\n",
    "import os\n",
    "import seaborn as sns\n",
    "# Set default Seaborn style\n",
    "sns.set(style=\"darkgrid\")\n",
    "#from scipy.stats import pearsonr\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def roundup(x):\n",
    "    return int(math.ceil(x / 10.0)) * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is still experimental\n",
    "\n",
    "This notebook allows you to calculate the **ACM Hub Observability** sizing for fleet management based on your inputs as explained in __Critical Input Parameters to Size__ section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology of Calculation\n",
    "1. We have obtained the number of time series per cluster by running https://github.com/stolostron/multicluster-observability-operator/tree/main/tools/simulator/metrics-collector/metrics-extractor\n",
    "\n",
    "1. From number of time series AND number of clusters, we infer\n",
    "    1. Memory requirement (2 hours of this time series data is stored in memory)\n",
    "    1. CPU Requirement\n",
    "    1. Disk needed for PVs (volume of data stored is dictated by settings in MultiCluster Observability CR)\n",
    "    1. Storage needed for Object store (volume of data stored is dictated by settings in MultiCluster Observability CR). This is has many simplifying assumptions.\n",
    "\n",
    "_All calculations are in this notebook below - and they are easy to follow._\n",
    "\n",
    "#### From Scale testing/Observation:\n",
    "We have data of CPU, Memory usage of the ACM hub as more managed cluster join till all transients stabilize. [Raw data is available here](http://e27-h01-000-r650.rdu2.scalelab.redhat.com:8081/results/ACM-2.8/20230817-211625-int-ztp-300-500b-43200i-83/acm-inspector-data-2/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9 entries, 0 to 8\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   cluster-count         9 non-null      int64  \n",
      " 1   etcdetcdDBSizeMB      9 non-null      float64\n",
      " 2   KubeAPIMemUsageWSSGB  9 non-null      float64\n",
      " 3   OtherMemUsageWSSGB    9 non-null      float64\n",
      " 4   ACMObsMemUsageWSSGB   9 non-null      float64\n",
      " 5   ACMOthMemUsageWSSGB   9 non-null      float64\n",
      " 6   ClusterMemUsageGB     9 non-null      float64\n",
      " 7   KubeAPICPUCoreUsage   9 non-null      float64\n",
      " 8   OtherCPUCoreUsage     9 non-null      float64\n",
      " 9   ACMObsCPUCoreUsage    9 non-null      float64\n",
      " 10  ACMOthCPUCoreUsage    9 non-null      float64\n",
      " 11  ClusterCPUCoreUsage   9 non-null      float64\n",
      " 12  ClusterCPUUsage       9 non-null      float64\n",
      " 13  TimeSeriesCount       9 non-null      float64\n",
      "dtypes: float64(13), int64(1)\n",
      "memory usage: 1.1 KB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster-count</th>\n",
       "      <th>etcdetcdDBSizeMB</th>\n",
       "      <th>KubeAPIMemUsageWSSGB</th>\n",
       "      <th>OtherMemUsageWSSGB</th>\n",
       "      <th>ACMObsMemUsageWSSGB</th>\n",
       "      <th>ACMOthMemUsageWSSGB</th>\n",
       "      <th>ClusterMemUsageGB</th>\n",
       "      <th>KubeAPICPUCoreUsage</th>\n",
       "      <th>OtherCPUCoreUsage</th>\n",
       "      <th>ACMObsCPUCoreUsage</th>\n",
       "      <th>ACMOthCPUCoreUsage</th>\n",
       "      <th>ClusterCPUCoreUsage</th>\n",
       "      <th>ClusterCPUUsage</th>\n",
       "      <th>TimeSeriesCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>492.132812</td>\n",
       "      <td>15.842926</td>\n",
       "      <td>29.345032</td>\n",
       "      <td>2.224129</td>\n",
       "      <td>4.217648</td>\n",
       "      <td>98.014347</td>\n",
       "      <td>2.005463</td>\n",
       "      <td>4.955723</td>\n",
       "      <td>0.087176</td>\n",
       "      <td>1.062161</td>\n",
       "      <td>7.125632</td>\n",
       "      <td>7.125632</td>\n",
       "      <td>1.208533e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>501</td>\n",
       "      <td>4765.730469</td>\n",
       "      <td>44.041397</td>\n",
       "      <td>71.184624</td>\n",
       "      <td>26.002499</td>\n",
       "      <td>12.360477</td>\n",
       "      <td>198.057091</td>\n",
       "      <td>27.162713</td>\n",
       "      <td>18.659946</td>\n",
       "      <td>6.976063</td>\n",
       "      <td>6.028077</td>\n",
       "      <td>51.071514</td>\n",
       "      <td>51.071514</td>\n",
       "      <td>1.774576e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001</td>\n",
       "      <td>6968.148438</td>\n",
       "      <td>70.522301</td>\n",
       "      <td>85.191090</td>\n",
       "      <td>53.609005</td>\n",
       "      <td>21.798447</td>\n",
       "      <td>265.963261</td>\n",
       "      <td>41.666794</td>\n",
       "      <td>24.313306</td>\n",
       "      <td>10.258325</td>\n",
       "      <td>6.897012</td>\n",
       "      <td>78.659244</td>\n",
       "      <td>78.659244</td>\n",
       "      <td>3.704551e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1501</td>\n",
       "      <td>8657.406250</td>\n",
