From 12e0b28e1302bc92d84b6e792a158e3a114699aa Mon Sep 17 00:00:00 2001 From: fierval Date: Thu, 21 May 2020 18:38:19 -0700 Subject: [PATCH] dropping the demo --- .gitignore | 345 + Notebooks/FPGA Data Merge.ipynb | 562 + Notebooks/NYTaxiBenchmarks.scala | 316 + Notebooks/Scratchpad.ipynb | 474 + Notebooks/Standardize Schema.ipynb | 1190 + Notebooks/Yellow Taxi Initial.ipynb | 676 + Notebooks/Yellow Taxi Stream-Local.ipynb | 280 + Notebooks/Yellow Taxi Stream.ipynb | 591 + Notebooks/fake tests.ipynb | 246 + README.md | 100 +- benchmarking/Mison/BenchmarkTaxi.cpp | 160 + benchmarking/listener/querylistener.scala | 86 + benchmarking/queries/README.md | 44 + benchmarking/queries/benchmark_magenta.scala | 68 + benchmarking/queries/benchmark_taxi.scala | 83 + .../queries/benchmark_taxi_fpga.scala | 94 + .../queries/benchmark_taxi_select.scala | 66 + benchmarking/queries/fix_data.py | 20 + benchmarking/queries/fpga_input_times.csv | 82 + benchmarking/queries/fpga_proc_times_q1.csv | 82 + benchmarking/queries/fpga_proc_times_q2.csv | 82 + benchmarking/queries/fpga_proc_times_q3.csv | 82 + benchmarking/queries/fpga_taxi_q1.csv | 82 + benchmarking/queries/fpga_taxi_q2.csv | 82 + benchmarking/queries/fpga_taxi_q3.csv | 82 + benchmarking/queries/fpga_taxi_template.csv | 82 + benchmarking/queries/line_count.csv | 82 + benchmarking/queries/logsample.json | 73 + benchmarking/queries/magenta.csv | 150 + benchmarking/queries/schemas.scala | 106 + benchmarking/queries/taxi_q0.csv | 82 + benchmarking/queries/taxi_q1.csv | 82 + benchmarking/queries/taxi_q1_e8.csv | 82 + benchmarking/queries/taxi_q2.csv | 82 + benchmarking/queries/taxi_q3.csv | 82 + benchmarking/queries/taxi_q4.csv | 82 + dataset/yellow_taxi_files.csv | 82 + demo/.gitignore | 23 + demo/README.md | 119 + demo/package-lock.json | 15420 ++++ demo/package.json | 38 + demo/public/favicon.ico | Bin 0 -> 3150 bytes demo/public/index.html | 43 + demo/public/logo192.png | Bin 0 -> 5347 bytes demo/public/logo512.png | Bin 0 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bytes demo/public/themes/purple/cpu.svg | 1 + demo/public/themes/purple/fpga.svg | 1 + demo/public/themes/purple/needle.svg | 1 + demo/public/themes/purple/start cpu.svg | 1 + demo/public/themes/purple/start fpga.svg | 1 + demo/public/themes/purple/stop cpu.svg | 1 + demo/public/themes/purple/stop fpga.svg | 1 + demo/src/App.css | 7 + demo/src/App.js | 172 + demo/src/App.test.js | 9 + demo/src/components/CPU.jsx | 27 + demo/src/components/FPGA.jsx | 27 + demo/src/components/Grid.jsx | 85 + demo/src/components/Meter.jsx | 390 + demo/src/components/ProgressGrower.jsx | 33 + demo/src/components/Query.jsx | 74 + demo/src/components/unused/ProgressSlider.jsx | 60 + demo/src/components/unused/Tabs.jsx | 82 + demo/src/data/cpu.json | 64392 ++++++++++++++++ demo/src/data/fpga.json | 47357 ++++++++++++ demo/src/index.css | 13 + demo/src/index.js | 17 + demo/src/logo.svg | 7 + demo/src/models/Record.js | 52 + demo/src/models/Theme.js | 31 + demo/src/queries/cpu/Query1A.csv | 82 + demo/src/queries/cpu/Query1B.csv | 82 + demo/src/queries/cpu/Query2.csv | 82 + demo/src/queries/cpu/Query3.csv | 82 + demo/src/queries/fpga/Query1.csv | 82 + demo/src/queries/fpga/Query2.csv | 82 + demo/src/queries/fpga/Query3.csv | 82 + demo/src/serviceWorker.js | 141 + demo/src/setupTests.js | 5 + demo/src/utilities/count.js | 11773 +++ demo/src/utilities/csv2json.js | 108 + docs/demo-approaches.md | 69 + docs/fpga_info.txt | 38 + docs/images/demo.png | Bin 0 -> 897072 bytes docs/images/spark_architecture.png | Bin 0 -> 27515 bytes docs/images/spark_overview.png | Bin 0 -> 69835 bytes docs/images/streaming.png | Bin 0 -> 198034 bytes fpga/vstream/VStreamDataSource/build.sbt | 7 + .../project/build.properties | 1 + ...pache.spark.sql.sources.DataSourceRegister | 1 + .../spark/VStreamRelationProvider.scala | 24 + .../scala/vstream/spark/VstreamRelation.scala | 29 + .../test/scala/VStreamDataSourceTest.scala | 24 + fpga/vstream/docs/Spark_VStream_V1.docx | Bin 0 -> 33828 bytes fpga/vstream/docs/read_vstream.cpp | 251 + fpga/vstream/examples/read_vstream.cpp | 251 + fpga/vstream/queries/CMakeLists.txt | 32 + fpga/vstream/queries/CMakeSettings.json | 29 + fpga/vstream/queries/cqueries.cpp | 52 + fpga/vstream/queries/helpers.hpp | 173 + fpga/vstream/queries/parser/field_iter.h | 136 + fpga/vstream/queries/parser/read_vstream.cpp | 11 + fpga/vstream/queries/parser/vstreamcore.h | 107 + fpga/vstream/queries/queries.hpp | 128 + fpga/vstream/queries/taxirow.hpp | 63 + ingestion/dataset/DestinationDataUsWest2.json | 87 + ingestion/dataset/ErrorFileUsWest2.json | 31 + ingestion/dataset/TaxiDatasetList.json | 32 + ingestion/dataset/TaxiSourceData.json | 30 + ingestion/linkedService/GetHttpTaxi.json | 13 + ingestion/linkedService/GetTaxiList.json | 12 + .../linkedService/PutTaxiFileUSWest2.json | 12 + .../linkedService/StagingBlobUSWest2.json | 12 + ingestion/pipeline/IterateAndPullUsWest2.json | 209 + magenta/pipeline/generate_data.py | 145 + tools/compute_data_size.ps1 | 37 + 159 files changed, 150193 insertions(+), 44 deletions(-) create mode 100644 .gitignore create mode 100644 Notebooks/FPGA Data Merge.ipynb create mode 100644 Notebooks/NYTaxiBenchmarks.scala create mode 100644 Notebooks/Scratchpad.ipynb create mode 100644 Notebooks/Standardize Schema.ipynb create mode 100644 Notebooks/Yellow Taxi Initial.ipynb create mode 100644 Notebooks/Yellow Taxi Stream-Local.ipynb create mode 100644 Notebooks/Yellow Taxi Stream.ipynb create mode 100644 Notebooks/fake tests.ipynb create mode 100644 benchmarking/Mison/BenchmarkTaxi.cpp create mode 100644 benchmarking/listener/querylistener.scala create mode 100644 benchmarking/queries/README.md create mode 100644 benchmarking/queries/benchmark_magenta.scala create mode 100644 benchmarking/queries/benchmark_taxi.scala create mode 100644 benchmarking/queries/benchmark_taxi_fpga.scala create mode 100644 benchmarking/queries/benchmark_taxi_select.scala create mode 100644 benchmarking/queries/fix_data.py create mode 100644 benchmarking/queries/fpga_input_times.csv create mode 100644 benchmarking/queries/fpga_proc_times_q1.csv create mode 100644 benchmarking/queries/fpga_proc_times_q2.csv create mode 100644 benchmarking/queries/fpga_proc_times_q3.csv create mode 100644 benchmarking/queries/fpga_taxi_q1.csv create mode 100644 benchmarking/queries/fpga_taxi_q2.csv create mode 100644 benchmarking/queries/fpga_taxi_q3.csv create mode 100644 benchmarking/queries/fpga_taxi_template.csv create mode 100644 benchmarking/queries/line_count.csv create mode 100644 benchmarking/queries/logsample.json create mode 100644 benchmarking/queries/magenta.csv create mode 100644 benchmarking/queries/schemas.scala create mode 100644 benchmarking/queries/taxi_q0.csv create mode 100644 benchmarking/queries/taxi_q1.csv create mode 100644 benchmarking/queries/taxi_q1_e8.csv create mode 100644 benchmarking/queries/taxi_q2.csv create mode 100644 benchmarking/queries/taxi_q3.csv create mode 100644 benchmarking/queries/taxi_q4.csv create mode 100644 dataset/yellow_taxi_files.csv create mode 100644 demo/.gitignore create mode 100644 demo/README.md create mode 100644 demo/package-lock.json create mode 100644 demo/package.json create mode 100644 demo/public/favicon.ico create mode 100644 demo/public/index.html create mode 100644 demo/public/logo192.png create mode 100644 demo/public/logo512.png create mode 100644 demo/public/manifest.json create mode 100644 demo/public/robots.txt create mode 100644 demo/public/themes/blue/background.jpg create mode 100644 demo/public/themes/blue/cpu.svg create mode 100644 demo/public/themes/blue/fpga.svg create mode 100644 demo/public/themes/blue/needle.svg create mode 100644 demo/public/themes/blue/start cpu.svg create mode 100644 demo/public/themes/blue/start fpga.svg create mode 100644 demo/public/themes/blue/stop cpu.svg create mode 100644 demo/public/themes/blue/stop fpga.svg create mode 100644 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demo/public/themes/magenta/cpu.svg create mode 100644 demo/public/themes/magenta/fpga.svg create mode 100644 demo/public/themes/magenta/needle.svg create mode 100644 demo/public/themes/magenta/start cpu.svg create mode 100644 demo/public/themes/magenta/start fpga.svg create mode 100644 demo/public/themes/magenta/stop cpu.svg create mode 100644 demo/public/themes/magenta/stop fpga.svg create mode 100644 demo/public/themes/orange/background.jpg create mode 100644 demo/public/themes/orange/cpu.svg create mode 100644 demo/public/themes/orange/fpga.svg create mode 100644 demo/public/themes/orange/needle.svg create mode 100644 demo/public/themes/orange/start cpu.svg create mode 100644 demo/public/themes/orange/start fpga.svg create mode 100644 demo/public/themes/orange/stop cpu.svg create mode 100644 demo/public/themes/orange/stop fpga.svg create mode 100644 demo/public/themes/purple/background.jpg create mode 100644 demo/public/themes/purple/cpu.svg create mode 100644 demo/public/themes/purple/fpga.svg create mode 100644 demo/public/themes/purple/needle.svg create mode 100644 demo/public/themes/purple/start cpu.svg create mode 100644 demo/public/themes/purple/start fpga.svg create mode 100644 demo/public/themes/purple/stop cpu.svg create mode 100644 demo/public/themes/purple/stop fpga.svg create mode 100644 demo/src/App.css create mode 100644 demo/src/App.js create mode 100644 demo/src/App.test.js create mode 100644 demo/src/components/CPU.jsx create mode 100644 demo/src/components/FPGA.jsx create mode 100644 demo/src/components/Grid.jsx create mode 100644 demo/src/components/Meter.jsx create mode 100644 demo/src/components/ProgressGrower.jsx create mode 100644 demo/src/components/Query.jsx create mode 100644 demo/src/components/unused/ProgressSlider.jsx create mode 100644 demo/src/components/unused/Tabs.jsx create mode 100644 demo/src/data/cpu.json create mode 100644 demo/src/data/fpga.json create mode 100644 demo/src/index.css create mode 100644 demo/src/index.js create mode 100644 demo/src/logo.svg create mode 100644 demo/src/models/Record.js create mode 100644 demo/src/models/Theme.js create mode 100644 demo/src/queries/cpu/Query1A.csv create mode 100644 demo/src/queries/cpu/Query1B.csv create mode 100644 demo/src/queries/cpu/Query2.csv create mode 100644 demo/src/queries/cpu/Query3.csv create mode 100644 demo/src/queries/fpga/Query1.csv create mode 100644 demo/src/queries/fpga/Query2.csv create mode 100644 demo/src/queries/fpga/Query3.csv create mode 100644 demo/src/serviceWorker.js create mode 100644 demo/src/setupTests.js create mode 100644 demo/src/utilities/count.js create mode 100644 demo/src/utilities/csv2json.js create mode 100644 docs/demo-approaches.md create mode 100644 docs/fpga_info.txt create mode 100644 docs/images/demo.png create mode 100644 docs/images/spark_architecture.png create mode 100644 docs/images/spark_overview.png create mode 100644 docs/images/streaming.png create mode 100644 fpga/vstream/VStreamDataSource/build.sbt create mode 100644 fpga/vstream/VStreamDataSource/project/build.properties create mode 100644 fpga/vstream/VStreamDataSource/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister create mode 100644 fpga/vstream/VStreamDataSource/src/main/scala/vstream/spark/VStreamRelationProvider.scala create mode 100644 fpga/vstream/VStreamDataSource/src/main/scala/vstream/spark/VstreamRelation.scala create mode 100644 fpga/vstream/VStreamDataSource/src/test/scala/VStreamDataSourceTest.scala create mode 100644 fpga/vstream/docs/Spark_VStream_V1.docx create mode 100644 fpga/vstream/docs/read_vstream.cpp create mode 100644 fpga/vstream/examples/read_vstream.cpp create mode 100644 fpga/vstream/queries/CMakeLists.txt create mode 100644 fpga/vstream/queries/CMakeSettings.json create mode 100644 fpga/vstream/queries/cqueries.cpp create mode 100644 fpga/vstream/queries/helpers.hpp create mode 100644 fpga/vstream/queries/parser/field_iter.h create mode 100644 fpga/vstream/queries/parser/read_vstream.cpp create mode 100644 fpga/vstream/queries/parser/vstreamcore.h create mode 100644 fpga/vstream/queries/queries.hpp create mode 100644 fpga/vstream/queries/taxirow.hpp create mode 100644 ingestion/dataset/DestinationDataUsWest2.json create mode 100644 ingestion/dataset/ErrorFileUsWest2.json create mode 100644 ingestion/dataset/TaxiDatasetList.json create mode 100644 ingestion/dataset/TaxiSourceData.json create mode 100644 ingestion/linkedService/GetHttpTaxi.json create mode 100644 ingestion/linkedService/GetTaxiList.json create mode 100644 ingestion/linkedService/PutTaxiFileUSWest2.json create mode 100644 ingestion/linkedService/StagingBlobUSWest2.json create mode 100644 ingestion/pipeline/IterateAndPullUsWest2.json create mode 100644 magenta/pipeline/generate_data.py create mode 100644 tools/compute_data_size.ps1 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c9ff023 --- /dev/null +++ b/.gitignore @@ -0,0 +1,345 @@ +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. +## +## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore + +# User-specific files +*.suo +*.user +*.userosscache +*.sln.docstates + +.idea/ +target/ +.image/ +build/ + +# User-specific files (MonoDevelop/Xamarin Studio) +*.userprefs + +# Build results +[Dd]ebug/ +[Dd]ebugPublic/ +[Rr]elease/ +[Rr]eleases/ +x64/ +x86/ +bld/ +[Bb]in/ +[Oo]bj/ +[Ll]og/ + +# Visual Studio 2015/2017 cache/options directory +.vs/ +.vscode/ +# Uncomment if you have tasks that create the project's static files in wwwroot +#wwwroot/ + +# Visual Studio 2017 auto generated files +Generated\ Files/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +# NUNIT +*.VisualState.xml +TestResult.xml + +# Build Results of an ATL Project +[Dd]ebugPS/ +[Rr]eleasePS/ +dlldata.c + +# Benchmark Results +BenchmarkDotNet.Artifacts/ + +# .NET Core +project.lock.json +project.fragment.lock.json +artifacts/ +**/Properties/launchSettings.json + +# StyleCop +StyleCopReport.xml + +# Files built by Visual Studio +*_i.c +*_p.c +*_i.h +*.ilk +*.meta +*.obj +*.iobj +*.pch +*.pdb +*.ipdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*.log +*.vspscc +*.vssscc +.builds +*.pidb +*.svclog +*.scc + +# Chutzpah Test files +_Chutzpah* + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opendb +*.opensdf +*.sdf +*.cachefile +*.VC.db +*.VC.VC.opendb +out/ + +# Visual Studio profiler +*.psess +*.vsp +*.vspx +*.sap + +# Visual Studio Trace Files +*.e2e + +# TFS 2012 Local Workspace +$tf/ + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper +*.DotSettings.user + +# JustCode is a .NET coding add-in +.JustCode + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# AxoCover is a Code Coverage Tool +.axoCover/* +!.axoCover/settings.json + +# Visual Studio code coverage results +*.coverage +*.coveragexml + +# NCrunch +_NCrunch_* +.*crunch*.local.xml +nCrunchTemp_* + +# MightyMoose +*.mm.* +AutoTest.Net/ + +# Web workbench (sass) +.sass-cache/ + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +# Note: Comment the next line if you want to checkin your web deploy settings, +# but database connection strings (with potential passwords) will be unencrypted +*.pubxml +*.publishproj + +# Microsoft Azure Web App publish settings. Comment the next line if you want to +# checkin your Azure Web App publish settings, but sensitive information contained +# in these scripts will be unencrypted +PublishScripts/ + +# NuGet Packages +*.nupkg +# The packages folder can be ignored because of Package Restore +**/[Pp]ackages/* +# except build/, which is used as an MSBuild target. +!**/[Pp]ackages/build/ +# Uncomment if necessary however generally it will be regenerated when needed +#!**/[Pp]ackages/repositories.config +# NuGet v3's project.json files produces more ignorable files +*.nuget.props +*.nuget.targets + +# Microsoft Azure Build Output +csx/ +*.build.csdef + +# Microsoft Azure Emulator +ecf/ +rcf/ + +# Windows Store app package directories and files +AppPackages/ +BundleArtifacts/ +Package.StoreAssociation.xml +_pkginfo.txt +*.appx + +# Visual Studio cache files +# files ending in .cache can be ignored +*.[Cc]ache +# but keep track of directories ending in .cache +!*.[Cc]ache/ + +# Others +ClientBin/ +~$* +*~ +*.dbmdl +*.dbproj.schemaview +*.jfm +*.pfx +*.publishsettings +orleans.codegen.cs + +# Including strong name files can present a security risk +# (https://github.com/github/gitignore/pull/2483#issue-259490424) +#*.snk + +# Since there are multiple workflows, uncomment next line to ignore bower_components +# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622) +#bower_components/ + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file +# to a newer Visual Studio version. Backup files are not needed, +# because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm +ServiceFabricBackup/ +*.rptproj.bak + +# SQL Server files +*.mdf +*.ldf +*.ndf + +# Business Intelligence projects +*.rdl.data +*.bim.layout +*.bim_*.settings +*.rptproj.rsuser + +# Microsoft Fakes +FakesAssemblies/ + +# GhostDoc plugin setting file +*.GhostDoc.xml + +# Node.js Tools for Visual Studio +.ntvs_analysis.dat +node_modules/ + +# Visual Studio 6 build log +*.plg + +# Visual Studio 6 workspace options file +*.opt + +# Visual Studio 6 auto-generated workspace file (contains which files were open etc.) +*.vbw + +# Visual Studio LightSwitch build output +**/*.HTMLClient/GeneratedArtifacts +**/*.DesktopClient/GeneratedArtifacts +**/*.DesktopClient/ModelManifest.xml +**/*.Server/GeneratedArtifacts +**/*.Server/ModelManifest.xml +_Pvt_Extensions + +# Paket dependency manager +.paket/paket.exe +paket-files/ + +# FAKE - F# Make +.fake/ + +# JetBrains Rider +.idea/ +*.sln.iml + +# CodeRush +.cr/ + +# Python Tools for Visual Studio (PTVS) +__pycache__/ +*.pyc +.ipynb_checkpoints/ + +# Cake - Uncomment if you are using it +# tools/** +# !tools/packages.config + +# Tabs Studio +*.tss + +# Telerik's JustMock configuration file +*.jmconfig + +# BizTalk build output +*.btp.cs +*.btm.cs +*.odx.cs +*.xsd.cs + +# OpenCover UI analysis results +OpenCover/ + +# Azure Stream Analytics local run output +ASALocalRun/ + +# MSBuild Binary and Structured Log +*.binlog + +# NVidia Nsight GPU debugger configuration file +*.nvuser + +# MFractors (Xamarin productivity tool) working folder +.mfractor/ + +#Spark +metastore_db/ +.metals/ +.scalafmt.conf + +*.zip \ No newline at end of file diff --git a/Notebooks/FPGA Data Merge.ipynb b/Notebooks/FPGA Data Merge.ipynb new file mode 100644 index 0000000..1b1c504 --- /dev/null +++ b/Notebooks/FPGA Data Merge.ipynb @@ -0,0 +1,562 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os, glob\n", + "import matplotlib.pylab as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "files_loc = os.path.expanduser(r\"~/git/PipeDream/benchmarking/queries\")\n", + "time_files = glob.glob(os.path.join(files_loc, \"fpga_*.csv\"))\n", + "res_files = glob.glob(os.path.join(files_loc, \"q1out*.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_time = pd.read_csv(time_files[0], header=None)\n", + "df_res = pd.read_csv(res_files[0], header=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = os.path.expanduser(r\"~/data/taxi_data_cleaned\")\n", + "all_files = os.listdir(os.path.expanduser(data_path))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame({\"files\":all_files, \"count\": df_res[0], \"time\": df_time[0] / (1000 * 1000)})\n", + "df.set_index(\"files\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yellow_tripdata_2017-09.csv207432.472827
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" + ], + "text/plain": [ + " count time\n", + "files \n", + "yellow_tripdata_2011-06.csv 14092413 22.215828\n", + "yellow_tripdata_2011-02.csv 13380122 22.411802\n", + "yellow_tripdata_2019-09.csv 14387371 22.519684\n", + "yellow_tripdata_2017-02.csv 14294783 22.341600\n", + "yellow_tripdata_2017-09.csv 2074 32.472827" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from multiprocessing import Pool, cpu_count\n", + "def get_count(f):\n", + " fn = os.path.join(data_path, f)\n", + " print(f\"Reading: {f}\")\n", + " with open(fn, \"r\") as fd:\n", + " l = fd.readlines()\n", + " return len(l)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading: yellow_tripdata_2011-09.csv\n", + "Reading: yellow_tripdata_2011-06.csv\n", + "Reading: yellow_tripdata_2017-09.csv\n", + "Reading: yellow_tripdata_2019-10.csv\n", + "Reading: yellow_tripdata_2015-06.csv\n", + "Reading: yellow_tripdata_2015-04.csv\n", + "Reading: yellow_tripdata_2018-05.csv\n", + "Reading: yellow_tripdata_2016-07.csv\n", + "Reading: yellow_tripdata_2018-03.csv\n", + "Reading: yellow_tripdata_2012-07.csv\n", + "Reading: yellow_tripdata_2011-02.csv\n", + "Reading: yellow_tripdata_2019-09.csv\n", + "Reading: yellow_tripdata_2017-02.csv\n", + "Reading: yellow_tripdata_2012-04.csv\n", + "Reading: yellow_tripdata_2019-07.csv\n", + "Reading: yellow_tripdata_2010-08.csv\n", + "Reading: yellow_tripdata_2017-04.csv\n", + "Reading: yellow_tripdata_2014-12.csv\n", + "Reading: yellow_tripdata_2014-11.csv\n", + "Reading: yellow_tripdata_2012-03.csv\n", + "Reading: yellow_tripdata_2015-05.csv\n", + "Reading: yellow_tripdata_2010-07.csv\n", + "Reading: yellow_tripdata_2017-01.csv\n", + "Reading: yellow_tripdata_2015-12.csv\n", + "Reading: yellow_tripdata_2018-01.csv\n", + "Reading: yellow_tripdata_2009-11.csv\n", + "Reading: yellow_tripdata_2014-04.csv\n", + "Reading: yellow_tripdata_2017-11.csv\n", + "Reading: yellow_tripdata_2014-08.csv\n", + "Reading: yellow_tripdata_2011-10.csv\n", + "Reading: yellow_tripdata_2010-05.csv\n", + "Reading: yellow_tripdata_2016-05.csv\n", + "Reading: yellow_tripdata_2018-07.csv\n", + "Reading: yellow_tripdata_2017-05.csv\n", + "Reading: yellow_tripdata_2019-05.csv\n", + "Reading: yellow_tripdata_2012-11.csv\n", + "Reading: yellow_tripdata_2015-02.csv\n", + "Reading: yellow_tripdata_2010-09.csv\n", + "Reading: yellow_tripdata_2010-06.csv\n", + "Reading: yellow_tripdata_2015-08.csv\n", + "Reading: yellow_tripdata_2016-03.csv\n", + "Reading: yellow_tripdata_2017-08.csv\n", + "Reading: yellow_tripdata_2013-10.csv\n", + "Reading: yellow_tripdata_2015-10.csv\n", + "Reading: yellow_tripdata_2009-03.csv\n", + "Reading: yellow_tripdata_2018-04.csv\n", + "Reading: yellow_tripdata_2009-06.csv\n", + "Reading: yellow_tripdata_2017-06.csv\n", + "Reading: yellow_tripdata_2010-12.csv\n", + "Reading: yellow_tripdata_2014-06.csv\n", + "Reading: yellow_tripdata_2014-10.csv\n", + "Reading: yellow_tripdata_2013-12.csv\n", + "Reading: yellow_tripdata_2012-10.csv\n", + "Reading: yellow_tripdata_2015-03.csv\n", + "Reading: yellow_tripdata_2019-02.csv\n", + "Reading: yellow_tripdata_2011-07.csv\n", + "Reading: yellow_tripdata_2011-03.csv\n", + "Reading: yellow_tripdata_2009-02.csv\n", + "Reading: yellow_tripdata_2018-12.csv\n", + "Reading: yellow_tripdata_2010-01.csv\n", + "Reading: yellow_tripdata_2013-01.csv\n", + "Reading: yellow_tripdata_2011-05.csv\n", + "Reading: yellow_tripdata_2019-08.csv\n", + "Reading: yellow_tripdata_2016-02.csv\n", + "Reading: yellow_tripdata_2014-09.csv\n", + "Reading: yellow_tripdata_2014-01.csv\n", + "Reading: yellow_tripdata_2016-09.csv\n", + "Reading: yellow_tripdata_2019-01.csv\n", + "Reading: yellow_tripdata_2012-02.csv\n", + "Reading: yellow_tripdata_2009-10.csv\n", + "Reading: yellow_tripdata_2012-01.csv\n", + "Reading: yellow_tripdata_2013-11.csv\n", + "Reading: yellow_tripdata_2009-08.csv\n", + "Reading: yellow_tripdata_2009-01.csv\n", + "Reading: yellow_tripdata_2017-07.csv\n", + "Reading: yellow_tripdata_2010-10.csv\n", + "Reading: yellow_tripdata_2010-11.csv\n", + "Reading: yellow_tripdata_2011-12.csv\n", + "Reading: yellow_tripdata_2016-11.csv\n", + "Reading: yellow_tripdata_2011-04.csv\n", + "Reading: yellow_tripdata_2015-09.csv\n", + "Reading: yellow_tripdata_2009-09.csv\n", + "Reading: yellow_tripdata_2015-11.csv\n", + "Reading: yellow_tripdata_2014-05.csv\n", + "Reading: yellow_tripdata_2012-09.csv\n", + "Reading: yellow_tripdata_2016-06.csv\n", + "Reading: yellow_tripdata_2009-05.csv\n", + "Reading: yellow_tripdata_2016-10.csv\n", + "Reading: yellow_tripdata_2012-06.csv\n", + "Reading: yellow_tripdata_2018-06.csv\n", + "Reading: yellow_tripdata_2014-02.csv\n", + "Reading: yellow_tripdata_2013-08.csv\n", + "Reading: yellow_tripdata_2016-04.csv\n", + "Reading: yellow_tripdata_2009-04.csv\n", + "Reading: yellow_tripdata_2012-12.csv\n", + "Reading: yellow_tripdata_2017-03.csv\n", + "Reading: yellow_tripdata_2017-10.csv\n", + "Reading: yellow_tripdata_2013-02.csv\n", + "Reading: yellow_tripdata_2012-08.csv\n", + "Reading: yellow_tripdata_2018-09.csv\n", + "Reading: yellow_tripdata_2011-01.csv\n", + "Reading: yellow_tripdata_2013-03.csv\n", + "Reading: yellow_tripdata_2019-12.csv\n", + "Reading: yellow_tripdata_2016-01.csv\n", + "Reading: yellow_tripdata_2019-11.csv\n", + "Reading: yellow_tripdata_2013-09.csv\n", + "Reading: yellow_tripdata_2012-05.csv\n", + "Reading: yellow_tripdata_2016-12.csv\n", + "Reading: yellow_tripdata_2011-11.csv\n", + "Reading: yellow_tripdata_2018-02.csv\n", + "Reading: yellow_tripdata_2013-04.csv\n", + "Reading: yellow_tripdata_2014-03.csv\n", + "Reading: yellow_tripdata_2011-08.csv\n", + "Reading: yellow_tripdata_2016-08.csv\n", + "Reading: yellow_tripdata_2013-05.csv\n", + "Reading: yellow_tripdata_2010-04.csv\n", + "Reading: yellow_tripdata_2013-07.csv\n", + "Reading: yellow_tripdata_2015-07.csv\n", + "Reading: yellow_tripdata_2017-12.csv\n", + "Reading: yellow_tripdata_2015-01.csv\n", + "Reading: yellow_tripdata_2019-06.csv\n", + "Reading: yellow_tripdata_2013-06.csv\n", + "Reading: yellow_tripdata_2009-07.csv\n", + "Reading: yellow_tripdata_2019-03.csv\n", + "Reading: yellow_tripdata_2009-12.csv\n", + "Reading: yellow_tripdata_2018-08.csv\n", + "Reading: yellow_tripdata_2018-10.csv\n", + "Reading: yellow_tripdata_2014-07.csv\n", + "Reading: yellow_tripdata_2019-04.csv\n", + "Reading: yellow_tripdata_2018-11.csv\n" + ] + } + ], + "source": [ + "lngths = Pool(cpu_count()).map(get_count, all_files)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"real_count\"] = lngths" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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counttimereal_count
files
yellow_tripdata_2011-06.csv1409241322.21582815097860
yellow_tripdata_2011-02.csv1338012222.41180214612198
yellow_tripdata_2019-09.csv1438737122.5196846567787
yellow_tripdata_2017-02.csv1429478322.3416009168824
yellow_tripdata_2017-09.csv207432.4728278945458
............
