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<h1 class="title">SparkR (R on Spark)</h1>
<ul id="markdown-toc">
<li><a href="#overview" id="markdown-toc-overview">Overview</a></li>
<li><a href="#sparkdataframe" id="markdown-toc-sparkdataframe">SparkDataFrame</a> <ul>
<li><a href="#starting-up-sparksession" id="markdown-toc-starting-up-sparksession">Starting Up: SparkSession</a></li>
<li><a href="#starting-up-from-rstudio" id="markdown-toc-starting-up-from-rstudio">Starting Up from RStudio</a></li>
<li><a href="#creating-sparkdataframes" id="markdown-toc-creating-sparkdataframes">Creating SparkDataFrames</a> <ul>
<li><a href="#from-local-data-frames" id="markdown-toc-from-local-data-frames">From local data frames</a></li>
<li><a href="#from-data-sources" id="markdown-toc-from-data-sources">From Data Sources</a></li>
<li><a href="#from-hive-tables" id="markdown-toc-from-hive-tables">From Hive tables</a></li>
</ul>
</li>
<li><a href="#sparkdataframe-operations" id="markdown-toc-sparkdataframe-operations">SparkDataFrame Operations</a> <ul>
<li><a href="#selecting-rows-columns" id="markdown-toc-selecting-rows-columns">Selecting rows, columns</a></li>
<li><a href="#grouping-aggregation" id="markdown-toc-grouping-aggregation">Grouping, Aggregation</a></li>
<li><a href="#operating-on-columns" id="markdown-toc-operating-on-columns">Operating on Columns</a></li>
<li><a href="#applying-user-defined-function" id="markdown-toc-applying-user-defined-function">Applying User-Defined Function</a> <ul>
<li><a href="#run-a-given-function-on-a-large-dataset-using-dapply-or-dapplycollect" id="markdown-toc-run-a-given-function-on-a-large-dataset-using-dapply-or-dapplycollect">Run a given function on a large dataset using <code class="highlighter-rouge">dapply</code> or <code class="highlighter-rouge">dapplyCollect</code></a> <ul>
<li><a href="#dapply" id="markdown-toc-dapply">dapply</a></li>
<li><a href="#dapplycollect" id="markdown-toc-dapplycollect">dapplyCollect</a></li>
</ul>
</li>
<li><a href="#run-a-given-function-on-a-large-dataset-grouping-by-input-columns-and-using-gapply-or-gapplycollect" id="markdown-toc-run-a-given-function-on-a-large-dataset-grouping-by-input-columns-and-using-gapply-or-gapplycollect">Run a given function on a large dataset grouping by input column(s) and using <code class="highlighter-rouge">gapply</code> or <code class="highlighter-rouge">gapplyCollect</code></a> <ul>
<li><a href="#gapply" id="markdown-toc-gapply">gapply</a></li>
<li><a href="#gapplycollect" id="markdown-toc-gapplycollect">gapplyCollect</a></li>
</ul>
</li>
<li><a href="#run-local-r-functions-distributed-using-sparklapply" id="markdown-toc-run-local-r-functions-distributed-using-sparklapply">Run local R functions distributed using <code class="highlighter-rouge">spark.lapply</code></a> <ul>
<li><a href="#sparklapply" id="markdown-toc-sparklapply">spark.lapply</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#eager-execution" id="markdown-toc-eager-execution">Eager execution</a></li>
</ul>
</li>
<li><a href="#running-sql-queries-from-sparkr" id="markdown-toc-running-sql-queries-from-sparkr">Running SQL Queries from SparkR</a></li>
</ul>
</li>
<li><a href="#machine-learning" id="markdown-toc-machine-learning">Machine Learning</a> <ul>
<li><a href="#algorithms" id="markdown-toc-algorithms">Algorithms</a> <ul>
<li><a href="#classification" id="markdown-toc-classification">Classification</a></li>
<li><a href="#regression" id="markdown-toc-regression">Regression</a></li>
<li><a href="#tree" id="markdown-toc-tree">Tree</a></li>
<li><a href="#clustering" id="markdown-toc-clustering">Clustering</a></li>
<li><a href="#collaborative-filtering" id="markdown-toc-collaborative-filtering">Collaborative Filtering</a></li>
<li><a href="#frequent-pattern-mining" id="markdown-toc-frequent-pattern-mining">Frequent Pattern Mining</a></li>
<li><a href="#statistics" id="markdown-toc-statistics">Statistics</a></li>
</ul>
</li>
<li><a href="#model-persistence" id="markdown-toc-model-persistence">Model persistence</a></li>
</ul>
</li>
<li><a href="#data-type-mapping-between-r-and-spark" id="markdown-toc-data-type-mapping-between-r-and-spark">Data type mapping between R and Spark</a></li>
<li><a href="#structured-streaming" id="markdown-toc-structured-streaming">Structured Streaming</a></li>
<li><a href="#apache-arrow-in-sparkr" id="markdown-toc-apache-arrow-in-sparkr">Apache Arrow in SparkR</a> <ul>
<li><a href="#ensure-arrow-installed" id="markdown-toc-ensure-arrow-installed">Ensure Arrow Installed</a></li>
<li><a href="#enabling-for-conversion-tofrom-r-dataframe-dapply-and-gapply" id="markdown-toc-enabling-for-conversion-tofrom-r-dataframe-dapply-and-gapply">Enabling for Conversion to/from R DataFrame, <code class="highlighter-rouge">dapply</code> and <code class="highlighter-rouge">gapply</code></a></li>
<li><a href="#supported-sql-types" id="markdown-toc-supported-sql-types">Supported SQL Types</a></li>
</ul>
</li>
<li><a href="#r-function-name-conflicts" id="markdown-toc-r-function-name-conflicts">R Function Name Conflicts</a></li>
<li><a href="#migration-guide" id="markdown-toc-migration-guide">Migration Guide</a></li>
</ul>
<h1 id="overview">Overview</h1>
<p>SparkR is an R package that provides a light-weight frontend to use Apache Spark from R.
In Spark 3.0.1, SparkR provides a distributed data frame implementation that
supports operations like selection, filtering, aggregation etc. (similar to R data frames,
<a href="https://github.com/hadley/dplyr">dplyr</a>) but on large datasets. SparkR also supports distributed
machine learning using MLlib.</p>
<h1 id="sparkdataframe">SparkDataFrame</h1>
<p>A SparkDataFrame is a distributed collection of data organized into named columns. It is conceptually
equivalent to a table in a relational database or a data frame in R, but with richer
optimizations under the hood. SparkDataFrames can be constructed from a wide array of sources such as:
structured data files, tables in Hive, external databases, or existing local R data frames.</p>
<p>All of the examples on this page use sample data included in R or the Spark distribution and can be run using the <code class="highlighter-rouge">./bin/sparkR</code> shell.</p>
<h2 id="starting-up-sparksession">Starting Up: SparkSession</h2>
<div data-lang="r">
<p>The entry point into SparkR is the <code class="highlighter-rouge">SparkSession</code> which connects your R program to a Spark cluster.
