468 строки
19 KiB
Plaintext
468 строки
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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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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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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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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"outputs": [],
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"source": [
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"su = \"pipelines-ghub-eus2-2\"\n",
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"start = \"2019-10-15T20:21:54.0330000Z\"\n",
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"end = \"2019-10-15T20:52:21.5370169Z\"\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 = \"pipelines\"\n",
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"hub = \"Actions\"\n",
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"locationName = \"pipelinesghubeus22\"\n",
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"mdmAccount = \"VSO-Pipelines\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"inputHidden": false,
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"outputHidden": false
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"!pip install --upgrade nimport azure-kusto-notebooks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"inputHidden": false,
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"outputHidden": false
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},
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"outputs": [],
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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 = False\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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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"inputHidden": false,
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"outputHidden": false
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},
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"outputs": [],
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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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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"inputHidden": false,
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"outputHidden": false
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},
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"outputs": [],
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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')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"inputHidden": false,
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"outputHidden": false
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},
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"outputs": [],
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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')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"inputHidden": false,
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"outputHidden": false
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},
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"outputs": [],
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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_mdm = os.path.join(queryPath, \"MDMAccount.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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"q_gen2 = os.path.join(impactPath, \"Gen2GCSpikes.csl\")\n",
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"q_dep = os.path.join(impactPath, \"Dependencies.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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" \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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" p8 = executor.submit(akn.execute_file, client, 'VSO', q_gen2, params)\n",
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" p9 = executor.submit(akn.execute_file, client, 'VSO', q_mdm, params)\n",
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" p10 = executor.submit(akn.execute_file, client, 'VSO', q_dep, 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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"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)\n",
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"\n",
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"q_gen2Df = akn.to_dataframe_from_future(p8)\n",
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"\n",
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"q_mdmDf = akn.to_dataframe_from_future(p9)\n",
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"params[\"mdmAccount\"] = q_mdmDf[\"monitoringAccount\"][0]\n",
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"\n",
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"q_depDf = akn.to_dataframe_from_future(p10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"inputHidden": false,
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"outputHidden": false
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},
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"outputs": [],
