Using Prometheus API.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pip > /dev/null\n",
    "!pip install prometheus-api-client > /dev/null\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prometheus_api_client import Metric, MetricsList, PrometheusConnect\n",
    "from prometheus_api_client.utils import parse_datetime, parse_timedelta\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = PrometheusConnect(url=\"https://prometheus-data-science-on-the-moc-openshift.k-apps.osh.massopen.cloud\", disable_ssl=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/app-root/lib/python3.6/site-packages/urllib3/connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
      "  InsecureRequestWarning)\n"
     ]
    }
   ],
   "source": [
    "start_time = parse_datetime(\"1d\")\n",
    "end_time = parse_datetime(\"now\")\n",
    "chunk_size = parse_timedelta(\"now\", \"1d\")\n",
    "\n",
    "metric_data = pc.get_metric_range_data(\n",
    "    'process_cpu_seconds_total{app=\"jupyterhub\",deployment=\"jupyterhub-4\",deploymentconfig=\"jupyterhub\",instance=\"10.128.5.18:8080\",job=\"kubernetes-pods\",kubernetes_namespace=\"data-science-on-the-moc-openshift\",kubernetes_pod_name=\"jupyterhub-4-xdwnk\"}',\n",
    "    start_time=start_time,\n",
    "    end_time=end_time,\n",
    "    chunk_size=chunk_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__name__': 'process_cpu_seconds_total', 'app': 'jupyterhub', 'deployment': 'jupyterhub-4', 'deploymentconfig': 'jupyterhub', 'instance': '10.128.5.18:8080', 'job': 'kubernetes-pods', 'kubernetes_namespace': 'data-science-on-the-moc-openshift', 'kubernetes_pod_name': 'jupyterhub-4-xdwnk'} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for metric in metric_data:\n",
    "    print(metric[\"metric\"], \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "<class 'prometheus_api_client.metric.Metric'>\n",
      "process_cpu_seconds_total {'app': 'jupyterhub', 'deployment': 'jupyterhub-4', 'deploymentconfig': 'jupyterhub', 'instance': '10.128.5.18:8080', 'job': 'kubernetes-pods', 'kubernetes_namespace': 'data-science-on-the-moc-openshift', 'kubernetes_pod_name': 'jupyterhub-4-xdwnk'} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "metrics_object_list = MetricsList(metric_data)\n",
    "print(len(metrics_object_list))\n",
    "for item in metrics_object_list:\n",
    "    print(type(item))\n",
    "    \n",
    "for item in metrics_object_list:\n",
    "    print(item.metric_name, item.label_config, \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'prometheus_api_client.metric.Metric'>\n"
     ]
    }
   ],
   "source": [
    "my_metric_object = metrics_object_list[0] # one of the metrics from the list\n",
    "print(type(my_metric_object))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'process_cpu_seconds_total'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_metric_object.metric_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-24 10:00:09.864</td>\n",
       "      <td>6819.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-24 10:01:09.864</td>\n",
       "      <td>6819.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-24 10:02:09.864</td>\n",
       "      <td>6819.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-24 10:03:09.864</td>\n",
       "      <td>6820.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-24 10:04:09.864</td>\n",
       "      <td>6820.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-03-24 10:05:09.864</td>\n",
       "      <td>6820.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-03-24 10:06:09.864</td>\n",
       "      <td>6821.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-03-24 10:07:09.864</td>\n",
       "      <td>6821.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-03-24 10:08:09.864</td>\n",
       "      <td>6821.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-03-24 10:09:09.864</td>\n",
       "      <td>6821.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-03-24 10:10:09.864</td>\n",
       "      <td>6822.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-03-24 10:11:09.864</td>\n",
       "      <td>6822.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-03-24 10:12:09.864</td>\n",
       "      <td>6822.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-03-24 10:13:09.864</td>\n",
       "      <td>6823.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-03-24 10:14:09.864</td>\n",
       "      <td>6823.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-03-24 10:15:09.864</td>\n",
       "      <td>6823.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-03-24 10:16:09.864</td>\n",
       "      <td>6823.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-03-24 10:17:09.864</td>\n",
       "      <td>6824.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-03-24 10:18:09.864</td>\n",
       "      <td>6824.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-03-24 10:19:09.864</td>\n",
       "      <td>6824.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-03-24 10:20:09.864</td>\n",
       "      <td>6825.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-03-24 10:21:09.864</td>\n",
       "      <td>6825.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-03-24 10:22:09.864</td>\n",
       "      <td>6825.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-03-24 10:23:09.864</td>\n",
       "      <td>6826.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-03-24 10:24:09.864</td>\n",
       "      <td>6826.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-03-24 10:25:09.864</td>\n",
       "      <td>6826.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-03-24 10:26:09.864</td>\n",
       "      <td>6826.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-03-24 10:27:09.864</td>\n",
       "      <td>6827.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-03-24 10:28:09.864</td>\n",
       "      <td>6827.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-03-24 10:29:09.864</td>\n",
       "      <td>6827.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>2020-03-24 22:30:09.864</td>\n",
       "      <td>7048.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>2020-03-24 22:31:09.864</td>\n",
       "      <td>7048.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>2020-03-24 22:32:09.864</td>\n",
       "      <td>7048.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>2020-03-24 22:33:09.864</td>\n",
       "      <td>7049.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>2020-03-24 22:34:09.864</td>\n",
       "      <td>7049.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>2020-03-24 22:35:09.864</td>\n",
       "      <td>7049.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>2020-03-24 22:36:09.864</td>\n",
       "      <td>7050.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>2020-03-24 22:37:09.864</td>\n",
       "      <td>7050.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>2020-03-24 22:38:09.864</td>\n",
       "      <td>7050.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>2020-03-24 22:39:09.864</td>\n",
       "      <td>7051.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>2020-03-24 22:40:09.864</td>\n",
       "      <td>7051.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>2020-03-24 22:41:09.864</td>\n",
       "      <td>7051.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>2020-03-24 22:42:09.864</td>\n",
       "      <td>7052.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>2020-03-24 22:43:09.864</td>\n",
       "      <td>7052.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2020-03-24 22:44:09.864</td>\n",
       "      <td>7052.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>2020-03-24 22:45:09.864</td>\n",
       "      <td>7053.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>2020-03-24 22:46:09.864</td>\n",
       "      <td>7053.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>2020-03-24 22:47:09.864</td>\n",
       "      <td>7053.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>2020-03-24 22:48:09.864</td>\n",
       "      <td>7053.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>2020-03-24 22:49:09.864</td>\n",
       "      <td>7054.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>2020-03-24 22:50:09.864</td>\n",
       "      <td>7054.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>2020-03-24 22:51:09.864</td>\n",
       "      <td>7054.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>2020-03-24 22:52:09.864</td>\n",