You can create a <code class="highlighter-rouge">SparkSession</code> using <code class="highlighter-rouge">sparkR.session</code> and pass in options such as the application name, any spark packages depended on, etc. Further, you can also work with SparkDataFrames via <code class="highlighter-rouge">SparkSession</code>. If you are working from the <code class="highlighter-rouge">sparkR</code> shell, the <code class="highlighter-rouge">SparkSession</code> should already be created for you, and you would not need to call <code class="highlighter-rouge">sparkR.session</code>.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">sparkR.session</span><span class="p">()</span></code></pre></figure>
</div>
<h2 id="starting-up-from-rstudio">Starting Up from RStudio</h2>
<p>You can also start SparkR from RStudio. You can connect your R program to a Spark cluster from
RStudio, R shell, Rscript or other R IDEs. To start, make sure SPARK_HOME is set in environment
(you can check <a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/Sys.getenv.html">Sys.getenv</a>),
load the SparkR package, and call <code class="highlighter-rouge">sparkR.session</code> as below. It will check for the Spark installation, and, if not found, it will be downloaded and cached automatically. Alternatively, you can also run <code class="highlighter-rouge">install.spark</code> manually.</p>
<p>In addition to calling <code class="highlighter-rouge">sparkR.session</code>,
you could also specify certain Spark driver properties. Normally these
<a href="configuration.html#application-properties">Application properties</a> and
<a href="configuration.html#runtime-environment">Runtime Environment</a> cannot be set programmatically, as the
driver JVM process would have been started, in this case SparkR takes care of this for you. To set
them, pass them as you would other configuration properties in the <code class="highlighter-rouge">sparkConfig</code> argument to
<code class="highlighter-rouge">sparkR.session()</code>.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">nchar</span><span class="p">(</span><span class="n">Sys.getenv</span><span class="p">(</span><span class="s2">"SPARK_HOME"</span><span class="p">))</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w">
</span><span class="n">Sys.setenv</span><span class="p">(</span><span class="n">SPARK_HOME</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"/home/spark"</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="n">library</span><span class="p">(</span><span class="n">SparkR</span><span class="p">,</span><span class="w"> </span><span class="n">lib.loc</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="n">file.path</span><span class="p">(</span><span class="n">Sys.getenv</span><span class="p">(</span><span class="s2">"SPARK_HOME"</span><span class="p">),</span><span class="w"> </span><span class="s2">"R"</span><span class="p">,</span><span class="w"> </span><span class="s2">"lib"</span><span class="p">)))</span><span class="w">
</span><span class="n">sparkR.session</span><span class="p">(</span><span class="n">master</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"local[*]"</span><span class="p">,</span><span class="w"> </span><span class="n">sparkConfig</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="n">spark.driver.memory</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"2g"</span><span class="p">))</span></code></pre></figure>
</div>
<p>The following Spark driver properties can be set in <code class="highlighter-rouge">sparkConfig</code> with <code class="highlighter-rouge">sparkR.session</code> from RStudio:</p>
<table class="table">
<tr><th>Property Name</th><th>Property group</th><th><code>spark-submit</code> equivalent</th></tr>
<tr>
<td><code>spark.master</code></td>
<td>Application Properties</td>
<td><code>--master</code></td>
</tr>
<tr>
<td><code>spark.kerberos.keytab</code></td>
<td>Application Properties</td>
<td><code>--keytab</code></td>
</tr>
<tr>
<td><code>spark.kerberos.principal</code></td>
<td>Application Properties</td>
<td><code>--principal</code></td>
</tr>
<tr>
<td><code>spark.driver.memory</code></td>
<td>Application Properties</td>
<td><code>--driver-memory</code></td>
</tr>
<tr>
<td><code>spark.driver.extraClassPath</code></td>
<td>Runtime Environment</td>
<td><code>--driver-class-path</code></td>
</tr>
<tr>
<td><code>spark.driver.extraJavaOptions</code></td>
<td>Runtime Environment</td>
<td><code>--driver-java-options</code></td>
</tr>
<tr>
<td><code>spark.driver.extraLibraryPath</code></td>
<td>Runtime Environment</td>
<td><code>--driver-library-path</code></td>
</tr>
</table>
</div>
<h2 id="creating-sparkdataframes">Creating SparkDataFrames</h2>
<p>With a <code class="highlighter-rouge">SparkSession</code>, applications can create <code class="highlighter-rouge">SparkDataFrame</code>s from a local R data frame, from a <a href="sql-data-sources-hive-tables.html">Hive table</a>, or from other <a href="sql-data-sources.html">data sources</a>.</p>
<h3 id="from-local-data-frames">From local data frames</h3>
<p>The simplest way to create a data frame is to convert a local R data frame into a SparkDataFrame. Specifically, we can use <code class="highlighter-rouge">as.DataFrame</code> or <code class="highlighter-rouge">createDataFrame</code> and pass in the local R data frame to create a SparkDataFrame. As an example, the following creates a <code class="highlighter-rouge">SparkDataFrame</code> based using the <code class="highlighter-rouge">faithful</code> dataset from R.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">df</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">as.DataFrame</span><span class="p">(</span><span class="n">faithful</span><span class="p">)</span><span class="w">
</span><span class="c1"># Displays the first part of the SparkDataFrame</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="w">
</span><span class="c1">## eruptions waiting</span><span class="w">
</span><span class="c1">##1 3.600 79</span><span class="w">
</span><span class="c1">##2 1.800 54</span><span class="w">
</span><span class="c1">##3 3.333 74</span></code></pre></figure>
</div>
<h3 id="from-data-sources">From Data Sources</h3>
<p>SparkR supports operating on a variety of data sources through the <code class="highlighter-rouge">SparkDataFrame</code> interface. This section describes the general methods for loading and saving data using Data Sources. You can check the Spark SQL programming guide for more <a href="sql-data-sources-load-save-functions.html#manually-specifying-options">specific options</a> that are available for the built-in data sources.</p>
<p>The general method for creating SparkDataFrames from data sources is <code class="highlighter-rouge">read.df</code>. This method takes in the path for the file to load and the type of data source, and the currently active SparkSession will be used automatically.
