Merge "[TOOL] Data Query Tool."

1 files changed, 974 insertions, 0 deletions

diff --git a/tools/datamanagement/Data-Query.ipynb b/tools/datamanagement/Data-Query.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ed320ed0",
+   "metadata": {},
+   "source": [
+    "# Data-Query\n",
+    "This notebook is provided as tool to extract data, based on data-model from thoth, from multiple sources."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63c2424e",
+   "metadata": {},
+   "source": [
+    "## Traget Sources\n",
+    "Phase-1:\n",
+    "1. Prometheus\n",
+    "2. Elasticsearch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0b349a9",
+   "metadata": {},
+   "source": [
+    "#########################################################################\n",
+    "\n",
+    "Copyright 2021 Spirent Communications\n",
+    "\n",
+    " Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    " you may not use this file except in compliance with the License.\n",
+    " You may obtain a copy of the License at\n",
+    "\n",
+    "   http://www.apache.org/licenses/LICENSE-2.0\n",
+    "\n",
+    " Unless required by applicable law or agreed to in writing, software\n",
+    " distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    " WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    " See the License for the specific language governing permissions and\n",
+    " limitations under the License.\n",
+    " \n",
+    "#########################################################################"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f11300d8",
+   "metadata": {},
+   "source": [
+    "### Import required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "d238a743",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "import numpy as np\n",
+    "from IPython.display import display\n",
+    "\n",
+    "import datetime\n",
+    "import time\n",
+    "import requests\n",
+    "\n",
+    "from pprint import pprint\n",
+    "import json\n",
+    "from datetime import datetime, timedelta\n",
+    "\n",
+    "#from elasticsearch import Elasticsearch\n",
+    "#from elasticsearch_dsl import Search\n",
+    "#from elasticsearch.connection import create_ssl_context\n",
+    "import ssl\n",
+    "import urllib3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af05e823",
+   "metadata": {},
+   "source": [
+    "### Sources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "416a1643",
+   "metadata": {},
+   "source": [
+    "### Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0c4690ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#function to make DF out of query json\n",
+    "\n",
+    "def convert_to_df(res_json):\n",
+    "\n",
+    "    data_list = res_json['data']['result']\n",
+    "    res_df = pd.DataFrame()\n",
+    "    if not data_list:\n",
+    "        return res_df\n",
+    "\n",
+    "    # making colums\n",
+    "    headers = data_list[0]\n",
+    "    for data in data_list:\n",
+    "        metrics = data['metric']\n",
+    "        for metric in metrics.keys():\n",
+    "            res_df[metric] = np.nan\n",
+    "        res_df['value'] = 0\n",
+    "    \n",
+    "    # filling the df\n",
+    "    for data in data_list:\n",
+    "        metrics = data['metric']\n",
+    "        metrics['value'] = data['value'][-1]\n",
+    "        res_df = res_df.append(metrics, ignore_index=True)      \n",
+    "\n",
+    "    return res_df\n",
+    "\n",
+    "def convert_to_df_range(res_json):\n",
+    "\n",
+    "    data_list = res_json['data']['result']\n",
+    "    res_df = pd.DataFrame()\n",
+    "    if not data_list:\n",
+    "        return res_df\n",
+    "\n",
+    "    # filling the df\n",
+    "    for data in data_list:\n",
+    "        metrics = data['metric']\n",
+    "        values = np.array(data['values'])\n",
+    "        for time, value in values:\n",
+    "            metrics['timestamp'] = time\n",
+    "            metrics['value'] = value\n",
+    "            res_df = res_df.append(metrics, ignore_index=True)      \n",
+    "\n",
+    "    return res_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0226e066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# functions to query\n",
+    "\n",
+    "def convert_to_timestamp(s):\n",
+    "    return time.mktime(datetime.strptime(s, \"%Y-%m-%d %H:%M:%S\").timetuple())\n",
+    "\n",
+    "def query_current(params={}):\n",
+    "    # input: params\n",
+    "    # type: dict\n",
+    "    # Example: {'query': 'container_cpu_user_seconds_total'}   \n",
+    "    # Output: dict, loaded json response of the query\n",
+    "\n",
+    "    res = requests.get(PROMETHEUS + '/api/v1/query', \n",
+    "                       params=params)\n",
+    "    return json.loads(res.text)\n",
+    "\n",
+    "\n",
+    "def query_range(start, end, params={}, steps = '30s'):\n",
+    "    # input: params\n",
+    "    # type: dict\n",
+    "    # Example: {'query': 'container_cpu_user_seconds_total'}\n",
+    "    \n",
