{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import json\n", "import plotly.express as xp\n", "import seaborn as sns\n", "sns.set_style(\"white\")\n", "import matplotlib.pyplot as plt\n", "from plotly.colors import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Initializations and Functions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Internal functions \n", "\n", "#function to calculate the number of distinct groups (tables) used in a query given the value of srctree\n", "def get_len(data):\n", " l = 1\n", " ar = []\n", " #split the value of srctree by comma to retrieve the different data sources (server__table__column)\n", " lst = data.split(',')\n", " for s in lst:\n", " #for each data source, retrieve the name of the table\n", " if len(s.split('__')) >=3:\n", " ar.append(s.split('__')[2])\n", " if len(set(ar)) >=0:\n", " #get the number of distinct table names\n", " l = len(set(ar))\n", " return l\n", "\n", "\n", "#function to return the distinct names of servers per row\n", "def get_servers(data):\n", " servers = []\n", " #split the value of srctree by comma to retrieve the different data sources (server__table__column)\n", " lst = data.split(',')\n", " for s in lst:\n", " #for each data source, retrieve the name of the server\n", " if len(s.split('__')) >=1:\n", " servers.append(s.split('__')[0])\n", " return set(servers)\n", "\n", "#function to return the number of servers per row\n", "def get_server_number(data):\n", " servers = []\n", " #split the value of srctree by comma to retrieve the different data sources (server__table__column)\n", " lst = data.split(',')\n", " for s in lst:\n", " #for each data source, retrieve the name of the server\n", " if len(s.split('__')) >=1:\n", " servers.append(s.split('__')[0])\n", " return len(set(servers))\n", "\n", "#function to calculate the number of columns from srctree \n", "def get_col_nb(data):\n", " l = 1\n", " ar = []\n", " lst = data.split(',')\n", " if len(lst) >= 0:\n", " l = len(lst)\n", " return str(l)\n", "\n", "#function to calculate the number of columns from db_mask\n", "def get_col_nb_novirtual(data):\n", " if pd.isnull(data):\n", " return 'All'\n", " try: \n", " float(data)\n", " return '1'\n", " except ValueError: \n", " s = data.split(',')\n", " l = len(s) \n", " return str(l)\n", "\n", "#function to plot histogram with percentage\n", "def plot_his_percentage(data, group_col, title, xtitle, ytitle):\n", " data_grouped = data[[group_col]].groupby([group_col]).size().reset_index(name='count')\n", " fig = xp.histogram(data_grouped.sort_values(by=['count'], ascending=False),histnorm = 'percent',x = group_col, y = 'count', title=title, width=600, height=400 ,color_discrete_sequence=['#75BDA7']).update_layout(xaxis_title = xtitle, yaxis_title=ytitle) \n", "\n", " fig.update_layout(yaxis={\"ticksuffix\":\"%\"})\n", " fig.show()\n", " \n", "\n", "# function to plot 100% stacked bar chart representing the percentage of a certan category\n", "def plot_stacked_chart(data, col, title):\n", " data_grouped=data[['Setup',col]].groupby(['Setup',col]).size().reset_index(name='count')\n", " data_grouped['Total'] = data_grouped['count'].sum()\n", " data_grouped['Percentage']=data_grouped['count']/data_grouped['Total']\n", " data_grouped['PercentageLabel']=data_grouped['Percentage'].transform(lambda x: '{:,.0%}'.format(x)) \n", " data_grouped['Label']= data_grouped[['PercentageLabel',col]].apply(lambda x : '{}: {}'.format(x[1],x[0]), axis=1)\n", " fig=xp.bar(data_grouped, x='Setup', y='Percentage', title=title, color=col, barmode ='stack', width=340, height=500, text = \"Label\")\n", " fig.update_layout(showlegend=False)\n", " fig.update_yaxes(showticklabels=False, visible=False)\n", " fig.update_xaxes(showticklabels=False, visible=False)\n", " fig.show()\n", " \n", "\n", "#function to plot scattered bar chart showing the relation between 2 numerical columns\n", "def plot_scattered_chart(data, xcol, ycol, xcol_label, ycol_label, title):\n", " fig = xp.scatter(data, y=ycol, x=xcol, width=600, height=400, title=title,render_mode='webgl',\n", "\n", " labels={\n", " xcol: xcol_label,\n", " ycol: ycol_label\n", " }\n", " )\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Processing" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Programs\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3049: DtypeWarning:\n", "\n", "Columns (19,20) have mixed types.Specify dtype option on import or set low_memory=False.