       "      <td>95.487778</td>\n",
       "      <td>100.638203</td>\n",
       "      <td>79.539074</td>\n",
       "      <td>27.120926</td>\n",
       "      <td>329.887955</td>\n",
       "      <td>29.254207</td>\n",
       "      <td>32.240631</td>\n",
       "      <td>14.904547</td>\n",
       "      <td>14.223816</td>\n",
       "      <td>84.916373</td>\n",
       "      <td>84.916373</td>\n",
       "      <td>5.483344e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>10417.035156</td>\n",
       "      <td>116.932346</td>\n",
       "      <td>117.019630</td>\n",
       "      <td>110.827610</td>\n",
       "      <td>33.914574</td>\n",
       "      <td>391.354710</td>\n",
       "      <td>38.871293</td>\n",
       "      <td>37.774763</td>\n",
       "      <td>23.447564</td>\n",
       "      <td>10.326685</td>\n",
       "      <td>104.121879</td>\n",
       "      <td>104.121879</td>\n",
       "      <td>7.229043e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2501</td>\n",
       "      <td>12133.246094</td>\n",
       "      <td>130.561272</td>\n",
       "      <td>132.690632</td>\n",
       "      <td>136.744621</td>\n",
       "      <td>39.276440</td>\n",
       "      <td>441.112297</td>\n",
       "      <td>46.194071</td>\n",
       "      <td>44.631129</td>\n",
       "      <td>24.730487</td>\n",
       "      <td>11.975998</td>\n",
       "      <td>116.910643</td>\n",
       "      <td>116.910643</td>\n",
       "      <td>9.022662e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3001</td>\n",
       "      <td>14056.968750</td>\n",
       "      <td>142.836929</td>\n",
       "      <td>147.665379</td>\n",
       "      <td>165.513916</td>\n",
       "      <td>44.791946</td>\n",
       "      <td>482.628525</td>\n",
       "      <td>44.892960</td>\n",
       "      <td>44.095892</td>\n",
       "      <td>31.291865</td>\n",
       "      <td>11.213243</td>\n",
       "      <td>126.479453</td>\n",
       "      <td>126.479453</td>\n",
       "      <td>1.081609e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3501</td>\n",
       "      <td>15887.367188</td>\n",
       "      <td>160.520748</td>\n",
       "      <td>163.741425</td>\n",
       "      <td>196.502529</td>\n",
       "      <td>52.538059</td>\n",
       "      <td>535.832123</td>\n",
       "      <td>44.998772</td>\n",
       "      <td>45.603580</td>\n",
       "      <td>36.887440</td>\n",
       "      <td>12.349075</td>\n",
       "      <td>130.973728</td>\n",
       "      <td>130.973728</td>\n",
       "      <td>1.261727e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3619</td>\n",
       "      <td>16190.859375</td>\n",
       "      <td>162.349144</td>\n",
       "      <td>184.511856</td>\n",
       "      <td>217.550026</td>\n",
       "      <td>53.207111</td>\n",
       "      <td>549.857502</td>\n",
       "      <td>43.223547</td>\n",
       "      <td>48.597584</td>\n",
       "      <td>37.383142</td>\n",
       "      <td>10.983012</td>\n",
       "      <td>117.938693</td>\n",
       "      <td>117.938693</td>\n",
       "      <td>1.306206e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster-count  etcdetcdDBSizeMB  KubeAPIMemUsageWSSGB  OtherMemUsageWSSGB  \\\n",
       "0              1        492.132812             15.842926           29.345032   \n",
       "1            501       4765.730469             44.041397           71.184624   \n",
       "2           1001       6968.148438             70.522301           85.191090   \n",
       "3           1501       8657.406250             95.487778          100.638203   \n",
       "4           2001      10417.035156            116.932346          117.019630   \n",
       "5           2501      12133.246094            130.561272          132.690632   \n",
       "6           3001      14056.968750            142.836929          147.665379   \n",
       "7           3501      15887.367188            160.520748          163.741425   \n",
       "8           3619      16190.859375            162.349144          184.511856   \n",
       "\n",
       "   ACMObsMemUsageWSSGB  ACMOthMemUsageWSSGB  ClusterMemUsageGB  \\\n",
       "0             2.224129             4.217648          98.014347   \n",
       "1            26.002499            12.360477         198.057091   \n",
       "2            53.609005            21.798447         265.963261   \n",
       "3            79.539074            27.120926         329.887955   \n",
       "4           110.827610            33.914574         391.354710   \n",
       "5           136.744621            39.276440         441.112297   \n",
       "6           165.513916            44.791946         482.628525   \n",
       "7           196.502529            52.538059         535.832123   \n",
       "8           217.550026            53.207111         549.857502   \n",
       "\n",
       "   KubeAPICPUCoreUsage  OtherCPUCoreUsage  ACMObsCPUCoreUsage  \\\n",
       "0             2.005463           4.955723            0.087176   \n",
       "1            27.162713          18.659946            6.976063   \n",
