Merge "LMA: Deployment of LMA solution.(ansible)"

1 files changed, 308 insertions, 0 deletions

diff --git a/tools/lma/logs/jupyter-notebooks/Trend-Analysis.ipynb b/tools/lma/logs/jupyter-notebooks/Trend-Analysis.ipynb
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@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trend Analysis\n",
+    "##### Contributor:\n",
+    "\n",
+    "- Adarsh Yadav <adiyadav0509@gmail.com> "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import matplotlib.dates as mdates\n",
+    "import numpy as np\n",
+    "import io \n",
+    "\n",
+    "from elasticsearch import Elasticsearch\n",
+    "from elasticsearch_dsl import Search\n",
+    "from elasticsearch.connection import create_ssl_context\n",
+    "import csv\n",
+    "import ssl\n",
+    "import urllib3\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Enter foldername and index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Give folder name\n",
+    "# foldername = \"results_2020-06-12_06-47-56\"\n",
+    "foldername = \"result-test1\"\n",
+    "#Give index name - \"node1*\" or \"node4*\"\n",
+    "index = \"node4*\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ssl_context = create_ssl_context()\n",
+    "ssl_context.check_hostname = False\n",
+    "ssl_context.verify_mode = ssl.CERT_NONE\n",
+    "urllib3.disable_warnings()\n",
+    "client = Elasticsearch(['https://elasticsearch:password123@10.10.120.211:31111'],verify_certs=False,ssl_context=ssl_context)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"/tmp/\"+foldername+\"/trex-liveresults-counts.dat\"\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"ts\").query(\"match_phrase\", log_path=filename)\n",
+    "\n",
+    "trex = pd.DataFrame()\n",
+    "trex_data = dict()\n",
+    "for hits in s.scan():\n",
+    "    trex_data['ts'] = hits.ts\n",
+    "    trex_data['rx_pkts'] = hits.rx_pkts\n",
+    "    trex_data['rx_port'] = hits.rx_port\n",
+    "    trex_data['tx_port'] = hits.tx_port\n",
+    "    trex = trex.append(trex_data, ignore_index=True)\n",
+    "if not trex.empty:\n",
+    "    #convert 'ts' to datetime\n",
+    "    trex['ts'] = pd.to_datetime(trex['ts'],unit='s')\n",
+    "    trex_grp = trex.groupby('rx_port')\n",
+    "    trex_rx_0 = trex_grp.get_group(0.0) \n",
+    "    trex_rx_1 = trex_grp.get_group(1.0) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not trex.empty:\n",
+    "    fig, ax = plt.subplots(2,figsize=(16, 10))\n",
+    "    ax[0].plot(trex_rx_0['ts'],\n",
+    "            trex_rx_0['rx_pkts'],\n",
+    "              'tab:orange')\n",
+    "    ax[0].title.set_text(\"At rx_port=0 & tx_port=1\")\n",
+    "    ax[0].set(xlabel=\"timestamp\")\n",
+    "    ax[0].set(ylabel=\"rx_pkts\")\n",
+    "\n",
+    "    ax[1].plot(trex_rx_1['ts'],\n",
+    "            trex_rx_1['rx_pkts'],\n",
+    "              'tab:green')\n",
+    "    ax[1].title.set_text(\"At rx_port=1 & tx_port=0\")\n",
+    "    ax[1].set(xlabel=\"timestamp\")\n",
+    "    ax[1].set(ylabel=\"rx_pkts\")\n",
+    "\n",
+    "    #change date format\n",
+    "    myFmt = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')\n",
+    "    for i in range(2):\n",
+    "        ax[i].xaxis.set_major_formatter(myFmt)   \n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No data Found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spirent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"/tmp/\"+foldername+\"/stc-liveresults.dat.rx\"\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"ts\").query(\"match_phrase\", log_path=filename)\n",
+    "\n",
+    "spirent = pd.DataFrame()\n",
+    "spirent_data = dict()\n",
+    "for hits in s.scan():\n",
+    "    spirent_data['ts'] = hits.ts\n",
+    "    spirent_data['RxPrt'] = hits.RxPrt\n",
+    "    spirent_data['FrCnt'] = hits.FrCnt\n",
+    "    spirent = spirent.append(spirent_data, ignore_index=True)\n",
+    "if not spirent.empty:\n",
+    "    #convert 'ts' to datetime\n",
+    "    spirent['ts'] = pd.to_datetime(spirent['ts'],unit='s')\n",
+    "    spirent_grp = spirent.groupby('RxPrt')\n",