+    "    # Output: dict, loaded json response of the query\n",
+    "    params[\"start\"] = convert_to_timestamp(start)\n",
+    "    params[\"end\"] = convert_to_timestamp(end)\n",
+    "    params[\"step\"] = steps\n",
+    "\n",
+    "    # print(params)\n",
+    "\n",
+    "    res = requests.get(PROMETHEUS + '/api/v1/query_range', \n",
+    "                       params=params,\n",
+    "                       )\n",
+    "\n",
+    "    return json.loads(res.text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a97f9a5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Elasticsearch [WIP]\n",
+    "# https://github.com/elastic/elasticsearch-dsl-py \n",
+    "def query_es_range(start, end, indx, cat, titl, desc):\n",
+    "    es = Elasticsearch([ELASTICSEARCH])\n",
+    "    # Build a DSL Search object on the 'commits' index, 'summary' document type\n",
+    "    request = elasticsearch_dsl.Search(using=es, index='commits',\n",
+    "                                       doc_type='summary')\n",
+    "    # Run the Search, using the scan interface to get all resuls\n",
+    "    response = request.scan() # or execute()?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21113203",
+   "metadata": {},
+   "source": [
+    "### Data Extractions as Dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "8fa9918d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#https://github.com/Naugrimm/promql-examples\n",
+    "def cpu_core_utilization(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    # If total is not available:\n",
+    "    #params = {'query' : \"100 * (1 - sum(collectd_cpu{type='idle'}) by (cpu) / sum(collectd_cpu{exported_instance='\" + node + \"'}) by (cpu))\"}\n",
+    "    params = {'query': \"100 * (1 - sum(collectd_cpu_total{type='idle'}) by (exported_instance) / sum(collectd_cpu_total) by (exported_instance))\"}\n",
+    "    target_cpu_usage_range = query_range(start, end, params, steps)\n",
+    "    current_cpu = query_current(params)\n",
+    "    df = convert_to_df_range(target_cpu_usage_range)\n",
+    "    display(df)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "fd8f91a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interface Dropped (both type 1 and 2, i.e rx and tx)\n",
+    "#TODO: Change this to separate functions later\n",
+    "def interface_dropped(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    drop_dfs = []\n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        df_0 = df #TODO: Change this\n",
+    "        df_1 = df #TODO: Change this\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_dropped_0{exported_instance='\" + node + \"'}\"}\n",
+    "\n",
+    "        interface_dropped_0 = query_range(start, end, params, steps)\n",
+    "        df_0 = convert_to_df_range(interface_dropped_0)\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_dropped_1{exported_instance='\" + node + \"'}\"}\n",
+    "        interface_dropped_1 = query_range(start, end, params, steps)\n",
+    "        df_1 = convert_to_df_range(interface_dropped_1)\n",
+    "\n",
+    "        #df_0 : interfaces_dropped_0_df\n",
+    "        #df_0 = df_0.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        drop_dfs.append(df_0)\n",
+    "\n",
+    "        #df_1 : interfaces_dropped_1_df\n",
+    "        #df_1 = df_1.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        drop_dfs.append(df_1)\n",
+    "\n",
+    "    return drop_dfs\n",
+    "\n",
+    "def virt_interface_dropped(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    drop_dfs = []\n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        df_0 = df #TODO: Change this\n",
+    "        df_1 = df #TODO: Change this\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_virt_if_dropped_0{exported_instance='\" + node + \"'}\"}\n",
+    "          \n",
+    "        interface_dropped_0 = query_range(start, end, params, steps)\n",
+    "        df_0 = convert_to_df_range(interface_dropped_0)\n",
+    "        \n",
+    "        params = {'query' : \"collectd_virt_if_dropped_0{exported_instance='\" + node + \"'}\"}\n",
+    "        interface_dropped_1 = query_range(start, end, params, steps)\n",
+    "        df_1 = convert_to_df_range(interface_dropped_1)\n",
+    "\n",
+    "        #df_0 : interfaces_dropped_0_df\n",
+    "        df_0 = df_0.drop(['instance', 'job'], axis = 1)\n",
+    "        drop_dfs.append(df_0)\n",
+    "\n",
+    "        #df_1 : interfaces_dropped_1_df\n",
+    "        df_1 = df_1.drop(['instance', 'job'], axis = 1)\n",
+    "        drop_dfs.append(df_1)\n",
+    "\n",
+    "    return drop_dfs\n",
+    "\n",
+    "\n",
+    "# Interface Errors (both type 1 and 2, i.e rx and tx)\n",
+    "#TODO: Change this to separate functions later\n",
+    "def interface_errors(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    error_dfs = []\n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        df_0 = df #TODO: Change this\n",