       "2            41.666794          24.313306           10.258325   \n",
       "3            29.254207          32.240631           14.904547   \n",
       "4            38.871293          37.774763           23.447564   \n",
       "5            46.194071          44.631129           24.730487   \n",
       "6            44.892960          44.095892           31.291865   \n",
       "7            44.998772          45.603580           36.887440   \n",
       "8            43.223547          48.597584           37.383142   \n",
       "\n",
       "   ACMOthCPUCoreUsage  ClusterCPUCoreUsage  ClusterCPUUsage  TimeSeriesCount  \n",
       "0            1.062161             7.125632         7.125632     1.208533e+04  \n",
       "1            6.028077            51.071514        51.071514     1.774576e+06  \n",
       "2            6.897012            78.659244        78.659244     3.704551e+06  \n",
       "3           14.223816            84.916373        84.916373     5.483344e+06  \n",
       "4           10.326685           104.121879       104.121879     7.229043e+06  \n",
       "5           11.975998           116.910643       116.910643     9.022662e+06  \n",
       "6           11.213243           126.479453       126.479453     1.081609e+07  \n",
       "7           12.349075           130.973728       130.973728     1.261727e+07  \n",
       "8           10.983012           117.938693       117.938693     1.306206e+07  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = os.path.join(\"..\",\"images\",\"sizing_summary_based_on_max_run_08222023.csv\")\n",
    "master_df = pandas.read_csv(fname,index_col=0)\n",
    "master_df.info()\n",
    "master_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpret the Test Results being used as Ground Truth\n",
    "\n",
    "### All managed clusters used in this testing was uniform. \n",
    "- They were all SNOs with same number of policies \n",
    "\n",
    "### Scatter plots confirm that the metrics are almost linearly related with managed cluster count.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Since relationships are linear, Therefore, let us calculate the Pearson's r with respect cluster-count\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "cluster-count           1.000000\n",
       "etcdetcdDBSizeMB        0.990125\n",
       "KubeAPIMemUsageWSSGB    0.989208\n",
       "OtherMemUsageWSSGB      0.986999\n",
       "ACMObsMemUsageWSSGB     0.997441\n",
       "ACMOthMemUsageWSSGB     0.996972\n",
       "ClusterMemUsageGB       0.992907\n",
       "KubeAPICPUCoreUsage     0.791195\n",
       "OtherCPUCoreUsage       0.958572\n",
       "ACMObsCPUCoreUsage      0.996040\n",
       "ACMOthCPUCoreUsage      0.752464\n",
       "ClusterCPUCoreUsage     0.930581\n",
       "ClusterCPUUsage         0.930581\n",
       "TimeSeriesCount         0.999965\n",
       "Name: cluster-count, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2016x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp = sns.pairplot(master_df, x_vars=['KubeAPICPUCoreUsage','ACMObsCPUCoreUsage','ACMOthCPUCoreUsage','OtherCPUCoreUsage'], y_vars='cluster-count', height=7, aspect=1)\n",
    "mp = sns.pairplot(master_df, x_vars=['KubeAPIMemUsageWSSGB','ACMObsMemUsageWSSGB','ACMOthMemUsageWSSGB','OtherMemUsageWSSGB'], y_vars='cluster-count', height=7, aspect=1)\n",
    "\n",
    "\n",
    "print(\"Since relationships are linear, Therefore, let us calculate the Pearson's r with respect cluster-count\")\n",
    "master_df.corr()['cluster-count']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plots confirm that the Observability metrics are almost linearly related with time series count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Since relationships are linear, Therefore, let us calculate the Pearson's r with respect TimeSeriesCount\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "cluster-count           0.999965\n",
       "etcdetcdDBSizeMB        0.990524\n",
       "KubeAPIMemUsageWSSGB    0.989758\n",
       "OtherMemUsageWSSGB      0.987241\n",
       "ACMObsMemUsageWSSGB     0.997343\n",
       "ACMOthMemUsageWSSGB     0.997402\n",
       "ClusterMemUsageGB       0.993287\n",
       "KubeAPICPUCoreUsage     0.792972\n",
       "OtherCPUCoreUsage       0.959208\n",
       "ACMObsCPUCoreUsage      0.995688\n",
       "ACMOthCPUCoreUsage      0.754722\n",
       "ClusterCPUCoreUsage     0.931622\n",
       "ClusterCPUUsage         0.931622\n",
       "TimeSeriesCount         1.000000\n",
       "Name: TimeSeriesCount, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp = sns.pairplot(master_df, x_vars=['ACMObsCPUCoreUsage','ACMObsMemUsageWSSGB'], y_vars='TimeSeriesCount', height=7, aspect=1)\n",
    "#mp = sns.pairplot(master_df, x_vars=['ACMObsMemUsageWSSGB'], y_vars='TimeSeriesCount', height=7, aspect=1)\n",
    "\n",
    "ax=master_df.plot.scatter(x='TimeSeriesCount', y='ACMObsCPUCoreUsage',label='CPU',s=20, c='red',xticks=[0,500,1000,1500])\n",
    "master_df.plot.scatter(x='TimeSeriesCount', y='ACMObsMemUsageWSSGB',s=20,label='Memory', c='blue',ax=ax)\n",
    "\n",
    "\n",
    "\n",