yellow_tripdata_2012-05.csv91688249.51171015567525
yellow_tripdata_2012-06.csv1029462812.13310915096468
yellow_tripdata_2018-09.csv1004618812.6019118040133
yellow_tripdata_2019-11.csv1010212411.1644336878111
yellow_tripdata_2011-08.csv965699311.46608513262441
\n", + "

100 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " count time real_count\n", + "files \n", + "yellow_tripdata_2011-06.csv 14092413 22.215828 15097860\n", + "yellow_tripdata_2011-02.csv 13380122 22.411802 14612198\n", + "yellow_tripdata_2019-09.csv 14387371 22.519684 6567787\n", + "yellow_tripdata_2017-02.csv 14294783 22.341600 9168824\n", + "yellow_tripdata_2017-09.csv 2074 32.472827 8945458\n", + "... ... ... ...\n", + "yellow_tripdata_2012-05.csv 9168824 9.511710 15567525\n", + "yellow_tripdata_2012-06.csv 10294628 12.133109 15096468\n", + "yellow_tripdata_2018-09.csv 10046188 12.601911 8040133\n", + "yellow_tripdata_2019-11.csv 10102124 11.164433 6878111\n", + "yellow_tripdata_2011-08.csv 9656993 11.466085 13262441\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(n=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(os.path.join(files_loc, \"fpga_results.csv\"), index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(os.path.join(files_loc, \"fpga_results.csv\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAo4Kh7RPz42gemtG7b3IEHAQAAADiK2AkEAAAAUMCEEai1tqG19mJr7ccTHHdOa+2N1tplw40HAAAAwBAmsxPojiSrDnZAa21Okv+U5MEBZgIAAABgYBNGoN77I0lemuCwzyb570leHGIoAAAAAIY17XsCtdbekeRfJvmz6Y8DAAAAwEwY4sbQ65P8h9777okObK2taa1taq1t2rFjxwBfDQAAAMBkDPGI+GVJ7m6tJcnCJB9prb3Re7/vzQf23m9LcluSLFu2rA/w3QAw642vfWBK67bNHXgQAACOatOOQL33U3/zc2vtjiR/daAABAAAAMDoTBiBWmt3JVmeZGFrbXuSG5IcmyS991tndDoAAAAABjFhBOq9XzHZk/Xer5nWNAAAAADMiCFuDA0AAADAEU4EAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoYGzUAwDAdI2vfWBK67bNHXgQAAA4gtkJBAAAAFCACAQAAABQgAgEAAAAUIB7AgEUMOV75tx4ycCTAAAAo2InEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABY6MegH9ofO0DU1q3be7AgwAAAACzip1AAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFjI16AAAAAA6DdfOnuG7XsHMAI2MnEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABbgwNAAAAh8n42gemtG7bjZcMPAkV2QkEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQwNioBwAAgFll3fwprts17BwA8CZ2AgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFDBhBGqtbWitvdha+/FbfH5Va+1HrbX/3Vp7vLW2dPgxAQAAAJiOyewEuiPJqoN8/vMkF/Xe/1mSLya5bYC5AAAAABjQ2EQH9N4faa2NH+Tzx/d7+WSSxdMfCwAAAIAhDX1PoE8n+Z8DnxMAAACAaZpwJ9BktdZWZE8E+oODHLMmyZokOeWUU4b6agAAAAAmMMhOoNbakiR/keTS3vvOtzqu935b731Z733ZokWLhvhqAAAAACZh2hGotXZKkr9Msrr3/tPpjwQAAADA0Ca8HKy1dleS5UkWtta2J7khybFJ0nu/NckXkixI8tXWWpK80XtfNlMDAwAAAHDoJvN0sCsm+PyPkvzRYBMBAAAAMLihnw4GAAAAwBFIBAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoYGzUAwAAAEeRdfOnsGbX8HMAcMjsBAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChgbNQDAAAAAMxa6+ZPcd2uYeeInUAAAAAAJYhAAAAAAAVMGIFaaxtaay+21n78Fp+31totrbVnW2s/aq29b/gxAQAAAJiOyewEuiPJqoN8/odJ3r3335okfzb9sQAAAAAY0oQRqPf+SJKXDnLIpUnu7Hs8meRtrbV/MtSAAAAAAEzfEPcEekeS5/d7vX3ve7+ltbamtbaptbZpx44dA3w1AAAAAJNxWG8M3Xu/rfe+rPe+bNGiRYfzqwEAAABKGyIC/SLJO/d7vXjvewAAAAAcIYaIQPcn+dTep4Sdl2RX7/3/DnBeAAAAAAYyNtEBrbW7kixPsrC1tj3JDUmOTZLe+61JvpXkI0meTfJKkmtnalgAAAAApmbCCNR7v2KCz3uSPx5sIgAAAAAGd1hvDA0AAADAaIhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYyNegAgybr5U1iza/g5AAAAmLXsBAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKEAEAgAAAChABAIAAAAoQAQCAAAAKGBs1AMAs9y6+VNct2vYOQAAAIqzEwgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoICxUQ8AwBFs3fwprts17BwAAMC02QkEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUMDYqAcAAAAAJrBu/hTX7Rp2Do5qdgIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQgAgEAAAAUIAIBAAAAFCACAQAAABQwqQjUWlvVWvub1tqzrbW1B/j8lNbaxtbaU621H7XWPjL8qAAAAABM1YQRqLU2J8lXkvxhkt9LckVr7ffedNh/TPLN3vvZST6R5KtDDwoAAADA1E1mJ9C5SZ7tvf+s9/5akruTXPqmY3qSf7T35/lJXhhuRAAAAACmazIR6B1Jnt/v9fa97+1vXZJPtta2J/lWks8e6ESttTWttU2ttU07duyYwrgAAAAATMVQN4a+IskdvffFST6S5L+21n7r3L3323rvy3rvyxYtWjTQVwMAAAAwkclEoF8keed+rxfvfW9/n07yzSTpvT+RZG6ShUMMCAAAAMD0TSYC/SDJu1trp7bWfid7bvx8/5uOeS7JxUnSWjsjeyKQ670AAAAAjhATRqDe+xtJ/iTJd5JszZ6ngG1prf1pa+2jew/7d0mua639dZK7klzTe+8zNTQAAAAAh2ZsMgf13r+VPTd83v+9L+z380+SXDDsaAAAAAAMZagbQwMAAABwBBOBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChCBAAAAAAoQgQAAAAAKEIEAAAAAChgb9QAAAAAwbevmT3HdrmHngCOYnUAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWIQAAAAAAFiEAAAAAABYhAAAAAAAWMjXoAAACKWTd/iut2DTsHABRjJxAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABIhAAAABAAfGjPQEAABtVSURBVCIQAAAAQAEiEAAAAEABIhAAAABAASIQAAAAQAEiEAAAAEABY6MeACZl3fwprts17BwAAABwlLITCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoAARCAAAAKAAEQgAAACgABEIAAAAoIBJRaDW2qrW2t+01p5tra19i2P+VWvtJ621La21/zbsmAAAAABMx9hEB7TW5iT5SpJ/nmR7kh+01u7vvf9kv2PeneRzSS7ovf9da+0fz9TAAAAAABy6yewEOjfJs733n/XeX0tyd5JL33TMdUm+0nv/uyTpvb847JgAAAAATMdkItA7kjy/3+vte9/b33uSvKe19lhr7cnW2qoDnai1tqa1tqm1tmnHjh1TmxgAAACAQzbUjaHHkrw7yfIkVyT589ba2958UO/9tt77st77skWLFg301QAAAABMZDIR6BdJ3rnf68V739vf9iT3995f773/PMlPsycKAQAAAHAEmEwE+kGSd7fWTm2t/U6STyS5/03H3Jc9u4DSWluYPZeH/WzAOQEAAACYhgkjUO/9jSR/kuQ7SbYm+WbvfUtr7U9bax/de9h3kuxsrf0kycYk/773vnOmhgYAAADg0Ez4iPgk6b1/K8m33vTeF/b7uSf5t3v/AQAAAHCEGerG0AAAAAAcwUQgAAAAgAJEIAAAAIACRCAAAACA/9/evQfPftf1HX+9k0hGCqaIASohhha8pJWbMdDWC1RqE20TijhAW0FLTacWZcCpxTIDIx0ZLgKDLRlJW1AcJSIopDVcpFyKwxCScgshBgOlJkghCGLlIg1594/vpv749STnR36bs2f3/XjMnJnd727OeX/mt9nd33O/3+8OIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAxwoAhUVedU1TVVdW1VPfVW7vdDVdVVddb6RgQAAADgsI4agarqxCQvTnJukjOTPLaqzjzC/e6c5ElJLlv3kAAAAAAczkH2BDo7ybXd/ZHu/lKSi5Ocf4T7/dskz0nyxTXOBwAAAMAaHCQC3TPJdXuuX7/a9v9U1YOS3Ku7f+fW/qKquqCqrqiqK2644YavelgAAAAAbptDnxi6qk5I8oIkP320+3b3Rd19Vnefdeqppx72nwYAAADggA4SgT6W5F57rp+22nazOyf5G0neWlUfTfKQJJc4OTQAAADA8eMgEejyJPetqntX1R2SPCbJJTff2N2f7e5v6O4zuvuMJO9Mcl53X3G7TAwAAADAV+2oEai7b0zyxCRvSHJ1kld291VV9cyqOu/2HhAAAACAwzvpIHfq7kuTXLpv29Nv4b4PPfxYAAAAAKzToU8MDQAAAMDxTwQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGOBAEaiqzqmqa6rq2qp66hFuf0pVfbCq3l9V/7Wqvmn9owIAAABwWx01AlXViUlenOTcJGcmeWxVnbnvbu9JclZ33y/Jq5I8d92DAgAAAHDbHWRPoLOTXNvdH+nuLyW5OMn5e+/Q3W/p7s+vrr4zyWnrHRMAAACAwzhIBLpnkuv2XL9+te2WPCHJ6w4zFAAAAADrddI6/7Kq+idJzkryvbdw+wVJLkiS008/fZ3/NAAAAAC34iB7An0syb32XD9tte0rVNXDkzwtyXnd/edH+ou6+6LuPqu7zzr11FNvy7wAAAAA3AYHiUCXJ7lvVd27qu6Q5DFJLtl7h6p6YJKXZAlAn1z/mAAAAAAcxlEjUHffmOSJSd6Q5Ookr+zuq6rqmVV13upuz0typyS/WVXvrapLbuGvAwAAAGADDnROoO6+NMml+7Y9fc/lh695LgAAAADW6CCHgwEAAACw5UQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABDhSBquqcqrqmqq6tqqce4faTq+o3VrdfVlVnrHtQAAAAAG67o0agqjoxyYuTnJvkzCSPraoz993tCUk+0933SfLCJM9Z96AAAAAA3HYH2RPo7CTXdvdHuvtLSS5Ocv6++5yf5FdWl1+V5PuqqtY3JgAAAACHcZAIdM8k1+25fv1q2xHv0903JvlskruuY0AAAAAADq+6+9bvUPWoJOd09z9bXf+RJA/u7ifuuc8HVve5fnX9w6v7fGrf33VBkgtWV78lyTXrWsgBfEOSTx31XtvL+rbbLq9vl9eWWN+2s77ttctrS6xv21nf9trltSXWt+2sb3sd67V9U3efeqQbTjrAf/yxJPfac/201bYj3ef6qjopySlJ/nj/X9TdFyW56CATr1tVXdHdZ23i3z4WrG+77fL6dnltifVtO+vbXru8tsT6tp31ba9dXltifdvO+rbX8bS2gxwOdnmS+1bVvavqDkkek+SSffe5JMnjV5cfleTNfbRdjAAAAAA4Zo66J1B331hVT0zyhiQnJnlpd19VVc9MckV3X5LkPyX51aq6Nsmns4QiAAAAAI4TBzkcLN19aZJL9217+p7LX0zyw+sdbe02chjaMWR9222X17fLa0usb9tZ3/ba5bUl1rftrG977fLaEuvbdta3vY6btR31xNAAAAAAbL+DnBMIAAAAgC0nAgEAAAAMIAIBAAAADHCgE0Nvo6r61iTnJ7nnatPHklzS3VdvbioOYvWzu2eSy7r7z/ZsP6e7X7+5ydajqs5O0t19eVWdmeScJL+/OgH7zqmql3f34zY9x+2hqr4rydlJPtDdb9z0PIdVVQ9OcnV3/2lVfW2SpyZ5UJIPJnlWd392owMeUlX9VJLf7u7rNj3LulXVHbJ8M+cfdfebquofJflbSa5OclF3/5+NDrgGVfVXkzwyyb2SfDnJh5L8enf/6UYHAwDYIjt5Yuiq+tdJHpvk4iTXrzafluUN8sXd/exNzXZ7q6of6+6XbXqO22r1S9q/zPKLywOSPKm7X7u67d3d/aBNzndYVfWMJOdmCbC/m+TBSd6S5O8meUN3//wGxzu0qrpk/6YkD0vy5iTp7vOO+VBrVFXv6u6zV5d/PMtj9beTfH+S/7ztzy1VdVWS+3f3jVV1UZLPJ3lVku9bbX/kRgc8pKr6bJLPJflwklck+c3uvmGzU61HVf1alueVOyb5kyR3SvJbWX521d2P3+B4h7Z6bfj7Sf5bkh9I8p4s6/yHSX6iu9+6uemAXVJVd+vuT256Dm6bqrprd//xpueA49muRqAPJfnr+z/5XH1SelV333czk93+quoPu/v0Tc9xW1XVlUn+Znf/WVWdkeUX0F/t7hdV1Xu6+4EbHfCQVut7QJKTk/yvJKft2evisu6+30YHPKSqeneWvUb+Y5LOEoFekSXAprvftrnpDm/vY7CqLk/yA919Q1X9pSTv7O5v3+yEh1NVV3f3t60uf0V0rar3dvcDNjfd4VXVe5J8R5KHJ3l0kvOS/Pcsj9Hf6u7/vcHxDqWq3t/d96uqk7Ls+fqN3f3lqqok79uB55YrkzxgtaY7Jrm0ux9aVacnee0OvDackuRnkzwiyd2yPH9+Mslrkzy7u/9kg+Pdrqrqdd197qbnOIyq+rosP7/Tkryuu399z20XdvdPbGy4NaiqeyR5RpKbkjw9yU8m+aEsH9g9qbs/vsHxDqWqvn7/piyvCw/M8nvSp4/9VOuzdy/61fPMC5J8Z5IPJHlyd39ik/MdVlU9O8kvdPenquqsJK/M8jj9miSP24H3ne/O8oHOK7r7w5ueZ91WP7PnZXnf8rNJXpplD/sPJbmgu9+zwfEOparulORnsjxXnpbkS1k+hPyl7v7lDY6WZHfPCXRTkm88wva/srptq1XV+2/hz5VJ7r7p+Q7phJsPAevujyZ5aJJzq+oFWV6Yt92N3f3l7v58kg/ffBhDd38hO/DYTHJWljdPT0vy2dWn81/o7rdt+wvxyglVdZequmuWN4c3JEl3fy7JjZsdbS0+UFU/trr8vtWLc6rqm5Ns/eFEWQ7DvKm739jdT8jyOnFhlkMyP7LZ0Q7thNUHHXfOsjfQKavtJ2d5M7wLbj6E/eQsezqlu/8wu7G+Vyb5TJKHdvfXd/dds+xF+ZnVbVutqh50C3++I8sHI9vuZVneo7w6yWOq6tVVdfLqtodsbqy1+eUsH/Bcl2Xv5S9k2SPv7Ul+aXNjrcWnsrxvufnPFVlOSfDu1eVt96w9l5+f5ONJ/kGSy5O8ZCMTrdcPdvenVpefl+TR3X2fLHvYP39zY63NXZL85SRvqap3VdWTq+pIv+NuqwuTPDfJ7yR5R5KXdPcpWU5HcOEmB1uDX8vy3vLvJfm5JL+Y5EeSPKyqnnVr/+GxsKt7Ap2T5N8n+YMsL1hJcnqS+yR54rafV6aqPpHlAfWZ/TcleUd3b+2TQ1W9OclTuvu9e7adlKUM/+PuPnFjw61BVV2W5GHd/fmqOqG7b1ptPyXJW7b9cLebVdVpSV6Y5BNJztvmvdP2qqqPZol1leWT+r/d3R9f1f7f24E9ZU5J8qIk353ljfGDsjyHXpfkp7r7fRsc79BubW/CqrrjKs5upap6cpZP50/M8sb3/CxvPh6S5FXd/XMbHO/QqupJSZ6Q5LIsj8/ndPfLqurUJK/u7u/Z6ICHVFXXdPe3fLW3bYuq+nKSt+XIH+Y8pLu/9hiPtFb795SsqqdliSTnJfndbX9t37cX7Ffscb7te4lW1U9nCQb/qruvXG37H919781Oth579+o9wuN0q392ybIHc5JvXx3G/s7ufsie267cgT209/78vjvL6U4emWUvvFd090WbnO+wjvLcstVHgFTV+7r7/nuuX97d31lVJyT5YHd/6wbH280TQ3f361efXJ+drzwx9OXd/eXNTbY2/yXJnfaGkptV1VuP/Thr9bjs26Oiu29M8riq2oVPLL6nu/88SW4OQCtfk2Srz9mxV3dfn+SHq+oHk+zMSVu7+4xbuOmmLOcm2Wq9nPj5R1eHNtw7y2vE9du+u/gej76lG7Y5ACVJd7+wqn5jdfmPqurlWQ57+w/d/a7NTnd4q0OC35Tk25I8v7t/f7X9hiRbHYBW/mdV/UySX7n5/7equnuSH81ffJi1za5O8s+7+w/231BVu7C+k/d+sNPdP19VH8tyDqs7bXa0tdh75MDL99221R/OdffzV8+dL1w9Fp+R5UOeXXG3qnpKlgD7dVVV/Rd7AOzCESEXJrl0dVjY66vqRVkOn/o7Sf6/35O2WXe/Pcnbq+ons4TLRyfZ6giU5ItV9f1Z9l7uqnpEd7+mqr43yxdAbLPPVdV3dffvVdV5ST6dLL//rQ7V36id3BMIAGBbVNVdsuz+fn6WcwIly56Ul2Q5J9D+PX+3SlU9KsmV3X3NEW57RHe/ZgNjrU1VPTfJG7v7Tfu2n5Pk3/WWn4uyqp6Z5Lm95xtbV9vvk+X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2= VeriFone Inc. + + tpep_pickup_datetime + The date and time when the meter was engaged. + + tpep_dropoff_datetime + The date and time when the meter was disengaged. + + Passenger_count + The number of passengers in the vehicle. + This is a driver-entered value. + + Trip_distance + The elapsed trip distance in miles reported by the taximeter. + + PULocationID + TLC Taxi Zone in which the taximeter was engaged + + DOLocationID + TLC Taxi Zone in which the taximeter was disengaged + + RateCodeID + The final rate code in effect at the end of the trip. + 1= Standard rate 2=JFK 3=Newark 4=Nassau or Westchester 5=Negotiated fare 6=Group ride + + Store_and_fwd_flag + This flag indicates whether the trip record was held in vehicle memory before sending to the vendor, aka “store and forward,” because the vehicle did not have a connection to the server. + Y= store and forward trip N= not a store and forward trip + + Payment_type + A numeric code signifying how the passenger paid for the trip. 1= Credit card 2= Cash 3= No charge 4= Dispute 5= Unknown 6= Voided trip + + Fare_amount + The time-and-distance fare calculated by the meter. + + Extra + Miscellaneous extras and surcharges. Currently, this only includes the $0.50 and $1 rush hour and overnight charges. + + MTA_tax + $0.50 MTA tax that is automatically triggered based on the metered rate in use. + + Improvement_surcharge + $0.30 improvement surcharge assessed trips at the flag drop. The improvement surcharge began being levied in 2015. + + Tip_amount + Tip amount – This field is automatically populated for credit card tips. Cash tips are not included. + + Tolls_amount + Total amount of all tolls paid in trip. Total_amount The total amount charged to passengers. Does not include cash tips. + + */ + +/* +Green Taxi Data Schema +====================== +Header +VendorID,lpep_pickup_datetime,Lpep_dropoff_datetime,Store_and_fwd_flag,RateCodeID,Pickup_longitude,Pickup_latitude,Dropoff_longitude,Dropoff_latitude,Passenger_count,Trip_distance,Fare_amount,Extra,MTA_tax,Tip_amount,Tolls_amount,Ehail_fee,Total_amount,Payment_type,Trip_type + + VendorID + A code indicating the LPEP provider that provided the record. + 1= Creative Mobile Technologies, LLC; 2= VeriFone Inc. lpep_pickup_datetime The date and time when the meter was engaged. lpep_dropoff_datetime The date and time when the meter was disengaged. + + Passenger_count + The number of passengers in the vehicle. + This is a driver-entered value. Trip_distance The elapsed trip distance in miles reported by the taximeter. PULocationID TLC Taxi Zone in which the taximeter was engaged DOLocationID TLC Taxi Zone in which the taximeter was disengaged + + RateCodeID + The final rate code in effect at the end of the trip. + 1= Standard rate 2=JFK 3=Newark 4=Nassau or Westchester 5=Negotiated fare 6=Group ride + + Store_and_fwd_flag + This flag indicates whether the trip record was held in vehicle memory before sending to the vendor, aka “store and forward,” because the vehicle did not have a connection to the server. + Y= store and forward trip N= not a store and forward trip + + Payment_type + A numeric code signifying how the passenger paid for the trip. 1= Credit card 2= Cash 3= No charge 4= Dispute 5= Unknown 6= Voided trip + + Fare_amount + The time-and-distance fare calculated by the meter. + + Extra + Miscellaneous extras and surcharges. Currently, this only includes the $0.50 and $1 rush hour and overnight charges. + + MTA_tax + $0.50 MTA tax that is automatically triggered based on the metered rate in use. + + Improvement_surcharge + $0.30 improvement surcharge assessed on hailed trips at the flag drop. The improvement surcharge began being levied in 2015. + + Tip_amount + Trip amount – This field is automatically populated for credit card tips. Cash tips are not included. + + Tolls_amount + Total amount of all tolls paid in trip. + + Total_amount + The total amount charged to passengers. Does not include cash tips. + + Trip_type + A code indicating whether the trip was a street-hail or a dispatch that is automatically assigned based on the metered rate in use but can be altered by the driver. + 1= Street-hail 2= Dispatch + + */ + +/* +Example Queries + + Query 1 + SELECT cab_type, count(*) FROM trips_csv GROUP BY cab_type; + + Query 2 + SELECT passenger_count, avg(total_amount) FROM trips_orc GROUP BY passenger_count + + Query 3 + SELECT passenger_count, year(pickup_datetime), count(*) FROM trips_orc GROUP BY passenger_count, year(pickup_datetime) + + Query 4 + SELECT passenger_count, year(pickup_datetime) trip_year, round(trip_distance), count(*) trips FROM trips_orc GROUP BY passenger_count, year(pickup_datetime), round(trip_distance) ORDER BY trip_year, trips desc; + + */ + +/* +Schema for Green Taxi + +case class GreenTaxiRide(VendorID: Byte, + lpep_pickup_datetime: TimestampType, + lpep_dropoff_datetime: TimestampType, + Store_and_fwd_flag: Boolean, + RateCodeID: Byte, + Pickup_longitude: Float, + Pickup_latitude: Float, + Dropoff_longitude: Float, + Dropoff_latitude: Float, + Passenger_count: Int, + Trip_distance: Float, + Fare_amount: Float, + Extra: Float, + MTA_tax: Float, + Tip_amount: FLoat, + Tolls_amount: Float, + Ehail_fee: Float, + Total_amount: Float, + Payment_type: Byte, + Trip_type: Byte) + + case class YellowTaxiRide ( + VendorID: Byte, + lpep_pickup_datetime: TimestampType, + lpep_dropoff_datetime: TimestampType, + passenger_count: Int, + trip_distance: Float, + RateCodeID: Byte, + store_and_fwd_flag: Boolean, + PULocationID: Int, + DOLocationID: Int, + payment_type: Byte, + fare_amount: Float, + extra: Float, + mta_tax: Float, + tip_amount: FLoat, + tolls_amount: Float, + improvement_surcharge:Float, + total_amount:Float + congestion_surcharge: Float + + */ + +object NyTaxiBenchmarks { + + // Converged schema with column projections + case class YellowTaxiRide ( + //VendorID: Byte, + pickup_datetime: TimestampType, + //dropoff_datetime: TimestampType, + passenger_count: Int, + trip_distance: Float, + //RateCodeID: Byte, + //store_and_fwd_flag: Boolean, + //PULocationID: Int, + //DOLocationID: Int, + //payment_type: Byte, + //fare_amount: Float, + //extra: Float, + //mta_tax: Float, + //tip_amount: FLoat, + //tolls_amount: Float, + //improvement_surcharge:Float, + total_amount:Float + //congestion_surcharge: Float + ) + + def main(args: Array[String]) { + + val spark = SparkSession.builder().appName("Spark SQL basic example").config("spark.some.config.option", "some-value").getOrCreate() + + // For implicit conversions like converting RDDs to DataFrames + import spark.implicits._ + + // Print working direcotory + System.getProperty("user.dir") + + // Define Programmatic Schema + val schema = StructType( + List( + StructField("pickup_datetime", TimestampType, nullable=true), + StructField("passenger_count", IntegerType, nullable=true), + StructField("trip_distance", FloatType, nullable=true), + StructField("total_amount", FloatType, nullable=true) + ) + ) + println(schema.toDDL) + + //runBasicCountQuery(spark) + + // Load yellow taxidata + val nyYellowTaxiDF = loadYellowTaxiData(spark, schema) + + // Query 1 + val totRides = spark.sql("select count(*) from nyyellowtaxi") + totRides.show() + + // Query 2 + val avgRides = spark.sql("SELECT passenger_count, avg(total_amount) FROM nyyellowtaxi GROUP BY passenger_count") + avgRides.show() + + // Query 3 + val psgCount = spark.sql("SELECT passenger_count, year(pickup_datetime), count(*) FROM nyyellowtaxi GROUP BY passenger_count, year(pickup_datetime)") + psgCount.show() + + // Query 4 + val psgYearCount = spark.sql("SELECT passenger_count, year(pickup_datetime) trip_year, round(trip_distance), count(*) trips FROM nyyellowtaxi GROUP BY passenger_count, year(pickup_datetime), round(trip_distance) ORDER BY trip_year, trips desc") + psgYearCount.show() + + spark.stop() + + } + + def loadYellowTaxiData(spark: SparkSession, schema: StructType): org.apache.spark.sql.DataFrame = { + + // NyTaxi RDD + val nyYellowTaxiRDD = spark.sparkContext.textFile("yellow_tripdata_2019-12.csv") + nyYellowTaxiRDD.take(5).foreach(println) + + // Convert records of ride to Rows + // Ensure there are no empty rows and fields are proper else filter appropriately + // e.g. rowRDD.filter(row => row != null && row.length>0) + val rowRDD = nyYellowTaxiRDD.map(_.split(",")).map(attr => Row( + Timestamp.valueOf(attr(1)), + attr(3).toInt, + attr(4).toFloat, + attr(16).toFloat + ) + ) + rowRDD.take(5).foreach(println) + //rowRDD.collect().foreach(f=>{println(f)}) + + // Apply the schema to the RDD + val nyYellowTaxiDF = spark.createDataFrame(rowRDD, schema) + //nyYellowTaxiDF.show() + + // Creates a temporary view using the DataFrame + nyYellowTaxiDF.createOrReplaceTempView("nyyellowtaxi") + // nytaxiDF.createGlobalTempView("nytaxi") + + //val totRides = spark.sql("select count(*) from nyyellowtaxi") + //totRides.show() + + return nyYellowTaxiDF; + } + + def runBasicCountQuery(spark: SparkSession): Unit = { + // NyTaxi RDD + val nyGreenTaxiRDD = spark.sparkContext.textFile("green_tripdata_2013-08.csv") + + // Programmatic Schema with string values + val schemaString = "VendorID Store_and_fwd_flag" + val fields = schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, nullable = true)) + val schema = StructType(fields) + + // Convert records of the RDD (people) to Rows + val rowRDD = nyGreenTaxiRDD.map(_.split(",")).map(attributes => Row(attributes(0), attributes(3).trim)) + // Apply the schema to the RDD + val nyGreenTaxiDF = spark.createDataFrame(rowRDD, schema) + + // Creates a temporary view using the DataFrame + nyGreenTaxiDF.createOrReplaceTempView("nygreentaxi") + // nyGreenTaxiDF.createGlobalTempView("nygreentaxi") + + // Run the query + val totRides = spark.sql("select count(*) from nygreentaxi") + + // Show results + totRides.show() + } + + +} + + + diff --git a/Notebooks/Scratchpad.ipynb b/Notebooks/Scratchpad.ipynb new file mode 100644 index 0000000..f481042 --- /dev/null +++ b/Notebooks/Scratchpad.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os, glob, shutil\n", + "import pandas as pd\n", + "import numpy as np\n", + "import csv\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "src= os.path.expanduser(r\"C:\\git\\neal\\PipeDream\\benchmarking\\queries\")\n", + "os.path.exists(src)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "query = 2\n", + "\n", + "counts = pd.read_csv(os.path.join(src, \"line_count.csv\"), header=None)\n", + "input_times = pd.read_csv(os.path.join(src, \"fpga_input_times.csv\"), header=None)\n", + "proc_times = pd.read_csv(os.path.join(src, f\"fpga_proc_times_q{query}.csv\"), header=None) / 1000000\n", + "save_times = os.path.join(src, f\"fpga_taxi_q{query}.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(os.path.join(src, \"fpga_taxi_template.csv\"), header=None)\n", + "\n", + "columns = [\"time\", \"total\", \"size\", \"input_throughput\", \"process_throughput\", \"input_time\", \"process_time\"]\n", + "columns = {i: v for i, v in enumerate(columns)}\n", + "\n", + "df = df.rename(columns = columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df['total'] = counts[0]\n", + "df['input_time'] = input_times[0]\n", + "df['process_time'] = proc_times[0]\n", + "df['input_throughput'] = df['total'] / df['input_time']\n", + "df['process_throughput'] = df['total'] / df['process_time']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " time total size input_throughput \\\n", + "0 2020-05-07T02:45:07.525Z 14092413 98 1.008073e+06 \n", + "1 2020-05-07T02:45:17.757Z 13380122 98 1.021916e+06 \n", + "2 2020-05-07T02:45:25.482Z 14387371 98 1.020568e+06 \n", + "3 2020-05-07T02:45:29.054Z 14294783 98 9.021051e+05 \n", + "4 2020-05-07T02:45:35.481Z 14796312 98 9.922677e+05 \n", + ".. ... ... ... ... \n", + "77 2020-05-07T02:50:21.053Z 6073357 98 1.019814e+06 \n", + "78 2020-05-07T02:50:28.426Z 6567787 98 1.476352e+07 \n", + "79 2020-05-07T02:50:35.988Z 7213889 98 9.544614e+05 \n", + "80 2020-05-07T02:50:43.080Z 6878111 98 1.467562e+07 \n", + "81 2020-05-07T02:50:49.974Z 6896315 98 9.440169e+05 \n", + "\n", + " process_throughput input_time process_time \n", + "0 1.498436e+07 13.979560 0.940475 \n", + "1 1.498245e+07 13.093169 0.893053 \n", + "2 1.488521e+07 14.097414 0.966555 \n", + "3 1.462753e+07 15.846028 0.977252 \n", + "4 1.465451e+07 14.911613 1.009676 \n", + ".. ... ... ... \n", + "77 2.725884e+05 5.955355 22.280323 \n", + "78 2.777633e+05 0.444866 23.645268 \n", + "79 1.378436e+05 7.558073 52.333855 \n", + "80 2.787216e+05 0.468676 24.677348 \n", + "81 1.370501e+05 7.305288 50.319654 \n", + "\n", + "[82 rows x 7 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "37.58087687091012" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['process_throughput'].max() * 98 / 1024 / 1024" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(save_times, header=None, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.rcParams['figure.figsize'] = (15,15)\n", + "_=df.plot.bar(y='process_time')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#orig_data_path=\"/data/home/boris/yellow/taxi_data/\"\n", + "src=\"/home/boris/data/taxi_data_cleaned_18_standard\"\n", + "dest=\"/home/boris/data/taxi_data_18\"\n", + "files = glob.glob(os.path.join(src, \"*.csv\"))\n", + "\n", + "len(files)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "fn = \"yellow_tripdata_2014-03.csv\"\n", + "\n", + "srcfl = os.path.join(src, fn)\n", + "destfl = os.path.join(dest, fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def num_cols(srcfl):\n", + " with open(srcfl, \"r\") as fd:\n", + " csvreader = csv.reader(fd)\n", + " for row in csvreader:\n", + " return len(row)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "lngths = dict(zip(files, map(num_cols, files)))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "fltrd = {k: v for k, v in lngths.items() if v == 18}" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "82" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(fltrd)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "if not os.path.exists(dest): os.makedirs(dest)\n", + "for f in fltrd.keys():\n", + " destfl = os.path.join(dest, os.path.split(f)[1])\n", + " shutil.move(f, destfl)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with open(files[0], \"r\") as fd:\n", + " lines = fd.readlines()\n", + "avg_lngths = np.mean([len(l) for l in lines])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "97.9367625610517" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avg_lngths" + ] + } + ], + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Notebooks/Standardize Schema.ipynb b/Notebooks/Standardize Schema.ipynb new file mode 100644 index 0000000..f76ff21 --- /dev/null +++ b/Notebooks/Standardize Schema.ipynb @@ -0,0 +1,1190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from multiprocessing import Pool\n", + "import multiprocessing\n", + "import os\n", + "import glob, shutil\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#orig_data_path=\"/data/home/boris/yellow/taxi_data/\"\n", + "src=\"/home/boris/data/taxi_data_cleaned_18_standard\"\n", + "dest=\"/home/boris/data/taxi_data_cleaned_18_standard\"\n", + "if not os.path.exists(dest):\n", + " os.makedirs(dest)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['/home/boris/data/taxi_data_cleaned_18_standard/yellow_tripdata_2011-06.csv',\n", + " '/home/boris/data/taxi_data_cleaned_18_standard/yellow_tripdata_2011-02.csv',\n", + " '/home/boris/data/taxi_data_cleaned_18_standard/yellow_tripdata_2019-09.csv',\n", + " '/home/boris/data/taxi_data_cleaned_18_standard/yellow_tripdata_2013-10.csv',\n", + " '/home/boris/data/taxi_data_cleaned_18_standard/yellow_tripdata_2011-04.csv']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "files = glob.glob(os.path.join(src, \"*.csv\"))\n", + "\n", + "files[0:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cleanup(f):\n", + "\n", + " dst = os.path.join(dest, os.path.split(f)[1])\n", + " print(f\"Reading: {os.path.split(f)[1]}\")\n", + " \n", + " df = pd.read_csv(f, header=None, error_bad_lines=False).replace('', 'N').fillna(78)\n", + " print(f\"Writing: {os.path.split(f)[1]}\")\n", + " \n", + " df.to_csv(dst, header=None, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_first(f):\n", + " \n", + " dst = os.path.join(dest, os.path.split(f)[1])\n", + " print(f\"Reading: {os.path.split(f)[1]}\")\n", + " \n", + " with open(f, \"r\") as fd:\n", + " l=fd.readlines()\n", + " \n", + " print(f\"Writing: {os.path.split(f)[1]}\") \n", + " with open(dst, \"w\") as fd:\n", + " fd.writelines(l[1:])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def check(f):\n", + " print(f\"Reading... {f}\")\n", + " with open(f, \"r\") as fd:\n", + " lns = fd.readlines()\n", + " empty = []\n", + " for i, l in zip(range(len(lns)), lns):\n", + " if l.find(',,') >= 0 or l.endswith(','): empty += [i]\n", + " if len(empty) > 0: print(f)\n", + " else: print(f\"No empty lines: {f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def clean_schema(f):\n", + " dst = os.path.join(dest, os.path.split(f)[1])\n", + " print(f\"Reading: {os.path.split(f)[1]}\")\n", + " \n", + " df = pd.read_csv(f, header=None)\n", + " df[0] = 1\n", + " df[5] = 1\n", + " df[6] = 'N'\n", + " df[7] = 145\n", + " df[8] = 226\n", + " df[9] = 2\n", + " df[11] = 0.0\n", + " df[10] = df[12]\n", + " df[16] = df[17]\n", + " df[17] = 0.0\n", + " print(f\"Writing: {os.path.split(f)[1]}\") \n", + " df.to_csv(dst, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading: yellow_tripdata_2013-11.csv\n", + "Reading: yellow_tripdata_2011-12.csv\n", + "Reading: yellow_tripdata_2012-07.csv\n", + "Reading: yellow_tripdata_2013-10.csv\n", + "Reading: yellow_tripdata_2011-01.csv\n", + "Reading: yellow_tripdata_2009-08.csv\n", + "Reading: yellow_tripdata_2010-08.csv\n", + "Reading: yellow_tripdata_2011-06.csv\n", + "Reading: yellow_tripdata_2009-03.csv\n", + "Reading: yellow_tripdata_2009-06.csv\n", + "Writing: yellow_tripdata_2011-06.csv\n", + "Reading: yellow_tripdata_2011-02.csv\n", + "Writing: yellow_tripdata_2011-02.csv\n", + "Reading: yellow_tripdata_2019-09.csv\n", + "Writing: yellow_tripdata_2019-09.csv\n", + "Reading: yellow_tripdata_2012-03.csv\n", + "Writing: yellow_tripdata_2010-08.csv\n", + "Reading: yellow_tripdata_2014-12.csv\n", + "Writing: yellow_tripdata_2011-01.csv\n", + "Writing: yellow_tripdata_2009-08.csv\n", + "Writing: yellow_tripdata_2013-11.csv\n", + "Writing: yellow_tripdata_2012-07.csv\n", + "Writing: yellow_tripdata_2009-06.csv\n", + "Writing: yellow_tripdata_2009-03.csv\n", + "Writing: yellow_tripdata_2013-10.csv\n", + "Writing: yellow_tripdata_2011-12.csv\n", + "Reading: yellow_tripdata_2012-04.csv\n", + "Reading: yellow_tripdata_2014-09.csv\n", + "Reading: yellow_tripdata_2009-05.csv\n", + "Reading: yellow_tripdata_2014-01.csv\n", + "Reading: yellow_tripdata_2011-04.csv\n", + "Reading: yellow_tripdata_2009-01.csv\n", + "Reading: yellow_tripdata_2013-08.csv\n", + "Reading: yellow_tripdata_2012-01.csv\n", + "Writing: yellow_tripdata_2014-12.csv\n", + "Writing: yellow_tripdata_2012-03.csv\n", + "Reading: yellow_tripdata_2014-11.csv\n", + "Reading: yellow_tripdata_2010-07.csv\n", + "Writing: yellow_tripdata_2014-09.csv\n", + "Reading: yellow_tripdata_2010-10.csv\n", + "Writing: yellow_tripdata_2012-04.csv\n", + "Writing: yellow_tripdata_2013-08.csv\n", + "Writing: yellow_tripdata_2014-01.csv\n", + "Reading: yellow_tripdata_2019-07.csv\n", + "Reading: yellow_tripdata_2019-10.csv\n", + "Writing: yellow_tripdata_2009-01.csv\n", + "Writing: yellow_tripdata_2019-10.csv\n", + "Writing: yellow_tripdata_2009-05.csv\n", + "Writing: yellow_tripdata_2011-04.csv\n", + "Writing: yellow_tripdata_2019-07.csv\n", + "Writing: yellow_tripdata_2012-01.csv\n", + "Reading: yellow_tripdata_2010-11.csv\n", + "Writing: yellow_tripdata_2014-11.csv\n", + "Reading: yellow_tripdata_2014-04.csv\n", + "Reading: yellow_tripdata_2010-05.csv\n", + "Reading: yellow_tripdata_2014-05.csv\n", + "Writing: yellow_tripdata_2010-07.csv\n", + "Reading: yellow_tripdata_2019-12.csv\n", + "Reading: yellow_tripdata_2011-09.csv\n", + "Reading: yellow_tripdata_2012-09.csv\n", + "Reading: yellow_tripdata_2010-09.csv\n", + "Reading: yellow_tripdata_2009-11.csv\n", + "Writing: yellow_tripdata_2010-10.csv\n", + "Writing: yellow_tripdata_2019-12.csv\n", + "Reading: yellow_tripdata_2014-06.csv\n", + "Reading: yellow_tripdata_2012-10.csv\n", + "Writing: yellow_tripdata_2010-11.csv\n", + "Reading: yellow_tripdata_2011-03.csv\n", + "Writing: yellow_tripdata_2014-04.csv\n", + "Reading: yellow_tripdata_2014-08.csv\n", + "Writing: yellow_tripdata_2010-05.csv\n", + "Writing: yellow_tripdata_2014-05.csv\n", + "Writing: yellow_tripdata_2012-09.csv\n", + "Writing: yellow_tripdata_2011-09.csv\n", + "Reading: yellow_tripdata_2019-05.csv\n", + "Writing: yellow_tripdata_2009-11.csv\n", + "Reading: yellow_tripdata_2013-01.csv\n", + "Writing: yellow_tripdata_2010-09.csv\n", + "Reading: yellow_tripdata_2019-01.csv\n", + "Reading: yellow_tripdata_2009-09.csv\n", + "Reading: yellow_tripdata_2012-12.csv\n", + "Reading: yellow_tripdata_2010-06.csv\n", + "Writing: yellow_tripdata_2014-06.csv\n", + "Writing: yellow_tripdata_2012-10.csv\n", + "Reading: yellow_tripdata_2014-10.csv\n", + "Writing: yellow_tripdata_2019-05.csv\n", + "Reading: yellow_tripdata_2019-02.csv\n", + "Reading: yellow_tripdata_2012-11.csv\n", + "Writing: yellow_tripdata_2019-01.csv\n", + "Writing: yellow_tripdata_2014-08.csv\n", + "Reading: yellow_tripdata_2012-02.csv\n", + "Reading: yellow_tripdata_2011-10.csv\n", + "Writing: yellow_tripdata_2011-03.csv\n", + "Reading: yellow_tripdata_2009-02.csv\n", + "Writing: yellow_tripdata_2019-02.csv\n", + "Reading: yellow_tripdata_2011-07.csv\n", + "Writing: yellow_tripdata_2013-01.csv\n", + "Reading: yellow_tripdata_2011-05.csv\n", + "Writing: yellow_tripdata_2009-09.csv\n", + "Writing: yellow_tripdata_2012-12.csv\n", + "Reading: yellow_tripdata_2009-04.csv\n", + "Writing: yellow_tripdata_2010-06.csv\n", + "Reading: yellow_tripdata_2012-05.csv\n", + "Reading: yellow_tripdata_2010-12.csv\n", + "Writing: yellow_tripdata_2014-10.csv\n", + "Writing: yellow_tripdata_2012-11.csv\n", + "Reading: yellow_tripdata_2013-12.csv\n", + "Reading: yellow_tripdata_2019-11.csv\n", + "Writing: yellow_tripdata_2012-02.csv\n", + "Writing: yellow_tripdata_2011-10.csv\n", + "Reading: yellow_tripdata_2009-10.csv\n", + "Writing: yellow_tripdata_2009-02.csv\n", + "Reading: yellow_tripdata_2013-09.csv\n", + "Reading: yellow_tripdata_2010-01.csv\n", + "Writing: yellow_tripdata_2019-11.csv\n", + "Writing: yellow_tripdata_2011-07.csv\n", + "Reading: yellow_tripdata_2011-08.csv\n", + "Reading: yellow_tripdata_2019-03.csv\n", + "Writing: yellow_tripdata_2011-05.csv\n", + "Writing: yellow_tripdata_2009-04.csv\n", + "Writing: yellow_tripdata_2010-12.csv\n", + "Reading: yellow_tripdata_2019-08.csv\n", + "Reading: yellow_tripdata_2013-02.csv\n", + "Writing: yellow_tripdata_2012-05.csv\n", + "Reading: yellow_tripdata_2009-07.csv\n", + "Reading: yellow_tripdata_2012-06.csv\n", + "Writing: yellow_tripdata_2013-12.csv\n", + "Reading: yellow_tripdata_2013-07.csv\n", + "Writing: yellow_tripdata_2019-03.csv\n", + "Reading: yellow_tripdata_2019-04.csv\n", + "Writing: yellow_tripdata_2019-08.csv\n", + "Reading: yellow_tripdata_2011-11.csv\n", + "Writing: yellow_tripdata_2011-11.csv\n", + "Writing: yellow_tripdata_2013-09.csv\n", + "Reading: yellow_tripdata_2013-04.csv\n", + "Writing: yellow_tripdata_2009-10.csv\n", + "Reading: yellow_tripdata_2014-07.csv\n", + "Writing: yellow_tripdata_2010-01.csv\n", + "Writing: yellow_tripdata_2011-08.csv\n", + "Reading: yellow_tripdata_2013-05.csv\n", + "Reading: yellow_tripdata_2014-02.csv\n", + "Writing: yellow_tripdata_2019-04.csv\n", + "Reading: yellow_tripdata_2019-06.csv\n", + "Writing: yellow_tripdata_2013-02.csv\n", + "Writing: yellow_tripdata_2009-07.csv\n", + "Reading: yellow_tripdata_2012-08.csv\n", + "Writing: yellow_tripdata_2013-07.csv\n", + "Writing: yellow_tripdata_2012-06.csv\n", + "Reading: yellow_tripdata_2013-06.csv\n", + "Writing: yellow_tripdata_2019-06.csv\n", + "Writing: yellow_tripdata_2014-07.csv\n", + "Writing: yellow_tripdata_2013-04.csv\n", + "Reading: yellow_tripdata_2013-03.csv\n", + "Writing: yellow_tripdata_2014-02.csv\n", + "Reading: yellow_tripdata_2014-03.csv\n", + "Writing: yellow_tripdata_2013-05.csv\n", + "Writing: yellow_tripdata_2012-08.csv\n", + "Reading: yellow_tripdata_2010-04.csv\n", + "Writing: yellow_tripdata_2013-06.csv\n", + "Reading: yellow_tripdata_2009-12.csv\n", + "Writing: yellow_tripdata_2013-03.csv\n", + "Writing: yellow_tripdata_2014-03.csv\n", + "Writing: yellow_tripdata_2010-04.csv\n", + "Writing: yellow_tripdata_2009-12.csv\n" + ] + } + ], + "source": [ + "n_cores = multiprocessing.cpu_count()\n", + "pool = Pool(processes=n_cores)\n", + "_ = pool.map(remove_first, files)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "fn = \"yellow_tripdata_2009-05.csv\"\n", + "\n", + "srcfl = os.path.join(src, fn)\n", + "destfl = os.path.join(dest, fn)\n", + "\n", + "df = pd.read_csv(files[5], header=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDYARN Application IDKindStateSpark UIDriver logCurrent session?
1application_1586146063972_0005pysparkidleLinkLink✔
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SparkSession available as 'spark'.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark.sql import SparkSession\n", + "import pyspark.sql.functions as F" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyspark.sql.types import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxi_file_loc=\"wasb://yellow@pipedreamdemo/taxi_data/yellow_tripdata_2019-12.csv\"\n", + "schema_cols = \"VendorID,tpep_pickup_datetime,tpep_dropoff_datetime,passenger_count,trip_distance,RatecodeID,store_and_fwd_flag,PULocationID,DOLocationID,payment_type,fare_amount,extra,mta_tax,tip_amount,tolls_amount,improvement_surcharge,total_amount,congestion_surcharge\".split(',')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxi_orig_schema = (\n", + " StructType()\n", + " .add(\"VendorID\", IntegerType())\n", + " .add(\"tpep_pickup_datetime\", TimestampType())\n", + " .add(\"tpep_dropoff_datetime\", TimestampType())\n", + " .add(\"passenger_count\", IntegerType())\n", + " .add(\"trip_distance\", DoubleType())\n", + " .add(\"RatecodeID\", IntegerType())\n", + " .add(\"store_and_fwd_flag\", StringType())\n", + " .add(\"PULocationID\", IntegerType())\n", + " .add(\"DOLocationID\", IntegerType())\n", + " .add(\"payment_type\", IntegerType())\n", + " .add(\"fare_amount\", DoubleType())\n", + " .add(\"extra\", DoubleType())\n", + " .add(\"mta_tax\", DoubleType())\n", + " .add(\"tip_amount\", DoubleType())\n", + " .add(\"tolls_amount\", DoubleType())\n", + " .add(\"improvement_surcharge\", DoubleType())\n", + " .add(\"total_amount\", DoubleType())\n", + " .add(\"congestion_surcharge\", DoubleType())\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read it" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An error was encountered:\n", + "Interpreter died:\n", + "\n", + "/usr/hdp/current/spark2-client/python/pyspark/context.py:261: RuntimeWarning: Failed to add file [file:/usr/hdp/current/spark2-client/python/lib/pyspark.zip] speficied in 'spark.submit.pyFiles' to Python path:\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/tmp\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/spark-b02c8e03-a6a6-40a4-a364-0e86f485fb21/userFiles-6c84950d-1e7c-4433-af02-3d504a2657da\n", + " /usr/hdp/current/spark2-client/python\n", + " /usr/hdp/current/spark2-client/python/lib/py4j-0.10.7-src.zip\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/pyspark.zip\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/py4j-0.10.7-src.zip\n", + " /usr/bin/anaconda/lib/python27.zip\n", + " /usr/bin/anaconda/lib/python2.7\n", + " /usr/bin/anaconda/lib/python2.7/plat-linux2\n", + " /usr/bin/anaconda/lib/python2.7/lib-tk\n", + " /usr/bin/anaconda/lib/python2.7/lib-old\n", + " /usr/bin/anaconda/lib/python2.7/lib-dynload\n", + " /usr/bin/anaconda/lib/python2.7/site-packages\n", + " /usr/bin/anaconda/lib/python2.7/site-packages/Sphinx-1.4.6-py2.7.egg\n", + " /var/lib/.jupyter/jupyterazure\n", + " /usr/bin/anaconda/lib/python2.7/site-packages/setuptools-27.2.0-py2.7.egg\n", + " RuntimeWarning)\n", + "/usr/hdp/current/spark2-client/python/pyspark/context.py:261: RuntimeWarning: Failed to add file [file:/usr/hdp/current/spark2-client/python/lib/py4j-0.10.7-src.zip] speficied in 'spark.submit.pyFiles' to Python path:\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/tmp\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/spark-b02c8e03-a6a6-40a4-a364-0e86f485fb21/userFiles-6c84950d-1e7c-4433-af02-3d504a2657da\n", + " /usr/hdp/current/spark2-client/python\n", + " /usr/hdp/current/spark2-client/python/lib/py4j-0.10.7-src.zip\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/pyspark.zip\n", + " /mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/py4j-0.10.7-src.zip\n", + " /usr/bin/anaconda/lib/python27.zip\n", + " /usr/bin/anaconda/lib/python2.7\n", + " /usr/bin/anaconda/lib/python2.7/plat-linux2\n", + " /usr/bin/anaconda/lib/python2.7/lib-tk\n", + " /usr/bin/anaconda/lib/python2.7/lib-old\n", + " /usr/bin/anaconda/lib/python2.7/lib-dynload\n", + " /usr/bin/anaconda/lib/python2.7/site-packages\n", + " /usr/bin/anaconda/lib/python2.7/site-packages/Sphinx-1.4.6-py2.7.egg\n", + " /var/lib/.jupyter/jupyterazure\n", + " /usr/bin/anaconda/lib/python2.7/site-packages/setuptools-27.2.0-py2.7.egg\n", + " RuntimeWarning)\n", + "\n", + "ERROR:fake_shell:execute_reply\n", + "Traceback (most recent call last):\n", + " File \"/mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/tmp/1656805614047440300\", line 318, in execute_request\n", + " result = node.execute()\n", + " File \"/mnt/resource/hadoop/yarn/local/usercache/livy/appcache/application_1586146063972_0005/container_1586146063972_0005_01_000001/tmp/1656805614047440300\", line 233, in execute\n", + " raise ExecutionError(sys.exc_info())\n", + "ExecutionError\n", + "\n" + ] + } + ], + "source": [ + "taxi_train_df = spark.read.schema(taxi_orig_schema).csv(taxi_file_loc, header=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "root\n", + " |-- VendorID: integer (nullable = true)\n", + " |-- tpep_pickup_datetime: timestamp (nullable = true)\n", + " |-- tpep_dropoff_datetime: timestamp (nullable = true)\n", + " |-- passenger_count: integer (nullable = true)\n", + " |-- trip_distance: double (nullable = true)\n", + " |-- RatecodeID: integer (nullable = true)\n", + " |-- store_and_fwd_flag: string (nullable = true)\n", + " |-- PULocationID: integer (nullable = true)\n", + " |-- DOLocationID: integer (nullable = true)\n", + " |-- payment_type: integer (nullable = true)\n", + " |-- fare_amount: double (nullable = true)\n", + " |-- extra: double (nullable = true)\n", + " |-- mta_tax: double (nullable = true)\n", + " |-- tip_amount: double (nullable = true)\n", + " |-- tolls_amount: double (nullable = true)\n", + " |-- improvement_surcharge: double (nullable = true)\n", + " |-- total_amount: double (nullable = true)\n", + " |-- congestion_surcharge: double (nullable = true)" + ] + } + ], + "source": [ + "taxi_train_df.printSchema()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+--------------------+\n", + "|VendorID|tpep_pickup_datetime|tpep_dropoff_datetime|passenger_count|trip_distance|RatecodeID|store_and_fwd_flag|PULocationID|DOLocationID|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|improvement_surcharge|total_amount|congestion_surcharge|\n", + "+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+--------------------+\n", + "+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+--------------------+" + ] + } + ], + "source": [ + "taxi_train_df.show(100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update the schema" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Queries" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxi_train_df.createOrReplaceTempView('nyyellowtaxi')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------+\n", + "| total|\n", + "+-------+\n", + "|6896317|\n", + "+-------+" + ] + } + ], + "source": [ + "\n", + "count_df = spark.sql(\"select count(*) as total from nyyellowtaxi\").show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------+----+-------+\n", + "|passenger_count|year| total|\n", + "+---------------+----+-------+\n", + "| 5|2009| 6|\n", + "| 6|2019| 147923|\n", + "| 9|2019| 16|\n", + "| 4|2019| 164921|\n", + "| 8|2019| 18|\n", + "| 5|2020| 1|\n", + "| 4|2009| 2|\n", + "| 6|2008| 1|\n", + "| 4|2008| 4|\n", + "| 1|2058| 3|\n", + "| 5|2019| 244875|\n", + "| 1|2009| 16|\n", + "| null|2019| 51018|\n", + "| 3|2019| 302987|\n", + "| 2|2008| 6|\n", + "| 1|2019|4783228|\n", + "| 1|2008| 8|\n", + "| 3|2020| 12|\n", + "| 4|2020| 4|\n", + "| 1|2026| 1|\n", + "+---------------+----+-------+\n", + "only showing top 20 rows" + ] + } + ], + "source": [ + "psg_count_dist = spark.sql(\"SELECT passenger_count, \\\n", + " year(tpep_pickup_datetime) year, count(*) total FROM nyyellowtaxi GROUP BY passenger_count, year(tpep_pickup_datetime)\").show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "avg_fare_psg_count = spark.sql(\"SELECT passenger_count, \\\n", + " avg(total_amount) avg_fare FROM nyyellowtaxi GROUP BY passenger_count\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------+------------------+\n", + "|passenger_count| avg_fare|\n", + "+---------------+------------------+\n", + "| null| 38.10385452193327|\n", + "| 1| 19.2689940821399|\n", + "| 6|19.365102653367924|\n", + "| 3|20.025870953485427|\n", + "| 5| 19.35781789152236|\n", + "| 9| 72.15124999999999|\n", + "| 4|20.576281717809266|\n", + "| 8| 94.81166666666665|\n", + "| 7| 61.69222222222221|\n", + "| 2| 20.35205234127457|\n", + "| 0| 18.94103797144808|\n", + "+---------------+------------------+" + ] + } + ], + "source": [ + "avg_fare_psg_count.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "psg_year_count = spark.sql(\"SELECT passenger_count, \\\n", + " year(tpep_pickup_datetime) trip_year, \\\n", + " round(trip_distance) distance, \\\n", + " count(*) trips \\\n", + " FROM nyyellowtaxi \\\n", + " GROUP BY passenger_count, \\\n", + " year(tpep_pickup_datetime), round(trip_distance) ORDER BY trip_year, trips desc\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------+---------+--------+-----+\n", + "|passenger_count|trip_year|distance|trips|\n", + "+---------------+---------+--------+-----+\n", + "| 1| 2008| 1.0| 3|\n", + "| 4| 2008| 1.0| 2|\n", + "| 3| 2008| 1.0| 2|\n", + "| 3| 2008| 3.0| 2|\n", + "| 1| 2008| 0.0| 2|\n", + "| 2| 2008| 1.0| 2|\n", + "| 3| 2008| 0.0| 1|\n", + "| 1| 2008| 10.0| 1|\n", + "| 3| 2008| 4.0| 1|\n", + "| 2| 2008| 8.0| 1|\n", + "| 1| 2008| 12.0| 1|\n", + "| 6| 2008| 18.0| 1|\n", + "| 5| 2008| 0.0| 1|\n", + "| 2| 2008| 3.0| 1|\n", + "| 2| 2008| 0.0| 1|\n", + "| 2| 2008| 17.0| 1|\n", + "| 5| 2008| 1.0| 1|\n", + "| 4| 2008| 2.0| 1|\n", + "| 5| 2008| 2.0| 1|\n", + "| 1| 2008| 8.0| 1|\n", + "+---------------+---------+--------+-----+\n", + "only showing top 20 rows" + ] + } + ], + "source": [ + "psg_year_count.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "PySpark", + "language": "", + "name": "pysparkkernel" + }, + "language_info": { + "codemirror_mode": { + "name": "python", + "version": 3 + }, + "mimetype": "text/x-python", + "name": "pyspark", + "pygments_lexer": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Notebooks/Yellow Taxi Stream-Local.ipynb b/Notebooks/Yellow Taxi Stream-Local.ipynb new file mode 100644 index 0000000..bce6d59 --- /dev/null +++ b/Notebooks/Yellow Taxi Stream-Local.ipynb @@ -0,0 +1,280 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Taxi Queries on a Structured Stream\n", + "\n", + "We have 106GB of NYC Yellow Taxi data that has been cleaned and normalized based on the [published schema](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql import SparkSession\n", + "import pyspark.sql.functions as F\n", + "from pyspark.sql.types import *\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "

SparkSession - hive

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TimestampType())\n", + " .add(\"tpep_dropoff_datetime\", TimestampType())\n", + " .add(\"passenger_count\", IntegerType())\n", + " .add(\"trip_distance\", DoubleType())\n", + " .add(\"RatecodeID\", IntegerType())\n", + " .add(\"store_and_fwd_flag\", StringType())\n", + " .add(\"PULocationID\", IntegerType())\n", + " .add(\"DOLocationID\", IntegerType())\n", + " .add(\"payment_type\", IntegerType())\n", + " .add(\"fare_amount\", DoubleType())\n", + " .add(\"extra\", DoubleType())\n", + " .add(\"mta_tax\", DoubleType())\n", + " .add(\"tip_amount\", DoubleType())\n", + " .add(\"tolls_amount\", DoubleType())\n", + " .add(\"improvement_surcharge\", DoubleType())\n", + " .add(\"total_amount\", DoubleType())\n", + " .add(\"congestion_surcharge\", DoubleType())\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Stream Source\n", + "\n", + "Also get the SQL table reference for the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "taxi_input_df = (\n", + " spark\n", + " .readStream\n", + " .schema(taxi_orig_schema)\n", + " .option(\"header\", \"false\")\n", + " .option(\"maxFilesPerTrigger\", 1)\n", + " .option(\"badRecordsPath\", bad_records_path)\n", + " .csv(taxi_file_dir) #the source\n", + ")\n", + "\n", + "#Create SQL table out of it\n", + "taxi_input_df.createOrReplaceTempView('nyyellowtaxi')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Queries\n", + "\n", + "```sql\n", + "1. select passenger_count, year(tpep_pickup_datetime) year from nyyellowtaxi\n", + "2. select passenger_count, avg(total_amount) as avg_amount from nyyellowtaxi group by passenger_count\n", + "3. select passenger_count, year(tpep_pickup_datetime) year, count(*) total \n", + " from nyyellowtaxi group by passenger_count, year\n", + "4. select passenger_count, year(tpep_pickup_datetime) as year, \n", + " cast(trip_distance as int) as distance, \n", + " count(*) as total \n", + " from nyyellowtaxi\n", + " group by passenger_count, year, distance\n", + " order by year, total desc\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "queries = [\"select passenger_count, year(tpep_pickup_datetime) year from nyyellowtaxi\",\n", + " \"select passenger_count, avg(total_amount) as avg_amount from nyyellowtaxi group by passenger_count\",\n", + " \"select passenger_count, year(tpep_pickup_datetime) year, count(*) total FROM $tableName group by passenger_count, year\",\n", + " \"\"\"select passenger_count, year(tpep_pickup_datetime) as year, \n", + " cast(trip_distance as int) as distance, \n", + " count(*) as total \n", + " from $tableName\n", + " group by passenger_count, year, distance\n", + " order by year, total desMc\"\"\"\n", + " ]\n", + "\n", + "query_names = [\"q{}\".format(i) for i in range(1, len(queries))]\n", + "out_dirs = [os.path.join(taxi_output_dir, n) for n in query_names]\n", + "checkpoint_locs = [os.path.join(checkpoint_loc, n) for n in query_names]\n", + "name_query_outdir = list(zip(query_names, queries, out_dirs, checkpoint_locs))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def start_query(query_desc):\n", + " query_name, query_str, output_dir, checkpoint_loc = query_desc\n", + " q = (\n", + " spark.sql(query_str)\n", + " .writeStream\n", + " .outputMode(\"append\")\n", + " .format(\"csv\")\n", + " .option(\"path\", output_dir)\n", + " .option(\"startingOffsets\", \"earliest\")\n", + " .option(\"checkpointLocation\", checkpoint_loc)\n", + " .queryName(query_name)\n", + " .trigger(processingTime='1 seconds') \n", + " .start()\n", + " )\n", + " return q" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "q1 = start_query(name_query_outdir[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'message': 'Processing new data',\n", + " 'isDataAvailable': True,\n", + " 'isTriggerActive': True}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q1.status" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "q1.stop()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7 - Spark (local)", + "language": "python", + "name": "spark-3-python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Notebooks/Yellow Taxi Stream.ipynb b/Notebooks/Yellow Taxi Stream.ipynb new file mode 100644 index 0000000..044f5cd --- /dev/null +++ b/Notebooks/Yellow Taxi Stream.ipynb @@ -0,0 +1,591 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Taxi Queries on a Single DF from CSV" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql import SparkSession\n", + "import pyspark.sql.functions as F" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql.types import *" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxi_file_loc=\"abfs:///taxi_data/yellow_tripdata_2019-12.csv\"\n", + "taxi_file_dir=\"/taxi_data/\"\n", + "taxi_output_dir=\"/taxi_data_year_count\"\n", + "schema_cols = \"VendorID,tpep_pickup_datetime,tpep_dropoff_datetime,passenger_count,trip_distance,RatecodeID,store_and_fwd_flag,PULocationID,DOLocationID,payment_type,fare_amount,extra,mta_tax,tip_amount,tolls_amount,improvement_surcharge,total_amount,congestion_surcharge\".split(',')\n", + "bad_recordset_path=\"/taxi_error\"\n", + "checkpoint_loc=\"/checkpoints\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define