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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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" 'location': locationName,\n",
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" 'account': params[\"mdmAccount\"]\n",
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"}\n",
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"\n",
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"display(params)\n",
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"\n",
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"startTime = akn.to_datetime(start)\n",
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"# jarvis\n",
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"jarvisLink = \"\"\"https://jarvis-west.dc.ad.msft.net/dashboard/VSO-ServiceInsights/DevOpsReports/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='LocationName']\",\"key\":\"value\",\"replacement\":\"%(location)s\"},\"\"\" \\\n",
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" \"\"\"{\"query\":\"//dataSources\",\"key\":\"namespace\",\"replacement\":\"%(su)s\"},\"\"\" \\\n",
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" \"\"\"{\"query\":\"//dataSources\",\"key\":\"account\",\"replacement\":\"%(account)s\"},\"\"\" \\\n",
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" \"\"\"{\"query\":\"//*[id='ApplicationEndpoint']\",\"key\":\"regex\",\"replacement\":\"*%(location)s*\"},\"\"\" \\\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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"# 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 config changes recorded during this period.\")\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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" vip_prefix = \"\"\n",
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" f_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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" delta = startTime.replace(tzinfo=None) - row.TIMESTAMP.replace(tzinfo=None)\n",
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" when = \"before\"\n",
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" if delta.total_seconds() < 0:\n",
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" when = \"after\"\n",
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" delta = row.TIMESTAMP.replace(tzinfo=None) - startTime.replace(tzinfo=None)\n",
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" hoursHappened = delta.total_seconds()//3600\n",
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" considerTime = hoursHappened <= 1\n",
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" def getText(row):\n",
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" return \"\"\"%s %s %s (%s days %s hours %s minutes %s the start time) \\n\\n\"\"\" % (row.TIMESTAMP, row.title, row.buildNumber, delta.days, delta.seconds//3600, delta.seconds//60, when)\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...\\n\\n\"\n",
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" text += getText(row)\n",
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" if(row.title.lower().find('mitigation') != -1):\n",
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" if considerTime and not mit_prefix:\n",
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" mit_prefix += \"Looks like, there are some mitigations by health agent...\\n\\n\"\n",
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" text += getText(row)\n",
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" if(row.title.lower().find('vip') != -1):\n",
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" if considerTime and not vip_prefix:\n",
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" vip_prefix += \"Looks like, there is VIP swap...\\n\\n\"\n",
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" text += getText(row) \n",
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" if(row.title.lower().find('feature flag') != -1):\n",
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" if considerTime and not f_prefix:\n",
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" f_prefix += \"Looks like, some feature flags are enabled...\\n\\n\"\n",
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" text += getText(row)\n",
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" if text:\n",
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" print(up_prefix + mit_prefix + f_prefix + vip_prefix + text)\n",
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" else:\n",
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" print(\"...no relevant changes during this period.\")\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(\"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...\") \n",
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" \n",
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"print()\n",
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"print('Dependencies insights =============================')\n",
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"r = q_depDf.describe()\n",
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"redis = r[\"avg_RedisExecutionTimeInMs\"]\n",
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"s2s = r[\"avg_VssClientExecutionTimeInMs\"]\n",