SparkR supports reading JSON, CSV and Parquet files natively, and through packages available from sources like <a href="https://spark.apache.org/third-party-projects.html">Third Party Projects</a>, you can find data source connectors for popular file formats like Avro. These packages can either be added by
specifying <code class="highlighter-rouge">--packages</code> with <code class="highlighter-rouge">spark-submit</code> or <code class="highlighter-rouge">sparkR</code> commands, or if initializing SparkSession with <code class="highlighter-rouge">sparkPackages</code> parameter when in an interactive R shell or from RStudio.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">sparkR.session</span><span class="p">(</span><span class="n">sparkPackages</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"org.apache.spark:spark-avro_2.12:3.0.1"</span><span class="p">)</span></code></pre></figure>
</div>
<p>We can see how to use data sources using an example JSON input file. Note that the file that is used here is <em>not</em> a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. For more information, please see <a href="http://jsonlines.org/">JSON Lines text format, also called newline-delimited JSON</a>. As a consequence, a regular multi-line JSON file will most often fail.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">people</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">read.df</span><span class="p">(</span><span class="s2">"./examples/src/main/resources/people.json"</span><span class="p">,</span><span class="w"> </span><span class="s2">"json"</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">people</span><span class="p">)</span><span class="w">
</span><span class="c1">## age name</span><span class="w">
</span><span class="c1">##1 NA Michael</span><span class="w">
</span><span class="c1">##2 30 Andy</span><span class="w">
</span><span class="c1">##3 19 Justin</span><span class="w">
</span><span class="c1"># SparkR automatically infers the schema from the JSON file</span><span class="w">
</span><span class="n">printSchema</span><span class="p">(</span><span class="n">people</span><span class="p">)</span><span class="w">
</span><span class="c1"># root</span><span class="w">
</span><span class="c1"># |-- age: long (nullable = true)</span><span class="w">
</span><span class="c1"># |-- name: string (nullable = true)</span><span class="w">
</span><span class="c1"># Similarly, multiple files can be read with read.json</span><span class="w">
</span><span class="n">people</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">read.json</span><span class="p">(</span><span class="nf">c</span><span class="p">(</span><span class="s2">"./examples/src/main/resources/people.json"</span><span class="p">,</span><span class="w"> </span><span class="s2">"./examples/src/main/resources/people2.json"</span><span class="p">))</span></code></pre></figure>
</div>
<p>The data sources API natively supports CSV formatted input files. For more information please refer to SparkR <a href="api/R/read.df.html">read.df</a> API documentation.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">df</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">read.df</span><span class="p">(</span><span class="n">csvPath</span><span class="p">,</span><span class="w"> </span><span class="s2">"csv"</span><span class="p">,</span><span class="w"> </span><span class="n">header</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"true"</span><span class="p">,</span><span class="w"> </span><span class="n">inferSchema</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"true"</span><span class="p">,</span><span class="w"> </span><span class="n">na.strings</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"NA"</span><span class="p">)</span></code></pre></figure>
</div>
<p>The data sources API can also be used to save out SparkDataFrames into multiple file formats. For example, we can save the SparkDataFrame from the previous example
to a Parquet file using <code class="highlighter-rouge">write.df</code>.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">write.df</span><span class="p">(</span><span class="n">people</span><span class="p">,</span><span class="w"> </span><span class="n">path</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"people.parquet"</span><span class="p">,</span><span class="w"> </span><span class="n">source</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"parquet"</span><span class="p">,</span><span class="w"> </span><span class="n">mode</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"overwrite"</span><span class="p">)</span></code></pre></figure>
</div>
<h3 id="from-hive-tables">From Hive tables</h3>
<p>You can also create SparkDataFrames from Hive tables. To do this we will need to create a SparkSession with Hive support which can access tables in the Hive MetaStore. Note that Spark should have been built with <a href="building-spark.html#building-with-hive-and-jdbc-support">Hive support</a> and more details can be found in the <a href="sql-getting-started.html#starting-point-sparksession">SQL programming guide</a>. In SparkR, by default it will attempt to create a SparkSession with Hive support enabled (<code class="highlighter-rouge">enableHiveSupport = TRUE</code>).</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">sparkR.session</span><span class="p">()</span><span class="w">
</span><span class="n">sql</span><span class="p">(</span><span class="s2">"CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"</span><span class="p">)</span><span class="w">
</span><span class="n">sql</span><span class="p">(</span><span class="s2">"LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src"</span><span class="p">)</span><span class="w">
</span><span class="c1"># Queries can be expressed in HiveQL.</span><span class="w">
</span><span class="n">results</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">sql</span><span class="p">(</span><span class="s2">"FROM src SELECT key, value"</span><span class="p">)</span><span class="w">
</span><span class="c1"># results is now a SparkDataFrame</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">results</span><span class="p">)</span><span class="w">
</span><span class="c1">## key value</span><span class="w">
</span><span class="c1">## 1 238 val_238</span><span class="w">
</span><span class="c1">## 2 86 val_86</span><span class="w">
</span><span class="c1">## 3 311 val_311</span></code></pre></figure>
</div>
<h2 id="sparkdataframe-operations">SparkDataFrame Operations</h2>
<p>SparkDataFrames support a number of functions to do structured data processing.
Here we include some basic examples and a complete list can be found in the <a href="api/R/index.html">API</a> docs:</p>
<h3 id="selecting-rows-columns">Selecting rows, columns</h3>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Create the SparkDataFrame</span><span class="w">
</span><span class="n">df</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">as.DataFrame</span><span class="p">(</span><span class="n">faithful</span><span class="p">)</span><span class="w">
</span><span class="c1"># Get basic information about the SparkDataFrame</span><span class="w">
</span><span class="n">df</span><span class="w">
</span><span class="c1">## SparkDataFrame[eruptions:double, waiting:double]</span><span class="w">
</span><span class="c1"># Select only the "eruptions" column</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">select</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">eruptions</span><span class="p">))</span><span class="w">
</span><span class="c1">## eruptions</span><span class="w">
</span><span class="c1">##1 3.600</span><span class="w">
</span><span class="c1">##2 1.800</span><span class="w">
</span><span class="c1">##3 3.333</span><span class="w">
</span><span class="c1"># You can also pass in column name as strings</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">select</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="s2">"eruptions"</span><span class="p">))</span><span class="w">
</span><span class="c1"># Filter the SparkDataFrame to only retain rows with wait times shorter than 50 mins</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">filter</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="m">50</span><span class="p">))</span><span class="w">
</span><span class="c1">## eruptions waiting</span><span class="w">
</span><span class="c1">##1 1.750 47</span><span class="w">