schema ahead of time for perf" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxi_orig_schema = (\n", + " StructType()\n", + " .add(\"VendorID\", IntegerType())\n", + " .add(\"tpep_pickup_datetime\", TimestampType())\n", + " .add(\"tpep_dropoff_datetime\", TimestampType())\n", + " .add(\"passenger_count\", IntegerType())\n", + " .add(\"trip_distance\", DoubleType())\n", + " .add(\"RatecodeID\", IntegerType())\n", + " .add(\"store_and_fwd_flag\", StringType())\n", + " .add(\"PULocationID\", IntegerType())\n", + " .add(\"DOLocationID\", IntegerType())\n", + " .add(\"payment_type\", IntegerType())\n", + " .add(\"fare_amount\", DoubleType())\n", + " .add(\"extra\", DoubleType())\n", + " .add(\"mta_tax\", DoubleType())\n", + " .add(\"tip_amount\", DoubleType())\n", + " .add(\"tolls_amount\", DoubleType())\n", + " .add(\"improvement_surcharge\", DoubleType())\n", + " .add(\"total_amount\", DoubleType())\n", + " .add(\"congestion_surcharge\", DoubleType())\n", + ")\n", + "\n", + "taxi_clean_chema = (\n", + " StructType()\n", + " .add(\"tpep_pickup_datetime\", TimestampType())\n", + " .add(\"tpep_dropoff_datetime\", TimestampType())\n", + " .add(\"passenger_count\", IntegerType())\n", + " .add(\"trip_distance\", DoubleType())\n", + " .add(\"payment_type\", IntegerType())\n", + " .add(\"fare_amount\", DoubleType())\n", + " .add(\"extra\", DoubleType())\n", + " .add(\"mta_tax\", DoubleType())\n", + " .add(\"tip_amount\", DoubleType())\n", + " .add(\"tolls_amount\", DoubleType())\n", + " .add(\"improvement_surcharge\", DoubleType())\n", + " .add(\"total_amount\", DoubleType())\n", + " .add(\"congestion_surcharge\", DoubleType())\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Input DF" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxi_input_df = (\n", + " spark\n", + " .readStream\n", + " .option(\"maxFilesPerTrigger\", 1)\n", + " .schema(taxi_orig_schema)\n", + " .option(\"badRecordsPath\", bad_recordset_path) #any bad records will go here\n", + " .option(\"header\", \"false\")\n", + " .csv(taxi_file_dir) #the source\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query= (\n", + " taxi_input_df\n", + " .writeStream\n", + " .format(\"parquet\")\n", + " .option(\"path\", taxi_output_dir)\n", + " .option(\"checkpointLocation\", checkpoint_loc)\n", + " .outputMode(\"append\")\n", + " .option(\"startingOffsets\", \"earliest\")\n", + " .queryName(\"taxi\")\n", + "# .trigger(processingTime='5 seconds')\n", + " .start()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query.stop()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Output DF (query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read it" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Queries" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "taxi_input_df.createOrReplaceTempView('nyyellowtaxi')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import uuid\n", + "\n", + "def write_batch_df(batch_df, epoch_id):\n", + " try:\n", + " batch_df.write.csv('{}/taxi_{}'.format(taxi_output_dir, epoch_id), mode='ignore')\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "q0 = (\n", + " spark.sql(\"SELECT passenger_count, \\\n", + " year(tpep_pickup_datetime) year, count(*) total FROM nyyellowtaxi GROUP BY passenger_count, year(tpep_pickup_datetime)\")\n", + " .writeStream\n", + " .outputMode(\"complete\")\n", + " .foreachBatch(write_batch_df)\n", + " .option(\"startingOffsets\", \"earliest\")\n", + " .queryName(\"year_count\")\n", + " .trigger(processingTime='20 seconds') \n", + " .start()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{u'message': u'Processing new data', u'isTriggerActive': True, u'isDataAvailable': True}" + ] + } + ], + "source": [ + "q0.status" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "amount = (\n", + "taxi_input_df\n", + ".filter(F.col(\"fare_amount\") > 11.0)\n", + ".writeStream\n", + ".format(\"memory\")\n", + ".queryName(\"query4\")\n", + ".outputMode(\"append\")\n", + ".start() \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{u'message': u'Processing new data', u'isTriggerActive': True, u'isDataAvailable': True}" + ] + } + ], + "source": [ + "amount.status" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "de48abab00f64efbbdde22e22121809b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%sql\n", + "select * from query4" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "avg_fare_psg_count = spark.sql(\"SELECT passenger_count, \\\n", + " avg(total_amount) avg_fare FROM nyyellowtaxi GROUP BY passenger_count\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame[passenger_count: int, avg_fare: double]" + ] + } + ], + "source": [ + "avg_fare_psg_count" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "psg_year_count = spark.sql(\"SELECT passenger_count, \\\n", + " year(tpep_pickup_datetime) trip_year, \\\n", + " round(trip_distance) distance, \\\n", + " count(*) trips \\\n", + " FROM nyyellowtaxi \\\n", + " GROUP BY passenger_count, \\\n", + " year(tpep_pickup_datetime), round(trip_distance) ORDER BY trip_year, trips desc\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "psg_year_count.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7 - Spark (local)", + "language": "python", + "name": "spark-3-python" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Notebooks/fake tests.ipynb b/Notebooks/fake tests.ipynb new file mode 100644 index 0000000..ff362f7 --- /dev/null +++ b/Notebooks/fake tests.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "from faker import Faker\n", + "import random as rnd\n", + "import os, argparse, logging\n", + "import pandas as pd\n", + "\n", + "logging.basicConfig(format='%(asctime)s %(levelname)-10s %(message)s', datefmt=\"%Y-%m-%d-%H-%M-%S\",\n", + " level=logging.INFO)\n", + "\n", + "def parse_args():\n", + " ap = argparse.ArgumentParser()\n", + " ap.add_argument(\"-o\", \"--outpath\", default=\".\", help=\"Output for csv files\")\n", + " ap.add_argument(\"-n\", \"--n-files\", default=5, type=int, help=\"number of files to generate\")\n", + " ap.add_argument(\"-s\", \"--file-size\", default=10 * 1024 * 1024, type=int, help=\"file size\")\n", + " return ap.parse_args()\n", + "\n", + "columns = (\n", + " [\"Text01\", \"Text02\", \"Text03\", \"Text04\", \"Text05\", \"Text06\", \"IntWritable01\", \"Text07\", \"Text08\"] +\n", + " [\"Text09\", \"Text10\", \"Text11\", \"Text12\", \"Text13\", \"LongWritable01\", \"LongWritable02\", \"LongWritable03\"] +\n", + " [\"LongWritable04\", \"LongWritable05\", \"LongWritable06\", \"Text14\", \"Text15\", \"IntWritable02\", \"Text16\", \"Text17\", \"Text18\", \"Text19\", \"Text20\",] +\n", + " [\"IntWritable03\", \"IntWritable04\", \"Text21\", \"IntWritable05\", \"Text22\", \"Text23\", \"Text24\", \"Text25\", \"Text26\", \"IntWritable06\"] +\n", + " [\"IntWritable07\", \"Text27\", \"IntWritable08\", \"Text28\", \"IntWritable09\", \"IntWritable10\", \"IntWritable11\", \"Text29\", \"Text30\", \"Text31\"]\n", + ")\n", + "\n", + "fake = Faker()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "def get_date():\n", + " return fake.date(pattern='%m:%d:%y %H:%M:%S:%f')[:-3]\n", + "\n", + "def get_dec(a, b):\n", + " return str(fake.pydecimal(left_digits=rnd.randint(a, b), right_digits=0))[:-2].replace('-', '')\n", + "\n", + "def str_exact_chars(n):\n", + " return fake.pystr(min_chars=n, max_chars=n)\n", + "\n", + "def generate_record():\n", + " Text01 = get_date()\n", + " Text02 = get_date()\n", + " Text03 = get_dec(1, 15)\n", + " Text04 = str_exact_chars(3)\n", + " Text05 = get_dec(1, 15)\n", + " Text06 = fake.pystr_format(string_format='###-###-###-###-##{{random_int}}')[:-4]\n", + " IntWriteable01 = get_dec(1, 1)\n", + " Text07 = fake.pystr_format(string_format='###-###-###-###-####{{random_int}}')[:-2]\n", + " Text08 = fake.ipv6()\n", + " Text09 = fake.ipv6()\n", + " Text10 = ''\n", + " Text11 = get_dec(5, 5)\n", + " Text12 = get_dec(5, 5)\n", + " Text13 = get_dec(2, 2)\n", + " LongWritable01 = get_dec(8, 12)\n", + " LongWritable02 = get_dec(8, 12)\n", + " LongWritable03 = get_dec(8, 12)\n", + " LongWritable04 = get_dec(8, 12)\n", + " LongWritable05 = get_dec(8, 12)\n", + " LongWritable06 = get_dec(8, 12)\n", + " Text14 = str_exact_chars(10)\n", + " Text15 = str_exact_chars(10)\n", + " IntWriteable02 = fake.pyint()\n", + " Text16 = str_exact_chars(12)\n", + " Text17= fake.pystr_format(string_format='##{{random_int}}')\n", + " Text18 = fake.ipv4()\n", + " Text19 = str_exact_chars(3)\n", + " Text20 = fake.pystr_format(string_format=\"???_?_??_???_????_?\")\n", + " IntWriteable03 = fake.pyint() \n", + " IntWriteable04 = fake.pyint() \n", + " Text21 = fake.pystr_format(string_format=\"#???###????###??\")\n", + " IntWriteable05 = fake.pyint() \n", + " Text22 = get_dec(1, 2)\n", + " Text23 = \"\"\n", + " Text24 = \"\"\n", + " Text25 = fake.pystr_format(string_format=\"##|###|#####|###|##|####\")\n", + " Text26 = fake.pystr_format(string_format=\"##:#######:###:####\")\n", + " IntWriteable06 = fake.pyint()\n", + " IntWriteable07 = fake.pyint()\n", + " Text27 = fake.ipv6()\n", + " IntWriteable08 = fake.pyint()\n", + " Text28 = fake.ipv6()\n", + " IntWriteable09 = fake.pyint()\n", + " IntWriteable10 = fake.pyint()\n", + " IntWriteable11 = fake.pyint()\n", + " Text29 = \"\"\n", + " Text30 = str_exact_chars(24)\n", + " Text31 = \"\"\n", + "\n", + " data = ([Text01, Text02, Text03, Text04, Text05, Text06, IntWriteable01, Text07, Text08, Text09, Text10] + \n", + " [Text11, Text12, Text13, LongWritable01, LongWritable02, LongWritable03, LongWritable04, LongWritable05, LongWritable06] +\n", + " [Text14, Text15, IntWriteable02, Text16, Text17, Text18, Text19, Text20, IntWriteable03, IntWriteable04, Text21] +\n", + " [IntWriteable05, Text22, Text23, Text24, Text25, Text26, IntWriteable06, IntWriteable07, Text27, IntWriteable08] +\n", + " [Text28, IntWriteable09, IntWriteable10, IntWriteable11, Text29, Text30, Text31]\n", + " )\n", + "\n", + " return data\n", + "\n", + "def generate_fake_data(out_path, file_size=1024 * 1024 * 10):\n", + " size = 0\n", + " data_dicts = []\n", + "\n", + " while size < file_size:\n", + "\n", + " data = generate_record()\n", + " size = sum([len(str(x)) for x in data])\n", + " data_dicts.append(dict(zip(columns, data)))\n", + "\n", + " df = pd.DataFrame(data_dicts)\n", + " df.to_csv(out_path)\n", + " logging.info(f\"Generated: {out_path}\")\n", + "\n", + "def generate_files(out_path, n_files = 5, file_size=1024 * 1024 * 10):\n", + "\n", + " file_path = os.path.join(out_path, fake.pystr_format(string_format=\"??????????.csv\"))\n", + "\n", + " for _ in range(n_files):\n", + " generate_fake_data(file_path, file_size)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "def gzip_file(file_path):\n", + "\n", + " base = os.path.splitext(file_path)[0]\n", + " dst_path = f\"{base}.gz\"\n", + "\n", + " with open(file_path, \"r\") as fd:\n", + " with gzip.open(dst_path, \"wb\") as gzfd:\n", + " shutil.copyfileobj(fd, gzfd)\n", + "\n", + " os.remove(file_path)\n", + " logging.info(f\"Compressed: {dst_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['04:04:06 06:17:23:000',\n", + " '09:09:07 04:47:56:000',\n", + " '237503428635467',\n", + " 'VZS',\n", + " '93163749932',\n", + " '069-678-346-387-77',\n", + " '2',\n", + " '388-630-450-251-613450',\n", + " '68af:b5ce:3e7f:1565:3c6:dc2a:5417:2447',\n", + " 'ca82:f7ed:b38d:d4f2:cf1f:f172:a761:e223',\n", + " '',\n", + " '63446',\n", + " '44989',\n", + " '23',\n", + " '97680902',\n", + " '170586192',\n", + " '78100466769',\n", + " '4131020837',\n", + " '622047749685',\n", + " '229398760573',\n", + " 'hXPMiqVClQ',\n", + " 'vdFhRsuEnf',\n", + " 5676,\n", + " 'dmmGfDPnQTbW',\n", + " '986369',\n", + " '198.4.249.177',\n", + " 'cts',\n", + " 'Bpb_A_HH_mGq_YaYU_u',\n", + " 9605,\n", + " 3593,\n", + " '7gvq572Bwqv923pS',\n", + " 7436,\n", + " '11',\n", + " '',\n", + " '',\n", + " '29|862|65115|481|49|5723',\n", + " '80:9163731:799:7541',\n", + " 7914,\n", + " 4187,\n", + " '175c:6e04:cb52:875e:8ace:d0f4:69de:105a',\n", + " 5298,\n", + " 'ac79:96f:415d:a84:7b01:eb18:8541:fe01',\n", + " 4469,\n", + " 7440,\n", + " 3138,\n", + " '',\n", + " 'URWJjesgxDqJTeQweycMgcyq',\n", + " '']" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/README.md b/README.md index 9322706..e6873df 100644 --- a/README.md +++ b/README.md @@ -1,64 +1,76 @@ ---- -page_type: sample -languages: -- csharp -products: -- dotnet -description: "Add 150 character max description" -urlFragment: "update-this-to-unique-url-stub" ---- +# Asessing CPU vs FPGA Performance Using Spark -# Official Microsoft Sample +We benchmark performance of a simple query using [NYC Taxi Dataset](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page). - +We use a subset of the Yellow Trip Data consisting of files with 18 columns. We further normalize the data in these files to conform to the schema published on the NYC Taxi Dataset site for [Yellow Taxi trips](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf). -Give a short description for your sample here. What does it do and why is it important? +To download the data locally: -## Contents +1. Clone this repo +1. Clone [nyc-taxi-data](https://github.com/toddwschneider/nyc-taxi-data) git repo +1. Edit `download_raw_data.sh` in the root of `nyc-taxi-data` repo replacing the default `setup_files/raw_data_urls.txt` with `/dataset/yellow_taxi_files.csv` +1. Run `./download_raw_data.sh` to download the dataset. +1. Normalize the downloaded dataset by going through the `Notebooks/Standardize Schema.ipynb` in this repo. The `clean_schema` function is what does normalization. -Outline the file contents of the repository. It helps users navigate the codebase, build configuration and any related assets. +Our dataset is 100 GB in size split into 82 files. -| File/folder | Description | -|-------------------|--------------------------------------------| -| `src` | Sample source code. | -| `.gitignore` | Define what to ignore at commit time. | -| `CHANGELOG.md` | List of changes to the sample. | -| `CONTRIBUTING.md` | Guidelines for contributing to the sample. | -| `README.md` | This README file. | -| `LICENSE` | The license for the sample. | +## Collecting Performance Data -## Prerequisites +We use local Spark configured with `local[*]` (default) and structured streaming to measure and aggregate performance on our dataset. Each streaming batch consists of a single CSV file. The profiled query is: -Outline the required components and tools that a user might need to have on their machine in order to run the sample. This can be anything from frameworks, SDKs, OS versions or IDE releases. +```sql +select payment_type, count(*) as total from nyctaxidata group by payment_type +``` -## Setup +As configured above, all local CPU cores are utilized for the query. -Explain how to prepare the sample once the user clones or downloads the repository. The section should outline every step necessary to install dependencies and set up any settings (for example, API keys and output folders). +### CPU -## Running the sample +1. [Install Apache Spark](https://www.apache.org/dyn/closer.lua/spark/spark-2.4.5/spark-2.4.5-bin-hadoop2.7.tgz). +1. In `/benchmarking/queries/benchmark_taxi.scala`, modify the following values as appropriate: -Outline step-by-step instructions to execute the sample and see its output. Include steps for executing the sample from the IDE, starting specific services in the Azure portal or anything related to the overall launch of the code. +```scala +val rootPath = s"~/data/taxi_data_cleaned_18_standard" //root of the dataset +val magentaOutDir = s"~/data/queries_e8/$queryName/processed/results" // query results +val checkpointLoc = s"~/data/queries_e8/$queryName/checkpoint" // checkpoint files +val logDir = s"~/data/queries_e8/$queryName/monitor/results" // profiling results +``` +3. Launch spark-shell with enough memory to stream the data: -## Key concepts +From the root of this repo: -Provide users with more context on the tools and services used in the sample. Explain some of the code that is being used and how services interact with each other. +```sh +$ cd benchmarking/queries +$ spark-shell --driver-memory 50G +``` -## Contributing +4. Load the relevant file and launch Spark processing -This project welcomes contributions and suggestions. Most contributions require you to agree to a -Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us -the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. +```scala +scala> :load benchmark_taxi.scala +scala> Benchmark.main(1) +``` -When you submit a pull request, a CLA bot will automatically determine whether you need to provide -a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions -provided by the bot. You will only need to do this once across all repos using our CLA. +Benchmarking results will be placed in the directories prefixed with `logDir`, so in the example above these will be: + ``` + ~/data/queries_e8/q1/monitor/results_0 + ~/data/queries_e8/q1/monitor/results_1 + ~/data/queries_e8/q1/monitor/results_2 + etc + ``` +5. Collect the results: +```sh +$ cat ~/data/queries_e8/q1/monitor/results_*/*.csv > taxi_q1_profile.csv +``` -This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). -For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or -contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. +### FPGA + +Provisioning an NP-10 machine in Azure and going through the above steps should yield the benchmarks for FPGA. For the demo we used a custom "one-off" implementation of this query to assess FPGA performance. + +## Web UI + +See this [README](demo/README.md) for instruction on how to visualize performance data in Web UI. Here is the [finished app](https://speeddemo.z5.web.core.windows.net/) \ No newline at end of file diff --git a/benchmarking/Mison/BenchmarkTaxi.cpp b/benchmarking/Mison/BenchmarkTaxi.cpp new file mode 100644 index 0000000..3c99cd5 --- /dev/null +++ b/benchmarking/Mison/BenchmarkTaxi.cpp @@ -0,0 +1,160 @@ +// ********************************************************************* +// Copyright (C) Microsoft. All rights reserved. +// ********************************************************************* +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "Mison/Config.h" +#include "Mison/CsvParser.h" +#include "Mison/CsvReader.h" +#include "Mison/CsvValue.h" +#include "Mison/Field.h" +#include "Mison/JsonParser.h" +#include "Mison/JsonReader.h" +#include "Mison/JsonValue.h" +#include "Mison/Nullable.h" +#include "Mison/Record.h" +#include "Mison/Records.h" + +using namespace Mison; + +/// +/// Extract three integer fields from TPC-H lineitem CSV file. +/// Expected parsing throughput: ~835MB/s (Intel Haswell 3.5GHz CPU). +/// +void CsvParserTpchBenchmark(std::string& path) { + std::cout << std::endl; + std::cout << "##### Csv Parser TPC-H lineitem parsing benchmark #####" << std::endl; + + // Extract the first, second, and fifth (integer) fields. + Config config; + config.csv.field_delimiter = ','; + + +for (size_t i = 0; i < 18; i++) { + std::vector fields; + + for (size_t j = 0; j <= i; j++) { + std::stringstream ss; + ss << "$" << j; + fields.push_back(ss.str()); + } + + Mison::CsvParser parser(fields, config); + + // Preload data into memory, to exclude I/O time. + std::unique_ptr file(new std::ifstream(path, std::ifstream::binary)); + + file->seekg(0, file->end); + size_t length = file->tellg(); + + char* buffer = new char[length]; + file->seekg(0, file->beg); + + // Start timer. + auto start = std::chrono::high_resolution_clock::now(); + + file->read(buffer, length); + + CsvRecords records = parser.Parse(buffer, length, false); + + size_t count = 0; + int v; + double d; + NullableString s; + + for (const CsvRecord& record : records) { + for (const CsvValue& field_value : record) { + StringRef string; + switch (field_value.FieldId()) { + case 0: + field_value.GetAsString(); + break; + case 1: + field_value.GetAsDateTime().Value(); + break; + case 2: + field_value.GetAsDateTime().Value(); + break; + case 3: + field_value.GetAsInt().Value(); + break; + case 4: + field_value.GetAsDouble().Value(); + break; + case 5: + field_value.GetAsDouble().Value(); + break; + case 6: + field_value.GetAsString().Value(); + break; + case 7: + field_value.GetAsString().Value(); + break; + case 8: + field_value.GetAsString().Value(); + break; + case 9: + field_value.GetAsString().Value(); + break; + case 10: + field_value.GetAsDouble().Value(); + break; + case 11: + field_value.GetAsRawText(); + break; + case 12: + field_value.GetAsDouble().Value(); + break; + case 13: + field_value.GetAsDouble().Value(); + break; + case 14: + field_value.GetAsDouble().Value(); + break; + case 15: + field_value.GetAsDouble().Value(); + break; + case 16: + field_value.GetAsDouble().Value(); + break; + case 17: + field_value.GetAsDouble().Value(); + break; + } + } + count++; + } + + // Stop timer. + auto end = std::chrono::high_resolution_clock::now(); + size_t time = std::chrono::duration_cast(end - start).count(); + + std::cout << "========================== Cols: " << fields.size() << std::endl; + std::cout << "Parsed " << count << " records." << std::endl; + std::cout << "Parsing time: " << time / 1000.0 << " ms" << std::endl; + std::cout << "Pasrsing throughput: " << static_cast(length) / time << " MB/s" + << std::endl; + + delete[] buffer; + } +} + +int main(int argc, char** argv) { + SetThreadAffinityMask(GetCurrentThread(), 1); + std::vector paths = {// R"(G:\taxi_data\yellow_tripdata_2019-12.csv)", + R"(G:\taxi_data\yellow_tripdata_2012-03.csv)"}; + + for (auto s : paths) { + CsvParserTpchBenchmark(s); + // CsvReaderTpchBenchmark(); + } + return 0; +} \ No newline at end of file diff --git a/benchmarking/listener/querylistener.scala b/benchmarking/listener/querylistener.scala new file mode 100644 index 0000000..f7a8fdc --- /dev/null +++ b/benchmarking/listener/querylistener.scala @@ -0,0 +1,86 @@ +import org.apache.spark.sql.Row + +import org.apache.spark.sql.streaming.StreamingQueryListener +import org.apache.spark.sql.streaming.StreamingQueryListener._ +import org.apache.spark.internal.Logging +import java.util.concurrent.locks.{Lock, ReentrantLock} + +import spark.implicits._ + +case class Record(timestamp : String, numRows : Long, avgRowSize : Int, + inputRowsPerSec : Double, processedRowsPerSec : Double, inputDurationSec : Double, processedDurationSec : Double) + +// this is how you use toDF +// 1. import spark.implicits._ from an existing spark session +// 2. define the Record case class containing the schema +// 3. Use in a separate object outside of the class definition +object ConvToDf { + def createDF(seqDF : List[Record]) = { + seqDF.toDF.na.fill(0) + } +} + +class StreamMonitor(val fileName : String, val rowSize: Int, val capacity: Int = 1) extends StreamingQueryListener() with Logging { + + private[this] val lock : Lock = new ReentrantLock() + private[this] var collectedRecs = List[Record]() + private[this] var queryDone = false + private[this] var batch = 0 + + override def onQueryProgress(queryProgress: QueryProgressEvent): Unit = { + + val progress = queryProgress.progress + val nr = progress.numInputRows + + // since we are streaming the query it's never done + // check what the next batch brings and terminate processing accordingly + if (nr == 0 || collectedRecs.length >= capacity) { + if (queryDone) return + try { + lock.lock() + if(!queryDone) { + queryDone = nr == 0 + + if (collectedRecs.length != 0) { + ConvToDf.createDF(collectedRecs.reverse) + .write + .option("header", "false") + .csv(s"${fileName}_$batch") + batch += 1 + } + collectedRecs = List[Record]() + } + } + catch { + case e: Throwable => { + logError(s"$e") + if (!queryDone) return + } + } + finally { + lock.unlock() + if (queryDone) return + } + } + + val ts = progress.timestamp + val inpRowsSec = progress.inputRowsPerSecond + val procRowsSec = progress.processedRowsPerSecond + val inpDurationSec = nr / inpRowsSec + val procDurationSec = nr / procRowsSec + + try { + lock.lock() + collectedRecs = Record(ts, nr, rowSize, inpRowsSec, procRowsSec, inpDurationSec, procDurationSec) :: collectedRecs + } + catch { + case e: Throwable => logError(s"$e") + } + finally { + lock.unlock() + } + } + + def onQueryStarted(event: QueryStartedEvent): Unit = () + def onQueryTerminated(event: QueryTerminatedEvent): Unit = () +} \ No newline at end of file diff --git a/benchmarking/queries/README.md b/benchmarking/queries/README.md new file mode 100644 index 0000000..ce6183a --- /dev/null +++ b/benchmarking/queries/README.md @@ -0,0 +1,44 @@ +To run, do following + +*Install AzureXRT stuff + cd AzureXRTPackages + sudo apt install ./*.deb +Reboot the machine. + +run + source /opt/xilinx/xrt/setup.sh + xbutil validate + +this should pass + +* Install required tools using following commands + sudo apt-get install uuid-dev + sudo apt-get install libssl-dev + sudo apt-get install build-essential + sudo apt-get install gcc-8 + sudo apt-get install g++-8 + sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 60 --slave /usr/bin/g++ g++ /usr/bin/g++-8 + sudo apt install python + +* Install cmake. Follow instructions from https://linux4one.com/how-to-install-cmake-on-ubuntu-18-04-linux/ + +* Setup environment variables (Add it in .bashrc file for convenience) + export WFDS_BUILD_ROOT=/home/boris/fpga_demo + export LD_LIBRARY_PATH=/home/boris/fpga_demo/cpp/external_libs/poco.1.9.2/lib + +* Build boost and Poco + Go to cpp/external_libs/poco.1.9.2 and run the buildall.sh script. + Go to cpp/external_libs/boost and run the buildall.sh script. + +* Go to build_output/global + cmake ../../ + cd accel + make acceltesthw + + +* Run executable like + go to fpga_demo + cpp/build_output/bin/acceltesthw hw -file yellow_tripdata_2019-12.csv +Generates vstream_ directory with VStream output +Use smaller csv file for now. Big ones have issues + diff --git a/benchmarking/queries/benchmark_magenta.scala b/benchmarking/queries/benchmark_magenta.scala new file mode 100644 index 0000000..a6aa259 --- /dev/null +++ b/benchmarking/queries/benchmark_magenta.scala @@ -0,0 +1,68 @@ +import org.apache.spark.sql.functions.{_ => F} +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ +import org.apache.spark.sql.streaming.Trigger +import org.apache.spark.sql.streaming.StreamingQuery +import org.apache.spark.sql.DataFrame + +import org.apache.log4j._ +import java.nio.file.Paths +import org.apache.spark.SparkConf + +:load ../listener/querylistener.scala +:load schemas.scala + +object BenchMagenta { + var cols = Array("") + + def getSchema() = Schemas.getMagentaSchema + + def loadData(filePath: String, queryName: String) = { + + val taxi_orig_schema = getSchema() + + val df = spark + .readStream + .schema(taxi_orig_schema) + .option("header", "false") + .option("maxFilesPerTrigger", 1) + .csv(filePath) + + df.createOrReplaceTempView(queryName) + cols = df.columns + println(s"Created sql table from dataframe") + } + + /** Our main function where the action happens */ + def runFile(magentaOutDir : String, checkpointLoc: String, tableName: String) = { + + spark.sql(s"select * from $tableName") + .writeStream + .format("parquet") + .outputMode("append") + .option("path", magentaOutDir) + .option("startingOffsets", "earliest") + .option("checkpointLocation", checkpointLoc) + .queryName("magenta_out") + .start() + } + + def main() = { + val rootPath = "~/yellow/magenta/raw" + val magentaOutDir = "~/yellow/magenta/processed" + val checkpointLoc = "~/yellow/magenta/checkpoint" + val logDir = "~/yellow/magenta/monitor/stats" + + println(s"spark.driver.memory: ${spark.conf.get("spark.driver.memory")}") + println(s"Running query") + + spark.streams.addListener(new StreamMonitor(logDir, 520, 5)) + + // start stream-reading + loadData(rootPath, "magenta") + + // start stsream-writing + runFile(magentaOutDir, checkpointLoc, "magenta") + } +} \ No newline at end of file diff --git a/benchmarking/queries/benchmark_taxi.scala b/benchmarking/queries/benchmark_taxi.scala new file mode 100644 index 0000000..5cb96c9 --- /dev/null +++ b/benchmarking/queries/benchmark_taxi.scala @@ -0,0 +1,83 @@ +import org.apache.spark.sql.functions.{_ => F} +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ +import org.apache.spark.sql.DataFrame +import org.apache.log4j._ +import java.nio.file.Paths +import org.apache.spark.SparkConf +import org.apache.spark.sql.streaming.Trigger + +:load schemas.scala +:load ../listener/querylistener.scala + +object Benchmark { + + def getSchema() = Schemas.getTaxiSchema + + def loadData(filePath: String, queryName: String) = { + + val taxi_orig_schema = getSchema() + + val df = spark + .readStream + .schema(taxi_orig_schema) + .option("header", "false") + .option("maxFilesPerTrigger", 1) + .csv(filePath) + + df.createOrReplaceTempView(queryName) + println(s"Created sql table from dataframe") + } + + /** Our main function where the action happens */ + def runFile(outDir: String, checkpointLoc: String, queryStr: String, tableName: String) = { + + spark.sql(queryStr) + .writeStream + .foreachBatch((df : DataFrame, epoch : Long) => { + df.write + .csv(s"${outDir}_$epoch") + }) + .outputMode("complete") + .option("startingOffsets", "earliest") + .option("checkpointLocation", checkpointLoc) + .trigger(Trigger.ProcessingTime("1 second")) + .queryName("magenta_out") + .start() + + } + + def main(queryIdx: Int) = { + val tableName = "nyyellowtaxi" + + val queryList = List(s"select count(*) as total from $tableName", + s"select payment_type, count(*) as total from $tableName group by payment_type", + s"select passenger_count, avg(total_amount) as avg_amount from $tableName group by passenger_count", + s"select passenger_count, year(tpep_pickup_datetime) year, count(*) total FROM $tableName group by passenger_count, year", + s"""select passenger_count, year(tpep_pickup_datetime) as year, + cast(trip_distance as int) as distance, + count(*) as total + from $tableName + group by passenger_count, year, distance + order by year, total desc + """ + ) + val queryName = s"q$queryIdx" + val rootPath = s"~/data/taxi_data_cleaned_18_standard" //root of the dataset + val magentaOutDir = s"~/data/queries_e8/$queryName/processed/results" // query results + val checkpointLoc = s"~/data/queries_e8/$queryName/checkpoint" // checkpoint files + val logDir = s"~/data/queries_e8/$queryName/monitor/results" // profiling results + + println(s"spark.driver.memory: ${spark.conf.get("spark.driver.memory")}") + println(s"Running query") + + spark.streams.addListener(new StreamMonitor(logDir, 98, 5)) + + // start stream-reading + loadData(rootPath, tableName) + + // start stsream-writing + runFile(magentaOutDir, checkpointLoc, queryList(queryIdx), tableName) + } +} diff --git a/benchmarking/queries/benchmark_taxi_fpga.scala b/benchmarking/queries/benchmark_taxi_fpga.scala new file mode 100644 index 0000000..f246009 --- /dev/null +++ b/benchmarking/queries/benchmark_taxi_fpga.scala @@ -0,0 +1,94 @@ +import org.apache.spark.sql.functions.{_ => F} +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ +import org.apache.spark.sql.DataFrame +import org.apache.log4j._ +import org.apache.spark.SparkConf +import org.apache.spark.sql.streaming.Trigger + +import sys.process._ +import java.io.File +import java.nio.file.{Files, Paths, StandardCopyOption} + +import scala.io.Source +import org.apache.commons.io.FilenameUtils + +object Benchmark { + + def main(queryIdx: Int = 1, data_path: String = "~/data/taxi_data_cleaned_18_standard", + home_dir: String = "~/git/PipeDream/.image", out_path: String="~/data/fpga_queries", + start: Int=0, count: Int = 0, verbose: Boolean = false) = { + + val taxi_files = new File(data_path).listFiles.drop(start) + val total = if (count > 0) count else taxi_files.length + + println(s"total files: $total") + + val taxi_idxs = start to (total - 1 + start) + val taxi_data_copy = s"${data_path}_copy" + + println(s"total idxs: ${taxi_idxs.size}") + + val out_dir_res = new File(s"${out_path}/q_${queryIdx}/results") + val times_dir = new File(s"${out_path}/q_${queryIdx}/monitor") + + if(! out_dir_res.exists()) { + out_dir_res.mkdirs() + } + + if(! times_dir.exists()) { + times_dir.mkdirs() + } + + for(file_id <- taxi_idxs zip taxi_files) { + file_id match { + case (idx, taxi_file) => { + // fix the file + val lineSize = Source.fromFile(taxi_file).getLines.take(1).toList.head.split(',').size + + val proc = + Process(s"${home_dir}/acceltesthw --file_name ${taxi_file} --query_mode=${queryIdx + 1} --accel_type=0 --num_cols=$lineSize", + new File(home_dir)) + + println(s"Starting. Cols $lineSize, name: $taxi_file") + if (verbose) proc.! else proc.!! + println(s"Completed") + + val suffix = FilenameUtils.getBaseName(taxi_file.toString) + + Files.move( + Paths.get(s"${home_dir}/fpga_time.csv"), + Paths.get(s"${times_dir.toString}/fpga_time_${suffix}.csv"), + StandardCopyOption.REPLACE_EXISTING + ) + for { + files <- Option(new File(home_dir).listFiles()) + file <- files if file.getName.endsWith(".log") + } file.delete() + + // Files.move( + // Paths.get(s"${home_dir}/q1out.csv"), + // Paths.get(s"${out_dir_res.toString}/q0out_${suffix}.csv"), + // StandardCopyOption.REPLACE_EXISTING + // ) + + Files.move( + Paths.get(s"${home_dir}/q${queryIdx + 1}out.csv"), + Paths.get(s"${out_dir_res.toString}/q${queryIdx}out_${suffix}.csv"), + StandardCopyOption.REPLACE_EXISTING + ) + + // val file_name = new File(taxi_file.toString).getName + // Files.move( + // Paths.get(taxi_file.toString), + // Paths.get(s"${taxi_data_copy}/${file_name}"), + // StandardCopyOption.REPLACE_EXISTING + // ) + + } + } + } + + } +} diff --git a/benchmarking/queries/benchmark_taxi_select.scala b/benchmarking/queries/benchmark_taxi_select.scala new file mode 100644 index 0000000..50c0069 --- /dev/null +++ b/benchmarking/queries/benchmark_taxi_select.scala @@ -0,0 +1,66 @@ +import org.apache.spark.sql.functions.{_ => F} +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ +import org.apache.spark.sql.DataFrame +import org.apache.log4j._ +import java.nio.file.Paths +import org.apache.spark.SparkConf + +:load schemas.scala + +object Benchmark { + var cols = Array("") + + def getSchema() = Schemas.getTaxiSchema + + def time[R](block: => R): R = { + val t0 = System.nanoTime() + val result = block // call-by-name + val t1 = System.nanoTime() + println(f"Elapsed time: ${(t1 - t0) / 1e9}%1.3fs") + result + } + + def loadTaxiData(fileName: String) = { + + val taxi_orig_schema = getSchema() + + val df = spark + .read + .schema(taxi_orig_schema) + .option("header", "false") + .csv(fileName) + + df.createOrReplaceTempView("nyyellowtaxi") + cols = df.columns + println(s"Created sql table from dataframe") + } + + /** Our main function where the action happens */ + def runFile(file_name: String) = { + val schema_fields = cols + val total_cols = schema_fields.length + + var i = 0 + for(i <- 1 to total_cols) { + val names = schema_fields.slice(0, i).mkString(", ") + val query_txt = spark.sql(s"select $names from nyyellowtaxi") + + println(s"cols ${i}:") + time {query_txt.collect()} + } + } + + def main(rootPath: String = "~/yellow/taxi/taxi_data") = { + val files = List("yellow_tripdata_2012-03.csv", "yellow_tripdata_2019-12.csv") + + println(s"spark.driver.memory: ${spark.conf.get("spark.driver.memory")}") + var f : String = "" + for (f <- files.map(Paths.get(rootPath, _).toString)) { + println(s"Running with $f") + loadTaxiData(f) + runFile(f) + } + } +} diff --git a/benchmarking/queries/fix_data.py b/benchmarking/queries/fix_data.py new file mode 100644 index 0000000..f4551b3 --- /dev/null +++ b/benchmarking/queries/fix_data.py @@ -0,0 +1,20 @@ +import os +import glob +from multiprocessing import Pool, cpu_count + +path_root = os.path.expanduser(r"~/data/taxi_data") +files = glob.glob(os.path.join(path_root, "*.csv")) + +def fixFile(f): + print(f"Reading: {os.path.split(f)[1]}") + with open(f, "r") as fd: + lines = fd.readlines() + + if len(lines[0].split(',')) < 18: + print(f"Will write: {os.path.split(f)[1]}") + with open(f, "w") as fd: + fd.writelines(lines[1:]) + +Pool(cpu_count()).map(fixFile, files) + + diff --git a/benchmarking/queries/fpga_input_times.csv b/benchmarking/queries/fpga_input_times.csv new file mode 100644 index 0000000..e93f50e --- /dev/null +++ b/benchmarking/queries/fpga_input_times.csv @@ -0,0 +1,82 @@ +13.97956 +13.093169 +14.097414 +15.846028 +14.911613 +14.005469 +13.39619 +13.455239 +13.741334 +15.320745 +14.549938 +14.609953 +14.595407 +14.979908 +15.349394 +14.608228 +14.411513 +12.299834 +15.770246 +13.928713 +13.639649 +13.964589 +13.515153 +0.984463 +15.802848 +1.002862 +19.031792 +1.021823 +14.604164 +13.236647 +15.921361 +15.627326 +14.276602 +14.624606 +14.722508 +14.773239 +15.843256 +15.191065 +15.420166 +15.035725 +14.145152 +14.213941 +15.519243 +14.478537 +13.46988 +14.247559 +14.295364 +13.556319 +15.724502 +14.577173 +14.808015 +14.066502 +13.398903 +12.168592 +13.732333 +1.024708 +13.890993 +13.546112 +13.30142 +12.761658 +14.967752 +14.205653 +15.752544 +13.605788 +12.791365 +12.320598 +13.025826 +13.799213 +12.897513 +12.576882 +7.382327 +6.735299 +7.496559 +7.114357 +7.276366 +6.652565 +6.770595 +5.955355 +0.444866 +7.558073 +0.468676 +7.305288 \ No newline at end of file diff --git a/benchmarking/queries/fpga_proc_times_q1.csv b/benchmarking/queries/fpga_proc_times_q1.csv new file mode 100644 index 0000000..bb36ff6 --- /dev/null +++ b/benchmarking/queries/fpga_proc_times_q1.csv @@ -0,0 +1,82 @@ +0.947825 +0.912538 +0.980564 +0.979225 +0.995022 +0.958112 +0.949534 +0.951962 +1.063844 +0.967885 +1.009119 +1.073668 +1.00906 +0.979072 +0.840295 +1.068371 +0.964575 +0.947867 +0.948287 +0.944065 +1.00033 +1.081844 +1.005351 +1.02761 +1.012189 +0.998865 +0.920604 +0.99245 +1.080438 +1.030567 +1.028355 +1.012393 +1.101289 +1.056697 +1.045737 +1.02883 +0.978511 +0.994138 +0.995688 +0.927906 +0.975625 +0.979342 +0.955342 +1.053516 +0.851215 +0.962986 +1.006164 +0.964013 +0.955302 +1.029765 +0.875359 +0.984822 +0.997328 +0.945503 +0.904668 +0.844486 +0.911973 +0.949751 +0.880459 +0.888663 +0.51473 +0.46568 +0.535507 +0.512967 +0.520184 +0.467202 +0.42576 +0.427634 +0.434916 +0.491674 +0.482278 +0.468268 +0.918656 +0.969311 +1.005039 +0.978991 +0.960973 +1.019734 +1.015870 +0.971850 +0.921574 +1.075810 \ No newline at end of file diff --git a/benchmarking/queries/fpga_proc_times_q2.csv b/benchmarking/queries/fpga_proc_times_q2.csv new file mode 100644 index 0000000..fab3ef7 --- /dev/null +++ b/benchmarking/queries/fpga_proc_times_q2.csv @@ -0,0 +1,82 @@ +940475 +893053 +966555 +977252 +1009676 +944347 +903349 +917968 +1010628 +1037121 +948746 +984671 +990188 +1054078 +1033425 +992200 +971913 +828716 +57656680 +970971 +927944 +970230 +918666 +995798 +1073673 +1031233 +1040431 +1019893 +1022448 +877899 +992017 +1045459 +967618 +999996 +990385 +997292 +1084605 +1042402 +1032976 +1026446 +960292 +964271 +982060 +994965 +910716 +970574 +991765 +940273 +1073958 +1005066 +1012261 +970688 +918614 +849510 +958614 +988098 +964627 +946995 +926526 +880872 +1018292 +978586 +1017643 +914257 +868684 +840789 +881766 +945202 +873355 +869790 +510038 +463499 +531702 +509655 +497279 +496703 +46059508 +22280323 +23645268 +52333855 +24677348 +50319654 diff --git a/benchmarking/queries/fpga_proc_times_q3.csv b/benchmarking/queries/fpga_proc_times_q3.csv new file mode 100644 index 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a/benchmarking/queries/schemas.scala b/benchmarking/queries/schemas.scala new file mode 100644 index 0000000..ffc59e6 --- /dev/null +++ b/benchmarking/queries/schemas.scala @@ -0,0 +1,106 @@ +import org.apache.spark.sql.types._ + +object Schemas { + def getTaxiSchema() = { + val schema_fields = + List( + StructField("VendorID", IntegerType), + StructField("tpep_pickup_datetime", TimestampType), + StructField("tpep_dropoff_datetime", TimestampType), + StructField("passenger_count", IntegerType), + StructField("trip_distance", DoubleType), + StructField("RatecodeID", IntegerType), + StructField("store_and_fwd_flag", StringType), + StructField("PULocationID", IntegerType), + StructField("DOLocationID", IntegerType), + StructField("payment_type", IntegerType), + StructField("fare_amount", DoubleType), + StructField("extra", DoubleType), + StructField("mta_tax", DoubleType), + StructField("tip_amount", DoubleType), + StructField("tolls_amount", DoubleType), + StructField("improvement_surcharge", DoubleType), + StructField("total_amount", DoubleType), + StructField("congestion_surcharge", DoubleType) + ) + StructType(schema_fields) + } + + def getMagentaSchema() = { + val schema_fields = + List( + StructField("Text01", StringType), + StructField("Text02", StringType), + StructField("Text03", StringType), + StructField("Text04", StringType), + StructField("Text05", StringType), + StructField("Text06", StringType), + StructField("IntWritable01", IntegerType), + StructField("Text07", StringType), + StructField("Text08", StringType), + StructField("Text09", StringType), + StructField("Text10", StringType), + StructField("Text11", StringType), + StructField("Text12", StringType), + StructField("Text13", StringType), + StructField("LongWritable01", LongType), + StructField("LongWritable02", LongType), + StructField("LongWritable03", LongType), + StructField("LongWritable04", LongType), + StructField("LongWritable05", LongType), + StructField("LongWritable06", LongType), + StructField("Text14", StringType), + StructField("Text15", StringType), + StructField("IntWritable02", IntegerType), + StructField("Text16", StringType), + StructField("Text17", StringType), + StructField("Text18", StringType), + StructField("Text19", StringType), + StructField("Text20", StringType), + StructField("IntWritable03", IntegerType), + StructField("IntWritable04", IntegerType), + StructField("Text21", StringType), + StructField("IntWritable05", IntegerType), + StructField("Text22", StringType), + StructField("Text23", StringType), + StructField("Text24", StringType), + StructField("Text25", StringType), + StructField("Text26", StringType), + StructField("IntWritable06", IntegerType), + StructField("IntWritable07", StringType), + StructField("Text27", StringType), + StructField("IntWritable08", IntegerType), + StructField("Text28", StringType), + StructField("IntWritable09", IntegerType), + StructField("IntWritable10", IntegerType), + StructField("IntWritable11", IntegerType), + StructField("Text29", StringType), + StructField("Text30", StringType), + StructField("Text31", StringType) + ) + + StructType(schema_fields) + } + + def getMonitorSchema() = { + val schema_fields = List( + StructField("timestamp", DateType), + StructField("numRows", LongType), + StructField("avgRowSize", IntegerType), + StructField("inputRowsPerSec", DoubleType), + StructField("processedRowsPerSec", DoubleType), + StructField("inputDurationSec", DoubleType), + StructField("processedDurationSec", DoubleType) + ) + } + + def getQ0Schema() = { + val schema_fields = List( + StructField("passenger_count", IntegerType), + StructField("year", IntegerType), + StructField("total", IntegerType) + ) + + StructType(schema_fields) + } +} \ No newline at end of file diff --git a/benchmarking/queries/taxi_q0.csv b/benchmarking/queries/taxi_q0.csv new file mode 100644 index 0000000..863282d --- /dev/null +++ b/benchmarking/queries/taxi_q0.csv @@ -0,0 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--- /dev/null +++ b/dataset/yellow_taxi_files.csv @@ -0,0 +1,82 @@ +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-01.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-02.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-03.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-04.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-05.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-06.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-07.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-08.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-09.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-10.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-11.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2009-12.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-01.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-04.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-05.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-06.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-07.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-08.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-09.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-10.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-11.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2010-12.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2011-01.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2011-02.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2011-03.csv 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+https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-05.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-06.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-07.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-08.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-09.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-10.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-11.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2012-12.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-01.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-02.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-03.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-04.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-05.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-06.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-07.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-08.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-09.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-10.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-11.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2013-12.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-01.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-02.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-03.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-04.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-05.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-06.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-07.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-08.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-09.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-10.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-11.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2014-12.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-01.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-02.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-03.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-04.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-05.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-06.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-07.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-08.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-09.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-10.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-11.csv +https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2019-12.csv \ No newline at end of file diff --git a/demo/.gitignore b/demo/.gitignore new file mode 100644 index 0000000..4d29575 --- /dev/null +++ b/demo/.gitignore @@ -0,0 +1,23 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js + +# testing +/coverage + +# production +/build + +# misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* diff --git a/demo/README.md b/demo/README.md new file mode 100644 index 0000000..c4d6b5d --- /dev/null +++ b/demo/README.md @@ -0,0 +1,119 @@ +*** This app uses React, React Router and React Responsive. *** + +*** To change settings in this app, in App.js *** + + 1. Set themes and query descriptions in the state. + 2. Create routes to display pairs of meters (CPU, FPGA) in either Grid (row, col) or Tabs components: + a. Pass selected record data sets, styled themes and query descriptions. + b. Pass scalar to set display size (defaults to 1) + c. Pass effects to turn on/off blur and saturation filters on the Meter component (defaults to true). + d. Use React Responsive Media Query with scalar for responsive scaling of the pairs of meters. + +*** The Project Hierarchy (with descriptions) *** + +public: + themes: (each folder contains color themed jpg and svg images for meter component) + blue: + green: + green2: + magenta: + orange: + purple: +src: + components: + App.js - loads the data, sets up the themes and query descriptions and handles the routing + Grid.jsx - composes the pairs of meters in a centered row of columns + Query.jsx - composes the paired CPU and FPGA components in a centered row of columns + CPU.jsx - presents a meter using CPU specific settings + FPGA.jsx - presents a meter using FPGA specific settings + Meter.jsx - displays a simulation of a live metering of data processing + ProgressGrower.jsx - displays a custom made progress bar + unused: + ProgressSlider.jsx - displays a custom progress spinner + Tabs.jsx - composes the pairs of meters in a tab navigation + data: + cpu.json - stores the data in the cpu csv files as arrays of records + fpga.json - stores the data in the fpgs csv files as arrays of records + + models: + Record.js - a javascript class model for a row of data from the CPU and FPGA csv query files + Theme.js - a javascript class model for a styled theme for the Grid and Meter components + queries: + cpu: + fpga: + utilities: + count.js - a node js app to verify the total data processed + csv2json.js - a node js app to convert csv data files to json (reads all files in src/queries folder and writes json files to src/data folder). + + + +*** THE FOLLOWING IS THE AUTO GENERATED README FOR PROJECTS BOOTSTRAPPED USING CREATE REACT APP *** + +This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app). + +## Available Scripts + +In the project directory, you can run: + +### `npm start` + +Runs the app in the development mode.
+Open [http://localhost:3000](http://localhost:3000) to view it in the browser. + +The page will reload if you make edits.
+You will also see any lint errors in the console. + +### `npm test` + +Launches the test runner in the interactive watch mode.
+See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information. + +### `npm run build` + +Builds the app for production to the `build` folder.
+It correctly bundles React in production mode and optimizes the build for the best performance. + +The build is minified and the filenames include the hashes.
+Your app is ready to be deployed! + +See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information. + +### `npm run eject` + +**Note: this is a one-way operation. Once you `eject`, you can’t go back!** + +If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. + +Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own. + +You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it. + +## Learn More + +You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started). + +To learn React, check out the [React documentation](https://reactjs.org/). + +### Code Splitting + +This section has moved here: https://facebook.github.io/create-react-app/docs/code-splitting + +### Analyzing the Bundle Size + +This section has moved here: https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size + +### Making a Progressive Web App + +This section has moved here: https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app + +### Advanced Configuration + +This section has moved here: https://facebook.github.io/create-react-app/docs/advanced-configuration + +### Deployment + +This section has moved here: https://facebook.github.io/create-react-app/docs/deployment + +### `npm run build` fails to minify + +This section has moved here: https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify diff --git a/demo/package-lock.json b/demo/package-lock.json new file mode 100644 index 0000000..12a2d01 --- /dev/null +++ b/demo/package-lock.json @@ -0,0 +1,15420 @@ +{ + "name": "nealanalyticsdemo", + "version": "0.1.0", + "lockfileVersion": 1, + "requires": true, + "dependencies": { + "@babel/code-frame": { + "version": "7.8.3", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.8.3.tgz", + "integrity": "sha512-a9gxpmdXtZEInkCSHUJDLHZVBgb1QS0jhss4cPP93EW7s+uC5bikET2twEF3KV+7rDblJcmNvTR7VJejqd2C2g==", + "requires": { + "@babel/highlight": "^7.8.3" + } + }, + "@babel/compat-data": { + "version": "7.9.0", + "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.9.0.tgz", + "integrity": "sha512-zeFQrr+284Ekvd9e7KAX954LkapWiOmQtsfHirhxqfdlX6MEC32iRE+pqUGlYIBchdevaCwvzxWGSy/YBNI85g==", + 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HcmV?d00001 diff --git a/demo/public/manifest.json b/demo/public/manifest.json new file mode 100644 index 0000000..080d6c7 --- /dev/null +++ b/demo/public/manifest.json @@ -0,0 +1,25 @@ +{ + "short_name": "React App", + "name": "Create React App Sample", + "icons": [ + { + "src": "favicon.ico", + "sizes": "64x64 32x32 24x24 16x16", + "type": "image/x-icon" + }, + { + "src": "logo192.png", + "type": "image/png", + "sizes": "192x192" + }, + { + "src": "logo512.png", + "type": "image/png", + "sizes": "512x512" + } + ], + "start_url": ".", + "display": "standalone", + "theme_color": "#000000", + "background_color": "#ffffff" +} diff --git a/demo/public/robots.txt b/demo/public/robots.txt new file mode 100644 index 0000000..e9e57dc --- /dev/null +++ b/demo/public/robots.txt @@ -0,0 +1,3 @@ +# https://www.robotstxt.org/robotstxt.html +User-agent: * +Disallow: diff --git a/demo/public/themes/blue/background.jpg b/demo/public/themes/blue/background.jpg new file mode 100644 index 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zc#&rp^8&12_Cm1u2&?^@&0uZOSQK*q=m5nc28;SlzHykZ5d^+6$88-GzB3h5;J-U$ zip%_wSlkdP2oOq9M*+!{O}~`}<_O>&z*)dZ>pg?BLU2LLbPCVS-RKP^Am|AewO?-Z z5G!c9JsMWNP7))tFd_~k+S4}C3h^-cTlTD+b&-Km*D(_PzP!aMz78}y@`7&u8_GG^ zedFqZNF^WQzV4|wGaTZ+KIju^LO?+%*n?RT7PUdWA-5a6eZ~I#Qz_ZL${nv^bl%;9 zBEtvD^1Hq9BEv)-*uyaZyw^hPQv!^wPee=I z9or}fZGs;9wz%XnFhJO&QeZhN5}o4s+o`dH=V(u_(gH5M6?m z^abZjF$+r8Sk%RcEucm~h;@885)cV2FK0O82FwoI@4hBN5P_l7^1w|C!HXlhU*6zb zbc4pdyhY6wAQXUDZzop9_XlX314eaU|3&uF>l(oIjw;kZr}6r5^2zIoC`jg?YUh98zR)T36Gnm}CD8Wk zzf3U=KVwlo`Z5YsL@UHyBa$iu>V{8RM{MBt!29Af7ZhXgk)XexZzxIzCg1OuH#hA? k1UK^PKbVfF<6{~qdRKg(nePt4SNvq92(cLX;(v$!518CPUSTARTSTOP \ No newline at end of file diff --git a/demo/public/themes/purple/fpga.svg b/demo/public/themes/purple/fpga.svg new file mode 100644 index 0000000..753a629 --- /dev/null +++ b/demo/public/themes/purple/fpga.svg @@ -0,0 +1 @@ +STARTSTOPFPGA \ No newline at end of file diff --git a/demo/public/themes/purple/needle.svg b/demo/public/themes/purple/needle.svg new file mode 100644 index 0000000..4789819 --- /dev/null +++ b/demo/public/themes/purple/needle.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/demo/public/themes/purple/start cpu.svg b/demo/public/themes/purple/start cpu.svg new file mode 100644 index 0000000..c5f62af --- /dev/null +++ b/demo/public/themes/purple/start cpu.svg @@ -0,0 +1 @@ +START \ No newline at end of file diff --git a/demo/public/themes/purple/start fpga.svg b/demo/public/themes/purple/start fpga.svg new file mode 100644 index 0000000..6603a00 --- /dev/null +++ b/demo/public/themes/purple/start fpga.svg @@ -0,0 +1 @@ +START \ No newline at end of file diff --git a/demo/public/themes/purple/stop cpu.svg b/demo/public/themes/purple/stop cpu.svg new file mode 100644 index 0000000..f810df0 --- /dev/null +++ b/demo/public/themes/purple/stop cpu.svg @@ -0,0 +1 @@ +STOP \ No newline at end of file diff --git a/demo/public/themes/purple/stop fpga.svg b/demo/public/themes/purple/stop fpga.svg new file mode 100644 index 0000000..82d9300 --- /dev/null +++ b/demo/public/themes/purple/stop fpga.svg @@ -0,0 +1 @@ +STOP \ No newline at end of file diff --git a/demo/src/App.css b/demo/src/App.css new file mode 100644 index 0000000..5427381 --- /dev/null +++ b/demo/src/App.css @@ -0,0 +1,7 @@ +ul { + justify-content: center; +} + +li { + list-style-type: none; +} \ No newline at end of file diff --git a/demo/src/App.js b/demo/src/App.js new file mode 100644 index 0000000..7deae60 --- /dev/null +++ b/demo/src/App.js @@ -0,0 +1,172 @@ +import React from 'react'; +import './App.css'; +import Record from './models/Record'; +import Theme from './models/Theme'; +import cpu from './data/cpu.json'; +import fpga from './data/fpga.json'; +import Grid from './components/Grid'; +import { + BrowserRouter as Router, + Switch, + Route +} from 'react-router-dom'; + +class App extends React.Component { + constructor(props) { + super(props); + this.state = { + recordSetsCPU: null, + recordSetsFPGA: null, + recordSetsIndex: 0, + recordSetsLoaded: false, + queryDescriptions: [ + { + title: "Spark Job", + sql: +

+ }, + { + title: "Query 1", + sql: +
    +
  • + SELECT payment_type, COUNT(*) as total +
    • +
  • +
      +
    • + FROM NewYorkYellowTaxi +
      • +
    • + GROUP BY payment_type +
      • +
    +
    • +
+ }, + { + title: "Query 2", + sql: +
    +
  • + SELECT passenger_count, +
    • +
  • + AVG(total_amount) as avg_amount +
    • +
  • +
      +
    • + FROM $tableName +
      • +
    • + GROUP BY passenger_count +
      • +
    +
    • +
+ }, + { + title: "Query 3", + sql: +
    +
  • + SELECT passenger_count, +
    • +
  • + year(tpep_pickup_datetime) year, +
    • +
  • + COUNT(*) total +
    • +
  • +
      +
    • + FROM $tableName +
      • +
    • + GROUP BY passenger_count, year +
      • +
    +
    • +
+ }, + ], + themes: [ + new Theme('themes', 'blue', '#0882D8', '#34c1ee', '#fff', '#8CDDF7', '#8cddf7', '#000', '#042246'), + new Theme('themes', 'green', '#44683A', '#44683A', '#fff', '#82B74F', '#82B74F', '#000', '#042246'), + new Theme('themes', 'green2', '#308950', '#308950', '#fff', '#80B173', '#80B173', '#000', '#042246'), + new Theme('themes', 'magenta', '#E3008C', '#E3008C', '#fff', '#B4009E', '#B4009E', '#fff', '#042246'), + new Theme('themes', 'orange', '#EB7415', '#EB7415', '#fff', '#F3AA6D', '#F3AA6D', '#000', '#042246'), + new Theme('themes', 'purple', '#5059C8', '#5059C8', '#fff', '#7B83EB', '#7B83EB', '#fff', '#042246') + ] + } + } + + componentDidMount() { + this.setState({ + recordSetsCPU: this.importRecordSets(cpu), + recordSetsFPGA: this.importRecordSets(fpga) + }, () => { + this.setState({ + recordSetsLoaded: true + }); + }); + } + + render() { + return this.state.recordSetsLoaded ? ( + + + + + + + + + + ) : null + } + + importRecordSets = (dataSets) => { + const recordSets = []; + const dl = dataSets.length; + for (let d = 0; d < dl; d++) { + const recordSet = []; + const dataSet = dataSets[d]; + const rl = dataSet.records.length; + for (let r = 0; r < rl; r++) { + recordSet.push(Record.fromData(dataSet.records[r])); + } + recordSets.push(recordSet); + } + return recordSets; + } +} + +export default App; \ No newline at end of file diff --git a/demo/src/App.test.js b/demo/src/App.test.js new file mode 100644 index 0000000..4db7ebc --- /dev/null +++ b/demo/src/App.test.js @@ -0,0 +1,9 @@ +import React from 'react'; +import { render } from '@testing-library/react'; +import App from './App'; + +test('renders learn react link', () => { + const { getByText } = render(); + const linkElement = getByText(/learn react/i); + expect(linkElement).toBeInTheDocument(); +}); diff --git a/demo/src/components/CPU.jsx b/demo/src/components/CPU.jsx new file mode 100644 index 0000000..d7069b7 --- /dev/null +++ b/demo/src/components/CPU.jsx @@ -0,0 +1,27 @@ +import React from 'react'; +import { Meter } from './Meter'; + +export class CPU extends React.Component { + render() { + const theme = this.props.theme; + return ( + + + + ) + } +} \ No newline at end of file diff --git a/demo/src/components/FPGA.jsx b/demo/src/components/FPGA.jsx new file mode 100644 index 0000000..c95b48a --- /dev/null +++ b/demo/src/components/FPGA.jsx @@ -0,0 +1,27 @@ +import React from 'react'; +import { Meter } from './Meter'; + +export class FPGA extends React.Component { + render() { + const theme = this.props.theme; + return ( + + + + ) + } +} \ No newline at end of file diff --git a/demo/src/components/Grid.jsx b/demo/src/components/Grid.jsx new file mode 100644 index 0000000..1cb2ce3 --- /dev/null +++ b/demo/src/components/Grid.jsx @@ -0,0 +1,85 @@ +import React from 'react'; +import Query from './Query'; + +class Grid extends React.Component { + render() { + return ( + +
+ { + this.props.queryDescriptions.map((queryDescription, index) => { + return ( + +
+
+
+ 99.7GB of NYC Yellow Taxi Trip Records +
+
+ + {queryDescription.title} + + {queryDescription.sql} +
+
+ +
+ + ); + }) + } +
+ + ) + } +} + +export default Grid; \ No newline at end of file diff --git a/demo/src/components/Meter.jsx b/demo/src/components/Meter.jsx new file mode 100644 index 0000000..3ee9fc3 --- /dev/null +++ b/demo/src/components/Meter.jsx @@ -0,0 +1,390 @@ +import React from 'react'; +import { ProgressGrower } from './ProgressGrower'; + +export class Meter extends React.Component { + constructor(props) { + super(props); + this.state = { + angleOffset: -130, + angle: 0, + mbPerSecond: 0, + mbProcessed: 0, + index: 0, + total: 0, + running: false + }; + } + + mapMbPerSecond = (input, start, end) => { + const inputStart = this.props.meterRange.start; + const inputEnd = this.props.meterRange.end; + const outputStart = start; + const outputEnd = end; + const inputRange = inputEnd - inputStart; + const outputRange = outputEnd - outputStart; + + const output = (input - inputStart) * outputRange / inputRange + outputStart; + return input === 0 ? 0 : output <= end ? output : end; + } + + mapMbProcessed = (input, start, end) => { + const inputStart = 0; + const inputEnd = this.state.total; + const outputStart = start; + const outputEnd = end; + const inputRange = inputEnd - inputStart; + const outputRange = outputEnd - outputStart; + + const output = (input - inputStart) * outputRange / inputRange + outputStart; + return input === 0 ? 0 : output <= end ? output : end; + } + + normalize(input, min, max) { + return ((input - min) * 100) / (max - min); + } + + start = (event) => { + this.setState({ + angle: 0, + mbPerSecond: 0, + mbProcessed: 0, + index: 0, + total: this.total(), + running: true + }, () => { + this.update(); + }) + } + + total = () => { + let total = 0; + const l = this.props.recordSet.length; + for (let i = 0; i < l; i++) { + const record = this.props.recordSet[i]; + total += +record.value; + } + return total; + } + + update = () => { + if (this.state.running) { + const record = this.props.recordSet[this.state.index]; + const seconds = +record.processedDurationSec * 1000; + const prevIndex = this.state.index - 1; + const nextIndex = this.state.index + 1; + const mbPerSecond = record.calculateValue(); + let mbProcessed = 0; + if (prevIndex >= 0) { + const prevRecord = this.props.recordSet[prevIndex]; + mbProcessed = prevRecord.calculateValue(); + } + if (nextIndex < this.props.recordSet.length) { + this.setState({ + angle: mbPerSecond, + mbPerSecond: mbPerSecond, + mbProcessed: this.state.mbProcessed + mbProcessed, + index: nextIndex + }, () => { + setTimeout(() => { + this.update(); + }, seconds); + }); + } else { + this.finish(); + } + } + } + + stop = (event) => { + this.setState({ + angle: 0, + mbPerSecond: 0, + mbProcessed: 0, + total: 0, + index: 0, + running: false + }) + } + + finish = () => { + this.setState({ + angle: 0, + mbPerSecond: 0 + }) + } + + noise = () => { + const value = this.state.running && this.state.mbPerSecond > 0 ? Math.sin(new Date().getTime()) * 1 : 0; + return value > 0 ? value : 0; + } + + componentDidUpdate(prevProps, prevState, snapshot) { + if (this.props.meterRange !== prevProps.meterRange) { + this.stop(); + } + } + + render() { + let blurValue = this.mapMbPerSecond(this.state.mbPerSecond, 0, 0.35); + let saturateValue = this.mapMbPerSecond(this.state.mbPerSecond, 1, 7); + saturateValue = saturateValue > 0 ? saturateValue : 1; + return ( + +
+
+ {this.props.background} + {/*
+
+ ({this.props.spec}) +
+
*/} +
+ needle +
+ +
+
+ {this.props.meterRange.start} +
+
+
+
+ {this.props.meterRange.end} +
+
+
+
+ {(this.state.mbPerSecond + this.noise()).toLocaleString(navigator.language, { minimumIntegerDigits: 3, maximumFractionDigits: 0 })} +
+
+
+
+ MB/SECOND +
+
+
+
+ {this.state.mbProcessed.toLocaleString(navigator.language, { minimumIntegerDigits: 1, maximumFractionDigits: 0 })} +
+
+
+
+ MB PROCESSED +
+
+
+
{ + e.target.style.backgroundColor = this.props.color; + e.target.style.color = this.props.hoverColor; + }} + onMouseOut={(e) => { + e.target.style.backgroundColor = 'transparent'; + e.target.style.color = this.props.color; + }} + onClick={this.start} + > + START +
+
+
+
{ + e.target.style.backgroundColor = this.props.color; + e.target.style.color = this.props.hoverColor; + }} + onMouseOut={(e) => { + e.target.style.backgroundColor = 'transparent'; + e.target.style.color = this.props.color; + }} + onClick={(e) => { + this.stop(); + }} + > + STOP +
+
+
+
+ + ); + } +} \ No newline at end of file diff --git a/demo/src/components/ProgressGrower.jsx b/demo/src/components/ProgressGrower.jsx new file mode 100644 index 0000000..687a2c3 --- /dev/null +++ b/demo/src/components/ProgressGrower.jsx @@ -0,0 +1,33 @@ +import React from 'react'; + +export class ProgressGrower extends React.Component { + render() { + return ( + +
+
+
+
+
+ + ); + } +} \ No newline at end of file diff --git a/demo/src/components/Query.jsx b/demo/src/components/Query.jsx new file mode 100644 index 0000000..7a52699 --- /dev/null +++ b/demo/src/components/Query.jsx @@ -0,0 +1,74 @@ +import React from 'react'; +import { CPU } from './CPU'; +import { FPGA } from './FPGA'; + +class Query extends React.Component { + constructor(props) { + super(props); + this.state = { + meterRange: { + start: 0, + end: 500 + } + } + } + + componentDidMount() { + this.setMeterRangeEnd(); + } + + componentDidUpdate(prevProps) { + if(this.props.index !== prevProps.index) { + this.setMeterRangeEnd(); + } + } + + render() { + return ( + +
+ + +
+ + ) + } + + setMeterRangeEnd = () => { + let maxValue = 0; + const l = this.props.recordSetFPGA.length; + for (let i = 0; i < l; i++) { + const record = this.props.recordSetFPGA[i]; + const value = Math.trunc(record.calculateValue()); + if (maxValue < value) { + maxValue = value; + } + } + this.setState({ + meterRange: { + start: 0, + end: maxValue + } + }) + } +} + +export default Query; \ No newline at end of file diff --git a/demo/src/components/unused/ProgressSlider.jsx b/demo/src/components/unused/ProgressSlider.jsx new file mode 100644 index 0000000..ff23522 --- /dev/null +++ b/demo/src/components/unused/ProgressSlider.jsx @@ -0,0 +1,60 @@ +import React from 'react'; + +export class ProgressSlider extends React.Component { + constructor(props) { + super(props); + this.state = { + position: 0 + }; + } + + update = () => { + if(this.props.running) { + this.setState({ + position: this.state.position < 245 ? this.state.position + 10 : -245 + }) + setTimeout(() => { + this.update(); + }, 1000/this.props.mapMbPerSecond(this.props.mbPerSecond, 30, 60)); + } + } + + componentDidMount() { + this.update(); + } + + componentDidUpdate(prevProps) { + if(prevProps.running !== this.props.running && this.props.running) { + this.update(); + } + } + + render() { + return ( + +
+
+
+
+ + ); + } +} \ No newline at end of file diff --git a/demo/src/components/unused/Tabs.jsx b/demo/src/components/unused/Tabs.jsx new file mode 100644 index 0000000..9051f0f --- /dev/null +++ b/demo/src/components/unused/Tabs.jsx @@ -0,0 +1,82 @@ +import React from 'react'; +import Query from './Query'; + +class Tabs extends React.Component { + constructor(props) { + super(props); + this.state = { + index: 0 + } + } + render() { + return ( + +
+
+
+
+ { + this.props.queryDescriptions.map((queryDescription, index) => { + return ( + this.setState({ index: index })} + style={{ + cursor: "pointer", + color: index === this.state.index ? this.props.themes[this.state.index].cpuColor : "#ffffff", + marginLeft: 20 + }} + > + {queryDescription.title} + + ); + }) + } +
+
+ {this.props.queryDescriptions[this.state.index].sql} +
+
+ +
+
+ + ) + } +} + +export default Tabs; \ No newline at end of file diff --git a/demo/src/data/cpu.json b/demo/src/data/cpu.json new file mode 100644 index 0000000..057d4be --- /dev/null +++ b/demo/src/data/cpu.json @@ -0,0 +1,64392 @@ +[ + { + "title": "Query1A", + "records": [ + { + "timestamp": "2020-05-07T03:12:19.244Z", + "numRows": "15097860", + "avgRowSize": "98", + "inputRowsPerSec": "0.0", + "processedRowsPerSec": "1908946.7695030977", + "inputDurationSec": "0.0", + "processedDurationSec": 1, + "value": 178.41032353525503 + }, + { + "timestamp": "2020-05-07T03:12:19.244Z", + "numRows": "15097860", + "avgRowSize": "98", + "inputRowsPerSec": "0.0", + "processedRowsPerSec": "1908946.7695030977", + "inputDurationSec": "0.0", + "processedDurationSec": 1, + "value": 178.41032353525503 + }, + { + "timestamp": "2020-05-07T03:12:19.244Z", + "numRows": "15097860", + "avgRowSize": "98", + "inputRowsPerSec": "0.0", + "processedRowsPerSec": "1908946.7695030977", + "inputDurationSec": "0.0", + 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100644 index 0000000..ec2585e --- /dev/null +++ b/demo/src/index.css @@ -0,0 +1,13 @@ +body { + margin: 0; + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', + 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', + sans-serif; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +code { + font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', + monospace; +} diff --git a/demo/src/index.js b/demo/src/index.js new file mode 100644 index 0000000..f5185c1 --- /dev/null +++ b/demo/src/index.js @@ -0,0 +1,17 @@ +import React from 'react'; +import ReactDOM from 'react-dom'; +import './index.css'; +import App from './App'; +import * as serviceWorker from './serviceWorker'; + +ReactDOM.render( + + + , + document.getElementById('root') +); + +// If you want your app to work offline and load faster, you can change +// unregister() to register() below. Note this comes with some pitfalls. +// Learn more about service workers: https://bit.ly/CRA-PWA +serviceWorker.unregister(); diff --git a/demo/src/logo.svg b/demo/src/logo.svg new file mode 100644 index 0000000..6b60c10 --- /dev/null +++ b/demo/src/logo.svg @@ -0,0 +1,7 @@ + + + + + + + diff --git a/demo/src/models/Record.js b/demo/src/models/Record.js new file mode 100644 index 0000000..de93ecc --- /dev/null +++ b/demo/src/models/Record.js @@ -0,0 +1,52 @@ +class Record { + constructor(timestamp, numRows, avgRowSize, inputRowsPerSec, processedRowsPerSec, inputDurationSec, processedDurationSec, value) { + this.timestamp = timestamp; + this.numRows = numRows; + this.avgRowSize = avgRowSize; + this.inputRowsPerSec = inputRowsPerSec; + this.processedRowsPerSec = processedRowsPerSec; + this.inputDurationSec = inputDurationSec; + this.processedDurationSec = processedDurationSec; + this.value = value; + } + + static fromData(data) { + return new Record( + data.timestamp, + data.numRows, + data.avgRowSize, + data.inputRowsPerSec, + data.processedRowsPerSec, + data.inputDurationSec, + data.processedDurationSec, + data.value + ); + } + + calculateValue = () => { + return +this.value; + // const prps = +this.processedRowsPerSec; + // const ars = +this.avgRowSize; + // const value = (prps * ars) / (1024 * 1024); + + // return value; + } + + // processingTime = () => { + // return +this.inputDurationSec + +this.processedDurationSec; + // //boris + // //return +this.processedDurationSec; + // } + + // throughput = () => { + // return +this.numRows / (+this.inputDurationSec + +this.processedDurationSec); + // //boris + // //return + this.processedRowsPerSec; + // } + + // totalBytesProcessed = () => { + // return +this.numRows * +this.avgRowSize; + // } +} + +export default Record; \ No newline at end of file diff --git a/demo/src/models/Theme.js b/demo/src/models/Theme.js new file mode 100644 index 0000000..9424293 --- /dev/null +++ b/demo/src/models/Theme.js @@ -0,0 +1,31 @@ +class Theme { + constructor( + path, + name, + cpuColor, + cpuSpecColor, + cpuHoverColor, + fpgaColor, + fpgaSpecColor, + fpgaHoverColor, + backgroundColor + ) { + this.cpuColor = cpuColor; + this.cpuSpecColor = cpuSpecColor; + this.cpuHoverColor = cpuHoverColor; + this.fpgaColor = fpgaColor; + this.fpgaSpecColor = fpgaSpecColor; + this.fpgaHoverColor = fpgaHoverColor; + this.backgroundColor = backgroundColor; + this.background = `${path}/${name}/background.jpg`; + this.needle = `${path}/${name}/needle.svg`; + this.cpu = `${path}/${name}/cpu`; + this.fpga = `${path}/${name}/fpga`; + this.startCPU = `${path}/${name}/start cpu.svg`; + this.stopCPU = `${path}/${name}/stop cpu.svg`; + this.startFPGA = `${path}/${name}/start fpga.svg`; + this.stopFPGA = `${path}/${name}/stop fpga.svg`; + } +} + +export default Theme; \ No newline at end of file diff --git a/demo/src/queries/cpu/Query1A.csv b/demo/src/queries/cpu/Query1A.csv new file mode 100644 index 0000000..004420b --- /dev/null +++ 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However, it also means that developers (and users) +// will only see deployed updates on subsequent visits to a page, after all the +// existing tabs open on the page have been closed, since previously cached +// resources are updated in the background. + +// To learn more about the benefits of this model and instructions on how to +// opt-in, read https://bit.ly/CRA-PWA + +const isLocalhost = Boolean( + window.location.hostname === 'localhost' || + // [::1] is the IPv6 localhost address. + window.location.hostname === '[::1]' || + // 127.0.0.0/8 are considered localhost for IPv4. + window.location.hostname.match( + /^127(?:\.(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){3}$/ + ) +); + +export function register(config) { + if (process.env.NODE_ENV === 'production' && 'serviceWorker' in navigator) { + // The URL constructor is available in all browsers that support SW. + const publicUrl = new URL(process.env.PUBLIC_URL, window.location.href); + if (publicUrl.origin !== window.location.origin) { + // Our service worker won't work if PUBLIC_URL is on a different origin + // from what our page is served on. This might happen if a CDN is used to + // serve assets; see https://github.com/facebook/create-react-app/issues/2374 + return; + } + + window.addEventListener('load', () => { + const swUrl = `${process.env.PUBLIC_URL}/service-worker.js`; + + if (isLocalhost) { + // This is running on localhost. Let's check if a service worker still exists or not. + checkValidServiceWorker(swUrl, config); + + // Add some additional logging to localhost, pointing developers to the + // service worker/PWA documentation. + navigator.serviceWorker.ready.then(() => { + console.log( + 'This web app is being served cache-first by a service ' + + 'worker. To learn more, visit https://bit.ly/CRA-PWA' + ); + }); + } else { + // Is not localhost. Just register service worker + registerValidSW(swUrl, config); + } + }); + } +} + +function registerValidSW(swUrl, config) { + navigator.serviceWorker + .register(swUrl) + .then(registration => { + registration.onupdatefound = () => { + const installingWorker = registration.installing; + if (installingWorker == null) { + return; + } + installingWorker.onstatechange = () => { + if (installingWorker.state === 'installed') { + if (navigator.serviceWorker.controller) { + // At this point, the updated precached content has been fetched, + // but the previous service worker will still serve the older + // content until all client tabs are closed. + console.log( + 'New content is available and will be used when all ' + + 'tabs for this page are closed. See https://bit.ly/CRA-PWA.' + ); + + // Execute callback + if (config && config.onUpdate) { + config.onUpdate(registration); + } + } else { + // At this point, everything has been precached. + // It's the perfect time to display a + // "Content is cached for offline use." message. + console.log('Content is cached for offline use.'); + + // Execute callback + if (config && config.onSuccess) { + config.onSuccess(registration); + } + } + } + }; + }; + }) + .catch(error => { + console.error('Error during service worker registration:', error); + }); +} + +function checkValidServiceWorker(swUrl, config) { + // Check if the service worker can be found. If it can't reload the page. + fetch(swUrl, { + headers: { 'Service-Worker': 'script' }, + }) + .then(response => { + // Ensure service worker exists, and that we really are getting a JS file. + const contentType = response.headers.get('content-type'); + if ( + response.status === 404 || + (contentType != null && contentType.indexOf('javascript') === -1) + ) { + // No service worker found. Probably a different app. Reload the page. + navigator.serviceWorker.ready.then(registration => { + registration.unregister().then(() => { + window.location.reload(); + }); + }); + } else { + // Service worker found. Proceed as normal. + registerValidSW(swUrl, config); + } + }) + .catch(() => { + console.log( + 'No internet connection found. App is running in offline mode.' + ); + }); +} + +export function unregister() { + if ('serviceWorker' in navigator) { + navigator.serviceWorker.ready + .then(registration => { + registration.unregister(); + }) + .catch(error => { + console.error(error.message); + }); + } +} diff --git a/demo/src/setupTests.js b/demo/src/setupTests.js new file mode 100644 index 0000000..74b1a27 --- /dev/null +++ b/demo/src/setupTests.js @@ -0,0 +1,5 @@ +// jest-dom adds custom jest matchers for asserting on DOM nodes. +// allows you to do things like: +// expect(element).toHaveTextContent(/react/i) +// learn more: https://github.com/testing-library/jest-dom +import '@testing-library/jest-dom/extend-expect'; diff --git a/demo/src/utilities/count.js b/demo/src/utilities/count.js new file mode 100644 index 0000000..5474b97 --- /dev/null +++ b/demo/src/utilities/count.js @@ -0,0 +1,11773 @@ +const cpu = [ + { + "timestamp": "2020-05-13T21:56:26.904Z", + "numRows": "15097860", + "avgRowSize": "98", + 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filePath.name.split('.')[0] + }; + }); + +const recordSets = []; + +files.forEach((file) => { + const data = fs.readFileSync(file.path, { encoding: 'utf-8' }); + + const parsedRecords = []; + const rows = data.split('\r\n'); + const rl = rows.length; + for (let r = 0; r < rl; r++) { + const row = rows[r]; + const columns = row.split(','); + if (columns.length === 7) { + parsedRecords.push({ + timestamp: columns[0], + numRows: columns[1], + avgRowSize: columns[2], + inputRowsPerSec: columns[3], + processedRowsPerSec: columns[4], + inputDurationSec: columns[5], + processedDurationSec: columns[6] + }); + } + } + + const sortedRecords = parsedRecords.sort((a, b) => { + const d1 = new Date(a.timestamp); + const d2 = new Date(b.timestamp); + if (d1 < d2) { + return -1; + } + if (d1 > d2) { + return 1; + } + return 0; + }); + + const records = []; + const srl = sortedRecords.length; + for(let r = 0; r < srl; r++) { + const record = sortedRecords[r]; + const duration = +record.processedDurationSec; + const seconds = Math.trunc(duration); + const remainder = duration % 1; + + const prps = +record.processedRowsPerSec; + const ars = +record.avgRowSize; + const value = (prps * ars) / (1024 * 1024); + + for (let s = 0; s < seconds; s++) { + records.push({ + timestamp: record.timestamp, + numRows: record.numRows, + avgRowSize: record.avgRowSize, + inputRowsPerSec: record.inputRowsPerSec, + processedRowsPerSec: record.processedRowsPerSec, + inputDurationSec: record.inputDurationSec, + processedDurationSec: 1, + value: value + }); + } + records.push({ + timestamp: record.timestamp, + numRows: record.numRows, + avgRowSize: record.avgRowSize, + inputRowsPerSec: record.inputRowsPerSec, + processedRowsPerSec: record.processedRowsPerSec, + inputDurationSec: record.inputDurationSec, + processedDurationSec: remainder, + value: value * remainder + }); + } + + recordSets.push({ + title: file.name, + records: records + }); +}); + +fs.writeFile(`${jsonDirPath}/${jsonFileName}.json`, JSON.stringify(recordSets), function (err) { + if (err) return console.log(err); + console.log(jsonFileName); +}); + +// node src/utilities/csv2json.js 'src/queries/cpu' 'cpu' 'src/data' +// node src/utilities/csv2json.js 'src/queries/fpga' 'fpga' 'src/data' \ No newline at end of file diff --git a/docs/demo-approaches.md b/docs/demo-approaches.md new file mode 100644 index 0000000..ce82187 --- /dev/null +++ b/docs/demo-approaches.md @@ -0,0 +1,69 @@ +# Taxi Dataset Demo Architecture + +## Overview + +We select a ~230 Gb subset of the [NYC Taxi dataset](https://github.com/toddwschneider/nyc-taxi-data) that covers Yellow Cab records for 2009 - 2019. The data is split into 130 CSV files and deposited in Azure Data Lake Storage Gen2. + +We then use Spark to demonstrate performance improvements resulting from use of Pipedream/FPGA as the backend compared to CPU. + +## Running on the cluster + +Spark abstracts the distributed computation paradigm. We setup the HDInsight Spark cluster in Azure. The dataflow is shown in the diagram below. + +![](images/spark_overview.png) + +The ruby squares show data stores pre- and post-processing. The cluster itself in the blue square is expanded below. + +![](images/spark_architecture.png) + +Driver (head) and workers are all instances in the HDFS under Yarn management in HDInsight. The resource manager is responsible for brokering data between external and cluster storage. + +## Running locally + +Spark runs locally as an application inside the JVM. + +## Demo flow + +Two sets of machines, local or cluster, used to showcase FPGA vs CPU perfromance + +### Prerequisites + +Download the dataset to the accessible store + +### Scenario + +We trigger Spark jobs for CPU and FPGA based machines and show performance gains in a compelling visualization + +### Demo on the cluster: structured streaming + +* Data is uploaded to Blob Storage (Data Lake Storage Gen2) all 233 Gb split into CSV files and the Spark streaming model is used to continuously write out query results: + +![streaming](images/streaming.png) + +* Processing is triggered every second creating a stream of CSV files that are then queried and query results are deposited into Blob Storage +* An Azure Function monitors the output storage and when new files arrive notifies the web-based visualization app + +#### Pros: + +* Scenario reflects real life + +#### Cons: + +* Network latency may consume performance gained from using FPGA + +### Demo locally + +* A single VM with Spark installation in Azure +* Data is loaded onto the local SSD drive +* Logs are written as query results become available +* The logs are served to the visualization app + +#### Pros + +* No network dependencies, FPGA performance clearly visible + + +#### Cons + +* Scenario not reflecting real life + diff --git a/docs/fpga_info.txt b/docs/fpga_info.txt new file mode 100644 index 0000000..5160cd8 --- /dev/null +++ b/docs/fpga_info.txt @@ -0,0 +1,38 @@ +File: boris@51.143.2.244:/home/boris/fpga_demo/yellow_tripdata_2011-06.csv + +(passwd: BuildDemo2019) + +Queries: + +0. select count(*) as total from $tableName +1. select payment_type, count(*) as total from $tableName group by payment_type +2. select passenger_count, avg(total_amount) as avg_amount from $tableName group by passenger_count +3. select passenger_count, year(tpep_pickup_datetime) year, count(*) total FROM $tableName group by passenger_count, year +4. select passenger_count, year(tpep_pickup_datetime) as year, + cast(trip_distance as int) as distance, + count(*) as total + from $tableName + group by passenger_count, year, distance + order by year, total desc + +Schema: (18 cols) + + +VendorID, String +tpep_pickup_datetime, Timestamp +tpep_dropoff_datetime, Timestamp +passenger_count, Integer +trip_distance, Double +PULocationID, Integer +DOLocationID, Integer +RateCodeID, Integer +store_and_fwd_flag, String +payment_type, Integer +fare_amount, Double +extra, Double +mta_tax, Double +improvement_surcharge, Double +tip_amount, Double +tolls_amount, Double +total_amount, Double +congestion_surcharge \ No newline at end of file diff --git a/docs/images/demo.png b/docs/images/demo.png new file mode 100644 index 0000000000000000000000000000000000000000..fec6019fa1edb25f1ba110552493ee6552d0225a GIT binary patch literal 897072 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+#define VSTREAM_LITTLE_ENDIAN 0x10101010 +#define VSTREAM_BIG_ENDIAN 0x11111111 + +#define VSTREAM_BEGIN_META 0x77202077 +#define VSTREAM_END_META 0x76202076 + +#define VSTREAM_SCHEMA_DEFINITION 0x75242475 +#define VSTREAM_END_SCHEMA_DEFINITION 0x75121275 +#define VSTREAM_COLUMN_DEFINITION 0x75464675 +#define VSTREAM_END_COLUMN_DEFINITION 0x75323275 + +// indicating no VStream data follows +#define VSTREAM_TERMINATE 0x7FFFFF7F + +// Typecodes are 32 bits in length +// +#define VSTREAM_TYPECODE_NULL 0 +#define VSTREAM_TYPECODE_BYTE 1 +#define VSTREAM_TYPECODE_CHAR 2 +#define VSTREAM_TYPECODE_INT8 3 +#define VSTREAM_TYPECODE_BOOL 4 + +#define VSTREAM_TYPECODE_INT16 5 +#define VSTREAM_TYPECODE_UINT16 6 +#define VSTREAM_TYPECODE_WCHAR32 7 +#define VSTREAM_TYPECODE_INT32 8 +#define VSTREAM_TYPECODE_UINT32 9 +#define VSTREAM_TYPECODE_INT64 10 +#define VSTREAM_TYPECODE_UINT64 11 + +// IEEE 754 4 byte +#define VSTREAM_TYPECODE_FLOAT 12 + +// IEEE 754 8 byte +#define VSTREAM_TYPECODE_DOUBLE 13 + +// Seconds since Unix epoch +#define VSTREAM_TYPECODE_TIMESTAMP_SEC 14 + +// Milliseconds into UNIX epoch +#define VSTREAM_TYPECODE_TIMESTAMP_MIL 15 + +// Microseconds into the UNIX epoch +#define VSTREAM_TYPECODE_TIMESTAMP_MIC 16 + +// Nanoseconds into the UNIX epoch +#define VSTREAM_TYPECODE_TIMESTAMP_NAN 17 + +#define VSTREAM_DYNAMIC_TYPE_MARKER 18 + +#define VSTREAM_TYPECODE_UUID 19 // Linux memory ordering + +// 64 bit length prefixed on all of these +// +#define VSTREAM_TYPECODE_UTF8_STRING 20 +#define VSTREAM_TYPEOCDE_UTF16_STRING 21 +#define VSTREAM_TYPECODE_UTF32_STRING 22 + +#define VSTREAM_TYPECODE_BUFFER 23 + +#define VSTREAM_TYPECODE_TUPLE 24 + +#define VSTREAM_TYPECODE_VECTOR 25 + +#define VSTREAM_TYPECODE_MAP 26 + +// 64 bit length prefix, otherwise encoded as UTF8 string +// +#define VSTREAM_TYPECODE_JSON 27 + +#define VSTREAM_TYPECODE_LAST 28 + + + +#define VSTREAM_DATEFORMAT_YYYY_MM_DD 0x0001 // Space, - or / delimiters +#define VSTREAM_DATEFORMAT_YYYYMMDD 0x0002 // No delimiters +#define VSTREAM_DATEFORMAT_MM_DD_YYYY 0x0004 +#define VSTREAM_DATEFORMAT_DD_MM_YYYY 0x0008 +#define VSTREAM_DATEFORMAT_DD_MONTHNAME_YYYY 0x0010 +#define VSTERAM_DATEFORMAT_MM_DD_YY 0x0020 +#define VSTREAM_DATEFORMAT_MNAME_DD_YYYY 0x0040 + +#define VSTREAM_TIMEFORMAT_HH_MM_SS 0x1000 +#define VSTREAM_TIMEFORMAT_HHMMSS 0x2000 +#define VSTREAM_TIMEFORMAT_HH_MM 0x4000 + +#define VSTREAM_DATEFORMAT_SPARK_DEFINED 0x8000 + + +#define VSTREAM_TIMEFORMAT_AM_PM 0x00010000 +#define VSTREAM_TIMEFORMAT_FRACTIONAL_SECONDS 0x00020000 + +#define VSTREAM_TIMEZONE_NONE 0x00040000 +#define VSTREAM_TIMEZONE_Z 0x00080000 +#define VSTREAM_TIMEZONE_UTCOFFSET 0x00100000 + + +// Walk over the fields of VStream + struct VSFieldIter { + char *p; + char *pm; + string GetString() { + if (p+8>pm) return ""; + uint64_t len = *(uint64_t*)p; + p += 8; + if (p+len>pm) return ""; + string s(p,len); + p += len; + return s; + } + + template + T GetVal() { + size_t sz = sizeof(T); + T val = *(T*)p; + p += sz; + return val; + } + + std::tm *GetUxTime(uint32_t type_code); + + uint32_t GetUInt32() { + if (p+4>pm) return 0; + uint32_t val = *(uint32_t*)p; + p += 4; + return val; + } + bool IsEndBlock() { + if (p+4>pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_END_BLOCK) { + p += 4; + return true; + } + return false; + } + bool IsBeginBlock() { + if (p+4>pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_BEGIN_BLOCK) { + p = p+4; + return true; + } + return false; + } + bool IsBeginRow() { + if (p+4>pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_BEGIN_ROW) { + p = p+4; + return true; + } + return false; + } + }; + + /* + TODO: You can use like this. Don’t worry about schema. + TODO: You can use a fixed schema. Let us start with all types as String + */ + + // now go through the Vstream fields now + VSFieldIter v0, v1; + v0.p = *(out_data_0->out_buf); + v0.pm = v0.p+out_data_0->block_size; + v1.p = *(out_data_1->out_buf); + v1.pm = v1.p+out_data_1->block_size; + + if (!v0.IsBeginBlock()) { + cout << "Missing Begin Block" << endl; + res = false; + } + if (!v1.IsBeginBlock()) { + cout << "Missing Begin Block" << endl; + res = false; + } + + int num_mismatch = 0; + if ( !v0.IsBeginRow() || !v1.IsBeginRow() ) { + cout << "Incorrect begin block" << endl; + res = false; + } + if (!res) return res; + + // Compare the payload. + for (int i=0; i(); + int64_t value1 = v1.GetVal(); + if (value0 != value1) { + cout << "Mismatch: \"" <(); + uint64_t value0DayLightSavings = value0; + + // NOTE: CPU implementation returns local time while + // FPGA implementation returns UTC time. Below CPU + // value is modified to be UTC. + // Since local time has day light savings, that scenario + // is also covered. + // TODO: This needs to be removed once there is an agreement + // in CPU/FPGA implementation. + if (VSTREAM_TYPECODE_TIMESTAMP_SEC == typeCode) { + value0 = value0 - 28800ull; + value0DayLightSavings = value0 + 3600ull; + } + else if (VSTREAM_TYPECODE_TIMESTAMP_MIL == typeCode) { + value0 = value0 - 28800000ull; + value0DayLightSavings = value0 + 3600000ull; + } + else if(VSTREAM_TYPECODE_TIMESTAMP_MIC == typeCode) { + value0 = value0 - 28800000000ull; + value0DayLightSavings = value0 + 3600000000ull; + } + else if (VSTREAM_TYPECODE_TIMESTAMP_NAN == typeCode) { + value0 = value0 - 28800000000000ull; + value0DayLightSavings = value0 + 3600000000000ull; + } + uint64_t value1 = v1.GetVal(); + if (value0 != value1 && value0DayLightSavings != value1) { + cout << "Mismatch: \"" <pm) return ""; + uint64_t len = *(uint64_t*)p; + p += 8; + if (p+len>pm) return ""; + string s(p,len); + p += len; + return s; + } + + template + T GetVal() { + size_t sz = sizeof(T); + T val = *(T*)p; + p += sz; + return val; + } + + std::tm *GetUxTime(uint32_t type_code); + + uint32_t GetUInt32() { + if (p+4>pm) return 0; + uint32_t val = *(uint32_t*)p; + p += 4; + return val; + } + bool IsEndBlock() { + if (p+4>pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_END_BLOCK) { + p += 4; + return true; + } + return false; + } + bool IsBeginBlock() { + if (p+4>pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_BEGIN_BLOCK) { + p = p+4; + return true; + } + return false; + } + bool IsBeginRow() { + if (p+4>pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_BEGIN_ROW) { + p = p+4; + return true; + } + return false; + } + }; + + /* + TODO: You can use like this. Don’t worry about schema. + TODO: You can use a fixed schema. Let us start with all types as String + */ + + // now go through the Vstream fields now + VSFieldIter v0, v1; + v0.p = *(out_data_0->out_buf); + v0.pm = v0.p+out_data_0->block_size; + v1.p = *(out_data_1->out_buf); + v1.pm = v1.p+out_data_1->block_size; + + if (!v0.IsBeginBlock()) { + cout << "Missing Begin Block" << endl; + res = false; + } + if (!v1.IsBeginBlock()) { + cout << "Missing Begin Block" << endl; + res = false; + } + + int num_mismatch = 0; + if ( !v0.IsBeginRow() || !v1.IsBeginRow() ) { + cout << "Incorrect begin block" << endl; + res = false; + } + if (!res) return res; + + // Compare the payload. + for (int i=0; i(); + int64_t value1 = v1.GetVal(); + if (value0 != value1) { + cout << "Mismatch: \"" <(); + uint64_t value0DayLightSavings = value0; + + // NOTE: CPU implementation returns local time while + // FPGA implementation returns UTC time. Below CPU + // value is modified to be UTC. + // Since local time has day light savings, that scenario + // is also covered. + // TODO: This needs to be removed once there is an agreement + // in CPU/FPGA implementation. + if (VSTREAM_TYPECODE_TIMESTAMP_SEC == typeCode) { + value0 = value0 - 28800ull; + value0DayLightSavings = value0 + 3600ull; + } + else if (VSTREAM_TYPECODE_TIMESTAMP_MIL == typeCode) { + value0 = value0 - 28800000ull; + value0DayLightSavings = value0 + 3600000ull; + } + else if(VSTREAM_TYPECODE_TIMESTAMP_MIC == typeCode) { + value0 = value0 - 28800000000ull; + value0DayLightSavings = value0 + 3600000000ull; + } + else if (VSTREAM_TYPECODE_TIMESTAMP_NAN == typeCode) { + value0 = value0 - 28800000000000ull; + value0DayLightSavings = value0 + 3600000000000ull; + } + uint64_t value1 = v1.GetVal(); + if (value0 != value1 && value0DayLightSavings != value1) { + cout << "Mismatch: \"" < +#include +#include +#include +#include +#include +#include + +#include "field_iter.h" +#include "taxirow.hpp" +#include "queries.hpp" + +using namespace std; + +int main(int argc, char* argv[]) { + + if (argc < 3) { + cout << "Specify vstream directory and query number on the command line:" << endl << " cqueries /path/to/vstream 3"; + return 1; + } + + string path(argv[1]); + + vector taxiRows; + + for (const auto& entry : filesystem::directory_iterator(path)) { + VSFieldIter vsParser(entry.path().string()); + + for (vector row = vsParser.getNextRow(); !row.empty(); row = vsParser.getNextRow()) { + + if (row.size() != 18) { + cout << "corrupt row found: " << taxiRows.size() << endl; + continue; + } + + taxiRows.push_back(parse_taxi_row(row)); + } + + cout << taxiRows.size() << endl; + //******************* + //TODO: DEBUG ONLY. Break after the frist file!!!!! + //******************** + break; + } + + Queries queries(taxiRows); + int n = atoi(argv[2]); + auto total_time = queries.timeQuery(n); + cout << "Runtime of Query "< +#include +#include +#include +#include +#include +#include + +using namespace std; + +typedef vector TaxiData; +typedef unordered_map IntTaxiGroup; +typedef unordered_map IntTaxiGroupAvg; +typedef tuple PassengerYearDistance; + +struct PassengerYearDistanceHash { + size_t operator()(PassengerYearDistance k) const { + return get<0>(k) ^ get<1>(k) ^ get<2>(k); + } +}; + +struct PassengerYearDistanceEq { + bool operator()(const PassengerYearDistance& v0, const PassengerYearDistance& v1) const + { + return ( + std::get<0>(v0) == std::get<0>(v1) && + std::get<1>(v0) == std::get<1>(v1) && + std::get<2>(v0) == std::get<2>(v1) + ); + } +}; + +typedef unordered_map TupleTaxiGroup; + +// satisfies all the cases of the parallel reduce BinaryOp +struct MapReducerPaymentType { + + IntTaxiGroup operator() (IntTaxiGroup& lhs, IntTaxiGroup& rhs) { + for (auto const& entry : rhs) { + if (lhs.find(entry.first) == lhs.end()) { + lhs.insert(IntTaxiGroup::value_type(entry.first, TaxiData())); + } + + lhs[entry.first].insert(lhs[entry.first].end(), entry.second.begin(), entry.second.end()); + } + return lhs; + } + + IntTaxiGroup operator()(IntTaxiGroup& g, TaxiRow t) { + if (g.find(t.paymentType) == g.end()) { + g.insert(IntTaxiGroup::value_type(t.paymentType, TaxiData())); + } + g[t.paymentType].push_back(t); + return g; + } + + IntTaxiGroup operator()(TaxiRow t, IntTaxiGroup& g) { + + return operator()(g, t); + } + + IntTaxiGroup operator()(TaxiRow t1, TaxiRow t2) { + IntTaxiGroup g; + g.insert(IntTaxiGroup::value_type(t1.paymentType, TaxiData())); + g[t1.paymentType].push_back(t1); + + if (g.find(t2.paymentType) == g.end()) { + g.insert(IntTaxiGroup::value_type(t2.paymentType, TaxiData())); + } + g[t2.paymentType].push_back(t2); + + return g; + } +}; + +// Passenger count reducer +struct MapReducerPassengerCount { + IntTaxiGroup operator() (IntTaxiGroup& lhs, IntTaxiGroup& rhs) { + for (auto const& entry : rhs) { + if (lhs.find(entry.first) == lhs.end()) { + lhs.insert(IntTaxiGroup::value_type(entry.first, TaxiData())); + } + + lhs[entry.first].insert(lhs[entry.first].end(), entry.second.begin(), entry.second.end()); + } + return lhs; + } + + IntTaxiGroup operator()(IntTaxiGroup& g, TaxiRow t) { + if (g.find(t.passengerCount) == g.end()) { + g.insert(IntTaxiGroup::value_type(t.passengerCount, TaxiData())); + } + g[t.passengerCount].push_back(t); + return g; + } + + IntTaxiGroup operator()(TaxiRow t, IntTaxiGroup& g) { + + return operator()(g, t); + } + + IntTaxiGroup operator()(TaxiRow t1, TaxiRow t2) { + IntTaxiGroup g; + g.insert(IntTaxiGroup::value_type(t1.passengerCount, TaxiData())); + g[t1.passengerCount].push_back(t1); + + if (g.find(t2.passengerCount) == g.end()) { + g.insert(IntTaxiGroup::value_type(t2.passengerCount, TaxiData())); + } + g[t2.passengerCount].push_back(t2); + + return g; + } +}; + +// Reducer by passenger_count, year, distance (optional) +// _includeDistance: set to "true" in order to group by the int of distance as well as passenger_count and year +struct MapReducerPassengerCountYear { + + MapReducerPassengerCountYear(bool _includeDistance = false) : includeDistance(_includeDistance) {} + + TupleTaxiGroup operator() (TupleTaxiGroup& lhs, TupleTaxiGroup& rhs) { + for (auto const& entry : rhs) { + if (lhs.find(entry.first) == lhs.end()) { + lhs.insert(TupleTaxiGroup::value_type(entry.first, TaxiData())); + } + + lhs[entry.first].insert(lhs[entry.first].end(), entry.second.begin(), entry.second.end()); + } + return lhs; + } + + TupleTaxiGroup operator()(TupleTaxiGroup& g, TaxiRow t) { + auto key = make_key(t); + if (g.find(key) == g.end()) { + g.insert(TupleTaxiGroup::value_type(key, TaxiData())); + } + g[key].push_back(t); + return g; + } + + TupleTaxiGroup operator()(TaxiRow t, TupleTaxiGroup& g) { + + return operator()(g, t); + } + + TupleTaxiGroup operator()(TaxiRow t1, TaxiRow t2) { + + TupleTaxiGroup g; + auto key1 = make_key(t1); + auto key2 = make_key(t2); + + g.insert(TupleTaxiGroup::value_type(key1, TaxiData())); + g[key1].push_back(t1); + + if (g.find(key2) == g.end()) { + g.insert(TupleTaxiGroup::value_type(key2, TaxiData())); + } + g[key2].push_back(t2); + + return g; + } + +private: + PassengerYearDistance make_key(TaxiRow& t) { + return make_tuple(t.passengerCount, get_year(t.pickupTime), includeDistance ? static_cast(t.tripDistance) : 0); + } + + bool includeDistance; +}; \ No newline at end of file diff --git a/fpga/vstream/queries/parser/field_iter.h b/fpga/vstream/queries/parser/field_iter.h new file mode 100644 index 0000000..b87b837 --- /dev/null +++ b/fpga/vstream/queries/parser/field_iter.h @@ -0,0 +1,136 @@ + #pragma once +#include +#include +#include +#include +#include +#include + +using namespace std; +using namespace boost::interprocess; + +#include "vstreamcore.h" + +class VSFieldIter { + +public: + VSFieldIter(string _fileName) + : fileName(_fileName), + mapped_file(_fileName.c_str(), read_only), + mapped_file_region(mapped_file, read_only) { + + + p = static_cast(mapped_file_region.get_address()); + sizeBytes = mapped_file_region.get_size(); + + // end of current block + pm = p + sizeBytes; + start = p; + } + + virtual ~VSFieldIter() {} + + template + T GetVal() { + size_t sz = sizeof(T); + if (p + sz > pm) throw - 1; + T val = *(T*)p; + p += sz; + return val; + } + + vector getNextRow() + { + vector row; + + // need to be at the start of the row or block + if ((p == start && !IsBeginBlock()) + || (p == start + 4 && !SkipSeq()) + || !IsBeginRow()) { + throw -2; // incorrect positioning + } + + if (IsEndBlock()) + { + return row; + } + + + do { + // this will be the column type which we can just ignore + // it looks like the 32-bit type + 32-bits of "0" + GetULong(); + row.push_back(GetString()); + + } while (!IsBeginRow(false)); + + return row; + } + +private: + + // file size + size_t sizeBytes; + const int rowSize = 300; + string fileName; + + file_mapping mapped_file; + mapped_region mapped_file_region; + + // TODO: Not thread safe + vector curRow; + char* p; + char* pm; + char* start; + + bool IsEndBlock() { + if (p + 4 > pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_END_BLOCK) { + return true; + } + return false; + } + bool IsBeginBlock() { + if (p + 4 > pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_BEGIN_BLOCK) { + p = p + 4; + return true; + } + return false; + } + bool IsBeginRow(bool advance = true) { + if (p + 4 > pm) return false; + uint32_t val = *(uint32_t*)p; + if (val == VSTREAM_BEGIN_ROW) { + p = advance ? p + 4 : p; + return true; + } + return false; + } + + uint32_t GetUInt32() { + return GetVal(); + } + + uint64_t GetULong() { + return GetVal(); + } + + string GetString() { + string out; + for (; *p >= 0x20 && !IsBeginRow(false); p++) { + out += *p; + } + + return out; + } + + bool SkipSeq() { + for(; !IsBeginRow(false); p++) { + ; + } + return true; + } + }; diff --git a/fpga/vstream/queries/parser/read_vstream.cpp b/fpga/vstream/queries/parser/read_vstream.cpp new file mode 100644 index 0000000..65848bf --- /dev/null +++ b/fpga/vstream/queries/parser/read_vstream.cpp @@ -0,0 +1,11 @@ +#include "field_iter.h" +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace std; diff --git a/fpga/vstream/queries/parser/vstreamcore.h b/fpga/vstream/queries/parser/vstreamcore.h new file mode 100644 index 0000000..f2ec7ce --- /dev/null +++ b/fpga/vstream/queries/parser/vstreamcore.h @@ -0,0 +1,107 @@ +#pragma once + +#define VSTREAM_VERSION 0x10101010 + + +#define VSTREAM_BEGIN_BLOCK 0x73A0A073 +#define VSTREAM_END_BLOCK 0x74A0A074 + +#define VSTREAM_BEGIN_ROW 0x71A0A071 + +#define VSTREAM_END_ROW 0x72A0A072 + +#define VSTREAM_LITTLE_ENDIAN 0x10101010 +#define VSTREAM_BIG_ENDIAN 0x11111111 + +#define VSTREAM_BEGIN_META 0x77202077 +#define VSTREAM_END_META 0x76202076 + +#define VSTREAM_SCHEMA_DEFINITION 0x75242475 +#define VSTREAM_END_SCHEMA_DEFINITION 0x75121275 +#define VSTREAM_COLUMN_DEFINITION 0x75464675 +#define VSTREAM_END_COLUMN_DEFINITION 0x75323275 + +// indicating no VStream data follows +#define VSTREAM_TERMINATE 0x7FFFFF7F + +// Typecodes are 32 bits in length +// +#define VSTREAM_TYPECODE_NULL 0 +#define VSTREAM_TYPECODE_BYTE 1 +#define VSTREAM_TYPECODE_CHAR 2 +#define VSTREAM_TYPECODE_INT8 3 +#define VSTREAM_TYPECODE_BOOL 4 + +#define VSTREAM_TYPECODE_INT16 5 +#define VSTREAM_TYPECODE_UINT16 6 +#define VSTREAM_TYPECODE_WCHAR32 7 +#define VSTREAM_TYPECODE_INT32 8 +#define VSTREAM_TYPECODE_UINT32 9 +#define VSTREAM_TYPECODE_INT64 10 +#define VSTREAM_TYPECODE_UINT64 11 + +// IEEE 754 4 byte +#define VSTREAM_TYPECODE_FLOAT 12 + +// IEEE 754 8 byte +#define VSTREAM_TYPECODE_DOUBLE 13 + +// Seconds since Unix epoch +#define VSTREAM_TYPECODE_TIMESTAMP_SEC 14 + +// Milliseconds into UNIX epoch +#define VSTREAM_TYPECODE_TIMESTAMP_MIL 15 + +// Microseconds into the UNIX epoch +#define VSTREAM_TYPECODE_TIMESTAMP_MIC 16 + +// Nanoseconds into the UNIX epoch +#define VSTREAM_TYPECODE_TIMESTAMP_NAN 17 + +#define VSTREAM_DYNAMIC_TYPE_MARKER 18 + +#define VSTREAM_TYPECODE_UUID 19 // Linux memory ordering + +// 64 bit length prefixed on all of these +// +#define VSTREAM_TYPECODE_UTF8_STRING 20 +#define VSTREAM_TYPEOCDE_UTF16_STRING 21 +#define VSTREAM_TYPECODE_UTF32_STRING 22 + +#define VSTREAM_TYPECODE_BUFFER 23 + +#define VSTREAM_TYPECODE_TUPLE 24 + +#define VSTREAM_TYPECODE_VECTOR 25 + +#define VSTREAM_TYPECODE_MAP 26 + +// 64 bit length prefix, otherwise encoded as UTF8 string +// +#define VSTREAM_TYPECODE_JSON 27 + +#define VSTREAM_TYPECODE_LAST 28 + + + +#define VSTREAM_DATEFORMAT_YYYY_MM_DD 0x0001 // Space, - or / delimiters +#define VSTREAM_DATEFORMAT_YYYYMMDD 0x0002 // No delimiters +#define VSTREAM_DATEFORMAT_MM_DD_YYYY 0x0004 +#define VSTREAM_DATEFORMAT_DD_MM_YYYY 0x0008 +#define VSTREAM_DATEFORMAT_DD_MONTHNAME_YYYY 0x0010 +#define VSTERAM_DATEFORMAT_MM_DD_YY 0x0020 +#define VSTREAM_DATEFORMAT_MNAME_DD_YYYY 0x0040 + +#define VSTREAM_TIMEFORMAT_HH_MM_SS 0x1000 +#define VSTREAM_TIMEFORMAT_HHMMSS 0x2000 +#define VSTREAM_TIMEFORMAT_HH_MM 0x4000 + +#define VSTREAM_DATEFORMAT_SPARK_DEFINED 0x8000 + + +#define VSTREAM_TIMEFORMAT_AM_PM 0x00010000 +#define VSTREAM_TIMEFORMAT_FRACTIONAL_SECONDS 0x00020000 + +#define VSTREAM_TIMEZONE_NONE 0x00040000 +#define VSTREAM_TIMEZONE_Z 0x00080000 +#define VSTREAM_TIMEZONE_UTCOFFSET 0x00100000 \ No newline at end of file diff --git a/fpga/vstream/queries/queries.hpp b/fpga/vstream/queries/queries.hpp new file mode 100644 index 0000000..5500333 --- /dev/null +++ b/fpga/vstream/queries/queries.hpp @@ -0,0 +1,128 @@ +#pragma once + +#include "taxirow.hpp" +#include +#include +#include +#include +#include +#include +#include +#include +#include "helpers.hpp" +#include + +using namespace std; + +class Queries { + +public: + Queries(TaxiData& _taxiData) : taxiData(_taxiData) { + + bindings.push_back(bind(&Queries::q1, this)); + bindings.push_back(bind(&Queries::q2, this)); + bindings.push_back(bind(&Queries::q3, this)); + bindings.push_back(bind(&Queries::q4, this)); + } + + function qfunc; + + size_t timeQuery(int nQuery) { + + assert(nQuery >= 0 && nQuery <= 4); + + return timeQuery(bindings[nQuery - 1]); + + } + + //select payment_type, count(*) as total from $tableName group by payment_type + void q1() { + + auto grp = reduce(execution::par_unseq, taxiData.begin(), taxiData.end(), IntTaxiGroup(), MapReducerPaymentType()); + cout << "total size: " << grp.size() << endl; + } + + //select passenger_count, avg(total_amount) as avg_amount from $tableName group by passenger_count + void q2() { + + // group by passenger count + auto grp = reduce(execution::par_unseq, taxiData.begin(), taxiData.end(), IntTaxiGroup(), MapReducerPassengerCount()); + vector> res(grp.size()); + + // create a new map from averages + transform(execution::par_unseq, grp.begin(), grp.end(), res.begin(), + + [](IntTaxiGroup::value_type& entry) { + + // average each amount + auto amount = transform_reduce(execution::par_unseq, entry.second.begin(), entry.second.end(), 0.0f, plus(), [](auto& r) {return r.totalAmount; }); + + amount /= entry.second.size(); + return make_tuple(entry.first, amount); + }); + + cout << "total size: " << res.size() << endl; + } + + //select passenger_count, year(tpep_pickup_datetime) year, count(*) total FROM $tableName group by passenger_count, year + void q3() { + // group by passenger count and year + auto grp = reduce(execution::par_unseq, taxiData.begin(), taxiData.end(), TupleTaxiGroup(), MapReducerPassengerCountYear(false)); + vector> res(grp.size()); + + transform(execution::par_unseq, grp.begin(), grp.end(), res.begin(), + [](auto& entry) {return make_tuple(get<0>(entry.first), get<1>(entry.first), entry.second.size()); }); + + cout << "total size: " << res.size() << endl; + } + + //select + // passenger_count, year(tpep_pickup_datetime) as year, cast(trip_distance as int) as distance, count(*) as total + // from $tableName + // group by passenger_count, year, distance + // order by year, total desc + void q4() { + auto grp = reduce(execution::par_unseq, taxiData.begin(), taxiData.end(), TupleTaxiGroup(), MapReducerPassengerCountYear(true)); + vector> res(grp.size()); + + transform(execution::par_unseq, grp.begin(), grp.end(), res.begin(), + [](auto& entry) { + + return make_tuple(get<0>(entry.first), get<1>(entry.first), get<2>(entry.first), entry.second.size()); + + }); + + // sort descending + sort(execution::par_unseq, res.begin(), res.end(), [](auto& t1, auto& t2) { + int y1, d1, y2, d2; + tie(ignore, y1, d1, ignore) = t1; + tie(ignore, y2, d2, ignore) = t2; + + if (y1 == y2) { + return d2 < d1; + } + + return y2 < y1; + + }); + cout << "total size: " << res.size() << endl; + + } + +private: + + TaxiData& taxiData; + + size_t timeQuery(function& qFunc) { + auto start = std::chrono::high_resolution_clock::now(); + + qFunc(); + + auto end = std::chrono::high_resolution_clock::now(); + + size_t total_time = std::chrono::duration_cast(end - start).count(); + return total_time; + } + + vector> bindings; +}; \ No newline at end of file diff --git a/fpga/vstream/queries/taxirow.hpp b/fpga/vstream/queries/taxirow.hpp new file mode 100644 index 0000000..3cedd1a --- /dev/null +++ b/fpga/vstream/queries/taxirow.hpp @@ -0,0 +1,63 @@ +#pragma once + +#include + +struct TaxiRow { + char vendorId; + std::tm pickupTime; + std::tm dropOffTime; + int passengerCount; + float tripDistance; + char rateCode; + int storeAndFwdFlag; + int puLocId; + int doLocId; + int paymentType; + float fareAmount; + float extra; + float taxAmount; + float tipAmount; + float tollsAmount; + float improvementSurcharge; + float totalAmount; + float congestionSurcharge; +}; + +void parse_time(tm* timestruct, string& dt) +{ + *timestruct = {}; + istringstream ss(dt); + ss >> get_time(timestruct, "%Y-%m-%d %H:%M:%S"); +} + +int get_year(tm& dt) +{ + return 1900 + dt.tm_year; +} + +TaxiRow parse_taxi_row(vector& row) { + + TaxiRow taxiRow; + + taxiRow.vendorId = stoi(row[0]); + parse_time(&taxiRow.pickupTime, row[1]); + parse_time(&taxiRow.dropOffTime, row[2]); + + taxiRow.passengerCount = stoi(row[3]); + taxiRow.tripDistance = stof(row[4]); + taxiRow.rateCode = stoi(row[5]); + taxiRow.storeAndFwdFlag = row[6][0]; + taxiRow.puLocId = stoi(row[7]); + taxiRow.doLocId = stoi(row[8]); + taxiRow.paymentType = stoi(row[9]); + taxiRow.fareAmount = stof(row[10]); + taxiRow.extra = stof(row[11]); + taxiRow.taxAmount = stof(row[12]); + taxiRow.tipAmount = stof(row[13]); + taxiRow.tollsAmount = stof(row[14]); + taxiRow.improvementSurcharge = stof(row[15]); + taxiRow.totalAmount = stof(row[16]); + taxiRow.congestionSurcharge = stof(row[17]); + return taxiRow; +} + diff --git a/ingestion/dataset/DestinationDataUsWest2.json b/ingestion/dataset/DestinationDataUsWest2.json new file mode 100644 index 0000000..27b1468 --- /dev/null +++ b/ingestion/dataset/DestinationDataUsWest2.json @@ -0,0 +1,87 @@ +{ + "name": "DestinationDataUsWest2", + "properties": { + "linkedServiceName": { + "referenceName": "PutTaxiFileUSWest2", + "type": "LinkedServiceReference" + }, + "parameters": { + "fileName": { + "type": "string" + } + }, + "annotations": [], + "type": "DelimitedText", + "typeProperties": { + "location": { + "type": "AzureBlobFSLocation", + "fileName": { + "value": "@dataset().fileName", + "type": "Expression" + }, + "folderPath": "taxi_data", + "fileSystem": "yellow" + }, + "columnDelimiter": ",", + "escapeChar": "\\", + "quoteChar": "\"" + }, + "schema": [ + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + }, + { + "type": "String" + } + ] + }, + "type": "Microsoft.DataFactory/factories/datasets" +} \ No newline at end of file diff --git a/ingestion/dataset/ErrorFileUsWest2.json b/ingestion/dataset/ErrorFileUsWest2.json new file mode 100644 index 0000000..6331200 --- /dev/null +++ b/ingestion/dataset/ErrorFileUsWest2.json @@ -0,0 +1,31 @@ +{ + "name": "ErrorFileUsWest2", + "properties": { + "linkedServiceName": { + "referenceName": "PutTaxiFileUSWest2", + "type": "LinkedServiceReference" + }, + "parameters": { + "fileName": { + "type": "string" + } + }, + "annotations": [], + "type": "DelimitedText", + "typeProperties": { + "location": { + "type": "AzureBlobFSLocation", + "fileName": { + "value": "@dataset().fileName", + "type": "Expression" + }, + "folderPath": "taxi_error", + "fileSystem": "yellow" + }, + "columnDelimiter": ",", + "escapeChar": "\\", + "quoteChar": "\"" + }, + "schema": [] + } +} \ No newline at end of file diff --git a/ingestion/dataset/TaxiDatasetList.json b/ingestion/dataset/TaxiDatasetList.json new file mode 100644 index 0000000..d2980db --- /dev/null +++ b/ingestion/dataset/TaxiDatasetList.json @@ -0,0 +1,32 @@ +{ + "name": "TaxiDatasetList", + "properties": { + "linkedServiceName": { + "referenceName": "PutTaxiFileUSWest2", + "type": "LinkedServiceReference" + }, + "parameters": { + "fileName": { + "type": "string" + } + }, + "annotations": [], + "type": "DelimitedText", + "typeProperties": { + "location": { + "type": "AzureBlobFSLocation", + "fileName": { + "value": "@dataset().fileName", + "type": "Expression" + }, + "folderPath": "taxi_files", + "fileSystem": "yellow" + }, + "columnDelimiter": ",", + "escapeChar": "\\", + "quoteChar": "\"" + }, + "schema": [] + }, + "type": "Microsoft.DataFactory/factories/datasets" +} \ No newline at end of file diff --git a/ingestion/dataset/TaxiSourceData.json b/ingestion/dataset/TaxiSourceData.json new file mode 100644 index 0000000..a45c5a2 --- /dev/null +++ b/ingestion/dataset/TaxiSourceData.json @@ -0,0 +1,30 @@ +{ + "name": "TaxiSourceData", + "properties": { + "linkedServiceName": { + "referenceName": "GetHttpTaxi", + "type": "LinkedServiceReference" + }, + "parameters": { + "fileName": { + "type": "string" + } + }, + "annotations": [], + "type": "DelimitedText", + "typeProperties": { + "location": { + "type": "HttpServerLocation", + "relativeUrl": { + "value": "@dataset().fileName", + "type": "Expression" + } + }, + "columnDelimiter": ",", + "escapeChar": "\\", + "quoteChar": "\"" + }, + "schema": [] + }, + "type": "Microsoft.DataFactory/factories/datasets" +} \ No newline at end of file diff --git a/ingestion/linkedService/GetHttpTaxi.json b/ingestion/linkedService/GetHttpTaxi.json new file mode 100644 index 0000000..4e01204 --- /dev/null +++ b/ingestion/linkedService/GetHttpTaxi.json @@ -0,0 +1,13 @@ +{ + "name": "GetHttpTaxi", + "type": "Microsoft.DataFactory/factories/linkedservices", + "properties": { + "annotations": [], + "type": "HttpServer", + "typeProperties": { + "url": " https://s3.amazonaws.com/nyc-tlc/trip+data/", + "enableServerCertificateValidation": true, + "authenticationType": "Anonymous" + } + } +} \ No newline at end of file diff --git a/ingestion/linkedService/GetTaxiList.json b/ingestion/linkedService/GetTaxiList.json new file mode 100644 index 0000000..88cd8db --- /dev/null +++ b/ingestion/linkedService/GetTaxiList.json @@ -0,0 +1,12 @@ +{ + "name": "GetTaxiList", + "type": "Microsoft.DataFactory/factories/linkedservices", + "properties": { + "annotations": [], + "type": "AzureBlobFS", + "typeProperties": { + "url": "https://pipedreamtaxi.dfs.core.windows.net", + "encryptedCredential": "ew0KICAiVmVyc2lvbiI6ICIyMDE3LTExLTMwIiwNCiAgIlByb3RlY3Rpb25Nb2RlIjogIktleSIsDQogICJTZWNyZXRDb250ZW50VHlwZSI6ICJQbGFpbnRleHQiLA0KICAiQ3JlZGVudGlhbElkIjogIlRBWElERl8wODk1MzVmMC1lN2VjLTRiNDYtYWJiOS1lNTY0M2UzODYyNmMiDQp9" + } + } +} \ No newline at end of file diff --git a/ingestion/linkedService/PutTaxiFileUSWest2.json b/ingestion/linkedService/PutTaxiFileUSWest2.json new file mode 100644 index 0000000..f36ae14 --- /dev/null +++ b/ingestion/linkedService/PutTaxiFileUSWest2.json @@ -0,0 +1,12 @@ +{ + "name": "PutTaxiFileUSWest2", + "type": "Microsoft.DataFactory/factories/linkedservices", + "properties": { + "annotations": [], + "type": "AzureBlobFS", + "typeProperties": { + "url": "https://pipedreamtaxi.dfs.core.windows.net", + "encryptedCredential": "ew0KICAiVmVyc2lvbiI6ICIyMDE3LTExLTMwIiwNCiAgIlByb3RlY3Rpb25Nb2RlIjogIktleSIsDQogICJTZWNyZXRDb250ZW50VHlwZSI6ICJQbGFpbnRleHQiLA0KICAiQ3JlZGVudGlhbElkIjogIlRBWElERl81N2FjMDFmNi0xZjBiLTRkNjYtOWJkNC1lNDk2N2ViMTAwZTUiDQp9" + } + } +} \ No newline at end of file diff --git a/ingestion/linkedService/StagingBlobUSWest2.json b/ingestion/linkedService/StagingBlobUSWest2.json new file mode 100644 index 0000000..dd13ad2 --- /dev/null +++ b/ingestion/linkedService/StagingBlobUSWest2.json @@ -0,0 +1,12 @@ +{ + "name": "StagingBlobUSWest2", + "type": "Microsoft.DataFactory/factories/linkedservices", + "properties": { + "annotations": [], + "type": "AzureBlobStorage", + "typeProperties": { + "connectionString": "DefaultEndpointsProtocol=https;AccountName=pipedreamtaxi;EndpointSuffix=core.windows.net;", + "encryptedCredential": "ew0KICAiVmVyc2lvbiI6ICIyMDE3LTExLTMwIiwNCiAgIlByb3RlY3Rpb25Nb2RlIjogIktleSIsDQogICJTZWNyZXRDb250ZW50VHlwZSI6ICJQbGFpbnRleHQiLA0KICAiQ3JlZGVudGlhbElkIjogIlRBWElERl8xZTQ3Y2ZiNC01YjkzLTQzOTMtYjlhNS0wMWY0NTU2ZjYyMDMiDQp9" + } + } +} \ No newline at end of file diff --git a/ingestion/pipeline/IterateAndPullUsWest2.json b/ingestion/pipeline/IterateAndPullUsWest2.json new file mode 100644 index 0000000..762e7f7 --- /dev/null +++ b/ingestion/pipeline/IterateAndPullUsWest2.json @@ -0,0 +1,209 @@ +{ + "name": "IterateAndPullUsWest2", + "properties": { + "activities": [ + { + "name": "GetListOfCsvFiles", + "type": "Lookup", + "dependsOn": [], + "policy": { + "timeout": "7.00:00:00", + "retry": 0, + "retryIntervalInSeconds": 30, + "secureOutput": false, + "secureInput": false + }, + "userProperties": [], + "typeProperties": { + "source": { + "type": "DelimitedTextSource", + "storeSettings": { + "type": "AzureBlobFSReadSettings", + "recursive": true + }, + "formatSettings": { + "type": "DelimitedTextReadSettings" + } + }, + "dataset": { + "referenceName": "TaxiDatasetList", + "type": "DatasetReference", + "parameters": { + "fileName": { + "value": "@pipeline().parameters.fileName", + "type": "Expression" + } + } + }, + "firstRowOnly": false + } + }, + { + "name": "Iterate", + "type": "ForEach", + "dependsOn": [ + { + "activity": "GetListOfCsvFiles", + "dependencyConditions": [ + "Succeeded" + ] + } + ], + "userProperties": [], + "typeProperties": { + "items": { + "value": "@activity('GetListOfCsvFiles').output.value", + "type": "Expression" + }, + "activities": [ + { + "name": "Download", + "type": "Copy", + "dependsOn": [], + "policy": { + "timeout": "7.00:00:00", + "retry": 0, + "retryIntervalInSeconds": 30, + "secureOutput": false, + "secureInput": false + }, + "userProperties": [], + "typeProperties": { + "source": { + "type": "DelimitedTextSource", + "storeSettings": { + "type": "HttpReadSettings", + "requestMethod": "GET" + }, + "formatSettings": { + "type": "DelimitedTextReadSettings", + "skipLineCount": 1 + } + }, + "sink": { + "type": "DelimitedTextSink", + "storeSettings": { + "type": "AzureBlobFSWriteSettings" + }, + "formatSettings": { + "type": "DelimitedTextWriteSettings", + "quoteAllText": true, + "fileExtension": ".txt" + } + }, + "enableStaging": true, + "stagingSettings": { + "linkedServiceName": { + "referenceName": "StagingBlobUSWest2", + "type": "LinkedServiceReference" + }, + "path": "yellow/temp" + } + }, + "inputs": [ + { + "referenceName": "TaxiSourceData", + "type": "DatasetReference", + "parameters": { + "fileName": { + "value": "@item().Prop_0", + "type": "Expression" + } + } + } + ], + "outputs": [ + { + "referenceName": "DestinationDataUsWest2", + "type": "DatasetReference", + "parameters": { + "fileName": { + "value": "@item().Prop_0", + "type": "Expression" + } + } + } + ] + }, + { + "name": "CopyFailed", + "type": "Copy", + "dependsOn": [ + { + "activity": "Download", + "dependencyConditions": [ + "Failed" + ] + } + ], + "policy": { + "timeout": "7.00:00:00", + "retry": 0, + "retryIntervalInSeconds": 30, + "secureOutput": false, + "secureInput": false + }, + "userProperties": [], + "typeProperties": { + "source": { + "type": "DelimitedTextSource", + "storeSettings": { + "type": "HttpReadSettings", + "requestMethod": "GET" + }, + "formatSettings": { + "type": "DelimitedTextReadSettings" + } + }, + "sink": { + "type": "DelimitedTextSink", + "storeSettings": { + "type": "AzureBlobFSWriteSettings" + }, + "formatSettings": { + "type": "DelimitedTextWriteSettings", + "quoteAllText": true, + "fileExtension": ".txt" + } + }, + "enableStaging": false + }, + "inputs": [ + { + "referenceName": "TaxiSourceData", + "type": "DatasetReference", + "parameters": { + "fileName": { + "value": "@item().Prop_0", + "type": "Expression" + } + } + } + ], + "outputs": [ + { + "referenceName": "ErrorFileUsWest2", + "type": "DatasetReference", + "parameters": { + "fileName": { + "value": "@item().Prop_0", + "type": "Expression" + } + } + } + ] + } + ] + } + } + ], + "parameters": { + "fileName": { + "type": "string", + "defaultValue": "yellow_data_urls.txt" + } + }, + "annotations": [] + }, + "type": "Microsoft.DataFactory/factories/pipelines" +} \ No newline at end of file diff --git a/magenta/pipeline/generate_data.py b/magenta/pipeline/generate_data.py new file mode 100644 index 0000000..f853941 --- /dev/null +++ b/magenta/pipeline/generate_data.py @@ -0,0 +1,145 @@ +from faker import Faker +import random as rnd +import os, argparse, logging, gzip, shutil +import pandas as pd +from multiprocessing import Pool +from functools import partial + +logging.basicConfig(format='%(asctime)s %(levelname)-10s %(message)s', datefmt="%Y-%m-%d-%H-%M-%S", + level=logging.INFO) + +def parse_args(): + ap = argparse.ArgumentParser() + ap.add_argument("-o", "--outpath", default=".", help="Output for csv files") + ap.add_argument("-n", "--n-files", default=5, type=int, help="number of files to generate") + ap.add_argument("-s", "--file-size", default=15, type=float, help="file size, MB") + ap.add_argument("--header", action="store_true", default=False, help="should keep header") + ap.add_argument("--gzip", action="store_true", default=False, help="Should compress?") + return ap.parse_args() + +columns = ( + ["Text01", "Text02", "Text03", "Text04", "Text05", "Text06", "IntWritable01", "Text07", "Text08"] + + ["Text09", "Text10", "Text11", "Text12", "Text13", "LongWritable01", "LongWritable02", "LongWritable03"] + + ["LongWritable04", "LongWritable05", "LongWritable06", "Text14", "Text15", "IntWritable02", "Text16", "Text17", "Text18", "Text19", "Text20",] + + ["IntWritable03", "IntWritable04", "Text21", "IntWritable05", "Text22", "Text23", "Text24", "Text25", "Text26", "IntWritable06"] + + ["IntWritable07", "Text27", "IntWritable08", "Text28", "IntWritable09", "IntWritable10", "IntWritable11", "Text29", "Text30", "Text31"] +) + +fake = Faker() + +def get_date(): + return fake.date(pattern='%m:%d:%y %H:%M:%S:%f')[:-3] + +def get_dec(a, b): + return str(fake.pydecimal(left_digits=rnd.randint(a, b), right_digits=0))[:-2].replace('-', '') + +def str_exact_chars(n): + return fake.pystr(min_chars=n, max_chars=n) + +def generate_record(): + Text01 = get_date() + Text02 = get_date() + Text03 = get_dec(1, 15) + Text04 = str_exact_chars(3) + Text05 = get_dec(1, 15) + Text06 = fake.pystr_format(string_format='###-###-###-###-##{{random_int}}')[:-4] + IntWriteable01 = get_dec(1, 1) + Text07 = fake.pystr_format(string_format='###-###-###-###-####{{random_int}}')[:-2] + Text08 = fake.ipv6() + Text09 = fake.ipv6() + Text10 = '' + Text11 = get_dec(5, 5) + Text12 = get_dec(5, 5) + Text13 = get_dec(2, 2) + LongWritable01 = get_dec(8, 12) + LongWritable02 = get_dec(8, 12) + LongWritable03 = get_dec(8, 12) + LongWritable04 = get_dec(8, 12) + LongWritable05 = get_dec(8, 12) + LongWritable06 = get_dec(8, 12) + Text14 = str_exact_chars(10) + Text15 = str_exact_chars(10) + IntWriteable02 = fake.pyint() + Text16 = str_exact_chars(12) + Text17 = fake.pystr_format(string_format='##{{random_int}}') + Text18 = fake.ipv4() + Text19 = str_exact_chars(3) + Text20 = fake.pystr_format(string_format="???_?_??_???_????_?") + IntWriteable03 = fake.pyint() + IntWriteable04 = fake.pyint() + Text21 = fake.pystr_format(string_format="#???###????###??") + IntWriteable05 = fake.pyint() + Text22 = get_dec(1, 2) + Text23 = "" + Text24 = "" + Text25 = fake.pystr_format(string_format="##|###|#####|###|##|####") + Text26 = fake.pystr_format(string_format="##:#######:###:####") + IntWriteable06 = fake.pyint() + IntWriteable07 = fake.pyint() + Text27 = fake.ipv6() + IntWriteable08 = fake.pyint() + Text28 = fake.ipv6() + IntWriteable09 = fake.pyint() + IntWriteable10 = fake.pyint() + IntWriteable11 = fake.pyint() + Text29 = "" + Text30 = str_exact_chars(24) + Text31 = "" + + data = ([Text01, Text02, Text03, Text04, Text05, Text06, IntWriteable01, Text07, Text08, Text09, Text10] + + [Text11, Text12, Text13, LongWritable01, LongWritable02, LongWritable03, LongWritable04, LongWritable05, LongWritable06] + + [Text14, Text15, IntWriteable02, Text16, Text17, Text18, Text19, Text20, IntWriteable03, IntWriteable04, Text21] + + [IntWriteable05, Text22, Text23, Text24, Text25, Text26, IntWriteable06, IntWriteable07, Text27, IntWriteable08] + + [Text28, IntWriteable09, IntWriteable10, IntWriteable11, Text29, Text30, Text31] + ) + + return data + +def generate_fake_data(file_size, header, out_path): + size = 0 + data_dicts = [] + + while size < file_size: + + data = generate_record() + size += sum([len(str(x)) for x in data]) + data_dicts.append(dict(zip(columns, data))) + + df = pd.DataFrame(data_dicts)[columns] + df.to_csv(out_path, index=False, header=columns if header else None) + logging.info(f"Generated: {out_path}") + +def gzip_file(file_path): + + dst_path = f"{file_path}.gz" + + with open(file_path, "rb") as fd: + with gzip.open(dst_path, "wb") as gzfd: + shutil.copyfileobj(fd, gzfd) + + os.remove(file_path) + logging.info(f"Compressed: {dst_path}") + +if __name__ == "__main__": + args = parse_args() + + if not os.path.exists(args.outpath): + os.makedirs(args.outpath) + + file_paths = [os.path.join(args.outpath, fake.pystr_format(string_format="??????????.csv")) for _ in range(args.n_files)] + generator = partial(generate_fake_data, args.file_size * 1024 * 1024, args.header) + + d = generate_record() + size = sum([len(str(x)) for x in d]) + logging.info(f"Record size: {size}") + + logging.info(f"Starting generation. Size: {args.file_size}. N-files {args.n_files}") + Pool(processes=args.n_files).map(generator, file_paths) + + logging.info("Generation complete.") + + if args.gzip: + logging.info("Starting compression") + + Pool(processes=args.n_files).map(gzip_file, file_paths) + \ No newline at end of file diff --git a/tools/compute_data_size.ps1 b/tools/compute_data_size.ps1 new file mode 100644 index 0000000..ca28c0f --- /dev/null +++ b/tools/compute_data_size.ps1 @@ -0,0 +1,37 @@ +# this script will show how to get the total size of the blobs in a container +# before running this, you need to create a storage account, create a container, +# and upload some blobs into the container +# note: this retrieves all of the blobs in the container in one command. +# if you are going to run this against a container with a lot of blobs +# (more than a couple hundred), use continuation tokens to retrieve +# the list of blobs. + +# these are for the storage account to be used +Connect-AzAccount + +$resourceGroup = "PipeDream" +$storageAccountName = "pipedreamtaxi1" +$containerName = "yellow" + +# get a reference to the storage account and the context +$storageAccount = Get-AzStorageAccount ` + -ResourceGroupName $resourceGroup ` + -Name $storageAccountName +$ctx = $storageAccount.Context + +# get a list of all of the blobs in the container +$listOfBLobs = Get-AzStorageBlob -Container $containerName -Context $ctx + +# zero out our total +$length = 0 + +# this loops through the list of blobs and retrieves the length for each blob +# and adds it to the total +$listOfBlobs | ForEach-Object {$length = $length + $_.Length} + +# output the blobs and their sizes and the total +Write-Host "List of Blobs and their size (length)" +Write-Host " " +$listOfBlobs | select Name, Length +Write-Host " " +Write-Host "Total Length = " ($length / 1024 / 1024 / 1024).ToString('N0')

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