312 строки
10 KiB
Plaintext
312 строки
10 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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"# SLA Investigation\n",
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"1. Run all cells! (click on Menu > Cell > 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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"triggerTime = \"2019-08-08T23:50:00.0000000Z\"\n",
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"scaleUnit = \"tfs-wcus-0\"\n",
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"service = \"tfs\"\n",
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"lookback = \"1h\"\n",
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"region = \"\""
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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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"\n",
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"# install packages, setup workspace root\n",
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"!pip install --upgrade pip azure-kusto-notebooks\n",
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"import os\n",
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"import sys\n",
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"import datetime\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"pd.options.display.html.table_schema = True\n",
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"import concurrent.futures\n",
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"from azure.kusto.notebooks import utils as akn\n",
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"\n",
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"# cwd should be workspace root\n",
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"if os.path.basename(os.getcwd()) == 'devops-pipelines':\n",
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" os.chdir(os.pardir)"
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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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"# find orchestrations that violate SLA\n",
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"params = {\n",
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" 'TriggerTime': akn.to_kusto_datetime(triggerTime),\n",
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" 'Lookback': akn.to_kusto_timespan(lookback),\n",
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" 'Service': '\"' + service + '\"', \n",
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" 'Region': '\"' + region + '\"',\n",
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" 'ScaleUnit': '\"' + scaleUnit + '\"'\n",
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"}\n",
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"query = os.path.join('devops-pipelines', 'queries', 'sla', 'SLADurationAnalysis.csl')\n",
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"violations = akn.execute_file(client, database='VSO', path=query, params=params)\n",
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"# violations"
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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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"# collect problematic orchestration ids\n",
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"result = violations.primary_results[0]\n",
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"oid_column_index = next((c.ordinal for c in result.columns if c.column_name == 'OrchestrationId'), None)\n",
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"su_column_index = next((c.ordinal for c in result.columns if c.column_name == 'ScaleUnit'), None)\n",
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"\n",
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"# group\n",
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"by_su = {}\n",
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"for r in result.rows:\n",
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" su = r[su_column_index]\n",
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" oid = r[oid_column_index]\n",
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" l = by_su.get(su, [])\n",
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" by_su[su] = l\n",
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" l.append(oid)\n",
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"\n",
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"max_scale_units = []\n",
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"max_problems = 0\n",
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"for k,v in by_su.items():\n",
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" c = len(v)\n",
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" if c > max_problems:\n",
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" max_problems = c\n",
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" max_scale_units = [k]\n",
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" elif c == max_problems:\n",
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" max_scale_units.append(k)\n",
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"max_scale_units.sort()\n",
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"\n",
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"# for su, oids in by_su.items():\n",
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"# print(su)\n",
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"# for oid in oids:\n",
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"# print(' ', oid)"
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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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"# collect visualization data sets\n",
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"query = os.path.join('devops-pipelines', 'queries', 'sla', 'SLAVisualization.csl')\n",
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"with concurrent.futures.ThreadPoolExecutor() as executor:\n",
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" hfs = [executor.submit(akn.execute_file, client, 'VSO', query, \n",
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" {\n",
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" 'ScaleUnit': '\"' + r[su_column_index] + '\"', \n",
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" 'OrchestrationId': '\"' + r[oid_column_index] + '\"'\n",
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" }) for r in result.rows]\n",
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" histories = [h.result() for h in concurrent.futures.as_completed(hfs)]\n",
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"\n",
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"# convert to data frames\n",
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"primary_results = [h.primary_results[0] for h in histories]\n",
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"dataframes = None\n",
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"with concurrent.futures.ThreadPoolExecutor() as executor:\n",
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" dataframe_futures = [executor.submit(akn.to_dataframe, r) for r in primary_results]\n",
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" dataframes = [dff.result() for dff in concurrent.futures.as_completed(dataframe_futures)]\n",
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"histories = None\n",
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"\n",
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"# try to filter out false positives? at least a certain number of phases must have been recorded.\n",
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"required_phases = ('RunAgentJob.SendJob', 'RunAgentJob.JobCompleted')\n",
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"filtered_dataframes = [df for df in dataframes if all([p in df['PhaseName'].values for p in required_phases])]\n",
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"number_of_false_positives = len(dataframes) - len(filtered_dataframes)\n",
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"dataframes = filtered_dataframes\n",
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"plans_out_of_sla = [df['PlanId'].iat[0] for df in dataframes]\n",
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"number_of_violations = len(dataframes)"