    "print(\"Since relationships are linear, Therefore, let us calculate the Pearson's r with respect TimeSeriesCount\")\n",
    "master_df.corr()['TimeSeriesCount']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion - Timeseries Count is a strong predictor for Observability Sizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internal data points  - do not touch!\n",
    "These are based on recordings at the lab. These should not be tweaked by anyone except from ACM Observability team who are familiar with the background data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#0.5 bytes - 1.5 bytes is maximum range. This is for storage in disk (not memory)\n",
    "observed_bytes_for_storage_per_ts=2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Critical Input Parameters to Size\n",
    ">- **Please tweak these values as needed**\n",
    ">- *Our calculations are solely based on what you input below*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Specs</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Number of Managed Clusters</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Number of time series per cluster</td>\n",
       "      <td>330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Number of metric samples per hour</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Base Operative time series count</td>\n",
       "      <td>49500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Number of hours of retention in Receiver PV</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Target days for storage of data assuming downs...</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Specs     Value\n",
       "0                         Number of Managed Clusters       150\n",
       "1                  Number of time series per cluster    330000\n",
       "2                  Number of metric samples per hour        12\n",
       "3                   Base Operative time series count  49500000\n",
       "4        Number of hours of retention in Receiver PV        24\n",
       "5  Target days for storage of data assuming downs...       365"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_managed_clusters=150\n",
    "number_of_time_series_per_cluster= 330000\n",
    "\n",
    "#sampling interval - how frequently do we send data to ACM for storage\n",
    "number_of_samples_per_hour=12\n",
    "number_of_hours_pv_retention_hrs=24\n",
    "number_of_days_for_storage=365\n",
    "\n",
    "# Present Sizing for Object Store\n",
    "input_data = {'Specs': ['Number of Managed Clusters', \n",
    "                        'Number of time series per cluster', \n",
    "                        'Number of metric samples per hour',\n",
    "                        'Base Operative time series count',\n",
    "                        'Number of hours of retention in Receiver PV',\n",
    "                        'Target days for storage of data assuming downsampling'], \n",
    "        'Value': [number_of_managed_clusters,number_of_time_series_per_cluster,\n",
    "                  number_of_samples_per_hour,\n",
    "                  number_of_managed_clusters*number_of_time_series_per_cluster,\n",
    "                  number_of_hours_pv_retention_hrs,number_of_days_for_storage]\n",
    "           } \n",
    "  \n",
    "# Create DataFrame \n",
    "input_df = pandas.DataFrame(input_data) \n",
    "  \n",
    "# Print the output. \n",
    "input_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Number of Time series for each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================================================\n",
      "Total Number of Time series per cluster:  330000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"=======================================================================\")\n",
    "print(\"Total Number of Time series per cluster: \",number_of_time_series_per_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inferring Memory Requirement\n",
    "*Thanos Receiver stores time series in memory for 2 hrs. This cannot be configured*\n",
    "- Though there are lots of pods which are running, the main consumer of memory is the Thanos Receiver\n",
    "- Therefore for large systems, basing SWAGs on Receiver memory is good enough cover for all Pods in the observability namespace\n",
    "- Also this total memory figure does not change if replication count of receiver is changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time Series count:  49500000\n",
      "------------------------------------\n",
      "alpha = [-3.78028111]\n",
      "beta = [[1.60469085e-05]]\n",
      "A rough Predicted Memory need in GB for 49500000 timeseries - IFF linearity holds = [[790.54168728]] GB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Projected Memory (GB)</th>\n",
       "      <th>Note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[790.5416872750741]]</td>\n",
       "      <td>This is good of linearity assumption holds</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Projected Memory (GB)                                        Note\n",