\n", "\n" ] } ], "source": [ "#Datasets are loaded from the csv consolidated datasets that are generated in the preparation phase\n", "#load KATRIN update requests\n", "katrin_update_data = pd.read_csv('./Data/KATRIN_Update_WithMessage.csv')\n", "#load KATRIN getdata requests\n", "katrin_read_data = pd.read_csv('./Data/KATRIN_get_no_rt.csv')\n", "#load KATRIN donwload requests\n", "katrin_download_data = pd.read_csv('./Data/Katrin_download.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Merge Datasets" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#define common columns\n", "common_columns = ['Timestamp', 'Setup', 'db_server', 'db_mask', 'Source', 'Request', 'PID', 'ClientID', 'db_name', 'db_group', 'srctree', 'window', 'resample', 'Latency', 'width', 'height', 'SessionID']\n", "#initialize width and height data for the download and Get datasets as they dont exist\n", "katrin_download_data['width'] = None\n", "katrin_read_data['width'] = None\n", "katrin_download_data['height'] = None\n", "katrin_read_data['height'] = None\n", "katrin_read_data['db_server'] = katrin_read_data['Server']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Merge the datasets\n", "katrin_update_data_c = katrin_update_data[common_columns]\n", "katrin_read_data_c = katrin_read_data[common_columns]\n", "katrin_download_data_c = katrin_download_data[common_columns]\n", "data_c = katrin_update_data_c\n", "data_c = data_c.append(katrin_read_data_c, ignore_index=True)\n", "data_c = data_c.append(katrin_download_data_c, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#remove duplicate requests - request with same request Id\n", "from datetime import datetime\n", "data_c['Timestamp'] = pd.to_datetime(data_c['Timestamp'])\n", "data_grouped_request = data_c.groupby([ 'Setup', 'db_server', 'db_mask', 'Source', 'Request', 'PID', 'ClientID', 'db_name', 'db_group', 'srctree', 'window', 'resample','width', 'height', 'SessionID'], dropna=False).agg({'Timestamp' : ['count',np.max], 'Latency':[np.max]}).reset_index()\n", "data_grouped_request.columns = [ 'Setup', 'db_server', 'db_mask', 'Source', 'Request', 'PID', 'ClientID', 'db_name', 'db_group', 'srctree', 'window', 'resample','width', 'height', 'SessionID', 'RequestCount', 'Timestamp', 'Latency']\n", "data_c = data_grouped_request" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate Data Recency, Duration (Time Span) and Records Limit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Case when window = '0'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_c.loc[data_c[\"window\"] == '0',\"period\"] = 'All'\n", "data_c.loc[data_c[\"window\"] == '0',\"data_age\"] = 'Begining'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Case when window starts with - : Get data from the begining of the experiment" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data_c.loc[data_c['window'].str.contains('^-', regex=True, na=False),\"period\"] = 'All'\n", "data_c.loc[~data_c['window'].str.contains('^-', regex=True, na=False),\"data_age\"] = 'Begining'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Initialize start and end timespans\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#split window by '-' to retrieve the start and end timespans\n", "data_c[['window_start','window_end']] = data_c['window'].str.split('-',expand=True)\n", "#remove data with empty window\n", "data_c = data_c.loc[~data_c['window'].isnull()]\n", "#clean start and end timespans\n", "data_c['window_start_clean'] = data_c['window_start'].str.split('.',expand=True)[0]\n", "data_c['window_end_clean'] = data_c['window_end'].str.split('.',expand=True)[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extract Query Limit \n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "data_c[['window_end_clean', 'limit']] = data_c['window_end_clean'].str.split(',',expand=True)\n", "data_c.loc[data_c['limit'] == 'undefined:undefined', 'limit'] = None\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.000255151508966024\n", "0.0\n", "1.9160602565803573\n" ] } ], "source": [ "# % of queries with data limit\n", "print(len(data_c[~data_c['limit'].isnull()])/len(data_c)*100)\n", "# % of queries that return the last value\n", "print(len(data_c[data_c['window'] == '-1'])/len(data_c)*100)\n", "# % of queries that return all the records\n", "print(len(data_c[data_c['window'] == '0'])/len(data_c)*100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clean start and end timestamps\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#Get timespan in second\n", "data_c['timestamp_sec'] = data_c.apply(lambda data: datetime.timestamp(data['Timestamp']) , axis = 1)\n", "#clean window start and window end values\n", "data_c.loc[data_c['window_start_clean'] != '','window_start_clean'] = data_c.loc[data_c['window_start_clean'] != '','window_start_clean'].astype('int')\n", "data_c['window_start_clean'] =data_c['window_start_clean'].replace(r'^\\s*$', np.nan, regex=True)\n", "data_c['window_end_clean'] =data_c['window_end_clean'].replace(r'^\\s*$', np.nan, regex=True)\n", "data_c.loc[(~data_c['window_end_clean'].isnull()) & (data_c[\"window_end_clean\"].astype('float') > data_c[\"timestamp_sec\"]),'window_end_clean'] = data_c.loc[(~data_c['window_end_clean'].isnull()) & (data_c[\"window_end_clean\"].astype('float') > data_c[\"timestamp_sec\"]),'timestamp_sec']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " #### Calculate duration in case window = number" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "data_c.loc[(~data_c['window_start_clean'].isnull() )& (data_c['window_start_clean'].astype('Int64')<= 31536000), 'duration_sec' ] = data_c.loc[(~data_c['window_start_clean'].isnull() )& (data_c['window_start_clean'].astype('Int64')<= 31536000), 'window_start_clean' ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Calculate duration for window with start and no end\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data_c.loc[(data_c['window_start_clean'] > 31536000) & (data_c['window_end_clean'].isnull()),'duration_sec'] = data_c.loc[(data_c['window_start_clean'] > 31536000) & (data_c['window_end_clean'].isnull()),'timestamp_sec'] - data_c.loc[(data_c['window_start_clean'] > 31536000) & (data_c['window_end_clean'].isnull()),'window_start_clean']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Calculate duration for window with start and end" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "data_c.loc[(data_c['window_start_clean'] > 31536000) & (~data_c['window_end_clean'].isnull() ),'duration_sec'] = data_c.loc[(data_c['window_start_clean'] > 31536000) & (~data_c['window_end_clean'].isnull() ),'window_end_clean'].astype('int') - data_c.loc[(data_c['window_start_clean'] > 31536000) & (~data_c['window_end_clean'].isnull() ),'window_start_clean'].astype('int')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "data_c['duration_min'] = data_c['duration_sec']/60\n", "data_c['duration_hour'] = data_c['duration_sec']/3600\n", "data_c['duration_day'] = data_c['duration_hour']/24" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate Recency\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "#extract data age for requests with window = duration (no separator)\n", "data_c.loc[data_c['window_start_clean'] <= 31536000,'query_from'] = data_c.loc[data_c['window_start_clean'] <= 