363 строки
14 KiB
Plaintext
363 строки
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Impact Investigation\n",
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"1. Run all cells.\n",
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"1. View report at the bottom."
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"source": [
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"su = \"tfs-cus-1\"\n",
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"start = \"2019-07-20T16:00:00.0000000Z\"\n",
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"end = \"2019-07-20T16:33:36.0000000Z\"\n",
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"url = \"https://notebooksv2.azure.com/yaananth/projects/06OasuNRs6rK/delays.ipynb\"\n",
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"baseUrl = \"https://notebooksv2.azure.com/yaananth/projects/06OasuNRs6rK\"\n",
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"service = \"tfs\"\n",
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"hub = \"Build\"\n",
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"locationName = \"tfsprodcus1\""
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"inputHidden": false,
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"outputHidden": false,
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"tags": [
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"parameters"
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]
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}
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},
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{
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"cell_type": "code",
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"source": [
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"%%capture\n",
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"!pip install nimport azure-kusto-notebooks"
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"inputHidden": false,
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"outputHidden": false
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Import the things we use\n",
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"\n",
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"# Note you can also use kql https://docs.microsoft.com/en-us/azure/data-explorer/kqlmagic\n",
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"# %kql is single line magic\n",
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"# %%kql is cell magic\n",
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"\n",
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"# https://nbviewer.jupyter.org/github/ipython/ipython/blob/4.0.x/examples/IPython%20Kernel/Rich%20Output.ipynb#HTML\n",
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"# https://ipython.readthedocs.io/en/stable/inte/magics.html\n",
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"from IPython.display import display, HTML, Markdown, Javascript, clear_output\n",
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"\n",
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"# http://pandas-docs.github.io/pandas-docs-travis/user_guide/reshaping.html\n",
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"import pandas as pd\n",
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"pd.options.display.html.table_schema = True\n",
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"from pandas import Series, DataFrame\n",
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"from datetime import datetime, timedelta, timezone\n",
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"from urllib.parse import urlencode, quote_plus\n",
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"from requests.utils import requote_uri\n",
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"import time\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"from nimport.utils import tokenize, open_nb\n",
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"import json\n",
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"import os\n",
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"import calendar as cal\n",
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"import concurrent.futures\n",
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"from azure.kusto.notebooks import utils as akn"
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"inputHidden": false,
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"outputHidden": false
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}
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},
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{
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"cell_type": "code",
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"source": [
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"params = {\n",
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" \"su\": su,\n",
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" \"start\": start,\n",
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" \"end\": end,\n",
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" \"url\": url,\n",
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" \"baseUrl\": baseUrl,\n",
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" \"service\": service\n",
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"}\n",
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"root = 'devops-pipelines' if os.path.basename(os.getcwd()) != 'devops-pipelines' else ''\n",
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"queryPath = os.path.join(root, 'queries')"
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"inputHidden": false,
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"outputHidden": false
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# authenticate kusto client\n",
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"# you will need to copy the token into a browser window for AAD auth. \n",
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"client = akn.get_client('https://vso.kusto.windows.net', 'VSO')"
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"inputHidden": false,
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"outputHidden": false
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# authenticate kusto client\n",
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"# you will need to copy the token into a browser window for AAD auth. \n",
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"icm_client = akn.get_client('https://icmcluster.kusto.windows.net', 'IcMDataWarehouse')"
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"outputHidden": false,
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"inputHidden": false
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}
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},
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{
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"cell_type": "code",
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"source": [
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"q_loc = os.path.join(queryPath, \"LocationName.csl\")\n",
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"q_whatChanged = os.path.join(queryPath, \"WhatChanged.csl\")\n",
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"q_vipSwap = os.path.join(queryPath, \"VIPSwap.csl\")\n",
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"\n",
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"impactPath = os.path.join(queryPath, \"impact\")\n",
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"q_commands = os.path.join(impactPath, \"CommandsReason.csl\")\n",
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"q_commandsAT = os.path.join(impactPath, \"CommandsAT.csl\")\n",
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"q_commandsDb = os.path.join(impactPath, \"CommandsDb.csl\")\n",
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"with concurrent.futures.ThreadPoolExecutor() as executor:\n",
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" # materialize location name immediately as we need this for other queries\n",
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" p1 = executor.submit(akn.execute_file, client, 'VSO', q_loc, params)\n",
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" locationNameResult = akn.to_dataframe_from_future(p1)\n",
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" locationName = locationNameResult[\"Tenant\"][0]\n",