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"sql = r[\"avg_SqlExecutionTimeInMs\"]\n",
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"queue = r[\"avg_QueueTimeInMs\"]\n",
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"maxs = [redis[\"max\"], s2s[\"max\"], sql[\"max\"], queue[\"max\"]]\n",
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"means = [redis[\"mean\"], s2s[\"mean\"], sql[\"mean\"], queue[\"mean\"]]\n",
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"up = redis[\"75%\"]\n",
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"lo = redis[\"25%\"]\n",
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"IQR = up - lo\n",
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"redisAnom = q_depDf[q_depDf[\"avg_RedisExecutionTimeInMs\"] > up + 3.5*IQR][[\"PreciseTimeStamp\", \"avg_RedisExecutionTimeInMs\"]].sort_values(by='PreciseTimeStamp') \n",
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"up = s2s[\"75%\"]\n",
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"lo = s2s[\"25%\"]\n",
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"IQR = up - lo\n",
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"s2sAnom = q_depDf[q_depDf[\"avg_VssClientExecutionTimeInMs\"] > up + 3.5*IQR][[\"PreciseTimeStamp\", \"avg_VssClientExecutionTimeInMs\"]].sort_values(by='PreciseTimeStamp')\n",
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"up = sql[\"75%\"]\n",
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"lo = sql[\"25%\"]\n",
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"IQR = up - lo\n",
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"sqlAnom = q_depDf[q_depDf[\"avg_SqlExecutionTimeInMs\"] > up + 3.5*IQR][[\"PreciseTimeStamp\", \"avg_SqlExecutionTimeInMs\"]].sort_values(by='PreciseTimeStamp')\n",
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"up = queue[\"75%\"]\n",
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"lo = queue[\"25%\"]\n",
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"IQR = up - lo\n",
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"queueAnom = q_depDf[q_depDf[\"avg_QueueTimeInMs\"] > up + 3.5*IQR][[\"PreciseTimeStamp\", \"avg_QueueTimeInMs\"]].sort_values(by='PreciseTimeStamp')\n",
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"reasons = [\"Redis\", \"S2S\", \"Sql\", \"RequestsQueued\"]\n",
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"anomdata = [redisAnom, s2sAnom, sqlAnom, queueAnom]\n",
|
|
"anom = [len(redisAnom), len(s2sAnom), len(sqlAnom), len(queueAnom)]\n",
|
|
"top2Anom = np.argsort(anom)[::-1][:2]\n",
|
|
"whenMax = [\n",
|
|
" redisAnom[redisAnom[\"avg_RedisExecutionTimeInMs\"]==maxs[0]],\n",
|
|
" s2sAnom[s2sAnom[\"avg_VssClientExecutionTimeInMs\"]==maxs[1]],\n",
|
|
" sqlAnom[sqlAnom[\"avg_SqlExecutionTimeInMs\"]==maxs[2]],\n",
|
|
" queueAnom[queueAnom[\"avg_QueueTimeInMs\"]==maxs[3]]\n",
|
|
"]\n",
|
|
"if len(top2Anom) > 0:\n",
|
|
" print(\"Found top anomolies...\")\n",
|
|
" for i in top2Anom:\n",
|
|
" # Wow, such a pain to convert numpy time to python time...\n",
|
|
" dt64 = whenMax[i][\"PreciseTimeStamp\"].values[0]\n",
|
|
" ts = (dt64 - np.datetime64('1970-01-01T00:00:00Z')) / np.timedelta64(1, 's')\n",
|
|
" whenMaxTime = datetime.utcfromtimestamp(ts)\n",
|
|
" delta = startTime.replace(tzinfo=None) - whenMaxTime.replace(tzinfo=None)\n",
|
|
" when = \"before\"\n",
|
|
" if delta.total_seconds() < 0:\n",
|
|
" when = \"after\"\n",
|
|
" delta = whenMaxTime.replace(tzinfo=None) - startTime.replace(tzinfo=None)\n",
|
|
" whenAnom = \"\"\"%s days %s hours %s minutes %s the start time - %s\"\"\" % (delta.days, delta.seconds//3600, delta.seconds//60, when, startTime)\n",
|
|
" print(\" %s (#buckets %s) (max %s) (mean %s) (Max is %s)\" % (reasons[i], anom[i], maxs[i], means[i], whenAnom)) \n",
|
|
" display(anomdata[i])\n",
|
|
"else:\n",
|
|
" print(\"Can't say there is...?\")\n",
|
|
"\n",
|
|
"# additional insights\n",
|
|
"print()\n",
|
|
"print('Additional insights =============================')\n",
|
|
"w3wpGc = q_gen2Df[ q_gen2Df[\"ProcessName\"] == \"w3wp\" ].sort_values(by=['Number'], ascending=False)\n",
|
|
"jaGc = q_gen2Df[ q_gen2Df[\"ProcessName\"] == \"TfsJobAgent\" ].sort_values(by=['Number'], ascending=False)\n",
|
|
"now3wpGc = True\n",
|
|
"if len(w3wpGc) > 0:\n",
|
|
" x = w3wpGc[:2]\n",
|
|
" if len(x) == 1:\n",
|
|
" xCount = x[\"Count\"].values\n",
|
|
" if xCount[0] > 25:\n",
|
|
" print(\"INSIGHT: There's a new spike in w3wp gc...\")\n",
|
|
" now3wpGc = False\n",
|
|
" if len(x) == 2:\n",
|
|
" xCount = x[\"Count\"].values\n",
|
|
" if xCount[1] > xCount[0]:\n",
|
|
" print(\"INSIGHT: There's a spike in w3wp gc...\")\n",
|
|
" now3wpGc = False\n",
|
|
"if now3wpGc:\n",
|
|
" print(\"INSIGHT: No gc spikes found from w3wp...\")\n",
|
|
" \n",
|
|
"nojaGc = True \n",
|
|
"if len(jaGc) > 0:\n",
|
|
" x = jaGc[:2]\n",
|
|
" if len(x) == 1:\n",
|
|
" xCount = x[\"Count\"].values\n",
|
|
" if xCount[0] > 25:\n",
|
|
" print(\"INSIGHT: There's a new spike in ja gc...\")\n",
|
|
" nojaGc = False\n",
|
|
" if len(x) == 2:\n",
|
|
" xCount = x[\"Count\"].values\n",
|
|
" if xCount[1] > xCount[0] and xCount[1] > 25:\n",
|
|
" print(\"INSIGHT: There's a spike in ja gc...\") \n",
|
|
" nojaGc = False\n",
|
|
"if nojaGc:\n",
|
|
" print(\"INSIGHT: No ja gc spikes found from w3wp...\") "
|
|
]
|
|
}
|
|
],
|
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