       "0  [[790.5416872750741]]  This is good of linearity assumption holds"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total Time Series count: \",number_of_managed_clusters*number_of_time_series_per_cluster)\n",
    "print(\"------------------------------------\")\n",
    "\n",
    "X = master_df['TimeSeriesCount'].values.reshape(-1,1)\n",
    "Y = master_df['ACMObsMemUsageWSSGB'].values.reshape(-1,1)\n",
    "\n",
    "regressor = LinearRegression()  \n",
    "#training the algorithm\n",
    "regressor.fit(X,Y) \n",
    "#To retrieve the intercept:\n",
    "alpha = regressor.intercept_\n",
    "print(f'alpha = {alpha}')\n",
    "#For retrieving the slope:\n",
    "beta = regressor.coef_\n",
    "print(f'beta = {beta}')\n",
    "\n",
    "y_pred = alpha + beta*number_of_managed_clusters*number_of_time_series_per_cluster\n",
    "print(f'A rough Predicted Memory need in GB for {number_of_managed_clusters*number_of_time_series_per_cluster} timeseries - IFF linearity holds = {y_pred} GB')\n",
    "\n",
    "memory_data = {'Projected Memory (GB)': [y_pred], \n",
    "        'Note': ['This is good of linearity assumption holds']\n",
    "           } \n",
    "  \n",
    "memory_df = pandas.DataFrame(memory_data) \n",
    "memory_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inferring CPU requirement\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Time Series count:  49500000\n",
      "------------------------------------\n",
      "alpha = [0.56711376]\n",
      "beta = [[2.83832114e-06]]\n",
      "A rough Predicted CPU vCPU needed for 49500000 timeseries - IFF linearity holds = [[141.06401003]] vCPU\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Projected CPU  (vCPU)</th>\n",
       "      <th>Note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[141.06401002697606]]</td>\n",
       "      <td>This is good of linearity assumption holds</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Projected CPU  (vCPU)                                        Note\n",
       "0  [[141.06401002697606]]  This is good of linearity assumption holds"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Total Time Series count: \",number_of_managed_clusters*number_of_time_series_per_cluster)\n",
    "print(\"------------------------------------\")\n",
    "\n",
    "X = master_df['TimeSeriesCount'].values.reshape(-1,1)\n",
    "Y = master_df['ACMObsCPUCoreUsage'].values.reshape(-1,1)\n",
    "\n",
    "regressor = LinearRegression()  \n",
    "#training the algorithm\n",
    "regressor.fit(X,Y) \n",
    "#To retrieve the intercept:\n",
    "alpha = regressor.intercept_\n",
    "print(f'alpha = {alpha}')\n",
    "#For retrieving the slope:\n",
    "beta = regressor.coef_\n",
    "print(f'beta = {beta}')\n",
    "\n",
    "y_pred = alpha + beta*number_of_managed_clusters*number_of_time_series_per_cluster\n",
    "print(f'A rough Predicted CPU vCPU needed for {number_of_managed_clusters*number_of_time_series_per_cluster} timeseries - IFF linearity holds = {y_pred} vCPU')\n",
    "\n",
    "cpu_data = {'Projected CPU  (vCPU)': [y_pred], \n",
    "        'Note': ['This is good of linearity assumption holds']\n",
    "           } \n",
    "  \n",
    "cpu_df = pandas.DataFrame(cpu_data) \n",
    "cpu_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inferring PVC Requirement\n",
    "*Thanos Receiver storage duration is controlled by customer as indicated above. So please make sure that the parameters is given correctly*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of hours of retention in Receiver PV:  24\n",
      "Assumed storage space per time series:  2  bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pod</th>\n",
       "      <th>Number of Replicas</th>\n",
       "      <th>Sizes Per Replica (GB)</th>\n",
       "      <th>Total Size (GB)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alertmanager</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>thanos-receiver</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>thanos-compactor</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thanos-rule</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>thanos-store</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Pod  Number of Replicas  Sizes Per Replica (GB)  \\\n",
       "0      alertmanager                   3                      10   \n",
       "1   thanos-receiver                   3                      30   \n",
       "2  thanos-compactor                   1                     100   \n",
       "3       thanos-rule                   3                      30   \n",
       "4      thanos-store                   3                     100   \n",
       "\n",
       "   Total Size (GB)  \n",
       "0               30  \n",
       "1               90  \n",
       "2              100  \n",
       "3               90  \n",
       "4              300  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#actual_number_of_time_series_local_retention=(\n",
    "#    actual_number_of_time_series_per_2h*(number_of_hours_pv_retention_hrs/2)\n",
    "#)\n",
    "\n",
    "actual_number_of_time_series_local_retention=(\n",
    "number_of_samples_per_hour*\n",
    "number_of_managed_clusters*\n",