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" params[\"locationName\"] = locationName\n",
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" p2 = executor.submit(akn.execute_file, client, 'VSO', q_whatChanged, params)\n",
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" p3 = executor.submit(akn.execute_file, client, 'VSO', q_vipSwap, params)\n",
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" \n",
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" p4 = executor.submit(akn.execute_file, client, 'VSO', q_commandsAT, params)\n",
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" p5 = executor.submit(akn.execute_file, client, 'VSO', q_commandsDb, params) \n",
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" p6 = executor.submit(akn.execute_file, client, 'VSO', q_commands, params)\n",
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" \n",
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" p7 = executor.submit(akn.execute_file, icm_client, 'IcmDataWarehouse', \n",
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" os.path.join(queryPath, 'ActiveIncidents.csl'), params)\n",
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"\n",
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"q_whatChanged_df = akn.to_dataframe_from_future(p2)\n",
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"\n",
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"vipSwapResultDf = akn.to_dataframe_from_future(p3)\n",
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"\n",
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"q_commandsAT_df = akn.to_dataframe_from_future(p4)\n",
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"\n",
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"q_commandsDb_df = akn.to_dataframe_from_future(p5)\n",
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"\n",
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"q_commands_df = akn.to_dataframe_from_future(p6)\n",
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"\n",
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"q_activeIncidentsResultDf = akn.to_dataframe_from_future(p7)"
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"inputHidden": false,
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"outputHidden": false
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}
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},
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{
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"cell_type": "code",
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"source": [
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"print('=' * 50)\n",
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"print('Report!')\n",
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"print('=' * 50, '\\n\\n')\n",
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"\n",
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"# jarvis params\n",
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"jarvisParams = {\n",
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" 'su': su, \n",
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" 'start': akn.get_time(start, -10), \n",
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" 'end': akn.get_time(end, 10), \n",
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" 'service': service \n",
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"}\n",
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"\n",
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"# jarvis\n",
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"jarvisLink = \"\"\"https://jarvis-west.dc.ad.msft.net/dashboard/VSO-ServiceInsights/DevOpsReports/TFS DevOpsReports\"\"\" \\\n",
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" \"\"\"?overrides=[{\"query\":\"//*[id='Service']\",\"key\":\"value\",\"replacement\":\"%(service)s\"},\"\"\" \\\n",
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" \"\"\"{\"query\":\"//*[id='RoleInstance']\",\"key\":\"value\",\"replacement\":\"\"},\"\"\" \\\n",
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" \"\"\"{\"query\":\"//*[id='ScaleUnit']\",\"key\":\"value\",\"replacement\":\"%(su)s\"}]\"\"\" \\\n",
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" \"\"\"&globalStartTime=%(start)s&globalEndTime=%(end)s&pinGlobalTimeRange=true\"\"\" % jarvisParams;\n",
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"print('Jarvis dashboard link:\\n', requote_uri(jarvisLink), '\\n')\n",
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"\n",
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"#\n",
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"# vip swap\n",
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"print()\n",
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"print('Vip Swap? =============================')\n",
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"if len(vipSwapResultDf.index) > 0:\n",
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" viptime = vipSwapResultDf[\"TIMESTAMP\"][0]\n",
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" starttime = akn.to_datetime(start)\n",
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" delta = starttime.replace(tzinfo=None) - viptime.replace(tzinfo=None)\n",
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" if delta.total_seconds() > 0:\n",
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" print(\"\"\"VIP SWAP happened: %s days %s hours %s minutes ago (%s) (issue start: %s)\"\"\" % (delta.days, delta.seconds//3600, (delta.seconds//60) % 60, viptime, start))\n",
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" else:\n",
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" print('...no swaps recorded in the given time range')\n",
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"else:\n",
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" print('...no swaps recorded in the given time range')\n",
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" \n",
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"# slow failed reason analysis\n",
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"print()\n",
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"print('Is it slow commands or failed commands? =============================')\n",
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"freq = q_commands_df[\"Frequency\"]\n",
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"coefficientOfVariance = freq.std()/freq.mean()\n",
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"failedCount = q_commands_df[q_commands_df[\"Reason\"] == \"failed\"][\"Frequency\"].values[0]\n",
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"slowCount = q_commands_df[q_commands_df[\"Reason\"] == \"slow\"][\"Frequency\"].values[0]\n",
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"reason = \"failed or slow\"\n",
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"if coefficientOfVariance > 0.5:\n",
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" if failedCount > slowCount:\n",
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" reason = \"failed\"\n",
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" else:\n",
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" reason = \"slow\"\n",
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"else:\n",
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" print(\"Slow and failed commands are too close, both might be contributing...\")\n",
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"if reason:\n",
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" print(\"Probably due to %s commands; Failed - %s, Slow - %s\" % (reason, failedCount, slowCount))\n",
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"\n",
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"# slow failed reason for AT?\n",
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"print()\n",
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"print('Is it %s because of AT? =============================' % (reason))\n",
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"failed = q_commandsAT_df[q_commandsAT_df[\"Reason\"] == \"failed\"]\n",
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"slow = q_commandsAT_df[q_commandsAT_df[\"Reason\"] == \"slow\"]\n",
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"data = q_commandsAT_df\n",
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"if reason == \"failed\":\n",
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" data = failed\n",