+    "        df_1 = df #TODO: Change this\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_errors_0_total{exported_instance='\" + node + \"'}\"}\n",
+    "        interfaces_errors_0 = query_range(start, end, params, steps)\n",
+    "        df_0 = convert_to_df_range(interfaces_errors_0)\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_errors_1_total{exported_instance='\" + node + \"'}\"}\n",
+    "        interface_errors_1 = query_range(start, end, params, steps)\n",
+    "        df_1 = convert_to_df_range(interface_errors_1)\n",
+    "\n",
+    "        \n",
+    "        #df_0 : interfaces_errors_0_df\n",
+    "        #df_0 = df_0.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        error_dfs.append(df_0)\n",
+    "\n",
+    "        #df_1 : interfaces_dropped_1_df\n",
+    "        #df_1 = df_1.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        error_dfs.append(df_1)\n",
+    "\n",
+    "    return error_dfs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "c98a59da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_memory_usage(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_memory{exported_instance='\" + node + \"'} / (1024*1024*1024) \"}        \n",
+    "        target_memory_usage_range = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(target_memory_usage_range)    \n",
+    "        #df = df.drop(['instance', 'job'], axis = 1)\n",
+    "    return df\n",
+    "\n",
+    "def get_virt_memory_usage(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    # Example value: collectd_virt_memory{exported_instance=\"instance-00001a33\", \n",
+    "    # instance=\"localhost:9103\",job=\"promc\",virt=\"total\"}\n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_virt_memory{exported_instance='\" + node + \"'} / (1024*1024*1024) \"}        \n",
+    "        target_memory_usage_range = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(target_memory_usage_range)\n",
+    "        \n",
+    "        #df = df.drop(['instance', 'job'], axis = 1)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3733a9a0",
+   "metadata": {},
+   "source": [
+    "### Main Extraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "bd5dd0f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract(timestamp, node=None, node_phy=True):\n",
+    "    virt = True\n",
+    "    # TODO: If node is provided, we may have to do some validations\n",
+    "    # If its a valid-node node of not.\n",
+    "    end = datetime.strptime(timestamp.split(',')[0], \"%Y-%m-%d %H:%M:%S\")\n",
+    "    start = end - timedelta(days=1)\n",
+    "    \n",
+    "    start = str(start)\n",
+    "    end = str(end)\n",
+    "    steps = '60s'\n",
+    "\n",
+    "    print(\"Starting Extraction from\",start,\"to\",end,'\\n\\n')\n",
+    "    #cpu\n",
+    "    print(\"===== CPU Data =====\\n\")\n",
+    "    cpu_utilization = cpu_core_utilization(start, end, steps=steps)\n",
+    "    display(cpu_utilization)\n",
+    "      \n",
+    "   \n",
+    "    #interface analysis\n",
+    "    print(\"=====Interfaces (including libvirt) Dropped / Errors=====\\n\")\n",
+    "    dropped_interfaces = interface_dropped(start=start, end=end, node=node, steps=steps)\n",
+    "    for drop_if in dropped_interfaces:\n",
+    "        display(drop_if)\n",
+    "    err_interfaces = interface_errors(start=start, end=end, node=node, steps=steps)\n",
+    "    for err_if in err_interfaces:\n",
+    "        display(err_if)\n",
+    "    print(err_interfaces)\n",
+    "    #vdropped_interfaces = virt_interface_dropped(start,end,node,steps)\n",
+    "    \n",
+    "       \n",
+    "    #Memory Analysis:\n",
+    "    print(\"=====Memory Data =====\\n\")\n",
+    "    mem = get_memory_usage(start, end, node, steps)\n",
+    "    display(mem)\n",
+    "    vmem = get_virt_memory_usage(start, end, node, steps)\n",
+    "    display(vmem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "e5d3ec9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PROMETHEUS = 'http://10.10.180.20:9090/'\n",
+    "ELASTICSEARCH = 'http://192.168.2.233:9200/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "14298851",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting Extraction from 2022-04-11 06:00:00 to 2022-04-12 06:00:00 \n",
+      "\n",
+      "\n",
+      "===== CPU Data =====\n",
+      "\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>exported_instance</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649660</td>\n",
+       "      <td>1.8592150264858165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649720</td>\n",
+       "      <td>1.859213509941382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649780</td>\n",
+       "      <td>1.8592120502819665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649840</td>\n",
+       "      <td>1.8592107627314003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649900</td>\n",
+       "      <td>1.8592092108782765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2455</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723160</td>\n",