31536000,'duration_sec'] \n", "#extract data age for requests with window start and no window end \n", "data_c.loc[(data_c['window_start_clean'] > 31536000) & (data_c['window_end_clean'].isnull()),'query_from'] = data_c.loc[(data_c['window_start_clean'] > 31536000) & (data_c['window_end_clean'].isnull()),'duration_sec']\n", "#extract data age for requests with window start and window end\n", "data_c.loc[(data_c['window_start_clean'] > 31536000) & (~data_c['window_end_clean'].isnull()),'query_from'] = data_c.loc[(data_c['window_start_clean'] > 31536000) & (~data_c['window_end_clean'].isnull()),'timestamp_sec'].astype(float) - data_c.loc[(data_c['window_start_clean'] > 31536000) & (~data_c['window_end_clean'].isnull()),'window_start_clean']\n", "data_c['data_age_sec'] = data_c['query_from']\n", "data_c['data_age_min'] = data_c['data_age_sec']/60\n", "data_c['data_age_hour'] = data_c['data_age_sec']/3600\n", "data_c['data_age_day'] = data_c['data_age_hour']/24\n", "\n", "data_c = data_c.loc[(data_c['data_age_day'] >=0) | (data_c['data_age_day'].isnull()) ]\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate Recency and Duration Categories\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "data_c.loc[(data_c[\"data_age_sec\"] <= 43200) & (data_c[\"data_age_sec\"] >0 ) ,\"data_age_cat\"] = '0-12hr'\n", "# data_c.loc[(data_c[\"data_age_sec\"] > 300) &(data_c[\"data_age_sec\"] <= 900) ,\"data_age_cat\"] = '5mn-15mn'\n", "\n", "# data_c.loc[(data_c[\"data_age_sec\"] > 900) &(data_c[\"data_age_sec\"] <= 3600) ,\"data_age_cat\"] = '15mn-1hr'\n", "# data_c.loc[(data_c[\"data_age_sec\"] > 3600) &(data_c[\"data_age_sec\"] <= 10800.0) ,\"data_age_cat\"] = '1hr-3hr'\n", "# data_c.loc[(data_c[\"data_age_sec\"] > 10800) &(data_c[\"data_age_sec\"] <= 21600) ,\"data_age_cat\"] = '3hr-6hr'\n", "# data_c.loc[(data_c[\"data_age_sec\"] > 21600) &(data_c[\"data_age_sec\"] <= 43200) ,\"data_age_cat\"] = '6hr-12hr'\n", "data_c.loc[(data_c[\"data_age_sec\"] > 43200) &(data_c[\"data_age_sec\"] <= 86400) ,\"data_age_cat\"] = '12hr-1day'\n", "\n", "# data_c.loc[(data_c[\"data_age_hour\"] > 24)& (data_c[\"data_age_hour\"] <=48) ,\"data_age_cat\"] = '1-2days'\n", "data_c.loc[(data_c[\"data_age_day\"] >1 )& (data_c[\"data_age_day\"] <=7) ,\"data_age_cat\"] = '1week'\n", "data_c.loc[(data_c[\"data_age_day\"] >7)& (data_c[\"data_age_day\"] <= 30) ,\"data_age_cat\"] = '1 month'\n", "data_c.loc[(data_c[\"data_age_day\"]>30)& (data_c[\"data_age_day\"] <= 90) ,\"data_age_cat\"] = '3 months'\n", "data_c.loc[(data_c[\"data_age_day\"]>90)& (data_c[\"data_age_day\"] <= 365) ,\"data_age_cat\"] = '1 year'\n", "data_c.loc[(data_c[\"data_age_day\"]>365)& (data_c[\"data_age_day\"] <= 730) ,\"data_age_cat\"] = '2 years'\n", "data_c.loc[data_c[\"data_age_day\"]>730 ,\"data_age_cat\"] = '> 2 years'\n", "\n", "data_c.loc[data_c[\"window\"] == '-1' ,\"data_age_cat\"] = '<=5 mn'\n", "data_c.loc[data_c[\"window\"] == '0' ,\"data_age_cat\"] = 'Beginning'\n", "data_c.loc[(data_c['window'].str.contains('^-', regex=True, na=False)) & (data_c['window'] != '-1'), 'data_age_cat'] = 'Beginning'\n", "\n", "\n", "\n", "data_c.loc[(data_c[\"duration_sec\"] <= 43200) & (data_c[\"duration_sec\"] >0 ) ,\"duration_cat\"] = '0-12 hr'\n", "# data_c.loc[(data_c[\"duration_sec\"] > 300) &(data_c[\"duration_sec\"] <= 900) ,\"duration_cat\"] = '5mn-15mn'\n", "\n", "# data_c.loc[(data_c[\"duration_sec\"] > 900) &(data_c[\"duration_sec\"] <= 3600) ,\"duration_cat\"] = '15mn-1hr'\n", "# data_c.loc[(data_c[\"duration_sec\"] > 3600) &(data_c[\"duration_sec\"] <= 10800.0) ,\"duration_cat\"] = '1hr-3hr'\n", "# data_c.loc[(data_c[\"duration_sec\"] > 10800) &(data_c[\"duration_sec\"] <= 21600) ,\"duration_cat\"] = '3hr-6hr'\n", "# data_c.loc[(data_c[\"duration_sec\"] > 21600) &(data_c[\"duration_sec\"] <= 43200) ,\"duration_cat\"] = '6hr-12hr'\n", "data_c.loc[(data_c[\"duration_sec\"] > 43200) &(data_c[\"duration_sec\"] <= 