</span><span class="c1">##2 1.750 47</span><span class="w">
</span><span class="c1">##3 1.867 48</span></code></pre></figure>
</div>
<h3 id="grouping-aggregation">Grouping, Aggregation</h3>
<p>SparkR data frames support a number of commonly used functions to aggregate data after grouping. For example, we can compute a histogram of the <code class="highlighter-rouge">waiting</code> time in the <code class="highlighter-rouge">faithful</code> dataset as shown below</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># We use the `n` operator to count the number of times each waiting time appears</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">summarize</span><span class="p">(</span><span class="n">groupBy</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="p">),</span><span class="w"> </span><span class="n">count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">n</span><span class="p">(</span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="p">)))</span><span class="w">
</span><span class="c1">## waiting count</span><span class="w">
</span><span class="c1">##1 70 4</span><span class="w">
</span><span class="c1">##2 67 1</span><span class="w">
</span><span class="c1">##3 69 2</span><span class="w">
</span><span class="c1"># We can also sort the output from the aggregation to get the most common waiting times</span><span class="w">
</span><span class="n">waiting_counts</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">summarize</span><span class="p">(</span><span class="n">groupBy</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="p">),</span><span class="w"> </span><span class="n">count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">n</span><span class="p">(</span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="p">))</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">arrange</span><span class="p">(</span><span class="n">waiting_counts</span><span class="p">,</span><span class="w"> </span><span class="n">desc</span><span class="p">(</span><span class="n">waiting_counts</span><span class="o">$</span><span class="n">count</span><span class="p">)))</span><span class="w">
</span><span class="c1">## waiting count</span><span class="w">
</span><span class="c1">##1 78 15</span><span class="w">
</span><span class="c1">##2 83 14</span><span class="w">
</span><span class="c1">##3 81 13</span></code></pre></figure>
</div>
<p>In addition to standard aggregations, SparkR supports <a href="https://en.wikipedia.org/wiki/OLAP_cube">OLAP cube</a> operators <code class="highlighter-rouge">cube</code>:</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">head</span><span class="p">(</span><span class="n">agg</span><span class="p">(</span><span class="n">cube</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="s2">"cyl"</span><span class="p">,</span><span class="w"> </span><span class="s2">"disp"</span><span class="p">,</span><span class="w"> </span><span class="s2">"gear"</span><span class="p">),</span><span class="w"> </span><span class="n">avg</span><span class="p">(</span><span class="n">df</span><span class="o">$</span><span class="n">mpg</span><span class="p">)))</span><span class="w">
</span><span class="c1">## cyl disp gear avg(mpg)</span><span class="w">
</span><span class="c1">##1 NA 140.8 4 22.8</span><span class="w">
</span><span class="c1">##2 4 75.7 4 30.4</span><span class="w">
</span><span class="c1">##3 8 400.0 3 19.2</span><span class="w">
</span><span class="c1">##4 8 318.0 3 15.5</span><span class="w">
</span><span class="c1">##5 NA 351.0 NA 15.8</span><span class="w">
</span><span class="c1">##6 NA 275.8 NA 16.3</span></code></pre></figure>
</div>
<p>and <code class="highlighter-rouge">rollup</code>:</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="n">head</span><span class="p">(</span><span class="n">agg</span><span class="p">(</span><span class="n">rollup</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="s2">"cyl"</span><span class="p">,</span><span class="w"> </span><span class="s2">"disp"</span><span class="p">,</span><span class="w"> </span><span class="s2">"gear"</span><span class="p">),</span><span class="w"> </span><span class="n">avg</span><span class="p">(</span><span class="n">df</span><span class="o">$</span><span class="n">mpg</span><span class="p">)))</span><span class="w">
</span><span class="c1">## cyl disp gear avg(mpg)</span><span class="w">
</span><span class="c1">##1 4 75.7 4 30.4</span><span class="w">
</span><span class="c1">##2 8 400.0 3 19.2</span><span class="w">
</span><span class="c1">##3 8 318.0 3 15.5</span><span class="w">
</span><span class="c1">##4 4 78.7 NA 32.4</span><span class="w">
</span><span class="c1">##5 8 304.0 3 15.2</span><span class="w">
</span><span class="c1">##6 4 79.0 NA 27.3</span></code></pre></figure>
</div>
<h3 id="operating-on-columns">Operating on Columns</h3>
<p>SparkR also provides a number of functions that can be directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Convert waiting time from hours to seconds.</span><span class="w">
</span><span class="c1"># Note that we can assign this to a new column in the same SparkDataFrame</span><span class="w">
</span><span class="n">df</span><span class="o">$</span><span class="n">waiting_secs</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="m">60</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="w">
</span><span class="c1">## eruptions waiting waiting_secs</span><span class="w">
</span><span class="c1">##1 3.600 79 4740</span><span class="w">
</span><span class="c1">##2 1.800 54 3240</span><span class="w">
</span><span class="c1">##3 3.333 74 4440</span></code></pre></figure>
</div>
<h3 id="applying-user-defined-function">Applying User-Defined Function</h3>
<p>In SparkR, we support several kinds of User-Defined Functions:</p>
<h4 id="run-a-given-function-on-a-large-dataset-using-dapply-or-dapplycollect">Run a given function on a large dataset using <code class="highlighter-rouge">dapply</code> or <code class="highlighter-rouge">dapplyCollect</code></h4>
<h5 id="dapply">dapply</h5>
<p>Apply a function to each partition of a <code class="highlighter-rouge">SparkDataFrame</code>. The function to be applied to each partition of the <code class="highlighter-rouge">SparkDataFrame</code>
and should have only one parameter, to which a <code class="highlighter-rouge">data.frame</code> corresponds to each partition will be passed. The output of function should be a <code class="highlighter-rouge">data.frame</code>. Schema specifies the row format of the resulting a <code class="highlighter-rouge">SparkDataFrame</code>. It must match to <a href="#data-type-mapping-between-r-and-spark">data types</a> of returned value.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Convert waiting time from hours to seconds.</span><span class="w">
</span><span class="c1"># Note that we can apply UDF to DataFrame.</span><span class="w">
</span><span class="n">schema</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">structType</span><span class="p">(</span><span class="n">structField</span><span class="p">(</span><span class="s2">"eruptions"</span><span class="p">,</span><span class="w"> </span><span class="s2">"double"</span><span class="p">),</span><span class="w"> </span><span class="n">structField</span><span class="p">(</span><span class="s2">"waiting"</span><span class="p">,</span><span class="w"> </span><span class="s2">"double"</span><span class="p">),</span><span class="w">
</span><span class="n">structField</span><span class="p">(</span><span class="s2">"waiting_secs"</span><span class="p">,</span><span class="w"> </span><span class="s2">"double"</span><span class="p">))</span><span class="w">
</span><span class="n">df1</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">dapply</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">x</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">cbind</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="o">$</span><span class="n">waiting</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="m">60</span><span class="p">)</span><span class="w"> </span><span class="p">},</span><span class="w"> </span><span class="n">schema</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">collect</span><span class="p">(</span><span class="n">df1</span><span class="p">))</span><span class="w">
</span><span class="c1">## eruptions waiting waiting_secs</span><span class="w">
</span><span class="c1">##1 3.600 79 4740</span><span class="w">
</span><span class="c1">##2 1.800 54 3240</span><span class="w">