+       "      <td>1.0958105540852525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2456</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723220</td>\n",
+       "      <td>1.0958088368217433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2457</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723280</td>\n",
+       "      <td>1.095807067448351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2458</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723340</td>\n",
+       "      <td>1.0958053357542963</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2459</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723400</td>\n",
+       "      <td>1.0958036155334194</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2460 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     exported_instance   timestamp               value\n",
+       "0          pod18-node4  1649649660  1.8592150264858165\n",
+       "1          pod18-node4  1649649720   1.859213509941382\n",
+       "2          pod18-node4  1649649780  1.8592120502819665\n",
+       "3          pod18-node4  1649649840  1.8592107627314003\n",
+       "4          pod18-node4  1649649900  1.8592092108782765\n",
+       "...                ...         ...                 ...\n",
+       "2455       pod18-node5  1649723160  1.0958105540852525\n",
+       "2456       pod18-node5  1649723220  1.0958088368217433\n",
+       "2457       pod18-node5  1649723280   1.095807067448351\n",
+       "2458       pod18-node5  1649723340  1.0958053357542963\n",
+       "2459       pod18-node5  1649723400  1.0958036155334194\n",
+       "\n",
+       "[2460 rows x 3 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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+       "      <th>value</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649660</td>\n",
+       "      <td>1.8592150264858165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649720</td>\n",
+       "      <td>1.859213509941382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649780</td>\n",
+       "      <td>1.8592120502819665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649840</td>\n",
+       "      <td>1.8592107627314003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>pod18-node4</td>\n",
+       "      <td>1649649900</td>\n",
+       "      <td>1.8592092108782765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2455</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723160</td>\n",
+       "      <td>1.0958105540852525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2456</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723220</td>\n",
+       "      <td>1.0958088368217433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2457</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723280</td>\n",
+       "      <td>1.095807067448351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2458</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723340</td>\n",
+       "      <td>1.0958053357542963</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2459</th>\n",
+       "      <td>pod18-node5</td>\n",
+       "      <td>1649723400</td>\n",
+       "      <td>1.0958036155334194</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2460 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     exported_instance   timestamp               value\n",
+       "0          pod18-node4  1649649660  1.8592150264858165\n",
+       "1          pod18-node4  1649649720   1.859213509941382\n",
+       "2          pod18-node4  1649649780  1.8592120502819665\n",
+       "3          pod18-node4  1649649840  1.8592107627314003\n",
+       "4          pod18-node4  1649649900  1.8592092108782765\n",
+       "...                ...         ...                 ...\n",
+       "2455       pod18-node5  1649723160  1.0958105540852525\n",
+       "2456       pod18-node5  1649723220  1.0958088368217433\n",
+       "2457       pod18-node5  1649723280   1.095807067448351\n",
+       "2458       pod18-node5  1649723340  1.0958053357542963\n",
+       "2459       pod18-node5  1649723400  1.0958036155334194\n",
+       "\n",
+       "[2460 rows x 3 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=====Interfaces (including libvirt) Dropped / Errors=====\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Empty DataFrame\n",
+      "Columns: []\n",
+      "Index: [], Empty DataFrame\n",
+      "Columns: []\n",
+      "Index: []]\n",
+      "=====Memory Data =====\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: []"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Call to extract any Node or all nodes...\n",
+    "# Default start is 1 days before the timestamp provided.\n",
+    "# Please modify in the extract function if you want anything lesser or more.\n",
+    "extract('2022-04-12 06:00:00', 'pod18-node4')\n",
+    "#extract('2021-09-03 18:45:00', node=\"MY-NODE-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26b72876",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}