86400) ,\"duration_cat\"] = '12hr-1day'\n", "# data_c.loc[(data_c[\"duration_sec\"] > 86400) &(data_c[\"duration_sec\"] <= 172800) ,\"duration_cat\"] = '1-2days'\n", "\n", "\n", "\n", "data_c.loc[(data_c[\"duration_day\"] > 1)& (data_c[\"duration_day\"] <=7) ,\"duration_cat\"] = '1week'\n", "data_c.loc[(data_c[\"duration_day\"] >7)& (data_c[\"duration_day\"] <= 30) ,\"duration_cat\"] = '1w-1month'\n", "data_c.loc[(data_c[\"duration_day\"]>30)& (data_c[\"duration_day\"] <= 90) ,\"duration_cat\"] = '1-3months'\n", "data_c.loc[(data_c[\"duration_day\"]>90)& (data_c[\"duration_day\"] <= 365) ,\"duration_cat\"] = '3m-1year'\n", "data_c.loc[(data_c[\"duration_day\"]>365)& (data_c[\"duration_day\"] <= 730) ,\"duration_cat\"] = '1-2 years'\n", "data_c.loc[data_c[\"duration_day\"]>730 ,\"duration_cat\"] = '> 2 years'\n", "\n", "data_c.loc[(data_c['window'].str.contains('^-', regex=True, na=False)) & (data_c['window'] != '-1'), 'duration_cat'] = 'All'\n", "data_c.loc[(data_c['window'] == '0'), 'duration_cat'] = 'All'\n", "data_c.loc[(data_c['window'] == '-1'), 'duration_cat'] = 'Last Value'\n", "\n", "data_c = data_c.loc[data_c['duration_hour'] > 0]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "data_c.loc[data_c['duration_day'] <=1 ,\"DurationType\"] = \"Short Duration\" \n", "data_c.loc[data_c['duration_day'] > 1,\"DurationType\"] = \"Long Duration\"\n", "\n", "data_c.loc[data_c['data_age_day'] <=1 ,\"RecencyType\"] = \"Recent\" \n", "data_c.loc[data_c['data_age_day'] > 1,\"RecencyType\"] = \"Old\"" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "84.81237902974047\n", "73.6760910736956\n" ] } ], "source": [ "#% of queries aiming to recent data\n", "print(len(data_c.loc[data_c.duration_hour <= 48])/len(data_c)*100)\n", "#% of queries with short time spans\n", "print(len(data_c.loc[data_c.data_age_hour <= 24])/len(data_c)*100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate Server, Table and Column number" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "#data_c.loc[~data_c['srctree'].isnull(),'group'] = data_c.loc[~data_c['srctree'].isnull(),'srctree'].str.split(',',expand=True)[0]\n", "#data_c.loc[~data_c['srctree'].isnull(),'server'] = data_c.loc[~data_c['srctree'].isnull(),'group'].str.split('__',expand=True)[0]\n", "#data_c.loc[~data_c['srctree'].isnull(),'database'] = data_c.loc[~data_c['srctree'].isnull(),'group'].str.split('__',expand=True)[1]\n", "\n", "data_c.loc[(data_c['db_group']=='-3')&(~data_c['srctree'].isnull()),'table_number'] = data_c.loc[(data_c['db_group']=='-3')&(~data_c['srctree'].isnull())].apply(lambda data: get_len(data['srctree']), axis = 1)\n", "\n", "data_c.loc[(data_c['db_group']=='-3')&(~data_c['srctree'].isnull()),'column_number'] = data_c.loc[(data_c['db_group']=='-3')&(~data_c['srctree'].isnull())].apply(lambda data: get_col_nb(data['srctree']), axis = 1)\n", "\n", "data_c.loc[data_c['db_group']!='-3','column_number'] = data_c.loc[data_c['db_group']!='-3'].apply(lambda data: get_col_nb_novirtual(data['db_mask']), axis = 1)\n", "data_c.loc[data_c['db_group']!='-3','table_number'] = 1\n", "data_c = data_c[~data_c.table_number.isnull()]\n", "data_c = data_c[data_c.column_number != 'All']\n", "data_c['column_number'] = data_c['column_number'].astype(int) \n", "\n", "data_c.loc[~data_c['srctree'].isnull(),'server_number'] = data_c.loc[~data_c['srctree'].isnull()].apply(lambda data: get_server_number(data['srctree']), axis = 1)\n", "data_c.loc[data_c['srctree'].isnull(),'server_number'] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Binning the column number" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "data_c.loc[data_c.column_number == 1, 'col_bin'] = '1'\n", "data_c.loc[(data_c.column_number >=2) & (data_c.column_number <=5), 'col_bin'] = '2-5'\n", "data_c.loc[(data_c.column_number >5) & (data_c.column_number <=10), 'col_bin'] = '6-10'\n", "data_c.loc[(data_c.column_number >10) & (data_c.column_number <=50), 