</span><span class="c1">##3 3.333 74 4440</span><span class="w">
</span><span class="c1">##4 2.283 62 3720</span><span class="w">
</span><span class="c1">##5 4.533 85 5100</span><span class="w">
</span><span class="c1">##6 2.883 55 3300</span></code></pre></figure>
</div>
<h5 id="dapplycollect">dapplyCollect</h5>
<p>Like <code class="highlighter-rouge">dapply</code>, apply a function to each partition of a <code class="highlighter-rouge">SparkDataFrame</code> and collect the result back. The output of function
should be a <code class="highlighter-rouge">data.frame</code>. But, Schema is not required to be passed. Note that <code class="highlighter-rouge">dapplyCollect</code> can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Convert waiting time from hours to seconds.</span><span class="w">
</span><span class="c1"># Note that we can apply UDF to DataFrame and return a R's data.frame</span><span class="w">
</span><span class="n">ldf</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">dapplyCollect</span><span class="p">(</span><span class="w">
</span><span class="n">df</span><span class="p">,</span><span class="w">
</span><span class="k">function</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w">
</span><span class="n">x</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">cbind</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="w"> </span><span class="s2">"waiting_secs"</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">x</span><span class="o">$</span><span class="n">waiting</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="m">60</span><span class="p">)</span><span class="w">
</span><span class="p">})</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">ldf</span><span class="p">,</span><span class="w"> </span><span class="m">3</span><span class="p">)</span><span class="w">
</span><span class="c1">## eruptions waiting waiting_secs</span><span class="w">
</span><span class="c1">##1 3.600 79 4740</span><span class="w">
</span><span class="c1">##2 1.800 54 3240</span><span class="w">
</span><span class="c1">##3 3.333 74 4440</span></code></pre></figure>
</div>
<h4 id="run-a-given-function-on-a-large-dataset-grouping-by-input-columns-and-using-gapply-or-gapplycollect">Run a given function on a large dataset grouping by input column(s) and using <code class="highlighter-rouge">gapply</code> or <code class="highlighter-rouge">gapplyCollect</code></h4>
<h5 id="gapply">gapply</h5>
<p>Apply a function to each group of a <code class="highlighter-rouge">SparkDataFrame</code>. The function is to be applied to each group of the <code class="highlighter-rouge">SparkDataFrame</code> and should have only two parameters: grouping key and R <code class="highlighter-rouge">data.frame</code> corresponding to
that key. The groups are chosen from <code class="highlighter-rouge">SparkDataFrame</code>s column(s).
The output of function should be a <code class="highlighter-rouge">data.frame</code>. Schema specifies the row format of the resulting
<code class="highlighter-rouge">SparkDataFrame</code>. It must represent R function&#8217;s output schema on the basis of Spark <a href="#data-type-mapping-between-r-and-spark">data types</a>. The column names of the returned <code class="highlighter-rouge">data.frame</code> are set by user.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Determine six waiting times with the largest eruption time in minutes.</span><span class="w">
</span><span class="n">schema</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">structType</span><span class="p">(</span><span class="n">structField</span><span class="p">(</span><span class="s2">"waiting"</span><span class="p">,</span><span class="w"> </span><span class="s2">"double"</span><span class="p">),</span><span class="w"> </span><span class="n">structField</span><span class="p">(</span><span class="s2">"max_eruption"</span><span class="p">,</span><span class="w"> </span><span class="s2">"double"</span><span class="p">))</span><span class="w">
</span><span class="n">result</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">gapply</span><span class="p">(</span><span class="w">
</span><span class="n">df</span><span class="p">,</span><span class="w">
</span><span class="s2">"waiting"</span><span class="p">,</span><span class="w">
</span><span class="k">function</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w">
</span><span class="n">y</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="w"> </span><span class="nf">max</span><span class="p">(</span><span class="n">x</span><span class="o">$</span><span class="n">eruptions</span><span class="p">))</span><span class="w">
</span><span class="p">},</span><span class="w">
</span><span class="n">schema</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">collect</span><span class="p">(</span><span class="n">arrange</span><span class="p">(</span><span class="n">result</span><span class="p">,</span><span class="w"> </span><span class="s2">"max_eruption"</span><span class="p">,</span><span class="w"> </span><span class="n">decreasing</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">)))</span><span class="w">
</span><span class="c1">## waiting max_eruption</span><span class="w">
</span><span class="c1">##1 64 5.100</span><span class="w">
</span><span class="c1">##2 69 5.067</span><span class="w">
</span><span class="c1">##3 71 5.033</span><span class="w">
</span><span class="c1">##4 87 5.000</span><span class="w">
</span><span class="c1">##5 63 4.933</span><span class="w">
</span><span class="c1">##6 89 4.900</span></code></pre></figure>
</div>
<h5 id="gapplycollect">gapplyCollect</h5>
<p>Like <code class="highlighter-rouge">gapply</code>, applies a function to each partition of a <code class="highlighter-rouge">SparkDataFrame</code> and collect the result back to R data.frame. The output of the function should be a <code class="highlighter-rouge">data.frame</code>. But, the schema is not required to be passed. Note that <code class="highlighter-rouge">gapplyCollect</code> can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Determine six waiting times with the largest eruption time in minutes.</span><span class="w">
</span><span class="n">result</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">gapplyCollect</span><span class="p">(</span><span class="w">
</span><span class="n">df</span><span class="p">,</span><span class="w">
</span><span class="s2">"waiting"</span><span class="p">,</span><span class="w">
</span><span class="k">function</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="w"> </span><span class="n">x</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w">
</span><span class="n">y</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="w"> </span><span class="nf">max</span><span class="p">(</span><span class="n">x</span><span class="o">$</span><span class="n">eruptions</span><span class="p">))</span><span class="w">
</span><span class="n">colnames</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="s2">"waiting"</span><span class="p">,</span><span class="w"> </span><span class="s2">"max_eruption"</span><span class="p">)</span><span class="w">
</span><span class="n">y</span><span class="w">
</span><span class="p">})</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">order</span><span class="p">(</span><span class="n">result</span><span class="o">$</span><span class="n">max_eruption</span><span class="p">,</span><span class="w"> </span><span class="n">decreasing</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">TRUE</span><span class="p">),</span><span class="w"> </span><span class="p">])</span><span class="w">
</span><span class="c1">## waiting max_eruption</span><span class="w">
</span><span class="c1">##1 64 5.100</span><span class="w">
</span><span class="c1">##2 69 5.067</span><span class="w">
</span><span class="c1">##3 71 5.033</span><span class="w">
</span><span class="c1">##4 87 5.000</span><span class="w">
</span><span class="c1">##5 63 4.933</span><span class="w">
</span><span class="c1">##6 89 4.900</span></code></pre></figure>
</div>
<h4 id="run-local-r-functions-distributed-using-sparklapply">Run local R functions distributed using <code class="highlighter-rouge">spark.lapply</code></h4>
<h5 id="sparklapply">spark.lapply</h5>
<p>Similar to <code class="highlighter-rouge">lapply</code> in native R, <code class="highlighter-rouge">spark.lapply</code> runs a function over a list of elements and distributes the computations with Spark.