    "number_of_time_series_per_cluster*\n",
    "number_of_hours_pv_retention_hrs\n",
    ")\n",
    "\n",
    "inferred_gb_storage_recv=(\n",
    "    (actual_number_of_time_series_local_retention*observed_bytes_for_storage_per_ts)/(1024*1024*1024)\n",
    ")\n",
    "\n",
    "inferred_gb_storage_am=10\n",
    "inferred_gb_storage_compactor=100\n",
    "inferred_gb_storage_rule=30\n",
    "inferred_gb_storage_store=100\n",
    "\n",
    "print(\"Number of hours of retention in Receiver PV: \", number_of_hours_pv_retention_hrs)\n",
    "print(\"Assumed storage space per time series: \",observed_bytes_for_storage_per_ts, \" bytes\")\n",
    "\n",
    "# Present Sizing for PVC\n",
    "pvc_data = {'Pod': ['alertmanager', 'thanos-receiver', \n",
    "                    'thanos-compactor', 'thanos-rule','thanos-store'], \n",
    "        'Number of Replicas': [3,3,1,3,3],\n",
    "        'Sizes Per Replica (GB)': [roundup(inferred_gb_storage_am), \n",
    "                                   roundup(inferred_gb_storage_recv), \n",
    "                                   roundup(inferred_gb_storage_compactor), \n",
    "                                   roundup(inferred_gb_storage_rule),\n",
    "                                   roundup(inferred_gb_storage_store)],\n",
    "        'Total Size (GB)': [roundup(inferred_gb_storage_am)*3, \n",
    "                            roundup(inferred_gb_storage_recv)*3, \n",
    "                            roundup(inferred_gb_storage_compactor)*1, \n",
    "                            roundup(inferred_gb_storage_rule)*3,\n",
    "                            roundup(inferred_gb_storage_store)*3]} \n",
    "  \n",
    "# Create DataFrame \n",
    "pvc_df = pandas.DataFrame(pvc_data) \n",
    "  \n",
    "# Print the output. \n",
    "pvc_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The above calculation shows the expected Receiver PVC size per instance of a receiever. This is calculated using assumptions. The time series data is saved in TSDB on the PVC. It is retained for duration of local retention. It is hard to estimate the size without looking at the data. Commonly, its seen to be between 1~2 bytes per record. Here we assume it to be 2 bytes. This however does not take into account the WAL size. [Background material can be found here.](https://www.robustperception.io/how-much-disk-space-do-prometheus-blocks-use/)\n",
    "- There are a few other PVCs needed (assuming replica of 3 where needed). We recommend the following sizes for these:\n",
    ">```\n",
    "alertmanager-db-observability-alertmanager-0 - 10GB\n",
    "alertmanager-db-observability-alertmanager-1 - 10GB\n",
    "alertmanager-db-observability-alertmanager-2 - 10GB\n",
    "data-observability-thanos-compact-0 - 100GB \n",
    "data-observability-thanos-receive-default-0 - calculated above\n",
    "data-observability-thanos-receive-default-1 - calculated above\n",
    "data-observability-thanos-receive-default-2 - calculated above\n",
    "data-observability-thanos-rule-0 - 30GB (could be lesser, but it is hard to estimate at this stage)       \n",
    "data-observability-thanos-rule-1 - 30GB (could be lesser, but it is hard to estimate at this stage)     \n",
    "data-observability-thanos-rule-2 - 30GB (could be lesser, but it is hard to estimate at this stage)  \n",
    "data-observability-thanos-store-shard-0-0 - 100 GB \n",
    "data-observability-thanos-store-shard-1-0 - 100 GB \n",
    "data-observability-thanos-store-shard-2-0 - 100 GB \n",
    "```\n",
    "- Compact [data-observability-thanos-compact-0] PVC needs increasing storage as the duration of data stored grows. Conceptually, as duration grows, it has to bring over proportional amount of that data to compact. It is spiked utilization. It uses this space during compaction and then the usage drops back to 0. As per [Thanos documentation](https://thanos.io/tip/components/compact.md/#disk/) ```The compactor needs local disk space to store intermediate data for its processing as well as bucket state cache. Generally, for medium sized bucket about 100GB should be enough to keep working as the compacted time ranges grow over time. However, this highly depends on size of the blocks. In worst case scenario compactor has to have space adequate to 2 times 2 weeks (if your maximum compaction level is 2 weeks) worth of smaller blocks to perform compaction. First, to download all of those source blocks, second to build on disk output of 2 week block composed of those smaller ones.```\n",
    "- Store-shard [data-observability-thanos-store-shard-*-*] PVC: As per [Thanos documentation](https://thanos.io/tip/components/store.md/) ```It acts primarily as an API gateway and therefore does not need significant amounts of local disk space. It joins a Thanos cluster on startup and advertises the data it can access. It keeps a small amount of information about all remote blocks on local disk and keeps it in sync with the bucket. This data is generally safe to delete across restarts at the cost of increased startup times.```. This PV may go away in future versions of ACM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inferring Object Store Requirement\n",