+    "    spirent_rx_1 = spirent_grp.get_group('Port //1/1') \n",
+    "    spirent_rx_2 = spirent_grp.get_group('Port //1/2') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not spirent.empty:\n",
+    "    fig, ax = plt.subplots(2,figsize=(16, 10))\n",
+    "    ax[0].plot(spirent_rx_1['ts'],\n",
+    "            spirent_rx_1['FrCnt'],\n",
+    "              'tab:orange')\n",
+    "    ax[0].title.set_text(\"At RxPrt=//1/1\")\n",
+    "    ax[0].set(xlabel=\"timestamp\")\n",
+    "    ax[0].set(ylabel=\"FrCnt\")\n",
+    "\n",
+    "    ax[1].plot(spirent_rx_2['ts'],\n",
+    "            spirent_rx_2['FrCnt'],\n",
+    "              'tab:green')\n",
+    "    ax[1].title.set_text(\"At RxPrt=//1/2\")\n",
+    "    ax[1].set(xlabel=\"timestamp\")\n",
+    "    ax[1].set(ylabel=\"FrCnt\")\n",
+    "\n",
+    "    #change date format\n",
+    "    myFmt = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')\n",
+    "    for i in range(2):\n",
+    "        ax[i].xaxis.set_major_formatter(myFmt)   \n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No data Found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ixia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"/tmp/\"+foldername+\"/Traffic Item Statistics.csv\"\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"msg\").query(\"match_phrase\", log_path=filename)\n",
+    "\n",
+    "for hits in s.scan():\n",
+    "    with open('./ixia-traffic.csv', 'a+') as f:\n",
+    "        f.write(hits.msg+\"\\n\")\n",
+    "        \n",
+    "ixia = pd.DataFrame()\n",
+    "if os.path.exists('./ixia-traffic.csv'):\n",
+    "    ixia = pd.read_csv('./ixia-traffic.csv')\n",
+    "    os.remove(f.name)\n",
+    "    f.close()\n",
+    "if not ixia.empty:\n",
+    "    ixia = ixia[['~ElapsedTime','Traffic Item 1:Frames Delta','Traffic Item 1:Loss %']].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not ixia.empty:\n",
+    "    fig, ax = plt.subplots(2,figsize=(16, 10))\n",
+    "    ax[0].plot(ixia['~ElapsedTime'],\n",
+    "            ixia['Traffic Item 1:Frames Delta'],\n",
+    "              'tab:orange')\n",
+    "    ax[0].set(xlabel=\"Elapsed Time\")\n",
+    "    ax[0].set(ylabel=\"Frames Delta\")\n",
+    "\n",
+    "    ax[1].plot(ixia['~ElapsedTime'],\n",
+    "            ixia['Traffic Item 1:Loss %'],\n",
+    "              'tab:green')\n",
+    "    ax[1].set(xlabel=\"Elapsed Time\")\n",
+    "    ax[1].set(ylabel=\"Loss %\")\n",
+    "\n",
+    "    plt.show()\n",
+    "else:\n",
+    "    print(\"No data Found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = \"/tmp/\"+foldername+\"/\"\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"setup_duration\").query(\"match_phrase\", log_path=filename)\n",
+    "for hits in s.scan():\n",
+    "    print(\"Setup duration: \", hits.setup_duration,\"s\")\n",
+    "\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"iteration_duration\").query(\"match_phrase\", log_path=filename)\n",
+    "for hits in s.scan():\n",
+    "    print(\"Iteration duration: \", hits.iteration_duration,\"s\")\n",
+    "\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"traffic_duration\").query(\"match_phrase\", log_path=filename)\n",
+    "for hits in s.scan():\n",
+    "    print(\"Traffic duration: \", hits.traffic_duration,\"s\")\n",
+    "\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"test_duration\").query(\"match_phrase\", log_path=filename)\n",
+    "for hits in s.scan():\n",
+    "    print(\"Test duration: \", hits.test_duration,\"s\")\n",
+    "\n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"report_duration\").query(\"match_phrase\", log_path=filename)\n",
+    "for hits in s.scan():\n",
+    "    print(\"Report duration: \", hits.report_duration,\"s\")\n",
+    "    \n",
+    "s = Search(index=index).using(client).query(\"exists\", field=\"vswitch_duration\").query(\"match_phrase\", log_path=filename)\n",
+    "for hits in s.scan():\n",
+    "    print(\"Vswitch starting duration: \", hits.vswitch_duration,\"s\")"
+   ]
+  }
+ ],
+ "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}