Applies a function in a manner that is similar to <code class="highlighter-rouge">doParallel</code> or <code class="highlighter-rouge">lapply</code> to elements of a list. The results of all the computations
should fit in a single machine. If that is not the case they can do something like <code class="highlighter-rouge">df &lt;- createDataFrame(list)</code> and then use
<code class="highlighter-rouge">dapply</code></p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Perform distributed training of multiple models with spark.lapply. Here, we pass</span><span class="w">
</span><span class="c1"># a read-only list of arguments which specifies family the generalized linear model should be.</span><span class="w">
</span><span class="n">families</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="s2">"gaussian"</span><span class="p">,</span><span class="w"> </span><span class="s2">"poisson"</span><span class="p">)</span><span class="w">
</span><span class="n">train</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">family</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w">
</span><span class="n">model</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">glm</span><span class="p">(</span><span class="n">Sepal.Length</span><span class="w"> </span><span class="o">~</span><span class="w"> </span><span class="n">Sepal.Width</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">Species</span><span class="p">,</span><span class="w"> </span><span class="n">iris</span><span class="p">,</span><span class="w"> </span><span class="n">family</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">family</span><span class="p">)</span><span class="w">
</span><span class="n">summary</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="w">
</span><span class="p">}</span><span class="w">
</span><span class="c1"># Return a list of model's summaries</span><span class="w">
</span><span class="n">model.summaries</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">spark.lapply</span><span class="p">(</span><span class="n">families</span><span class="p">,</span><span class="w"> </span><span class="n">train</span><span class="p">)</span><span class="w">
</span><span class="c1"># Print the summary of each model</span><span class="w">
</span><span class="n">print</span><span class="p">(</span><span class="n">model.summaries</span><span class="p">)</span></code></pre></figure>
</div>
<h3 id="eager-execution">Eager execution</h3>
<p>If eager execution is enabled, the data will be returned to R client immediately when the <code class="highlighter-rouge">SparkDataFrame</code> is created. By default, eager execution is not enabled and can be enabled by setting the configuration property <code class="highlighter-rouge">spark.sql.repl.eagerEval.enabled</code> to <code class="highlighter-rouge">true</code> when the <code class="highlighter-rouge">SparkSession</code> is started up.</p>
<p>Maximum number of rows and maximum number of characters per column of data to display can be controlled by <code class="highlighter-rouge">spark.sql.repl.eagerEval.maxNumRows</code> and <code class="highlighter-rouge">spark.sql.repl.eagerEval.truncate</code> configuration properties, respectively. These properties are only effective when eager execution is enabled. If these properties are not set explicitly, by default, data up to 20 rows and up to 20 characters per column will be showed.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Start up spark session with eager execution enabled</span><span class="w">
</span><span class="n">sparkR.session</span><span class="p">(</span><span class="n">master</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"local[*]"</span><span class="p">,</span><span class="w">
</span><span class="n">sparkConfig</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="n">spark.sql.repl.eagerEval.enabled</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"true"</span><span class="p">,</span><span class="w">
</span><span class="n">spark.sql.repl.eagerEval.maxNumRows</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">as.integer</span><span class="p">(</span><span class="m">10</span><span class="p">)))</span><span class="w">
</span><span class="c1"># Create a grouped and sorted SparkDataFrame</span><span class="w">
</span><span class="n">df</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">faithful</span><span class="p">)</span><span class="w">
</span><span class="n">df2</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">arrange</span><span class="p">(</span><span class="n">summarize</span><span class="p">(</span><span class="n">groupBy</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="p">),</span><span class="w"> </span><span class="n">count</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">n</span><span class="p">(</span><span class="n">df</span><span class="o">$</span><span class="n">waiting</span><span class="p">)),</span><span class="w"> </span><span class="s2">"waiting"</span><span class="p">)</span><span class="w">
</span><span class="c1"># Similar to R data.frame, displays the data returned, instead of SparkDataFrame class string</span><span class="w">
</span><span class="n">df2</span><span class="w">
</span><span class="c1">##+-------+-----+</span><span class="w">
</span><span class="c1">##|waiting|count|</span><span class="w">
</span><span class="c1">##+-------+-----+</span><span class="w">
</span><span class="c1">##| 43.0| 1|</span><span class="w">
</span><span class="c1">##| 45.0| 3|</span><span class="w">
</span><span class="c1">##| 46.0| 5|</span><span class="w">
</span><span class="c1">##| 47.0| 4|</span><span class="w">
</span><span class="c1">##| 48.0| 3|</span><span class="w">
</span><span class="c1">##| 49.0| 5|</span><span class="w">
</span><span class="c1">##| 50.0| 5|</span><span class="w">
</span><span class="c1">##| 51.0| 6|</span><span class="w">
</span><span class="c1">##| 52.0| 5|</span><span class="w">
</span><span class="c1">##| 53.0| 7|</span><span class="w">
</span><span class="c1">##+-------+-----+</span><span class="w">
</span><span class="c1">##only showing top 10 rows</span></code></pre></figure>
</div>
<p>Note that to enable eager execution in <code class="highlighter-rouge">sparkR</code> shell, add <code class="highlighter-rouge">spark.sql.repl.eagerEval.enabled=true</code> configuration property to the <code class="highlighter-rouge">--conf</code> option.</p>
<h2 id="running-sql-queries-from-sparkr">Running SQL Queries from SparkR</h2>
<p>A SparkDataFrame can also be registered as a temporary view in Spark SQL and that allows you to run SQL queries over its data.