    "*The data retention and downsampling settings in the MCO CR affect these. We make some simplifying assumptions here and do the calculation based on max number of data the data needs to be stored.*\n",
    "*We make a simplifying assumption that the space required in PV and Object Store follow the same 2 byte/time-series logic*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target days for storage of data assuming downsampling:  365\n",
      "Inferred Object Storage needed:  29076.486825942993 GB\n"
     ]
    },
    {
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       "      <th>Steps</th>\n",
       "      <th>Value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Raw storage for 1 day (GB)</td>\n",
       "      <td>26.553869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>number of days to be stored</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Raw storage for all days (GB)</td>\n",
       "      <td>9692.162275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5m storage for all days (GB)</td>\n",
       "      <td>9692.162275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1hr storage for all days (GB)</td>\n",
       "      <td>9692.162275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total storage for all days (GB)</td>\n",
       "      <td>29080.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Steps         Value\n",
       "0       Raw storage for 1 day (GB)     26.553869\n",
       "1      number of days to be stored    365.000000\n",
       "2    Raw storage for all days (GB)   9692.162275\n",
       "3     5m storage for all days (GB)   9692.162275\n",
       "4    1hr storage for all days (GB)   9692.162275\n",
       "5  Total storage for all days (GB)  29080.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this is needed just in case the number_of_hours_pv_retention_hrs is set to something other than 24hrs\n",
    "inferred_obj_storage_per_day=inferred_gb_storage_recv*24/number_of_hours_pv_retention_hrs\n",
    "inferred_obj_storage_for_life=inferred_obj_storage_per_day*number_of_days_for_storage*3\n",
    "\n",
    "print(\"Target days for storage of data assuming downsampling: \", number_of_days_for_storage)\n",
    "print(\"Inferred Object Storage needed: \", inferred_obj_storage_for_life , \"GB\")\n",
    "\n",
    "# Present Sizing for Object Store\n",
    "obj_data = {'Steps': ['Raw storage for 1 day (GB)', \n",
    "                      'number of days to be stored', \n",
    "                      'Raw storage for all days (GB)', \n",
    "                      '5m storage for all days (GB)',\n",
    "                      '1hr storage for all days (GB)',\n",
    "                      'Total storage for all days (GB)'], \n",
    "        'Value': [inferred_obj_storage_per_day,\n",
    "                  number_of_days_for_storage,\n",
    "                  inferred_obj_storage_for_life/3,\n",
    "                  inferred_obj_storage_for_life/3,\n",
    "                  inferred_obj_storage_for_life/3,\n",
    "                  roundup(inferred_obj_storage_for_life)]\n",
    "           } \n",
    "  \n",
    "# Create DataFrame \n",
    "objstore_df = pandas.DataFrame(obj_data) \n",
    "  \n",
    "# Print the output. \n",
    "objstore_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the simplifying assumptions. You may have set:\n",
    ">- raw data to be stored for 14 days\n",
    ">- 5m interval data to be stored for 3 months\n",
    ">- 1hour interval data to be stored for 12 months\n",
    "\n",
    "We simplify the above to:\n",
    "- 12 months aka 365 days of storage\n",
    "- and since we have 3 raw and 5m and 1hr interval needed to be stored, we multiply by 3\n",
    "- and we also assuming the data storage size be appx 2 bytes per time series\n",
    "\n",
    "[Background material can be found here.](https://thanos.io/tip/thanos/design.md/#cost)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********************************************************************************\n",
      "******** High Level Estimates for sizing of ACM Hub Observability  ***************\n",
      "** These are based on utilization numbers seen during Lab testing.\n",
      "** Lab Testing included collecting metrics and alerts across the managed clusters\n",
      "** and out of the box rule analysis.\n",
      "** These are provided as inputs to customer capacity planning needs.\n",
      "**********************************************************************************\n",
      "\n",
      "******* Inputs to the System ************\n",
      "----------------------------------------------------------------------\n",
      "|    | Specs                                                 |    Value |\n",
      "|---:|:------------------------------------------------------|---------:|\n",
      "|  0 | Number of Managed Clusters                            |      150 |\n",
      "|  1 | Number of time series per cluster                     |   330000 |\n",
      "|  2 | Number of metric samples per hour                     |       12 |\n",
