251 строка
7.0 KiB
Plaintext
251 строка
7.0 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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"# The Plan\n",
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"Everything we know about the plan.\n",
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"\n",
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"### Instructions\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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"#planId = \"98db70e2-cee5-4e2d-ae15-dca389fa8f41\"\n",
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"planId = \"f38f1a4b-49d7-4f08-a9b9-c81b2c39aff6\""
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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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"# install packages, setup workspace root\n",
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"!pip install azure-kusto-notebooks plotly\n",
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"import os\n",
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"from azure.kusto.notebooks import utils as akn\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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"\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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"# collect basic plan info\n",
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"plan_info = akn.Query(\n",
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" client, 'VSO', \n",
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" path=os.path.join('devops-pipelines', 'queries', 'run', 'PlanInfo.csl'), \n",
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" params={'OrchestrationId': akn.quote(planId)})\n",
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"\n",
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"# collect full plan history\n",
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"what_happened = akn.Query(client, 'VSO',\n",
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" path=os.path.join('devops-pipelines', 'queries', 'run', 'WhatHappened.csl'),\n",
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" params={'OrchestrationId': akn.quote(planId)})\n",
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"\n",
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"# fetch data in parallel\n",
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"akn.run((plan_info, what_happened))"
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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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"# draw basic info\n",
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"\n",
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"# compute relative time stamps\n",
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"history = what_happened.dataframe\n",
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"t0 = history['PreciseTimeStamp'].iloc[0]\n",
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"history['Time'] = history.apply(lambda row: row['PreciseTimeStamp'] - t0, axis=1)\n",
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"history.OrchestrationId = history.apply(lambda row: row.OrchestrationId[37:], axis=1)\n",
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"\n",
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"# record critical times\n",
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"def find_time(message):\n",
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" r = history[history.Message.str.startswith(message)]\n",
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" if len(r.index) > 0:\n",
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" return r['PreciseTimeStamp'].iloc[0]\n",
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"\n",
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"create_time = find_time('Created plan')\n",
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"start_time = find_time('Started plan')\n",
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"end_time = find_time('Completed orchestration with result')\n",
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"total_duration = end_time - start_time if end_time and start_time else None\n",
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"\n",
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"import importlib\n",
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"importlib.reload(akn)\n",
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"# info will only exist if the plan has started at least one job :(\n",
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"d = akn.pandas_row_to_dictionary(plan_info.dataframe)\n",
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"d['create time'] = create_time\n",
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"d['start time'] = start_time\n",
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"d['end time'] = end_time\n",
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"d['total duration'] = total_duration\n",
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"r = akn.Report()\n",
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"r.write(akn.to_md_table(d))\n",
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"\n",
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"from IPython.display import Markdown\n",
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"Markdown(r.content)"
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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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"# SLA analysis\n",
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"su = akn.quote(d.get('ScaleUnit', ''))\n",
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"oids = [akn.quote(joid) for joid in d.get('JobOrchestrationIds', [])]\n",
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"slas = [akn.Query(client, 'VSO', \n",
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" os.path.join('devops-pipelines', 'queries', 'sla', 'SLAVisualization.csl'),\n",
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" params=dict(ScaleUnit=su, OrchestrationId=oid)) \n",
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" for oid in oids]\n",
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"akn.run(slas)"
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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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"# draw all slas\n",
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"from _plotly_future_ import v4_subplots\n",
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"from plotly.subplots import make_subplots\n",
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"import plotly.graph_objects as go\n",
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"import math\n",
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"if not slas:\n",
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" print(\"There are no jobs associated with this plan.\")\n",
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"else:\n",
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" number_of_graphs = min(25, len(slas))\n",
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" names = [n[37:] for n in d.get('JobOrchestrationIds',[])]\n",
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" fig = make_subplots(cols=2, rows=int(math.ceil(number_of_graphs / 2)), \n",
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" subplot_titles=names,\n",
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" shared_xaxes=True, \n",
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" vertical_spacing=0.1)\n",
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"\n",
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" for i in range(len(slas)):\n",
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" df = slas[i].dataframe\n",
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" row = int(i / 2) + 1\n",
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" col = int(i % 2) + 1\n",
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" name = names[i]\n",
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" \n",
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" df = slas[0].dataframe\n",
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" fig.add_trace(go.Bar(x=df.PhaseName, y=df.PercentDifference, name=name), \n",
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" row=row, col=col)\n",
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" fig.update_xaxes(showgrid=False, tickangle=-60, automargin=True)\n",
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" fig.update_xaxes(showgrid=True, zeroline=True, automargin=True)\n",
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" fig.update_layout(height=150 * number_of_graphs, \n",
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" width=1000, showlegend=False,\n",
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" title_text=\"Analysis!\")\n",
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"\n",
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" fig.show()"
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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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"# draw full history\n",
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"columns_to_ignore = ('source_', 'PreciseTimeStamp')\n",
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"columns = ['Time'] + [c for c in history.columns if c not in columns_to_ignore and c != 'Time']\n",
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"\n",
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"from IPython.display import HTML\n",
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"HTML(history[columns].to_html(index=False))"
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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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}
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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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"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": "3.7.4"
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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": 2
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}
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