The <code class="highlighter-rouge">sql</code> function enables applications to run SQL queries programmatically and returns the result as a <code class="highlighter-rouge">SparkDataFrame</code>.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Load a JSON file</span><span class="w">
</span><span class="n">people</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">read.df</span><span class="p">(</span><span class="s2">"./examples/src/main/resources/people.json"</span><span class="p">,</span><span class="w"> </span><span class="s2">"json"</span><span class="p">)</span><span class="w">
</span><span class="c1"># Register this SparkDataFrame as a temporary view.</span><span class="w">
</span><span class="n">createOrReplaceTempView</span><span class="p">(</span><span class="n">people</span><span class="p">,</span><span class="w"> </span><span class="s2">"people"</span><span class="p">)</span><span class="w">
</span><span class="c1"># SQL statements can be run by using the sql method</span><span class="w">
</span><span class="n">teenagers</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">sql</span><span class="p">(</span><span class="s2">"SELECT name FROM people WHERE age &gt;= 13 AND age &lt;= 19"</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">teenagers</span><span class="p">)</span><span class="w">
</span><span class="c1">## name</span><span class="w">
</span><span class="c1">##1 Justin</span></code></pre></figure>
</div>
<h1 id="machine-learning">Machine Learning</h1>
<h2 id="algorithms">Algorithms</h2>
<p>SparkR supports the following machine learning algorithms currently:</p>
<h4 id="classification">Classification</h4>
<ul>
<li><a href="api/R/spark.logit.html"><code class="highlighter-rouge">spark.logit</code></a>: <a href="ml-classification-regression.html#logistic-regression"><code class="highlighter-rouge">Logistic Regression</code></a></li>
<li><a href="api/R/spark.mlp.html"><code class="highlighter-rouge">spark.mlp</code></a>: <a href="ml-classification-regression.html#multilayer-perceptron-classifier"><code class="highlighter-rouge">Multilayer Perceptron (MLP)</code></a></li>
<li><a href="api/R/spark.naiveBayes.html"><code class="highlighter-rouge">spark.naiveBayes</code></a>: <a href="ml-classification-regression.html#naive-bayes"><code class="highlighter-rouge">Naive Bayes</code></a></li>
<li><a href="api/R/spark.svmLinear.html"><code class="highlighter-rouge">spark.svmLinear</code></a>: <a href="ml-classification-regression.html#linear-support-vector-machine"><code class="highlighter-rouge">Linear Support Vector Machine</code></a></li>
</ul>
<h4 id="regression">Regression</h4>
<ul>
<li><a href="api/R/spark.survreg.html"><code class="highlighter-rouge">spark.survreg</code></a>: <a href="ml-classification-regression.html#survival-regression"><code class="highlighter-rouge">Accelerated Failure Time (AFT) Survival Model</code></a></li>
<li><a href="api/R/spark.glm.html"><code class="highlighter-rouge">spark.glm</code></a> or <a href="api/R/glm.html"><code class="highlighter-rouge">glm</code></a>: <a href="ml-classification-regression.html#generalized-linear-regression"><code class="highlighter-rouge">Generalized Linear Model (GLM)</code></a></li>
<li><a href="api/R/spark.isoreg.html"><code class="highlighter-rouge">spark.isoreg</code></a>: <a href="ml-classification-regression.html#isotonic-regression"><code class="highlighter-rouge">Isotonic Regression</code></a></li>
</ul>
<h4 id="tree">Tree</h4>
<ul>
<li><a href="api/R/spark.decisionTree.html"><code class="highlighter-rouge">spark.decisionTree</code></a>: <code class="highlighter-rouge">Decision Tree for</code> <a href="ml-classification-regression.html#decision-tree-regression"><code class="highlighter-rouge">Regression</code></a> <code class="highlighter-rouge">and</code> <a href="ml-classification-regression.html#decision-tree-classifier"><code class="highlighter-rouge">Classification</code></a></li>
<li><a href="api/R/spark.gbt.html"><code class="highlighter-rouge">spark.gbt</code></a>: <code class="highlighter-rouge">Gradient Boosted Trees for</code> <a href="ml-classification-regression.html#gradient-boosted-tree-regression"><code class="highlighter-rouge">Regression</code></a> <code class="highlighter-rouge">and</code> <a href="ml-classification-regression.html#gradient-boosted-tree-classifier"><code class="highlighter-rouge">Classification</code></a></li>
<li><a href="api/R/spark.randomForest.html"><code class="highlighter-rouge">spark.randomForest</code></a>: <code class="highlighter-rouge">Random Forest for</code> <a href="ml-classification-regression.html#random-forest-regression"><code class="highlighter-rouge">Regression</code></a> <code class="highlighter-rouge">and</code> <a href="ml-classification-regression.html#random-forest-classifier"><code class="highlighter-rouge">Classification</code></a></li>
</ul>
<h4 id="clustering">Clustering</h4>
<ul>
<li><a href="api/R/spark.bisectingKmeans.html"><code class="highlighter-rouge">spark.bisectingKmeans</code></a>: <a href="ml-clustering.html#bisecting-k-means"><code class="highlighter-rouge">Bisecting k-means</code></a></li>
<li><a href="api/R/spark.gaussianMixture.html"><code class="highlighter-rouge">spark.gaussianMixture</code></a>: <a href="ml-clustering.html#gaussian-mixture-model-gmm"><code class="highlighter-rouge">Gaussian Mixture Model (GMM)</code></a></li>
<li><a href="api/R/spark.kmeans.html"><code class="highlighter-rouge">spark.kmeans</code></a>: <a href="ml-clustering.html#k-means"><code class="highlighter-rouge">K-Means</code></a></li>
<li><a href="api/R/spark.lda.html"><code class="highlighter-rouge">spark.lda</code></a>: <a href="ml-clustering.html#latent-dirichlet-allocation-lda"><code class="highlighter-rouge">Latent Dirichlet Allocation (LDA)</code></a></li>
<li><a href="api/R/spark.powerIterationClustering.html"><code class="highlighter-rouge">spark.powerIterationClustering (PIC)</code></a>: <a href="ml-clustering.html#power-iteration-clustering-pic"><code class="highlighter-rouge">Power Iteration Clustering (PIC)</code></a></li>
</ul>
<h4 id="collaborative-filtering">Collaborative Filtering</h4>
<ul>
<li><a href="api/R/spark.als.html"><code class="highlighter-rouge">spark.als</code></a>: <a href="ml-collaborative-filtering.html#collaborative-filtering"><code class="highlighter-rouge">Alternating Least Squares (ALS)</code></a></li>
</ul>
<h4 id="frequent-pattern-mining">Frequent Pattern Mining</h4>
<ul>
<li><a href="api/R/spark.fpGrowth.html"><code class="highlighter-rouge">spark.fpGrowth</code></a> : <a href="ml-frequent-pattern-mining.html#fp-growth"><code class="highlighter-rouge">FP-growth</code></a></li>
<li><a href="api/R/spark.prefixSpan.html"><code class="highlighter-rouge">spark.prefixSpan</code></a> : <a href="ml-frequent-pattern-mining.html#prefixSpan"><code class="highlighter-rouge">PrefixSpan</code></a></li>
</ul>
<h4 id="statistics">Statistics</h4>
<ul>
<li><a href="api/R/spark.kstest.html"><code class="highlighter-rouge">spark.kstest</code></a>: <code class="highlighter-rouge">Kolmogorov-Smirnov Test</code></li>
</ul>
<p>Under the hood, SparkR uses MLlib to train the model. Please refer to the corresponding section of MLlib user guide for example code.
Users can call <code class="highlighter-rouge">summary</code> to print a summary of the fitted model, <a href="api/R/predict.html">predict</a> to make predictions on new data, and <a href="api/R/write.ml.html">write.ml</a>/<a href="api/R/read.ml.html">read.ml</a> to save/load fitted models.
