├── Dockerfile ├── LICENSE ├── README.md ├── example ├── DFML_demo_local.ipynb ├── example_basic.ipynb └── sparksql_basic.ipynb ├── hive-bootstrap.sh └── hive-site.xml /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM prasanthj/docker-hive-on-tez 2 | 3 | ## install spark 4 | RUN curl -s http://apache.stu.edu.tw/spark/spark-1.4.1/spark-1.4.1-bin-hadoop2.4.tgz | tar -xz -C /usr/local/ 5 | RUN cd /usr/local \ 6 | && ln -s spark-1.4.1-bin-hadoop2.4 spark 7 | 8 | ENV SPARK_JAR hdfs:///spark/spark-assembly-1.4.1-hadoop2.4.0.jar \ 9 | SPARK_HOME /usr/local/spark \ 10 | PATH $PATH:$SPARK_HOME/bin:$HADOOP_PREFIX/bin \ 11 | PYTHONPATH $SPARK_HOME/python/:$PYTHONPATH \ 12 | PYTHONPATH $SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$PYTHONPATH 13 | 14 | ADD hive-site.xml $SPARK_HOME/conf/hive-site.xml 15 | ADD hive-bootstrap.sh /etc/hive-bootstrap.sh 16 | RUN chown root:root /etc/hive-bootstrap.sh && chmod 700 /etc/hive-bootstrap.sh 17 | 18 | ## install ipython 19 | RUN apt-get update && apt-get -y install python-pip python-dev build-essential python-tk libpng-dev liblapack-dev libatlas-base-dev gfortran libfreetype6-dev wget pkg-config python-matplotlib\ 20 | && pip install pip --upgrade \ 21 | && pip install -U jupyter \ 22 | && pip install pandas scipy scikit-learn 23 | 24 | 25 | CMD /etc/hive-bootstrap.sh && /usr/local/spark/sbin/start-master.sh && IPYTHON_OPTS="notebook --no-browser --ip=0.0.0.0 --port 8888 --notebook-dir=/home/" /usr/local/spark/bin/pyspark 26 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | 676 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # docker-spark-hive-ipython 2 | # Introduction 3 | 4 | - Apache Hive (trunk version) 5 | - Apache Tez 0.5.2 6 | - Apache Hadoop 2.5.2 7 | - PostgreSQL 9.3 (Hive metastore backend) 8 | - Apach Spark 1.4.1 9 | - Jupyter 10 | 11 | # Install Docker 12 | 13 | - Linux：https://docs.docker.com/linux/step_one/ 14 | - Mac：https://docs.docker.com/installation/mac/ 15 | - Install boot2docker 16 | - **Recommand** Simulate a pure Linux(like Ubuntu) enviroment in VMWARE and install docker in it. 17 | 18 | # **Attention** for boot2docker user 19 | The default memory of boot2docker is only 2G, and recommended system requirement is 4G. 20 | There may be some unexpection problem if memory is not enough. 21 | You can change the default memory setting in following way: 22 | 23 | - `vim ~/.boot2docker/profile` 24 | - add `Memory = 4096` in this file. 25 | * **Attention** The following process will reset your boot2docker, it's mean your images and caontainers will be erased. 26 | - boot2docker stop 27 | - boot2docker destroy 28 | - boot2docker init 29 | - boot2docker start 30 | 31 | # Recommended System Requirement 32 | - CPU 4core 33 | - RAM 4G up 34 | - HDD 10G up (4G for Docker images) 35 | 36 | # Pull The Docker Image 37 | 38 | - `docker pull bryanyang0528/docker-spark-hive-ipython` 39 | 40 | # Build The Docker Image 41 | 42 | - Install git first 43 | - Enter a apropriate directory 44 | - `git clone https://github.com/bryanyang0528/docker-spark-hive-ipython.git` 45 | - `cd docker-spark-hive-ipython` 46 | - `docker build .` 47 | - `docker images` confirm the images id 48 | - `docker tag docker-spark-hive-ipython:latest` 49 | 50 | # Run the Docker Image 51 | 52 | - `docker run -d -p 8888:8888 -p 4040:4040 --name pyspark bryanyang0528/docker-spark-hive-ipython` 53 | 54 | # Launch the Jupyter 55 | 56 | - linux: Type the web address `http://localhost:8888` in any browser. The link of Spark UI is `http://localhost:4040`. 57 | - Mac:(for boot2docker) In your terminal, press `boot2docker ip` confirm the ip address of boot2docker，the type `http://:8888`in the browser. 58 | 59 | #### SparkContext(as **sc**) and SqlContext(as **sqlContext**) will launch automatically when you open a notebook. 60 | -------------------------------------------------------------------------------- /example/example_basic.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Basic RDD Operation Example" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "list_a = [x for x in range(10)]" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "metadata": { 25 | "collapsed": false 26 | }, 27 | "outputs": [ 28 | { 29 | "data": { 30 | "text/plain": [ 31 | "55" 32 | ] 33 | }, 34 | "execution_count": 2, 35 | "metadata": {}, 36 | "output_type": "execute_result" 37 | } 38 | ], 39 | "source": [ 40 | "sc.parallelize(list_a).map(lambda x: x+1).reduce(lambda x , y :x+y)" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "## SparkSQL Example" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 3, 53 | "metadata": { 54 | "collapsed": true 55 | }, 56 | "outputs": [], 57 | "source": [ 58 | "from pyspark.sql import HiveContext, Row\n", 59 | "sqlContext= HiveContext(sc)" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 4, 65 | "metadata": { 66 | "collapsed": false 67 | }, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "text/plain": [ 72 | "DataFrame[tableName: string, isTemporary: boolean]" 73 | ] 74 | }, 75 | "execution_count": 4, 76 | "metadata": {}, 77 | "output_type": "execute_result" 78 | } 79 | ], 80 | "source": [ 81 | "sqlContext.sql(\"show tables\")" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 5, 87 | "metadata": { 88 | "collapsed": false 89 | }, 90 | "outputs": [ 91 | { 92 | "name": "stdout", 93 | "output_type": "stream", 94 | "text": [ 95 | "+---------+----------+----+\n", 96 | "| category| product| rev|\n", 97 | "+---------+----------+----+\n", 98 | "|cellphone| thin|6000|\n", 99 | "| tablet| normal|1500|\n", 100 | "| tablet| mini|5500|\n", 101 | "|cellphone|ultra thin|5000|\n", 102 | "|cellphone|very thing|6000|\n", 103 | "| tablet| big|2500|\n", 104 | "|cellphone| bendable|3000|\n", 105 | "|cellphone| foldable|3000|\n", 106 | "| tablet| pro|4500|\n", 107 | "| tablet| pro2|6500|\n", 108 | "+---------+----------+----+\n", 109 | "\n" 110 | ] 111 | } 112 | ], 113 | "source": [ 114 | "df = sc.parallelize([Row(product=\"thin\",category=\"cellphone\",rev=\"6000\"),Row(product=\"normal\",category=\"tablet\",rev=\"1500\"), Row(product=\"mini\",category=\"tablet\",rev=\"5500\"),Row(product=\"ultra thin\",category=\"cellphone\",rev=\"5000\"),Row(product=\"very thing\",category=\"cellphone\",rev=\"6000\"),Row(product=\"big\",category=\"tablet\",rev=\"2500\"),Row(product=\"bendable\",category=\"cellphone\",rev=\"3000\"),Row(product=\"foldable\",category=\"cellphone\",rev=\"3000\"),Row(product=\"pro\",category=\"tablet\",rev=\"4500\"),Row(product=\"pro2\",category=\"tablet\",rev=\"6500\")])\n", 115 | "productRevenue_df = sqlContext.createDataFrame(df)\n", 116 | "productRevenue_df.show()" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": {}, 122 | "source": [ 123 | "## Shell Script Example" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 16, 129 | "metadata": { 130 | "collapsed": false 131 | }, 132 | "outputs": [ 133 | { 134 | "name": "stdout", 135 | "output_type": "stream", 136 | "text": [ 137 | "/home\r\n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "!pwd" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 17, 148 | "metadata": { 149 | "collapsed": false 150 | }, 151 | "outputs": [ 152 | { 153 | "name": "stdout", 154 | "output_type": "stream", 155 | "text": [ 156 | "Untitled.ipynb\tderby.log metastore_db\r\n" 157 | ] 158 | } 159 | ], 160 | "source": [ 161 | "!ls" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "## Spark Mllib Example" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 8, 174 | "metadata": { 175 | "collapsed": true 176 | }, 177 | "outputs": [], 178 | "source": [ 179 | "from sklearn.datasets import make_blobs\n", 180 | "from pyspark.mllib.clustering import KMeans" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 9, 186 | "metadata": { 187 | "collapsed": true 188 | }, 189 | "outputs": [], 190 | "source": [ 191 | "X, y = make_blobs(50,20, 5)" 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": 10, 197 | "metadata": { 198 | "collapsed": false 199 | }, 200 | "outputs": [ 201 | { 202 | "name": "stdout", 203 | "output_type": "stream", 204 | "text": [ 205 | "[[ 8.79265169 8.69813553 7.26584523 -0.95237571 7.66482599\n", 206 | " -1.84022236 4.62125024 2.5017508 -10.41165513 2.09050148\n", 207 | " 3.17796425 7.85348358 10.24431579 -1.88136637 4.73685396\n", 208 | " -4.22206454 1.3553198 8.96417314 0.96634182 -0.1082746 ]\n", 209 | " [ -0.57021603 -2.95011861 2.8014989 -3.77173248 2.4647076\n", 210 | " 5.81325698 1.42435662 -7.04384883 -3.05966505 1.6740405\n", 211 | " -9.84026239 8.24840516 -5.1131863 -2.22311618 0.95482552\n", 212 | " -3.38819958 -2.10016993 2.59753474 1.76189002 9.40326368]\n", 213 | " [ -2.28491968 4.78324522 8.63483306 -4.35186232 -4.60251651\n", 214 | " -7.03618689 -8.32221764 0.66676661 7.53789095 -0.7162713\n", 215 | " -10.19716729 -11.58301531 -9.63346717 1.21774221 -3.09186347\n", 216 | " -1.61226172 -3.38826404 -5.90932636 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301 | " [ -8.9643237 -0.75453883 8.50413004 -7.02518689 -9.98634559\n", 302 | " -0.82849811 -4.53274002 -9.09084082 -6.6357981 6.83042158\n", 303 | " -4.42255934 9.00979303 -9.3140614 -4.14881835 5.6429822\n", 304 | " 0.09036695 -1.23564314 -9.11116981 -2.82218242 3.26987051]\n", 305 | " [ 6.12513995 8.99674188 9.65724553 -1.21327036 5.97868271\n", 306 | " 0.58261886 3.23666666 3.45474845 -10.39400999 1.72747319\n", 307 | " 1.04407464 8.52157082 8.3749836 -1.49563235 5.21648981\n", 308 | " -5.11484133 1.59931938 9.36096256 2.02000206 -0.25519271]\n", 309 | " [ 0.42800271 -4.75157392 0.67068223 -6.63799638 2.30012038\n", 310 | " 5.88094233 0.17829208 -4.89444755 -3.98047312 0.14807241\n", 311 | " -8.49625553 9.03629778 -4.19923372 -2.32524137 0.24132932\n", 312 | " 1.32375501 -3.56663785 3.5635277 0.47411429 8.68253044]\n", 313 | " [ -1.97170984 4.73907768 8.5960669 -2.47929819 -5.67235034\n", 314 | " -8.39811441 -5.6700071 2.95260447 6.45600225 -0.64717907\n", 315 | " -7.93264074 -9.06398649 -7.22674135 2.16432398 -2.51049852\n", 316 | " -5.49224367 -3.72170812 -5.5418101 -3.11545286 -9.43508282]\n", 317 | " [ 5.5404227 10.63362173 6.15231789 -0.07974176 5.93787708\n", 318 | " -0.73781044 5.14992505 3.48997197 -8.93728186 2.32245306\n", 319 | " 3.69498089 8.35523137 10.30829502 -2.70288028 7.24147297\n", 320 | " -3.36114892 1.9437317 9.31858424 0.94674363 -0.44561825]\n", 321 | " [-10.63279579 -0.66113613 5.69772772 -8.40089973 -7.59638738\n", 322 | " -2.373329 -5.18061728 -8.07305814 -7.20407407 7.14676877\n", 323 | " -3.29742097 8.01483303 -8.96479687 -2.44997393 6.16384879\n", 324 | " 2.30791879 -2.31465654 -9.68337043 -2.88746672 4.2631566 ]\n", 325 | " [ -3.69508504 3.82906398 9.07353261 -3.33653134 -6.01945723\n", 326 | " -8.65407873 -6.55893289 1.23018717 7.34892996 0.0579159\n", 327 | " -9.03702647 -7.44114238 -9.73628371 2.89247218 -0.89702567\n", 328 | " -3.58736906 -3.86314433 -5.30858454 0.7791437 -9.24063714]\n", 329 | " [ 8.08644222 8.87941365 9.55340897 -1.30607678 4.9260196\n", 330 | " -3.6240853 5.15678805 2.82427402 -9.32367323 1.47574584\n", 331 | " 2.14141527 8.08307953 9.40738853 -3.88530459 5.98481482\n", 332 | " -4.53187247 0.45451155 10.25104408 0.25276069 -1.06347681]\n", 333 | " [ -2.74628449 3.31815634 8.14577466 -2.06380547 -7.28394\n", 334 | " -8.83086112 -7.16667963 3.31967965 6.82192358 0.16597437\n", 335 | " -11.24025004 -9.41793648 -9.04037246 4.14180995 0.84967642\n", 336 | " -6.27998076 -2.75612904 -6.74960893 0.89383426 -10.0398925 ]\n", 337 | " [ -5.42344655 -5.32197792 4.24357795 6.08716412 -8.04891978\n", 338 | " 7.03299931 -0.16837288 -8.42380817 5.71020325 3.93160216\n", 339 | " -8.68375605 7.9532002 6.29923985 6.1721293 -2.40228886\n", 340 | " -1.17646738 -9.20062379 -9.05222142 -4.14841751 -9.44238908]\n", 341 | " [ -9.69575602 -1.26165659 7.17574298 -7.44571536 -9.20489898\n", 342 | " -1.05552674 -2.24857561 -5.77235264 -4.90003167 7.35809601\n", 343 | " -4.37268835 7.73192904 -9.91806323 -0.95139636 5.61510012\n", 344 | " 0.04955683 -2.37936598 -9.32269386 -1.228146 4.60322034]\n", 345 | " [ -7.06918785 -4.53619792 3.49272374 7.30552997 -8.42555883\n", 346 | " 7.61654036 -0.41245482 -8.375508 5.04293329 3.47474554\n", 347 | " -9.71920488 6.42720035 5.97988831 7.16019699 -1.76321135\n", 348 | " -1.68806574 -8.29167598 -10.0597838 -5.23408344 -9.84310148]\n", 349 | " [ -2.05858916 -5.93886336 3.59727058 -5.48880491 1.75878514\n", 350 | " 6.3795424 -0.16205924 -5.66474524 -4.29749796 1.45742668\n", 351 | " -9.18978875 8.31342714 -4.39611176 -2.36679532 -0.17316643\n", 352 | " -1.29472262 -2.03622453 3.16314402 -0.03492922 9.23281076]\n", 353 | " [ 7.92722089 8.04099126 5.82785243 0.20723055 5.22372309\n", 354 | " -1.29561513 4.43317771 1.40867907 -8.20111531 1.80932808\n", 355 | " 1.89302643 10.23653117 9.35520272 -3.79136589 6.94000326\n", 356 | " -5.19072538 0.82532701 9.37551211 2.82581378 -0.31815209]\n", 357 | " [ 7.28484896 9.83327378 7.97406688 -2.15559887 6.72369761\n", 358 | " -2.07408644 4.64012786 2.81919429 -9.79647173 1.282567\n", 359 | " 2.67484452 9.44119935 8.62662785 -2.42891041 7.10834997\n", 360 | " -4.36657481 4.29044979 8.4482797 3.54488407 1.36132838]\n", 361 | " [ -2.98063191 4.074572 8.98369123 -5.03446163 -6.15695148\n", 362 | " -9.32677563 -7.07704334 3.45671378 7.75018594 -0.24121346\n", 363 | " -12.86203222 -11.01295 -7.74739849 3.5447667 -0.40269482\n", 364 | " -5.35323143 -4.33959903 -5.31024094 0.93685153 -10.66204779]\n", 365 | " [ -5.32426187 -5.79372405 4.39744709 5.20303173 -8.83290436\n", 366 | " 5.01711329 -0.56582087 -10.00838395 3.94702946 1.99261785\n", 367 | " -10.42930071 6.23734522 7.1082643 6.75586154 -4.02747233\n", 368 | " -1.89817641 -9.73914688 -10.88380192 -6.32326931 -8.66369578]\n", 369 | " [ -0.80088647 -6.59404667 4.96584639 -6.5966571 2.4049069\n", 370 | " 4.51482955 3.66068208 -3.98320439 -4.61739624 -0.913527 -7.262703\n", 371 | " 7.93022824 -3.92490351 -3.82614015 0.231209 0.22197534\n", 372 | " -2.53968922 3.95178895 2.23285485 8.38337081]\n", 373 | " [ -5.20347485 -4.76200642 4.78065089 7.80650627 -6.99190434\n", 374 | " 6.60218947 -0.75750626 -9.93655922 3.55925751 0.18589963\n", 375 | " -10.99735713 5.62395171 9.54326579 7.56091331 -5.92309349\n", 376 | " -2.0497414 -8.24049052 -10.24857496 -4.82350147 -8.9755812 ]\n", 377 | " [ 6.35284627 9.36685896 7.14045089 -2.95191837 4.50853949\n", 378 | " -3.85719013 4.67085388 2.95068103 -7.33265801 0.41501278\n", 379 | " 2.21337601 9.89184692 10.20355969 -2.09113752 5.75919196\n", 380 | " -3.96184032 0.56571767 8.68900251 0.8324208 -0.94634905]\n", 381 | " [ 7.00613987 9.63080174 9.39265305 -0.99138488 6.53977301\n", 382 | " -1.86539383 5.08117662 1.57432817 -9.66341266 1.57295707\n", 383 | " 3.40884724 9.07733361 7.79035121 -0.32150351 6.94189509\n", 384 | " -3.94901528 2.36947411 11.08678972 2.27115378 1.08629576]\n", 385 | " [ -3.67115226 2.94766368 8.70206218 -4.21991573 -6.60319089\n", 386 | " -7.05401522 -7.47685667 2.03894497 8.76000929 -1.76306672\n", 387 | " -10.59755549 -8.02661475 -6.56361209 0.81500712 -2.06300179\n", 388 | " -5.29620904 -2.62132026 -3.72818738 0.13529043 -8.75662355]\n", 389 | " [ -4.4385462 -5.86517207 4.22034216 6.81720356 -6.3163222\n", 390 | " 7.47084369 0.69087844 -9.51944839 5.68193768 3.74646092\n", 391 | " -10.13513082 6.69211503 6.30429168 7.98231544 -4.01880065\n", 392 | " -1.54689604 -8.92889236 -10.49976794 -5.70977596 -10.91617866]\n", 393 | " [ 7.40529978 9.21222707 6.76048215 -2.18044132 5.33469714\n", 394 | " -2.35195585 5.1215056 3.6254503 -10.23488585 2.88027529\n", 395 | " 2.76962641 10.17128967 8.91038073 -1.99085837 7.11521375\n", 396 | " -5.0265937 0.78647388 10.48042086 0.65197898 -1.77420073]\n", 397 | " [ 0.10021932 -6.33457931 3.6229593 -4.29785852 2.4271323\n", 398 | " 6.50368944 3.11400521 -2.10474759 -2.7370379 -1.24326915\n", 399 | " -9.33573515 10.00896148 -4.64442439 -1.40697846 -1.0308081\n", 400 | " -2.6851808 -1.39260628 4.2399263 1.30515791 9.58972213]\n", 401 | " [ -4.65253395 -4.56866878 4.41349972 7.88223699 -7.51775694\n", 402 | " 5.32978152 0.44765337 -7.92145155 3.86751537 0.94237832\n", 403 | " -9.32159783 8.06150206 5.93296409 7.07037184 -4.13101316\n", 404 | " -1.56841113 -11.65892599 -10.66518394 -6.00710412 -11.42686344]]\n" 405 | ] 406 | } 407 | ], 408 | "source": [ 409 | "print X" 410 | ] 411 | }, 412 | { 413 | "cell_type": "code", 414 | "execution_count": 11, 415 | "metadata": { 416 | "collapsed": true 417 | }, 418 | "outputs": [], 419 | "source": [ 420 | "data=sc.parallelize(X[1:10])" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 12, 426 | "metadata": { 427 | "collapsed": true 428 | }, 429 | "outputs": [], 430 | "source": [ 431 | "model = KMeans.train(data, k = 5)" 432 | ] 433 | }, 434 | { 435 | "cell_type": "code", 436 | "execution_count": 14, 437 | "metadata": { 438 | "collapsed": false 439 | }, 440 | "outputs": [ 441 | { 442 | "data": { 443 | "text/plain": [ 444 | "[array([-9.52977885, -0.21949739, 7.40559298, -7.35162615, -8.60554592,\n", 445 | " -3.11771962, -3.56486519, -8.50363237, -6.48018487, 7.52695109,\n", 446 | " -3.85337117, 9.54175262, -9.03974626, -3.39330424, 4.9353669 ,\n", 447 | " 0.43595824, -2.38499406, -9.70736034, -2.65217937, 2.6921451 ]),\n", 448 | " array([ -2.28491968, 4.78324522, 8.63483306, -4.35186232,\n", 449 | " -4.60251651, -7.03618689, -8.32221764, 0.66676661,\n", 450 | " 7.53789095, -0.7162713 , -10.19716729, -11.58301531,\n", 451 | " -9.63346717, 1.21774221, -3.09186347, -1.61226172,\n", 452 | " -3.38826404, -5.90932636, -0.85335682, -8.50578432]),\n", 453 | " array([ -5.90885199, -5.54495586, 3.82752788, 5.47445191,\n", 454 | " -7.56901466, 6.17678723, -1.67866919, -8.18495808,\n", 455 | " 4.4351337 , 3.01243834, -9.64523205, 6.18695345,\n", 456 | " 6.18979228, 5.44298096, -6.51295969, -3.63470336,\n", 457 | " -8.37506596, -8.80310855, -5.08267496, -10.22274691]),\n", 458 | " array([ -1.01893618e+00, -4.69772631e+00, 3.19034668e+00,\n", 459 | " -4.77147910e+00, 1.78000059e+00, 6.04453602e+00,\n", 460 | " 1.19143029e+00, -5.57071535e+00, -3.71489146e+00,\n", 461 | " 9.73624545e-01, -9.70951008e+00, 8.43654627e+00,\n", 462 | " -5.30731287e+00, -2.07384086e+00, -5.77645726e-03,\n", 463 | " -2.28189013e+00, -1.34796861e+00, 2.73544779e+00,\n", 464 | " 1.00295494e+00, 9.14007282e+00]),\n", 465 | " array([-9.49650876, -1.0219336 , 5.35451856, -7.49414541, -9.12113299,\n", 466 | " -3.84506743, -2.52210559, -8.34878782, -5.95092744, 8.55325433,\n", 467 | " -6.49382995, 9.91160201, -8.59122399, -1.56711779, 4.7464535 ,\n", 468 | " 0.10183576, -2.61129868, -8.5574042 , -1.65643489, 3.69093277])]" 469 | ] 470 | }, 471 | "execution_count": 14, 472 | "metadata": {}, 473 | "output_type": "execute_result" 474 | } 475 | ], 476 | "source": [ 477 | "model.centers" 478 | ] 479 | }, 480 | { 481 | "cell_type": "code", 482 | "execution_count": null, 483 | "metadata": { 484 | "collapsed": true 485 | }, 486 | "outputs": [], 487 | "source": [] 488 | } 489 | ], 490 | "metadata": { 491 | "kernelspec": { 492 | "display_name": "Python 2", 493 | "language": "python", 494 | "name": "python2" 495 | }, 496 | "language_info": { 497 | "codemirror_mode": { 498 | "name": "ipython", 499 | "version": 2 500 | }, 501 | "file_extension": ".py", 502 | "mimetype": "text/x-python", 503 | "name": "python", 504 | "nbconvert_exporter": "python", 505 | "pygments_lexer": "ipython2", 506 | "version": "2.7.6" 507 | } 508 | }, 509 | "nbformat": 4, 510 | "nbformat_minor": 0 511 | } 512 | -------------------------------------------------------------------------------- /example/sparksql_basic.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# **SparkSQL Lab: **\n", 8 | "#### From this lab, you would write code to execute SQL query in Spark. Makes your analytic life simpler and faster.\n", 9 | "#### ** During this lab we will cover: **\n", 10 | "#### *Part 1:* Linking with SparkSQL\n", 11 | "#### *Part 2:* Loading data programmatically\n", 12 | "#### *Part 3:* User-Defined Functions \n", 13 | "#### *Part 4:* Caching for performance \n", 14 | "#### *Part 5:* Your show time - How many authors tagged as spam?\n", 15 | "#### Reference for Spark RDD [Spark's Python API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD)" 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [ 22 | "##Part 1: Linking with SparkSQL" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "from pyspark.sql import SQLContext, Row\n", 34 | "sqlContext = SQLContext(sc)" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": null, 40 | "metadata": { 41 | "collapsed": false 42 | }, 43 | "outputs": [], 44 | "source": [ 45 | "sqlContext" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": null, 51 | "metadata": { 52 | "collapsed": true 53 | }, 54 | "outputs": [], 55 | "source": [ 56 | "from pyspark.sql import HiveContext, Row\n", 57 | "sqlContext= HiveContext(sc)" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": null, 63 | "metadata": { 64 | "collapsed": false 65 | }, 66 | "outputs": [], 67 | "source": [ 68 | "sqlContext" 69 | ] 70 | }, 71 | { 72 | "cell_type": "markdown", 73 | "metadata": {}, 74 | "source": [ 75 | "#### HiveContext, a superset of SQLContext, was recommended for most use cases. Please make sure you are using HiveContext now!" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "##Part 2: Loading data programmatically" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "#### ** (2a) Read local JSON file to DataFrame **\n", 90 | "#### Now, try to read json file from Spark Example. Thank for the hashed spam data from PIXNET [PIXNET HACKATHON 2015](https://pixnethackathon2015.events.pixnet.net/)" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": null, 96 | "metadata": { 97 | "collapsed": true 98 | }, 99 | "outputs": [], 100 | "source": [ 101 | "jsonfile = \"file:///opt/spark-1.4.1-bin-hadoop2.6/examples/src/main/resources/people.json\"\n", 102 | "df = sqlContext.read.load(jsonfile, format=\"json\")" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "####Show time: Query top 2 row" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": null, 115 | "metadata": { 116 | "collapsed": false 117 | }, 118 | "outputs": [], 119 | "source": [ 120 | "# TODO: Replace with appropriate code\n", 121 | "df." 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "metadata": { 128 | "collapsed": false 129 | }, 130 | "outputs": [], 131 | "source": [ 132 | "#print df's schema\n", 133 | "df.printSchema()" 134 | ] 135 | }, 136 | { 137 | "cell_type": "markdown", 138 | "metadata": {}, 139 | "source": [ 140 | "#### ** (2b) Read from Hive **\n", 141 | "####Don't forget the configuration of Hive should be done by placing your hive-site.xml file in conf/." 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": null, 147 | "metadata": { 148 | "collapsed": false 149 | }, 150 | "outputs": [], 151 | "source": [ 152 | "sqlContext.sql(\"SHOW TABLES\").show()" 153 | ] 154 | }, 155 | { 156 | "cell_type": "markdown", 157 | "metadata": {}, 158 | "source": [ 159 | "#### In this class, you will use *pixnet_user_log_1000* for further works" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "#### Print the schema! What do we have?" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": null, 172 | "metadata": { 173 | "collapsed": false 174 | }, 175 | "outputs": [], 176 | "source": [ 177 | "sqlContext.sql(\"SELECT * FROM pixnet_user_log_1000\").printSchema()" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "#### How many rows in *pixnet_user_log*" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": null, 190 | "metadata": { 191 | "collapsed": false 192 | }, 193 | "outputs": [], 194 | "source": [ 195 | "from datetime import datetime\n", 196 | "start_time = datetime.now()\n", 197 | "\n", 198 | "df2 = sqlContext.sql(\"SELECT * FROM pixnet_user_log_1000\")\n", 199 | "\n", 200 | "end_time = datetime.now()\n", 201 | "print df2.count()\n", 202 | "print('Duration: {}'.format(end_time - start_time))" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": null, 208 | "metadata": { 209 | "collapsed": false 210 | }, 211 | "outputs": [], 212 | "source": [ 213 | "df2.select('time').show(2)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "##Part 3: User-Defined Functions" 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "metadata": {}, 226 | "source": [ 227 | "###In part 3, you will create your first UDF in Spark SQL with elegant lambda" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": null, 233 | "metadata": { 234 | "collapsed": true 235 | }, 236 | "outputs": [], 237 | "source": [ 238 | "#registers this RDD as a temporary table using the given name.\n", 239 | "df2.registerTempTable(\"people\")\n", 240 | "\n", 241 | "# Create an UDF for how long some text is\n", 242 | "# example from user guide, length function\n", 243 | "sqlContext.registerFunction(\"strLenPython\", lambda x: len(x)) \n", 244 | "\n", 245 | "# split function for parser\n", 246 | "sqlContext.registerFunction(\"strDate\", lambda x: x.split(\"T\")[0])\n", 247 | "\n", 248 | "# put udf with expected columns\n", 249 | "results = sqlContext.sql(\"SELECT author, \\\n", 250 | " strDate(time) AS dt, \\\n", 251 | " strLenPython(action) AS lenAct \\\n", 252 | " FROM people\")" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": null, 258 | "metadata": { 259 | "collapsed": false 260 | }, 261 | "outputs": [], 262 | "source": [ 263 | "# print top 5 results\n", 264 | "results.show(5)" 265 | ] 266 | }, 267 | { 268 | "cell_type": "markdown", 269 | "metadata": {}, 270 | "source": [ 271 | "##Part 4: Caching for performance" 272 | ] 273 | }, 274 | { 275 | "cell_type": "markdown", 276 | "metadata": {}, 277 | "source": [ 278 | "#### ** Saving to persistent tables**\n", 279 | "#### `saveAsTable ` : Saves the contents of this DataFrame to a data source as a table." 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": 19, 285 | "metadata": { 286 | "collapsed": true 287 | }, 288 | "outputs": [], 289 | "source": [ 290 | "sqlContext.cacheTable(\"people\")" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": null, 296 | "metadata": { 297 | "collapsed": false 298 | }, 299 | "outputs": [], 300 | "source": [ 301 | "start_time = datetime.now()\n", 302 | "\n", 303 | "sqlContext.sql(\"SELECT * FROM people\").count()\n", 304 | "\n", 305 | "end_time = datetime.now()\n", 306 | "print('Duration: {}'.format(end_time - start_time))" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": null, 312 | "metadata": { 313 | "collapsed": false 314 | }, 315 | "outputs": [], 316 | "source": [ 317 | "sqlContext.sql(\"SELECT strDate(time) AS dt,\\\n", 318 | " count(distinct author) AS cnt \\\n", 319 | " FROM people \\\n", 320 | " GROUP BY strDate(time)\").show(5)" 321 | ] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": null, 326 | "metadata": { 327 | "collapsed": false 328 | }, 329 | "outputs": [], 330 | "source": [ 331 | "sqlContext.uncacheTable(\"people\")" 332 | ] 333 | }, 334 | { 335 | "cell_type": "markdown", 336 | "metadata": {}, 337 | "source": [ 338 | "##Part 5: Your show time - How many authors are tagged as spam from pixnet_user_log?\n", 339 | "\n", 340 | "#### Here are two hive tables you will need:\n", 341 | "#### (1) author with action *pixnet_user_spam*\n", 342 | "#### (2) author with spam tag *pixnet_user_log_1000*" 343 | ] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": null, 348 | "metadata": { 349 | "collapsed": true 350 | }, 351 | "outputs": [], 352 | "source": [ 353 | "# TODO: Replace with appropriate code\n", 354 | "result = " 355 | ] 356 | }, 357 | { 358 | "cell_type": "markdown", 359 | "metadata": {}, 360 | "source": [ 361 | "### Don't forget to stop sc" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": 23, 367 | "metadata": { 368 | "collapsed": true 369 | }, 370 | "outputs": [], 371 | "source": [ 372 | "sc.stop()" 373 | ] 374 | } 375 | ], 376 | "metadata": { 377 | "kernelspec": { 378 | "display_name": "Python 2", 379 | "language": "python", 380 | "name": "python2" 381 | }, 382 | "language_info": { 383 | "codemirror_mode": { 384 | "name": "ipython", 385 | "version": 2 386 | }, 387 | "file_extension": ".py", 388 | "mimetype": "text/x-python", 389 | "name": "python", 390 | "nbconvert_exporter": "python", 391 | "pygments_lexer": "ipython2", 392 | "version": "2.7.9" 393 | } 394 | }, 395 | "nbformat": 4, 396 | "nbformat_minor": 0 397 | } 398 | -------------------------------------------------------------------------------- /hive-bootstrap.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | echo "Starting postgresql server..." 4 | sudo -u postgres $POSTGRESQL_BIN --config-file=$POSTGRESQL_CONFIG_FILE & 5 | 6 | /etc/bootstrap.sh 7 | echo "Leaving namenode safemode..." 8 | $HADOOP_PREFIX/bin/hdfs dfsadmin -safemode leave 9 | -------------------------------------------------------------------------------- /hive-site.xml: -------------------------------------------------------------------------------- 1 | 2 | Branch: master docker-hive-on-tez/hive-site.xml 3 | @prasanthjprasanthj on Oct 26, 2014 misc config fixes. sample data and query added. 4 | 1 contributor 5 | RawBlameHistory 154 lines (154 sloc) 3.98 KB 6 | 7 | 8 | hive.metastore.cache.pinobjtypes 9 | Table,Database,Type,FieldSchema,Order 10 | 11 | 12 | javax.jdo.option.ConnectionDriverName 13 | org.postgresql.Driver 14 | 15 | 16 | javax.jdo.option.ConnectionUserName 17 | hive 18 | 19 | 20 | hive.auto.convert.join 21 | true 22 | 23 | 24 | fs.hdfs.impl.disable.cache 25 | true 26 | 27 | 28 | fs.file.impl.disable.cache 29 | true 30 | 31 | 32 | hive.metastore.warehouse.dir 33 | /apps/hive/warehouse 34 | 35 | 36 | hive.auto.convert.sortmerge.join 37 | true 38 | 39 | 40 | hive.metastore.client.socket.timeout 41 | 60 42 | 43 | 44 | hive.optimize.bucketmapjoin 45 | true 46 | 47 | 48 | hive.optimize.bucketmapjoin.sortedmerge 49 | true 50 | 51 | 52 | hive.optimize.index.filter 53 | true 54 | 55 | 56 | hive.auto.convert.join.noconditionaltask.size 57 | 1000000000 58 | 59 | 60 | hive.auto.convert.join.noconditionaltask 61 | true 62 | 63 | 64 | hive.mapjoin.bucket.cache.size 65 | 10000 66 | 67 | 68 | hive.vectorized.execution.enabled 69 | true 70 | 71 | 72 | hive.security.authorization.enabled 73 | false 74 | 75 | 76 | hive.optimize.reducededuplication.min.reducer 77 | 4 78 | 79 | 80 | hive.server2.enable.doAs 81 | true 82 | 83 | 84 | hive.mapred.reduce.tasks.speculative.execution 85 | false 86 | 87 | 88 | javax.jdo.option.ConnectionURL 89 | jdbc:postgresql://localhost/metastore 90 | 91 | 92 | hive.enforce.bucketing 93 | true 94 | 95 | 96 | hive.metastore.execute.setugi 97 | true 98 | 99 | 100 | hive.enforce.sorting 101 | true 102 | 103 | 104 | hive.security.authorization.manager 105 | org.apache.hadoop.hive.ql.security.authorization.DefaultHiveAuthorizationProvider 106 | 107 | 108 | hive.map.aggr 109 | true 110 | 111 | 112 | hive.optimize.reducededuplication 113 | true 114 | 115 | 116 | hive.execution.engine 117 | tez 118 | 119 | 120 | hive.vectorized.execution.enabled 121 | true 122 | 123 | 124 | hive.vectorized.groupby.maxentries 125 | 10000 126 | 127 | 128 | hive.vectorized.groupby.checkinterval 129 | 10000 130 | 131 | 132 | hive.input.format 133 | org.apache.hadoop.hive.ql.io.HiveInputFormat 134 | 135 | 136 | javax.jdo.option.ConnectionPassword 137 | hive 138 | 139 | 140 | tez.am.node-blacklisting.enabled 141 | false 142 | 143 | 144 | hive.prewarm.numcontainers 145 | 3 146 | 147 | Controls the number of containers to prewarm for tez (hadoop 2 only) 148 | 149 | 150 | 151 | mapred.tez.java.opts 152 | -Xmx256m 153 | 154 | 155 | hive.tez.container.size 156 | 256 157 | 158 | 159 | 160 | --------------------------------------------------------------------------------