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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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"worst_phaseName = ''\n",
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"worst_count = 0\n",
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"worst_team = ''\n",
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"\n",
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"if dataframes:\n",
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" # what was the worst phase?\n",
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" combined = pd.concat(dataframes, ignore_index=True)\n",
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" df = combined.loc[combined['Level'] == 2].groupby(['PhaseName']).size().to_frame('Count').nlargest(1, 'Count')\n",
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" if len(df.index) > 0:\n",
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" worst_phaseName = df.index[0]\n",
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" worst_count = df.iat[0, 0]\n",
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" worst_team = worst_phaseName.split('.')[0]\n",
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" \n",
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" # what was the worst plan?\n",
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" violations_df = akn.to_dataframe(violations.primary_results[0])\n",
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" df = violations_df.groupby(['PlanId']).size().to_frame('Count').nlargest(1, 'Count')\n",
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" plan_with_most_violations = df.index[0]\n",
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" plan_with_most_violations_count = df.iat[0, 0]"
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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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"if number_of_false_positives:\n",
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" print(number_of_false_positives, 'plans are likely missing kusto data and were ignored.')\n",
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"if number_of_violations <= 0:\n",
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" print('no problems detected')\n",
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"else:\n",
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" for su in max_scale_units:\n",
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" print(max_problems, 'of the problems were in', su)\n",
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" \n",
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" print(number_of_violations, \n",
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" 'plans' if number_of_violations > 1 else 'plan', \n",
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" 'had no apparent data problems and', \n",
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" 'are' if number_of_violations > 1 else 'is', \n",
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" 'out of SLA.')\n",
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" \n",
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" if plan_with_most_violations in plans_out_of_sla:\n",
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" print(plan_with_most_violations, 'had the most violations with', plan_with_most_violations_count)\n",
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" \n",
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" if worst_phaseName:\n",
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" print('\"' + worst_phaseName + '\"', 'was the slowest phase in', worst_count, \n",
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" 'of the', number_of_violations, 'SLA violations.')\n",
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" \n",
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" print ('\\nConclusion:')\n",
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" if number_of_violations > 5: \n",
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" print('This is likely a real problem. Open icm against scale units:', max_scale_units)\n",
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" print('Initially route it to: ', worst_team)\n",
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" else: \n",
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" print('Too much uncertainty -- do not open any ICMs.')\n",
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" \n",
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" if number_of_false_positives and float(number_of_false_positives) / float(max_problems) > .5:\n",
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" for su in max_scale_units:\n",
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" print(su, 'might be unhealthy based on the number of plans missing kusto data.')\n",
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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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"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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"%matplotlib inline\n",
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"plt.rcdefaults()\n",
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"\n",
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"if dataframes:\n",
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" number_of_graphs = min(25, len(dataframes))\n",
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" fig, axes = plt.subplots(nrows=number_of_graphs, ncols=1, figsize=(8, 6 * number_of_graphs), constrained_layout=True)\n",
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" for i in range(number_of_graphs):\n",
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" df = dataframes[i]\n",
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" ax = axes[i] if number_of_graphs > 1 else axes\n",
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" ax.axhline(0, color='k')\n",
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"\n",
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" x = df['PhaseName']\n",
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" xpos = np.arange(len(x))\n",
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" y = df['PercentDifference']\n",
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" plan_id = df['PlanId'].iloc[0]\n",
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" violation_row = violations_df.loc[violations_df['PlanId'] == plan_id]\n",
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" title = '\\n'.join([\n",
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" 'plan id:' + plan_id,\n",
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" 'scale unit:' + str(violation_row['ScaleUnit'].iloc[0]),\n",
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" 'definition:' + str(df['DefinitionName'].iloc[0]),\n",
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" 'plan duration: ' + str(violation_row['PlanDuration'].iloc[0]),\n",
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" 'sla duration: ' + str(violation_row['TotalSLADuration'].iloc[0]),\n",
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" ])\n",
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" ax.title.set_text(title)\n",
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"\n",
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" ax.bar(x=xpos, height=y)\n",
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" ax.set_xticks(xpos)\n",
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" ax.set_xticklabels(x, rotation=45, ha=\"right\")\n",
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"\n",
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"# output_filename = 'analysis.svg'\n",
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"# plt.savefig(output_filename, format='svg')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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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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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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},
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"nteract": {
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"version": "nteract-on-jupyter@2.1.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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