--- /dev/null
+{
+ "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": {
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+ " <thead>\n",
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+ " <th></th>\n",
+ " <th>exported_instance</th>\n",
+ " <th>timestamp</th>\n",
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+ "2458 pod18-node5 1649723340 1.0958053357542963\n",
+ "2459 pod18-node5 1649723400 1.0958036155334194\n",
+ "\n",
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+ "output_type": "display_data"
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+ " <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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+ "text": [
+ "[Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: [], Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: []]\n",
+ "=====Memory Data =====\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
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+ {
+ "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",
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+ "metadata": {},
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+ ],
+ "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
+}
Code Review / thoth.git / commitdiff