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| |
| <h1>Scalding Interpreter for Apache Zeppelin</h1> |
| |
| <div id="toc"></div> |
| |
| <p><a href="https://github.com/twitter/scalding">Scalding</a> is an open source Scala library for writing MapReduce jobs.</p> |
| |
| <h2>Building the Scalding Interpreter</h2> |
| |
| <p>You have to first build the Scalding interpreter by enable the <strong>scalding</strong> profile as follows:</p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">mvn clean package -Pscalding -DskipTests |
| </code></pre></div> |
| <h2>Enabling the Scalding Interpreter</h2> |
| |
| <p>In a notebook, to enable the <strong>Scalding</strong> interpreter, click on the <strong>Gear</strong> icon,select <strong>Scalding</strong>, and hit <strong>Save</strong>.</p> |
| |
| <p><center></p> |
| |
| <p><img src="../assets/themes/zeppelin/img/docs-img/scalding-InterpreterBinding.png" alt="Interpreter Binding"></p> |
| |
| <p><img src="../assets/themes/zeppelin/img/docs-img/scalding-InterpreterSelection.png" alt="Interpreter Selection"></p> |
| |
| <p></center></p> |
| |
| <h2>Configuring the Interpreter</h2> |
| |
| <p>Scalding interpreter runs in two modes:</p> |
| |
| <ul> |
| <li>local</li> |
| <li>hdfs</li> |
| </ul> |
| |
| <p>In the local mode, you can access files on the local server and scalding transformation are done locally.</p> |
| |
| <p>In hdfs mode you can access files in HDFS and scalding transformation are run as hadoop map-reduce jobs.</p> |
| |
| <p>Zeppelin comes with a pre-configured Scalding interpreter in local mode.</p> |
| |
| <p>To run the scalding interpreter in the hdfs mode you have to do the following:</p> |
| |
| <p><strong>Set the classpath with ZEPPELIN_CLASSPATH_OVERRIDES</strong></p> |
| |
| <p>In conf/zeppelin<em>env.sh, you have to set |
| ZEPPELIN</em>CLASSPATH_OVERRIDES to the contents of 'hadoop classpath' |
| and directories with custom jar files you need for your scalding commands.</p> |
| |
| <p><strong>Set arguments to the scalding repl</strong></p> |
| |
| <p>The default arguments are: "--local --repl"</p> |
| |
| <p>For hdfs mode you need to add: "--hdfs --repl"</p> |
| |
| <p>If you want to add custom jars, you need to add: |
| "-libjars directory/<em>:directory/</em>"</p> |
| |
| <p>For reducer estimation, you need to add something like: |
| "-Dscalding.reducer.estimator.classes=com.twitter.scalding.reducer_estimation.InputSizeReducerEstimator"</p> |
| |
| <p><strong>Set max.open.instances</strong></p> |
| |
| <p>If you want to control the maximum number of open interpreters, you have to select "scoped" interpreter for note |
| option and set max.open.instances argument.</p> |
| |
| <h2>Testing the Interpreter</h2> |
| |
| <h3>Local mode</h3> |
| |
| <p>In example, by using the <a href="https://gist.github.com/johnynek/a47699caa62f4f38a3e2">Alice in Wonderland</a> tutorial, |
| we will count words (of course!), and plot a graph of the top 10 words in the book.</p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">%scalding |
| |
| import scala.io.Source |
| |
| // Get the Alice in Wonderland book from gutenberg.org: |
| val alice = Source.fromURL("http://www.gutenberg.org/files/11/11.txt").getLines |
| val aliceLineNum = alice.zipWithIndex.toList |
| val alicePipe = TypedPipe.from(aliceLineNum) |
| |
| // Now get a list of words for the book: |
| val aliceWords = alicePipe.flatMap { case (text, _) => text.split("\\s+").toList } |
| |
| // Now lets add a count for each word: |
| val aliceWithCount = aliceWords.filterNot(_.equals("")).map { word => (word, 1L) } |
| |
| // let's sum them for each word: |
| val wordCount = aliceWithCount.group.sum |
| |
| print ("Here are the top 10 words\n") |
| val top10 = wordCount |
| .groupAll |
| .sortBy { case (word, count) => -count } |
| .take(10) |
| top10.dump |
| </code></pre></div><div class="highlight"><pre><code class="text language-text" data-lang="text">%scalding |
| |
| val table = "words\t count\n" + top10.toIterator.map{case (k, (word, count)) => s"$word\t$count"}.mkString("\n") |
| print("%table " + table) |
| </code></pre></div> |
| <p>If you click on the icon for the pie chart, you should be able to see a chart like this: |
| <img src="../assets/themes/zeppelin/img/docs-img/scalding-pie.png" alt="Scalding - Pie - Chart"></p> |
| |
| <h3>HDFS mode</h3> |
| |
| <p><strong>Test mode</strong></p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">%scalding |
| mode |
| </code></pre></div> |
| <p>This command should print:</p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">res4: com.twitter.scalding.Mode = Hdfs(true,Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml, hdfs-site.xml) |
| </code></pre></div> |
| <p><strong>Test HDFS read</strong></p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">val testfile = TypedPipe.from(TextLine("/user/x/testfile")) |
| testfile.dump |
| </code></pre></div> |
| <p>This command should print the contents of the hdfs file /user/x/testfile.</p> |
| |
| <p><strong>Test map-reduce job</strong></p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">val testfile = TypedPipe.from(TextLine("/user/x/testfile")) |
| val a = testfile.groupAll.size.values |
| a.toList |
| </code></pre></div> |
| <p>This command should create a map reduce job.</p> |
| |
| <h2>Future Work</h2> |
| |
| <ul> |
| <li>Better user feedback (hadoop url, progress updates)</li> |
| <li>Ability to cancel jobs</li> |
| <li>Ability to dynamically load jars without restarting the interpreter</li> |
| <li>Multiuser scalability (run scalding interpreters on different servers)</li> |
| </ul> |
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