      "|  3 | Base Operative time series count                      | 49500000 |\n",
      "|  4 | Number of hours of retention in Receiver PV           |       24 |\n",
      "|  5 | Target days for storage of data assuming downsampling |      365 |\n",
      "\n",
      "\n",
      "******* Recommendations ************\n",
      "----------------------------------------------------------------------------\n",
      "Memory and CPU Inferred Value for HA Configuration\n",
      "==> this is sum of resources all pods in observability namspace\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "|    |   Projected Memory (GB) | Note                                       |\n",
      "|---:|------------------------:|:-------------------------------------------|\n",
      "|  0 |                 790.542 | This is good of linearity assumption holds |\n",
      "|    |   Projected CPU  (vCPU) | Note                                       |\n",
      "|---:|------------------------:|:-------------------------------------------|\n",
      "|  0 |                 141.064 | This is good of linearity assumption holds |\n",
      "\n",
      "\n",
      "Storage Inferred Value for Persistent Volume (Block storage) for all replicas:\n",
      "----------------------------------------------------------------------------------------------\n",
      "|    | Pod              |   Number of Replicas |   Sizes Per Replica (GB) |   Total Size (GB) |\n",
      "|---:|:-----------------|---------------------:|-------------------------:|------------------:|\n",
      "|  0 | alertmanager     |                    3 |                       10 |                30 |\n",
      "|  1 | thanos-receiver  |                    3 |                       30 |                90 |\n",
      "|  2 | thanos-compactor |                    1 |                      100 |               100 |\n",
      "|  3 | thanos-rule      |                    3 |                       30 |                90 |\n",
      "|  4 | thanos-store     |                    3 |                      100 |               300 |\n",
      "\n",
      "Storage Inferred Value for Object Store:\n",
      "----------------------------------------------------\n",
      "|    | Steps                           |      Value |\n",
      "|---:|:--------------------------------|-----------:|\n",
      "|  0 | Raw storage for 1 day (GB)      |    26.5539 |\n",
      "|  1 | number of days to be stored     |   365      |\n",
      "|  2 | Raw storage for all days (GB)   |  9692.16   |\n",
      "|  3 | 5m storage for all days (GB)    |  9692.16   |\n",
      "|  4 | 1hr storage for all days (GB)   |  9692.16   |\n",
      "|  5 | Total storage for all days (GB) | 29080      |\n"
     ]
    }
   ],
   "source": [
    "print(\"**********************************************************************************\")\n",
    "print(\"******** High Level Estimates for sizing of ACM Hub Observability  ***************\")\n",
    "#print(\"********************************************************************************\")\n",
    "print(\"** These are based on utilization numbers seen during Lab testing.\")\n",
    "print(\"** Lab Testing included collecting metrics and alerts across the managed clusters\")\n",
    "print(\"** and out of the box rule analysis.\")\n",
    "print(\"** These are provided as inputs to customer capacity planning needs.\")\n",
    "print(\"**********************************************************************************\")\n",
    "print(\"\")\n",
    "#print(\"*****************************************\")\n",
    "print(\"******* Inputs to the System ************\")\n",
    "#print(\"*****************************************\")\n",
    "print(\"----------------------------------------------------------------------\")\n",
    "print(input_df.to_markdown())\n",
    "print(\"\")\n",
    "print(\"\")\n",
    "#print(\"*****************************************\")\n",
    "print(\"******* Recommendations ************\")\n",
    "#print(\"*****************************************\")\n",
    "print(\"----------------------------------------------------------------------------\")\n",
    "print(\"Memory and CPU Inferred Value for HA Configuration\")\n",
    "print(\"==> this is sum of resources all pods in observability namspace\")\n",
    "print(\"----------------------------------------------------------------------------\")\n",
    "print(\"\")\n",
    "print(memory_df.to_markdown())\n",
    "print(cpu_df.to_markdown())\n",
    "print(\"\")\n",
    "print(\"\")\n",
    "print(\"Storage Inferred Value for Persistent Volume (Block storage) for all replicas:\")\n",
    "print(\"----------------------------------------------------------------------------------------------\")\n",
    "print(pvc_df.to_markdown())\n",
    "print(\"\")\n",
    "print(\"Storage Inferred Value for Object Store:\")\n",
    "print(\"----------------------------------------------------\")\n",
    "print(objstore_df.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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