       "      <td>7055.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>2020-03-24 22:53:09.864</td>\n",
       "      <td>7055.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>2020-03-24 22:54:09.864</td>\n",
       "      <td>7055.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>2020-03-24 22:55:09.864</td>\n",
       "      <td>7056.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>2020-03-24 22:56:09.864</td>\n",
       "      <td>7056.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>2020-03-24 22:57:09.864</td>\n",
       "      <td>7056.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>2020-03-24 22:58:09.864</td>\n",
       "      <td>7056.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>2020-03-24 22:59:09.864</td>\n",
       "      <td>7057.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>780 rows × 2 columns</p>\n",
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      ],
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       "                         ds        y\n",
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       "1   2020-03-24 10:01:09.864  6819.70\n",
       "2   2020-03-24 10:02:09.864  6819.99\n",
       "3   2020-03-24 10:03:09.864  6820.27\n",
       "4   2020-03-24 10:04:09.864  6820.58\n",
       "5   2020-03-24 10:05:09.864  6820.85\n",
       "6   2020-03-24 10:06:09.864  6821.04\n",
       "7   2020-03-24 10:07:09.864  6821.23\n",
       "8   2020-03-24 10:08:09.864  6821.48\n",
       "9   2020-03-24 10:09:09.864  6821.78\n",
       "10  2020-03-24 10:10:09.864  6822.10\n",
       "11  2020-03-24 10:11:09.864  6822.42\n",
       "12  2020-03-24 10:12:09.864  6822.73\n",
       "13  2020-03-24 10:13:09.864  6823.03\n",
       "14  2020-03-24 10:14:09.864  6823.32\n",
       "15  2020-03-24 10:15:09.864  6823.64\n",
       "16  2020-03-24 10:16:09.864  6823.93\n",
       "17  2020-03-24 10:17:09.864  6824.23\n",
       "18  2020-03-24 10:18:09.864  6824.54\n",
       "19  2020-03-24 10:19:09.864  6824.84\n",
       "20  2020-03-24 10:20:09.864  6825.14\n",
       "21  2020-03-24 10:21:09.864  6825.45\n",
       "22  2020-03-24 10:22:09.864  6825.73\n",
       "23  2020-03-24 10:23:09.864  6826.04\n",
       "24  2020-03-24 10:24:09.864  6826.34\n",
       "25  2020-03-24 10:25:09.864  6826.66\n",
       "26  2020-03-24 10:26:09.864  6826.97\n",
       "27  2020-03-24 10:27:09.864  6827.28\n",
       "28  2020-03-24 10:28:09.864  6827.57\n",
       "29  2020-03-24 10:29:09.864  6827.86\n",
       "..                      ...      ...\n",
       "750 2020-03-24 22:30:09.864  7048.37\n",
       "751 2020-03-24 22:31:09.864  7048.68\n",
       "752 2020-03-24 22:32:09.864  7048.98\n",
       "753 2020-03-24 22:33:09.864  7049.34\n",
       "754 2020-03-24 22:34:09.864  7049.64\n",
       "755 2020-03-24 22:35:09.864  7049.99\n",
       "756 2020-03-24 22:36:09.864  7050.29\n",
       "757 2020-03-24 22:37:09.864  7050.58\n",
       "758 2020-03-24 22:38:09.864  7050.87\n",
       "759 2020-03-24 22:39:09.864  7051.18\n",
       "760 2020-03-24 22:40:09.864  7051.51\n",
       "761 2020-03-24 22:41:09.864  7051.81\n",
       "762 2020-03-24 22:42:09.864  7052.09\n",
       "763 2020-03-24 22:43:09.864  7052.39\n",
       "764 2020-03-24 22:44:09.864  7052.70\n",
       "765 2020-03-24 22:45:09.864  7053.04\n",
       "766 2020-03-24 22:46:09.864  7053.34\n",
       "767 2020-03-24 22:47:09.864  7053.62\n",
       "768 2020-03-24 22:48:09.864  7053.89\n",
       "769 2020-03-24 22:49:09.864  7054.19\n",
       "770 2020-03-24 22:50:09.864  7054.51\n",
       "771 2020-03-24 22:51:09.864  7054.80\n",
       "772 2020-03-24 22:52:09.864  7055.08\n",
       "773 2020-03-24 22:53:09.864  7055.39\n",
       "774 2020-03-24 22:54:09.864  7055.67\n",
       "775 2020-03-24 22:55:09.864  7056.00\n",
       "776 2020-03-24 22:56:09.864  7056.30\n",
       "777 2020-03-24 22:57:09.864  7056.59\n",
       "778 2020-03-24 22:58:09.864  7056.90\n",
       "779 2020-03-24 22:59:09.864  7057.59\n",
       "\n",
       "[780 rows x 2 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_metric_object.metric_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb02811ddd8>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_metric_object.metric_values.iloc[:,1].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}