'col_bin'] = '11-50'\n", "data_c.loc[(data_c.column_number >50) & (data_c.column_number <=100), 'col_bin'] = '51-100'\n", "data_c.loc[data_c.column_number > 100, 'col_bin'] = '100+'\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# latency sec\n", "data_c['Latency_sec'] = data_c['Latency']/1000000\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate Query Status column" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Programs\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "D:\\Programs\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1598: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "D:\\Programs\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1719: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "D:\\Programs\\anaconda3\\lib\\site-packages\\pandas\\core\\strings\\accessor.py:101: UserWarning:\n", "\n", "This pattern has match groups. To actually get the groups, use str.extract.\n", "\n", "D:\\Programs\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n" ] } ], "source": [ "# content exists only in update requests\n", "columns = ['Timestamp', 'Setup', 'db_server', 'db_mask', 'Source', 'Request', 'PID', 'ClientID', 'db_name', 'db_group', 'srctree', 'window', 'resample', 'Latency', 'width', 'height', 'SessionID', 'Content']\n", "data_latency = katrin_update_data[columns]\n", "data_latency['Timestamp'] = pd.to_datetime(data_latency['Timestamp'])\n", "\n", "data_latency.loc[data_latency['Content'].str.contains('Timeout locking file'),'Status'] = 'LockingFileError'\n", "data_latency.loc[data_latency['Content'].str.contains('DRAW class have not access to the temporary directory'),'Status'] = 'AccessDirectoryError'\n", "data_latency.loc[data_latency['Content'].str.contains('INTERVAL window have undefined size'),'Status'] = 'WindowSizeError'\n", "data_latency.loc[data_latency['Content'].str.contains('(timeout expired)'),'Status'] = 'TimeoutError'\n", "data_latency.loc[data_latency['Content'].str.contains('Connection to the Caching MySQL Server is failed'),'Status'] = 'MYSQLCachingError'\n", "data_latency.loc[data_latency['Content'].str.contains('find any column'),'Status'] = 'ColumnNotFoundError'\n", "data_latency.loc[data_latency['Content'].str.contains('The graph dimmensions'),'Status'] = 'GraphDimensionError'\n", "data_latency.loc[data_latency['Content'].str.contains('SQLDriverConnect'),'Status'] = 'SQLError'\n", "data_latency.loc[data_latency['Content'].str.contains('New request is received'),'Status'] = 'Received'\n", "data_latency.loc[data_latency['Content'].str.contains('Request processing is finished'),'Status'] = 'Finished'\n", "data_latency['Latency_sec'] = data_latency['Latency']/1000000\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Expand server names" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "data_c.loc[~data_c['srctree'].isnull(),'servers'] = data_c.loc[~data_c['srctree'].isnull()].apply(lambda data: get_servers(data['srctree']), axis = 1)\n", "data_c.loc[data_c.db_server != 'virtual', 'servers'] = data_c.loc[data_c.db_server != 'virtual', ['db_server']]\n", "expanded_servers = data_c['servers'].explode('servers').reset_index(drop=True)\n", "expanded_servers = expanded_servers.to_frame()\n", "expanded_servers = expanded_servers.loc[~expanded_servers.servers.isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot requests per type" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hoverlabel": { "namelength": 0 }, "hovertemplate": "Request_Type=%{x}