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"elif reason == \"slow\":\n",
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" data = slow\n",
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"\n",
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"coefficientOfVariance = data[\"Frequency\"].std()/data[\"Frequency\"].mean()\n",
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" \n",
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"if coefficientOfVariance > 0.5:\n",
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" print(\"Found variance in AT's for %s commands\" % (reason))\n",
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" print(data.head(30))\n",
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"else:\n",
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" print(\"Seems be same across AT's for %s commands\" % (reason))\n",
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" \n",
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"# slow failed reason for Db?\n",
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"print()\n",
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"print('Is it %s because of Db? =============================' % (reason))\n",
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"failed = q_commandsDb_df[q_commandsDb_df[\"Reason\"] == \"failed\"]\n",
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"slow = q_commandsDb_df[q_commandsDb_df[\"Reason\"] == \"slow\"]\n",
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"data = q_commandsDb_df\n",
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"if reason == \"failed\":\n",
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" data = failed\n",
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"elif reason == \"slow\":\n",
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" data = slow\n",
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"\n",
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"coefficientOfVariance = data[\"Frequency\"].std()/data[\"Frequency\"].mean()\n",
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" \n",
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"if coefficientOfVariance > 0.5:\n",
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" print(\"Found variance in Db's for %s commands\" % (reason))\n",
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" print(\"Suffix '%s' to database server name\" % (\".database.windows.net\"))\n",
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" print(\"Prefix '%s' to database name\" % (params[\"service\"] + \"_\" + params[\"locationName\"] + \"_\"))\n",
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" print(data.head(30))\n",
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"else:\n",
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" print(\"Seems be same across Db's for %s commands\" % (reason)) \n",
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" \n",
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"# what changed? analysis\n",
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"print()\n",
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"print('What changed? =============================')\n",
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"if(len(q_whatChanged_df.index) == 0):\n",
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" print(\"No relevant changes found...\")\n",
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"else:\n",
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" up_prefix = \"\";\n",
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" mit_prefix = \"\";\n",
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" text = \"\";\n",
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" for index, row in q_whatChanged_df.iterrows():\n",
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" if(row.title.lower().find('upgrade') != -1):\n",
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" if not up_prefix:\n",
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" up_prefix += \"Looks like, there's upgrade in progress...\\n\";\n",
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" text += \"\"\"%s %s %s \\n\"\"\" % (row.TIMESTAMP, row.title, row.buildNumber);\n",
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" if(row.title.lower().find('mitigation') != -1):\n",
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" if not mit_prefix:\n",
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" mit_prefix += \"Looks like, there are some mitigations by health agent...\\n\";\n",
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" state += states[\"healthagent\"];\n",
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" text += \"\"\"%s %s %s\"\"\" % (row.TIMESTAMP, row.title, row.buildNumber);\n",
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" \n",
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" if text:\n",
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" print(up_prefix + mit_prefix + text)\n",
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" else:\n",
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" print(q_whatChanged_df)\n",
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" \n",
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"# active incidents?\n",
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"print()\n",
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"print('Active incidents? =============================')\n",
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"otherIncidentsCount = 0;\n",
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"for index, row in q_activeIncidentsResultDf.iterrows():\n",
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" if(row.Title.find(\"TFS Customer Impact Monitor\") == -1):\n",
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" otherIncidentsCount+=1;\n",
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" \n",
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"if(otherIncidentsCount > 0):\n",
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" print(\"We found some incidents during the time period, check if they are related...\")\n",
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" # styling\n",
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" def make_clickable(url, text):\n",
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" return '{0}'.format(url)\n",
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"\n",
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" newDf = q_activeIncidentsResultDf.assign(URL=[*map(lambda x: make_clickable(\"\"\"https://icm.ad.msft.net/imp/v3/incidents/details/%s/home\"\"\" % (x), \"ICMLink\"), q_activeIncidentsResultDf.IncidentId)])\n",
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" print(\"ICM link to copy - \" + \"https://icm.ad.msft.net/imp/v3/incidents/details/INCIDENTID/home\")\n",
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" print(newDf[['IncidentId','Severity','Title']])\n",
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"else:\n",
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" print(\"No active incidents that could be related are found...\") "
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],
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"outputs": [],
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"inputHidden": false,
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"outputHidden": false
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}
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}
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],
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"metadata": {
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"kernel_info": {
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"name": "python3"
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},
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"kernelspec": {
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"name": "python3",
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"language": "python",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python",
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"version": "3.7.4",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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},
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"nteract": {
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"version": "0.14.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |