├── 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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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 | # 前言 3 | 4 | 感謝各位參加Hadoop Conference 2015 5 | 為了方便各位實作，將協助各位使用docker在本機上建立spark教學環境． 6 | 熟悉Docker的朋友可以直接跳過這段． 7 | 8 | # Docker安裝教學 9 | 10 | - Linux環境餐請參考：https://docs.docker.com/linux/step_one/ 11 | - Mac環境請參考：https://docs.docker.com/installation/mac/ 12 | - 可以安裝boot2docker 13 | - **推薦先用VMWARE建立純linux環境後再安裝docker** 14 | 15 | # 使用boot2docker特別注意 16 | 因為預設的記憶體只有2G，本包可能需要至少**4G**的記憶體，若執行時遇到記憶體不足的問題，請按照下列步驟修改． 17 | - `vim ~/.boot2docker/profile` 18 | - 在檔案中加入 Memory = 4096 19 | *!!以下步驟將會重置您的boot2docker，亦即所有的images都會刪除，請謹慎使用!!* 20 | - boot2docker stop 21 | - boot2docker destroy 22 | - boot2docker init 23 | - boot2docker start 24 | 25 | # 系統需求 26 | - CPU 4core 27 | - RAM 4G以上 28 | - HDD 10G以上(Docker images檔約4G) 29 | 30 | # 方法一：直接拉取Docker Images 31 | 32 | - `docker pull bryanyang0528/docker-spark-hive-ipython` 33 | (拉取成功後就不用再自行Build Images，直接跳執行) 34 | 35 | # 方法二：建立Docker Images 36 | 37 | - 請確認您的電腦上已經安裝git 38 | - 進入任意合適的目錄 39 | - `git clone https://github.com/bryanyang0528/docker-spark-hive-ipython.git` 40 | - `cd docker-spark-hive-ipython` 41 | - `docker build .` 此步驟將會開始建立docker images 42 | - `docker images` 確認新建立的images id (一個英數組合) 43 | - `docker tag docker-spark-hive-ipython:latest` 44 | 45 | # 執行Docker Images 46 | 47 | - `docker run -d -p 8888:8888 -p 4040:4040 --name pyspark bryanyang0528/docker-spark-hive-ipython` 48 | 49 | # 進入ipython 50 | 51 | - linux: 直接在瀏覽器輸入`http://localhost:8888` , Spark的UI在`http://localhost:4040` 52 | - Mac: 請先在terminal中輸入 `boot2docker ip` 確認ip位置，再到瀏覽器中輸入`http://:8888` 53 | 54 | #### SparkContext(as sc) and SqlContext(as sqlContext) will launch automatically when you open a notebook. 55 | -------------------------------------------------------------------------------- /example/DFML_demo_local.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "# 設定Spark\n", 12 | "import os\n", 13 | "import sys\n", 14 | "# SPARK_HOME=\"/opt/spark-1.4.1-bin-hadoop2.6\"\n", 15 | "# os.environ[\"SPARK_HOME\"] = SPARK_HOME\n", 16 | "# sys.path.append(os.path.join(SPARK_HOME, 'python'))\n", 17 | "# sys.path.append(os.path.join(SPARK_HOME, 'python/lib/py4j-0.8.2.1-src.zip'))\n", 18 | "#----\n", 19 | "import numpy as np\n", 20 | "import pandas as pd\n", 21 | "import matplotlib.pyplot as plt\n", 22 | "%matplotlib inline\n", 23 | "# import seaborn as sns\n", 24 | "# sns.set(rc={\"figure.figsize\": (14, 5)},palette=sns.color_palette(\"Set1\", 2))\n", 25 | "from pyspark.sql.types import *\n", 26 | "from pyspark.sql.functions import *\n", 27 | "from pyspark.ml.feature import *\n", 28 | "from pyspark.ml.classification import *\n", 29 | "from pyspark.ml.param import *\n", 30 | "from pyspark.ml import *\n", 31 | "# from pyspark import HiveContext\n", 32 | "# ctx = HiveContext(sc)\n", 33 | "ctx=sqlContext " 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": {}, 39 | "source": [ 40 | "#DataFrame與Pipeline使用範例\n", 41 | "\n", 42 | "1. 使用 DataFrame 探索資料，觀察時間與spam的關係。\n", 43 | "2. 使用ML Pipeline進行spam預測。" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": {}, 49 | "source": [ 50 | "#資料說明\n", 51 | "\n", 52 | "資料來源是 2015痞客邦 PIXNET HACKATHON 活動中開放的[資料集](https://pixnethackathon2015.events.pixnet.net/dataset/readfirst.html)\n", 53 | "

[痞客邦](https://www.pixnet.net/)

\n", 54 | "痞客邦目前華文世界最大部落格服務網站，當中蘊含大量的優質文章，但也不乏惡意灌水的廣告文章，目前站方已經設計多種演算法來偵測劣質文章，也希望多方好手及駭客高手腦力激盪來找出更好的方法揪出網路流氓。身為台灣社群龍頭，以每天2800萬次瀏覽到訪網站的流量，痞客邦開放海量數據資料，讓參賽者可以看見台灣網友更豐富多元的面向與使用行為。 \n", 55 | "\n", 56 | "---\n", 57 | "文章資料集蘊含正常使用者所發佈的優質文章，及spam使用者所發佈的劣質廣告文章，資料收集時間為2015/4。\n", 58 | "因為data使用規定，本demo無法直接提供資料，請於資料連結內下載。\n", 59 | "資料欄位說明：\n", 60 | "```\n", 61 | "\"post_at\": 文章發佈時間 (Unix Timestamp),\n", 62 | "\"author\": 作者 ID ,\n", 63 | "\"tags\": [文章歸屬的標籤列表],\n", 64 | "\"title\": 文章的標題,\n", 65 | "\"hits\": 文章總人氣,\n", 66 | "\"content\": 部落格本文\n", 67 | "\"comment_count\": 多少人 comment 過,\n", 68 | "\"comment_ids\": [留言者ID] ,\n", 69 | "\"category\": 文章的分類,\n", 70 | "\"spam\": 是否為 Spam 文章,1為spam，0為正常。\n", 71 | "```" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "#讀取資料集" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 12, 84 | "metadata": { 85 | "collapsed": false 86 | }, 87 | "outputs": [ 88 | { 89 | "name": "stdout", 90 | "output_type": "stream", 91 | "text": [ 92 | "root\n", 93 | " |-- category: string (nullable = true)\n", 94 | " |-- comment_count: long (nullable = true)\n", 95 | " |-- comment_ids: array (nullable = true)\n", 96 | " | |-- element: string (containsNull = true)\n", 97 | " |-- hits: long (nullable = true)\n", 98 | " |-- tags: array (nullable = true)\n", 99 | " | |-- element: string (containsNull = true)\n", 100 | " |-- title: string (nullable = true)\n", 101 | " |-- spam: double (nullable = true)\n", 102 | " |-- post_at: long (nullable = true)\n", 103 | "\n" 104 | ] 105 | } 106 | ], 107 | "source": [ 108 | "path = <> # the path of file: articles-half-a.json.\n", 109 | "panda_df = pd.read_json(path) #如果無法讀入，可以試試python的json套件\n", 110 | "df=ctx.createDataFrame(panda_df)\n", 111 | "df=df.withColumn('spam2',df['spam'].astype(DoubleType())).drop('spam').withColumnRenamed('spam2','spam') # 1.4 bug\n", 112 | "df=df.withColumn('post_at2',(df['post_at']/1000000000).astype(LongType())).drop('post_at').withColumnRenamed('post_at2','post_at') # 1.4 bug\n", 113 | "df.printSchema()" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": 13, 119 | "metadata": { 120 | "collapsed": false 121 | }, 122 | "outputs": [ 123 | { 124 | "name": "stdout", 125 | "output_type": "stream", 126 | "text": [ 127 | "+--------+-------------+----+----------+--------------------+----+\n", 128 | "|category|comment_count|hits| post_at| title|spam|\n", 129 | "+--------+-------------+----+----------+--------------------+----+\n", 130 | "| 時尚流行| 0| 0|1427897290|LACOSTE 2013 春夏新品預覽會| 1.0|\n", 131 | "| 汽機車| 0| 445|1428297431| VIRAGE 引擎抖動、不順!| 0.0|\n", 132 | "+--------+-------------+----+----------+--------------------+----+\n", 133 | "\n" 134 | ] 135 | } 136 | ], 137 | "source": [ 138 | "# 觀察table內的數值\n", 139 | "df.select('category','comment_count','hits','post_at','title','spam').show(2) " 140 | ] 141 | }, 142 | { 143 | "cell_type": "markdown", 144 | "metadata": {}, 145 | "source": [ 146 | "---" 147 | ] 148 | }, 149 | { 150 | "cell_type": "markdown", 151 | "metadata": {}, 152 | "source": [ 153 | "#使用 DataFrame 探索資料，觀察時間與spam的關係\n", 154 | "我們想要知道spam和時間的關係，是否spam大部份發生在晚上或白天，非spam是否有分布上的差異。 \n", 155 | "原始資料是巨大的表格形式，不利於觀察，我們利用Spark處理大資料的能力將大資料聚集成小資料，再利用Pandas將它視覺化。 \n", 156 | "\n", 157 | "步驟：\n", 158 | "1. 觀察原始時間資料。\n", 159 | "2. 定義解析時間用的UDF ,用udf將utc數字轉成時間類型。\n", 160 | "2. 計算每一小時的spam和非sapm數量，也就是聚集時間和spam成group並進行count。\n", 161 | "3. 視覺化，轉成pandas畫圖。" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "###觀察原始時間資料\n", 169 | "原始時間在欄位'post_at',格式是UTC second." 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 14, 175 | "metadata": { 176 | "collapsed": false, 177 | "scrolled": true 178 | }, 179 | "outputs": [ 180 | { 181 | "name": "stdout", 182 | "output_type": "stream", 183 | "text": [ 184 | "+----------+----+\n", 185 | "| post_at|spam|\n", 186 | "+----------+----+\n", 187 | "|1427897290| 1.0|\n", 188 | "|1428297431| 0.0|\n", 189 | "|1428749080| 1.0|\n", 190 | "+----------+----+\n", 191 | "\n" 192 | ] 193 | } 194 | ], 195 | "source": [ 196 | "df.select('post_at','spam').show(3)" 197 | ] 198 | }, 199 | { 200 | "cell_type": "markdown", 201 | "metadata": {}, 202 | "source": [ 203 | "###定義解析時間用的UDF \n", 204 | "1. 設定台灣時差\n", 205 | "2. 定義把utc時間轉成當天小時時間的function\n", 206 | "3. 定義SQL UDF" 207 | ] 208 | }, 209 | { 210 | "cell_type": "code", 211 | "execution_count": 15, 212 | "metadata": { 213 | "collapsed": false 214 | }, 215 | "outputs": [], 216 | "source": [ 217 | "from datetime import datetime,tzinfo,timedelta\n", 218 | "# 定義把utc時間轉成當天小時時間的function\n", 219 | "get_hour = lambda x: (datetime.utcfromtimestamp(float(x)) + timedelta(hours=8)).hour \n", 220 | "# 定義SQL UDF\n", 221 | "hourOfDay = udf(get_hour, IntegerType()) " 222 | ] 223 | }, 224 | { 225 | "cell_type": "markdown", 226 | "metadata": {}, 227 | "source": [ 228 | "###計算每一小時的spam和非sapm數量\n", 229 | "1. 使用udf，增加hour 欄位 \n", 230 | "2. group by (spam , hour) \n", 231 | "3. 計算每個group內的數量並且命名為 'count' \n", 232 | "4. 照時間順序排序結果" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 16, 238 | "metadata": { 239 | "collapsed": false 240 | }, 241 | "outputs": [ 242 | { 243 | "name": "stdout", 244 | "output_type": "stream", 245 | "text": [ 246 | "root\n", 247 | " |-- spam: double (nullable = true)\n", 248 | " |-- hour: integer (nullable = true)\n", 249 | " |-- count: long (nullable = false)\n", 250 | "\n" 251 | ] 252 | } 253 | ], 254 | "source": [ 255 | "df_hour=(df.withColumn('hour',hourOfDay(df['post_at'])) # 使用上述定義的UDF，增加hour欄位\n", 256 | " .groupBy('spam','hour') # group by (spam , hour) \n", 257 | " .agg(count('*').alias('count')) # 計算每個group內的數量並且命名為 'count' \n", 258 | " .orderBy(asc('hour'))) # 照時間順序排序結果\n", 259 | " \n", 260 | "df_hour.printSchema()" 261 | ] 262 | }, 263 | { 264 | "cell_type": "markdown", 265 | "metadata": {}, 266 | "source": [ 267 | "###視覺化\n", 268 | "圖片藍色是spam文章，紅色是正常文章。 \n", 269 | "經由視覺化更有效地觀察資料，我們可以發現，發文數量呈現日多夜少的情形。\n", 270 | "非spam的發文數量於晚間12點達到最高峰，晚餐和睡眠時間最低。\n", 271 | "spam方面與非spam的發文數量相比，spam半夜特別多，且變化較大。 \n", 272 | "\n", 273 | "步驟：\n", 274 | "1. spark dataframe轉成pandas dataframe\n", 275 | "2. 使用pandas畫圖" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 17, 281 | "metadata": { 282 | "collapsed": false 283 | }, 284 | "outputs": [ 285 | { 286 | "data": { 287 | "text/plain": [ 288 | "" 289 | ] 290 | }, 291 | "execution_count": 17, 292 | "metadata": {}, 293 | "output_type": "execute_result" 294 | }, 295 | { 296 | "data": { 297 | "image/png": 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298 | "text/plain": [ 299 | "" 300 | ] 301 | }, 302 | "metadata": {}, 303 | "output_type": "display_data" 304 | } 305 | ], 306 | "source": [ 307 | "# spark dataframe轉成pandas dataframe\n", 308 | "pandas_df = df_hour.toPandas() \n", 309 | "# 轉一下格式方便畫圖\n", 310 | "pandas_df = pandas_df.pivot_table(index='hour',columns='spam',values='count') \n", 311 | "pandas_df.set_axis(1,['not spam','spam'])\n", 312 | "# 使用pandas畫圖\n", 313 | "ax=pandas_df.plot(kind='bar') \n", 314 | "ax.set_title('count by hour')\n", 315 | "ax.set_ylabel('count')\n", 316 | "ax.set_xlabel('hour')" 317 | ] 318 | }, 319 | { 320 | "cell_type": "markdown", 321 | "metadata": {}, 322 | "source": [ 323 | "----" 324 | ] 325 | }, 326 | { 327 | "cell_type": "markdown", 328 | "metadata": {}, 329 | "source": [ 330 | "#使用ML Pipeline進行spam預測\n", 331 | "步驟：\n", 332 | "1. 前處理。 \n", 333 | "3. 建立pipeline。\n", 334 | "4. 分成訓練和測試資料。\n", 335 | "5. 使用訓練資料訓練pipeline。\n", 336 | "7. 使用測試資料評估性能。" 337 | ] 338 | }, 339 | { 340 | "cell_type": "markdown", 341 | "metadata": {}, 342 | "source": [ 343 | "###前處理 \n", 344 | "特徵抽取，加入時間特徵，利用上述dataframe udf加入hour特徵。" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": 18, 350 | "metadata": { 351 | "collapsed": false 352 | }, 353 | "outputs": [], 354 | "source": [ 355 | "# 加入時間特徵\n", 356 | "df_ml = df.withColumn('hour',hourOfDay(df['post_at']).astype(DoubleType())) " 357 | ] 358 | }, 359 | { 360 | "cell_type": "markdown", 361 | "metadata": {}, 362 | "source": [ 363 | "###建立 Pipeline\n", 364 | "本demo目的為Pipeline的使用，為了簡單起見，本demo只挑選幾個簡單的特徵：\n", 365 | "- 時間\"hour\", 時間特徵使用上述的發文小時時間, 在前處理步驟已經加入。\n", 366 | "- 點擊數\"hits\",\n", 367 | "- 留言數量'comment_count'。 \n", 368 | "\n", 369 | "我們使用線性分類器logistic regression，這種分類器只接受數值特徵，類別特徵必須先經過編碼轉換才能通過分類器。留言數和點擊數本身是數值特徵， 小時時間類別是類別特徵需要經過編碼轉成數值特徵。\n", 370 | "\n", 371 | "Pipeline流程圖：\n", 372 | "```\n", 373 | "發文時間 ---- 類別數值化 ------------\\ \n", 374 | " \\ \n", 375 | "點擊數量 -------------------------- 結合 ---> 分類器 \n", 376 | " / \n", 377 | "留言數量 --------------------------/ \n", 378 | "```" 379 | ] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": 19, 384 | "metadata": { 385 | "collapsed": false 386 | }, 387 | "outputs": [], 388 | "source": [ 389 | "# 發文時間編碼\n", 390 | "hour_encoder = OneHotEncoder(inputCol=\"hour\", outputCol=\"hour_code\") \n", 391 | "\n", 392 | "# 結合所有特徵\n", 393 | "assembler = VectorAssembler(inputCols=[\"hour_code\",\"hits\",'comment_count'], outputCol=\"features\")\n", 394 | "\n", 395 | "# 分類器\n", 396 | "log_regressor = LogisticRegression(featuresCol=\"features\",labelCol=\"spam\")\n", 397 | "\n", 398 | "#機器學習管線\n", 399 | "pipeline = Pipeline(stages=[hour_encoder,assembler,log_regressor])" 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "###把資料分成訓練和測試資料\n", 407 | "在比例上，訓練資料佔七成，測試資料佔三成" 408 | ] 409 | }, 410 | { 411 | "cell_type": "code", 412 | "execution_count": 20, 413 | "metadata": { 414 | "collapsed": true 415 | }, 416 | "outputs": [], 417 | "source": [ 418 | "df_train,df_test=df_ml.randomSplit([7.,3.],123)" 419 | ] 420 | }, 421 | { 422 | "cell_type": "markdown", 423 | "metadata": {}, 424 | "source": [ 425 | "###訓練pipeline\n", 426 | "pipeline是Estimator所以有fit方法。" 427 | ] 428 | }, 429 | { 430 | "cell_type": "code", 431 | "execution_count": 21, 432 | "metadata": { 433 | "collapsed": true 434 | }, 435 | "outputs": [], 436 | "source": [ 437 | "model = pipeline.fit(df_train)" 438 | ] 439 | }, 440 | { 441 | "cell_type": "markdown", 442 | "metadata": {}, 443 | "source": [ 444 | "###評估pipeline\n", 445 | "1. 用訓練好的pipeline進行預測操是資料集。\n", 446 | "3. 使用DataFrame計算預測精準度。\n", 447 | "3. 利用dataframe觀察資料以利檢討模型。" 448 | ] 449 | }, 450 | { 451 | "cell_type": "code", 452 | "execution_count": 22, 453 | "metadata": { 454 | "collapsed": true 455 | }, 456 | "outputs": [], 457 | "source": [ 458 | "# 用訓練好的pipeline進行預測測試資料集。\n", 459 | "# 訓練好的pipeline是Transformer所以有transform方法。\n", 460 | "df_pred = model.transform(df_test)" 461 | ] 462 | }, 463 | { 464 | "cell_type": "code", 465 | "execution_count": 23, 466 | "metadata": { 467 | "collapsed": false 468 | }, 469 | "outputs": [ 470 | { 471 | "name": "stdout", 472 | "output_type": "stream", 473 | "text": [ 474 | "root\n", 475 | " |-- category: string (nullable = true)\n", 476 | " |-- comment_count: long (nullable = true)\n", 477 | " |-- comment_ids: array (nullable = true)\n", 478 | " | |-- element: string (containsNull = true)\n", 479 | " |-- hits: long (nullable = true)\n", 480 | " |-- tags: array (nullable = true)\n", 481 | " | |-- element: string (containsNull = true)\n", 482 | " |-- title: string (nullable = true)\n", 483 | " |-- spam: double (nullable = true)\n", 484 | " |-- post_at: long (nullable = true)\n", 485 | " |-- hour: double (nullable = true)\n", 486 | " |-- hour_code: vector (nullable = true)\n", 487 | " |-- features: vector (nullable = true)\n", 488 | " |-- rawPrediction: vector (nullable = true)\n", 489 | " |-- probability: vector (nullable = true)\n", 490 | " |-- prediction: double (nullable = true)\n", 491 | "\n" 492 | ] 493 | } 494 | ], 495 | "source": [ 496 | "# 觀察transofrm對dataframe做了什麼事，所有pipeline設定的特徵和預測都加入dataframe了。\n", 497 | "df_pred.printSchema()" 498 | ] 499 | }, 500 | { 501 | "cell_type": "code", 502 | "execution_count": 24, 503 | "metadata": { 504 | "collapsed": false 505 | }, 506 | "outputs": [ 507 | { 508 | "name": "stdout", 509 | "output_type": "stream", 510 | "text": [ 511 | "+------------------+\n", 512 | "| accuracy|\n", 513 | "+------------------+\n", 514 | "|0.7128378378378378|\n", 515 | "+------------------+\n", 516 | "\n" 517 | ] 518 | } 519 | ], 520 | "source": [ 521 | "pred_label = df_pred.select(\"prediction\", \"spam\")\n", 522 | "# 建立accuracy function，使用內建dataframe functions達成UDAF\n", 523 | "accuracy = avg((pred_label.prediction==pred_label['spam']).astype(IntegerType())).alias(\"accuracy\") \n", 524 | "# 需要groupBy才能使用此function, 所以就group所有data吧\n", 525 | "pred_label.groupBy().agg(accuracy).show()" 526 | ] 527 | }, 528 | { 529 | "cell_type": "code", 530 | "execution_count": 25, 531 | "metadata": { 532 | "collapsed": false 533 | }, 534 | "outputs": [ 535 | { 536 | "name": "stdout", 537 | "output_type": "stream", 538 | "text": [ 539 | "+----+----+-------------+----+----------+\n", 540 | "|hour|hits|comment_count|spam|prediction|\n", 541 | "+----+----+-------------+----+----------+\n", 542 | "|13.0| 189| 0| 0.0| 1.0|\n", 543 | "|12.0| 34| 0| 0.0| 1.0|\n", 544 | "|20.0| 0| 0| 1.0| 0.0|\n", 545 | "|20.0| 0| 0| 1.0| 0.0|\n", 546 | "|20.0| 0| 0| 1.0| 0.0|\n", 547 | "|20.0| 0| 0| 1.0| 0.0|\n", 548 | "|15.0| 0| 0| 0.0| 1.0|\n", 549 | "|10.0|2012| 0| 0.0| 1.0|\n", 550 | "|15.0|3652| 4| 0.0| 1.0|\n", 551 | "|13.0| 0| 0| 0.0| 1.0|\n", 552 | "+----+----+-------------+----+----------+\n", 553 | "\n" 554 | ] 555 | } 556 | ], 557 | "source": [ 558 | "# 檢討模型效能，觀察什麼樣的特徵導致錯誤預測。\n", 559 | "df_pred.filter(df_pred['spam']!=df_pred['prediction'])\\\n", 560 | " .select('hour','hits','comment_count','spam','prediction')\\\n", 561 | " .show(10)" 562 | ] 563 | }, 564 | { 565 | "cell_type": "markdown", 566 | "metadata": {}, 567 | "source": [ 568 | "author: Wayne-Lin \n", 569 | "#=====使用完請隨手關燈sc.stop(),感謝您======" 570 | ] 571 | }, 572 | { 573 | "cell_type": "code", 574 | "execution_count": 7, 575 | "metadata": { 576 | "collapsed": false 577 | }, 578 | "outputs": [], 579 | "source": [ 580 | "sc.stop()" 581 | ] 582 | }, 583 | { 584 | "cell_type": "code", 585 | "execution_count": null, 586 | "metadata": { 587 | "collapsed": false 588 | }, 589 | "outputs": [], 590 | "source": [] 591 | } 592 | ], 593 | "metadata": { 594 | "kernelspec": { 595 | "display_name": "Python 2", 596 | "language": "python", 597 | "name": "python2" 598 | }, 599 | "language_info": { 600 | "codemirror_mode": { 601 | "name": "ipython", 602 | "version": 2 603 | }, 604 | "file_extension": ".py", 605 | "mimetype": "text/x-python", 606 | "name": "python", 607 | "nbconvert_exporter": "python", 608 | "pygments_lexer": "ipython2", 609 | "version": "2.7.10" 610 | } 611 | }, 612 | "nbformat": 4, 613 | "nbformat_minor": 0 614 | } 615 | -------------------------------------------------------------------------------- /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": 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-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 HiveContext, Row\n", 34 | "sqlContext= HiveContext(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": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "#### HiveContext, a superset of SQLContext, was recommended for most use cases. Please make sure you are using HiveContext now!" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "##Part 2: Loading data programmatically" 60 | ] 61 | }, 62 | { 63 | "cell_type": "markdown", 64 | "metadata": {}, 65 | "source": [ 66 | "#### ** (2a) Read local JSON file to DataFrame **\n", 67 | "#### 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/)" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": null, 73 | "metadata": { 74 | "collapsed": true 75 | }, 76 | "outputs": [], 77 | "source": [ 78 | "jsonfile = \"file:///opt/spark-1.4.1-bin-hadoop2.6/examples/src/main/resources/people.json\"\n", 79 | "df = sqlContext.read.load(jsonfile, format=\"json\")" 80 | ] 81 | }, 82 | { 83 | "cell_type": "markdown", 84 | "metadata": {}, 85 | "source": [ 86 | "####Show time: Query top 2 row" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": null, 92 | "metadata": { 93 | "collapsed": false 94 | }, 95 | "outputs": [], 96 | "source": [ 97 | "# TODO: Replace with appropriate code\n", 98 | "df." 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": null, 104 | "metadata": { 105 | "collapsed": false 106 | }, 107 | "outputs": [], 108 | "source": [ 109 | "#print df's schema\n", 110 | "df.printSchema()" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "#### ** (2b) Read from Hive **\n", 118 | "####Don't forget the configuration of Hive should be done by placing your hive-site.xml file in conf/." 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": { 125 | "collapsed": false 126 | }, 127 | "outputs": [], 128 | "source": [ 129 | "sqlContext.sql(\"SHOW TABLES\").show()" 130 | ] 131 | }, 132 | { 133 | "cell_type": "markdown", 134 | "metadata": {}, 135 | "source": [ 136 | "#### In this class, you will use *pixnet_user_log_1000* for further works" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "#### Print the schema! What do we have?" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": null, 149 | "metadata": { 150 | "collapsed": false 151 | }, 152 | "outputs": [], 153 | "source": [ 154 | "sqlContext.sql(\"SELECT *\\ \n", 155 | " FROM pixnet_user_log_1000\").printSchema()" 156 | ] 157 | }, 158 | { 159 | "cell_type": "markdown", 160 | "metadata": {}, 161 | "source": [ 162 | "#### How many rows in *pixnet_user_log*" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": null, 168 | "metadata": { 169 | "collapsed": false 170 | }, 171 | "outputs": [], 172 | "source": [ 173 | "from datetime import datetime\n", 174 | "start_time = datetime.now()\n", 175 | "\n", 176 | "df2 = sqlContext.sql(\"SELECT * \\\n", 177 | " FROM pixnet_user_log_1000\")\n", 178 | "\n", 179 | "end_time = datetime.now()\n", 180 | "\n", 181 | "print df2.count()\n", 182 | "print('Duration: {}'.format(end_time - start_time))" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": null, 188 | "metadata": { 189 | "collapsed": false 190 | }, 191 | "outputs": [], 192 | "source": [ 193 | "df2.select('time').show(2)" 194 | ] 195 | }, 196 | { 197 | "cell_type": "markdown", 198 | "metadata": {}, 199 | "source": [ 200 | "##Part 3: User-Defined Functions" 201 | ] 202 | }, 203 | { 204 | "cell_type": "markdown", 205 | "metadata": {}, 206 | "source": [ 207 | "###In part 3, you will create your first UDF in Spark SQL with elegant lambda" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": null, 213 | "metadata": { 214 | "collapsed": true 215 | }, 216 | "outputs": [], 217 | "source": [ 218 | "#registers this RDD as a temporary table using the given name.\n", 219 | "df2.registerTempTable(\"people\")\n", 220 | "\n", 221 | "# Create an UDF for how long some text is\n", 222 | "# example from user guide, length function\n", 223 | "sqlContext.registerFunction(\"strLenPython\", lambda x: len(x)) \n", 224 | "\n", 225 | "# split function for parser\n", 226 | "sqlContext.registerFunction(\"strDate\", lambda x: x.split(\"T\")[0])\n", 227 | "\n", 228 | "# put udf with expected columns\n", 229 | "results = sqlContext.sql(\"SELECT author, \\\n", 230 | " strDate(time) AS dt, \\\n", 231 | " strLenPython(action) AS lenAct \\\n", 232 | " FROM people\")" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": null, 238 | "metadata": { 239 | "collapsed": true 240 | }, 241 | "outputs": [], 242 | "source": [ 243 | "# or you could use df\n", 244 | "df2.select('author').show(3)\n", 245 | "\n", 246 | "#\n", 247 | "strLenPython = udf(lambda s: len(s), IntegerType())\n", 248 | "df2.select(strLenPython(df2.action).alias('action_len')).show(2)\n" 249 | ] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": null, 254 | "metadata": { 255 | "collapsed": false 256 | }, 257 | "outputs": [], 258 | "source": [ 259 | "# print top 5 results\n", 260 | "results.show(5)" 261 | ] 262 | }, 263 | { 264 | "cell_type": "markdown", 265 | "metadata": {}, 266 | "source": [ 267 | "##Part 4: Caching for performance" 268 | ] 269 | }, 270 | { 271 | "cell_type": "markdown", 272 | "metadata": {}, 273 | "source": [ 274 | "#### ** Saving to persistent tables**\n", 275 | "#### `saveAsTable ` : Saves the contents of this DataFrame to a data source as a table." 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 19, 281 | "metadata": { 282 | "collapsed": true 283 | }, 284 | "outputs": [], 285 | "source": [ 286 | "sqlContext.cacheTable(\"people\")" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": null, 292 | "metadata": { 293 | "collapsed": false 294 | }, 295 | "outputs": [], 296 | "source": [ 297 | "start_time = datetime.now()\n", 298 | "\n", 299 | "sqlContext.sql(\"SELECT * FROM people\").count()\n", 300 | "\n", 301 | "end_time = datetime.now()\n", 302 | "print('Duration: {}'.format(end_time - start_time))" 303 | ] 304 | }, 305 | { 306 | "cell_type": "code", 307 | "execution_count": null, 308 | "metadata": { 309 | "collapsed": false 310 | }, 311 | "outputs": [], 312 | "source": [ 313 | "sqlContext.sql(\"SELECT strDate(time) AS dt,\\\n", 314 | " count(distinct author) AS cnt \\\n", 315 | " FROM people \\\n", 316 | " GROUP BY strDate(time)\").show(5)" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": null, 322 | "metadata": { 323 | "collapsed": false 324 | }, 325 | "outputs": [], 326 | "source": [ 327 | "sqlContext.uncacheTable(\"people\")" 328 | ] 329 | }, 330 | { 331 | "cell_type": "markdown", 332 | "metadata": {}, 333 | "source": [ 334 | "##Part 5: Your show time - How many authors are tagged as spam from pixnet_user_log?\n", 335 | "\n", 336 | "#### Here are two hive tables you will need:\n", 337 | "#### (1) author with action *pixnet_user_spam*\n", 338 | "#### (2) author with spam tag *pixnet_user_log_1000*" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": null, 344 | "metadata": { 345 | "collapsed": true 346 | }, 347 | "outputs": [], 348 | "source": [ 349 | "# TODO: Replace with appropriate code\n", 350 | "result = " 351 | ] 352 | }, 353 | { 354 | "cell_type": "markdown", 355 | "metadata": {}, 356 | "source": [ 357 | "### Don't forget to stop sc" 358 | ] 359 | }, 360 | { 361 | "cell_type": "code", 362 | "execution_count": 23, 363 | "metadata": { 364 | "collapsed": true 365 | }, 366 | "outputs": [], 367 | "source": [ 368 | "sc.stop()" 369 | ] 370 | } 371 | ], 372 | "metadata": { 373 | "kernelspec": { 374 | "display_name": "Python 2", 375 | "language": "python", 376 | "name": "python2" 377 | }, 378 | "language_info": { 379 | "codemirror_mode": { 380 | "name": "ipython", 381 | "version": 2 382 | }, 383 | "file_extension": ".py", 384 | "mimetype": "text/x-python", 385 | "name": "python", 386 | "nbconvert_exporter": "python", 387 | "pygments_lexer": "ipython2", 388 | "version": "2.7.5" 389 | } 390 | }, 391 | "nbformat": 4, 392 | "nbformat_minor": 0 393 | } 394 | -------------------------------------------------------------------------------- /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 | --------------------------------------------------------------------------------