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"white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Requests per query type" }, "width": 600, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Query Type" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Count" } } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#merge the counts of all request types\n", "data_counts = {'Request_Type' : ['data analysis', 'data export'],\n", " 'Count': [len(katrin_update_data), len(katrin_read_data) + len(katrin_download_data)]\n", " }\n", "\n", "data_counts = pd.DataFrame(data_counts)\n", "#plot request count per type\n", "fig = xp.bar(data_counts, x = 'Request_Type', y = 'Count', title='Requests per query type', width=600, height=400,color_discrete_sequence=['#75BDA7']).update_layout(xaxis_title = 'Query Type', yaxis_title='Count') \n", "fig.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Requests Time Span (Duration) " ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "histnorm": "percent", "hoverlabel": { "namelength": 0 }, "hovertemplate": "duration_cat=%{x}
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0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "KATRIN Requests Duration" }, "width": 600, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Category" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "ticksuffix": "%", "title": { "text": "Requests %" } } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_his_percentage(data_c, 'duration_cat', 'KATRIN Requests Duration', 'Category', 'Requests %')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Requests with short vs long duration" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_stacked_chart(data_c, 'DurationType', \"Requests with Short vs Long Duration\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Data Recency " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "histnorm": "percent", "hoverlabel": { "namelength": 0 }, "hovertemplate": "data_age_cat=%{x}
count of count=%{y}", "legendgroup": "", "marker": { "color": "#75BDA7" }, "name": "", "offsetgroup": "", "orientation": "v", "showlegend": false, "type": "histogram", "x": [ "0-12hr", "12hr-1day", "1 month", "1week", "1 year", "3 months", "2 years", "> 2 years" ], "xaxis": "x", "y": [ 335132, 231110, 84641, 68328, 32043, 13972, 1858, 1523 ], "yaxis": "y" } ], "layout": { "barmode": "relative", "height": 400, "legend": { "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "KATRIN Requests Recency" }, "width": 600, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Request Recency" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "ticksuffix": "%", "title": { "text": "Requests %" } } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_his_percentage(data_c, 'data_age_cat', 'KATRIN Requests Recency', 'Request Recency', 'Requests %')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Requests with recent vs old data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_stacked_chart(data_c, 'RecencyType', \"Requests Targeting Recent vs Old Data\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Recency vs Time Span\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'data_age_day', 'duration_day', 'Recency (day)', 'Time Span (day)', 'KATRIN Requests Recency vs Time Span')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Requests by Column Number" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_his_percentage(data_c, 'col_bin', 'KATRIN Requests by Sensor #', 'Sensor #', 'Requests %')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Requests by Group Number\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = data_c.loc[~data_c.table_number.isnull()]\n", "data['table_number'] = data['table_number'].astype('int')\n", "\n", "data['table_number'] = data['table_number'].astype('str')\n", "plot_his_percentage(data, 'table_number', 'KATRIN Requests by group #', 'Group #', 'Requests %')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot by server number\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = data_c.copy()\n", "data['server_number'] = data['server_number'].astype('int')\n", "data['server_number'] = data['server_number'].astype('str')\n", "\n", "plot_his_percentage(data, 'server_number', 'KATRIN Requests by subsystem #', 'Subsystem #', 'Requests %')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Requests by subsystem " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "histnorm": "percent", "hoverlabel": { "namelength": 0 }, "hovertemplate": "servers=%{x}
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\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_his_percentage(expanded_servers, 'servers', 'KATRIN Requests per subsystem', 'Subsystem', 'Requests %')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Request Recency vs Column number" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'data_age_day', 'column_number', 'Recency (day)', 'sensor #', 'KATRIN Requests Recency vs Sensor #')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Request Recency vs Group number" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'data_age_day', 'table_number', 'Recency (day)', 'Group #', 'KATRIN Requests Recency vs Group #')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Latency Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency vs Time Span" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'Latency_sec', 'duration_day', 'Latency (sec)', 'Time Span (day)', 'KATRIN Requests Latency vs Time Span')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency vs Recency" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'Latency_sec', 'data_age_day', 'Latency (sec)', 'Recency (day)', 'KATRIN Requests Latency vs Recency')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency Box Plot" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hoverlabel": { "namelength": 0 }, "hovertemplate": "Latency (sec)=%{y}", "legendgroup": "", "marker": { "color": "#636efa" }, "name": 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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "KATRIN Requets Latency Box Plot" }, "width": 600, "xaxis": { "anchor": "y", "domain": [ 0, 1 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Latency (sec)" } } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = xp.box(data_c, y=\"Latency_sec\", labels={'Latency_sec': 'Latency (sec)'}, title='KATRIN Requets Latency Box Plot', width=600,height=400)\n", "\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency vs Group number" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'Latency_sec', 'table_number', 'Latency (sec)', 'Group #', 'KATRIN Requests Latency vs Group #')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency vs Column Number" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'Latency_sec', 'column_number', 'Latency (sec)', 'Column #', 'KATRIN Requests Latency vs Column #')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency vs width" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_scattered_chart(data_c, 'Latency_sec', 'width', 'Latency (sec)', 'width', 'KATRIN Requests Latency vs Width')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency by Server Response" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# fig = xp.box(data_latency, x=\"Status\", y=\"Latency_sec\", title='KATRIN Requests Latency by Status')\n", "# fig.show()\n", "\n", "ax = sns.catplot(x='Status', y = 'Latency_sec', kind = 'box',data = data_latency)\n", "plt.gcf().set_size_inches(12, 6)\n", "\n", "plt.xticks(rotation = 45)\n", "ax.fig.suptitle('KATRIN Requests Latency by Status', y=1.3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latency vs time of the day" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_c['hour'] =data_c.apply(lambda data: data['Timestamp'].hour , axis = 1)\n", "data_c['hour'] = data_c['hour'].astype('int')\n", "plot_scattered_chart(data_c, 'Latency_sec', 'hour', 'latency (sec)', 'hour', 'Latency per hour of the day')\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_c['day'] =data_c.apply(lambda data: data['Timestamp'].day , axis = 1)\n", "data_c['day'] = data_c['day'].astype('int')\n", "plot_scattered_chart(data_c, 'Latency_sec', 'day', 'latency (sec)', 'day', 'Latency per day')\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 5 }