SparkR supports a subset of the available R formula operators for model fitting, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘.</p>
<h2 id="model-persistence">Model persistence</h2>
<p>The following example shows how to save/load a MLlib model by SparkR.</p>
<div class="highlight"><pre class="codehilite"><code><span class="n">training</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">read.df</span><span class="p">(</span><span class="s2">"data/mllib/sample_multiclass_classification_data.txt"</span><span class="p">,</span><span class="w"> </span><span class="n">source</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"libsvm"</span><span class="p">)</span><span class="w">
</span><span class="c1"># Fit a generalized linear model of family "gaussian" with spark.glm</span><span class="w">
</span><span class="n">df_list</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">randomSplit</span><span class="p">(</span><span class="n">training</span><span class="p">,</span><span class="w"> </span><span class="nf">c</span><span class="p">(</span><span class="m">7</span><span class="p">,</span><span class="m">3</span><span class="p">),</span><span class="w"> </span><span class="m">2</span><span class="p">)</span><span class="w">
</span><span class="n">gaussianDF</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">df_list</span><span class="p">[[</span><span class="m">1</span><span class="p">]]</span><span class="w">
</span><span class="n">gaussianTestDF</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">df_list</span><span class="p">[[</span><span class="m">2</span><span class="p">]]</span><span class="w">
</span><span class="n">gaussianGLM</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">spark.glm</span><span class="p">(</span><span class="n">gaussianDF</span><span class="p">,</span><span class="w"> </span><span class="n">label</span><span class="w"> </span><span class="o">~</span><span class="w"> </span><span class="n">features</span><span class="p">,</span><span class="w"> </span><span class="n">family</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"gaussian"</span><span class="p">)</span><span class="w">
</span><span class="c1"># Save and then load a fitted MLlib model</span><span class="w">
</span><span class="n">modelPath</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">tempfile</span><span class="p">(</span><span class="n">pattern</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"ml"</span><span class="p">,</span><span class="w"> </span><span class="n">fileext</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">".tmp"</span><span class="p">)</span><span class="w">
</span><span class="n">write.ml</span><span class="p">(</span><span class="n">gaussianGLM</span><span class="p">,</span><span class="w"> </span><span class="n">modelPath</span><span class="p">)</span><span class="w">
</span><span class="n">gaussianGLM2</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">read.ml</span><span class="p">(</span><span class="n">modelPath</span><span class="p">)</span><span class="w">
</span><span class="c1"># Check model summary</span><span class="w">
</span><span class="n">summary</span><span class="p">(</span><span class="n">gaussianGLM2</span><span class="p">)</span><span class="w">
</span><span class="c1"># Check model prediction</span><span class="w">
</span><span class="n">gaussianPredictions</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">predict</span><span class="p">(</span><span class="n">gaussianGLM2</span><span class="p">,</span><span class="w"> </span><span class="n">gaussianTestDF</span><span class="p">)</span><span class="w">
</span><span class="n">head</span><span class="p">(</span><span class="n">gaussianPredictions</span><span class="p">)</span><span class="w">
</span><span class="n">unlink</span><span class="p">(</span><span class="n">modelPath</span><span class="p">)</span></code></pre></div>
<div><small>Find full example code at "examples/src/main/r/ml/ml.R" in the Spark repo.</small></div>
<h1 id="data-type-mapping-between-r-and-spark">Data type mapping between R and Spark</h1>
<table class="table">
<tr><th>R</th><th>Spark</th></tr>
<tr>
<td>byte</td>
<td>byte</td>
</tr>
<tr>
<td>integer</td>
<td>integer</td>
</tr>
<tr>
<td>float</td>
<td>float</td>
</tr>
<tr>
<td>double</td>
<td>double</td>
</tr>
<tr>
<td>numeric</td>
<td>double</td>
</tr>
<tr>
<td>character</td>
<td>string</td>
</tr>
<tr>
<td>string</td>
<td>string</td>
</tr>
<tr>
<td>binary</td>
<td>binary</td>
</tr>
<tr>
<td>raw</td>
<td>binary</td>
</tr>
<tr>
<td>logical</td>
<td>boolean</td>
</tr>
<tr>
<td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/DateTimeClasses.html">POSIXct</a></td>
<td>timestamp</td>
</tr>
<tr>
<td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/DateTimeClasses.html">POSIXlt</a></td>
<td>timestamp</td>
</tr>
<tr>
<td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/Dates.html">Date</a></td>
<td>date</td>
</tr>
<tr>
<td>array</td>
<td>array</td>
</tr>
<tr>
<td>list</td>
<td>array</td>
</tr>
<tr>
<td>env</td>
<td>map</td>
</tr>
</table>
<h1 id="structured-streaming">Structured Streaming</h1>
<p>SparkR supports the Structured Streaming API. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. For more information see the R API on the <a href="structured-streaming-programming-guide.html">Structured Streaming Programming Guide</a></p>
<h1 id="apache-arrow-in-sparkr">Apache Arrow in SparkR</h1>
<p>Apache Arrow is an in-memory columnar data format that is used in Spark to efficiently transfer data between JVM and R processes. See also PySpark optimization done, <a href="sql-pyspark-pandas-with-arrow.html">PySpark Usage Guide for Pandas with Apache Arrow</a>. This guide targets to explain how to use Arrow optimization in SparkR with some key points.</p>
<h2 id="ensure-arrow-installed">Ensure Arrow Installed</h2>
<p>Arrow R library is available on CRAN and it can be installed as below.</p>
<div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Rscript <span class="nt">-e</span> <span class="s1">'install.packages("arrow", repos="https://cloud.r-project.org/")'</span>
</code></pre></div></div>
<p>Please refer <a href="https://arrow.apache.org/docs/r/">the official documentation of Apache Arrow</a> for more detials.</p>
<p>Note that you must ensure that Arrow R package is installed and available on all cluster nodes.
The current supported minimum version is 0.15.1; however, this might change between the minor releases since Arrow optimization in SparkR is experimental.</p>
<h2 id="enabling-for-conversion-tofrom-r-dataframe-dapply-and-gapply">Enabling for Conversion to/from R DataFrame, <code class="highlighter-rouge">dapply</code> and <code class="highlighter-rouge">gapply</code></h2>
<p>Arrow optimization is available when converting a Spark DataFrame to an R DataFrame using the call <code class="highlighter-rouge">collect(spark_df)</code>,
when creating a Spark DataFrame from an R DataFrame with <code class="highlighter-rouge">createDataFrame(r_df)</code>, when applying an R native function to each partition
via <code class="highlighter-rouge">dapply(...)</code> and when applying an R native function to grouped data via <code class="highlighter-rouge">gapply(...)</code>.
To use Arrow when executing these, users need to set the Spark configuration ‘spark.sql.execution.arrow.sparkr.enabled’
to ‘true’ first. This is disabled by default.</p>
<p>Whether the optimization is enabled or not, SparkR produces the same results. In addition, the conversion
between Spark DataFrame and R DataFrame falls back automatically to non-Arrow optimization implementation
when the optimization fails for any reasons before the actual computation.</p>
<div data-lang="r">
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="c1"># Start up spark session with Arrow optimization enabled</span><span class="w">
</span><span class="n">sparkR.session</span><span class="p">(</span><span class="n">master</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"local[*]"</span><span class="p">,</span><span class="w">
</span><span class="n">sparkConfig</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nf">list</span><span class="p">(</span><span class="n">spark.sql.execution.arrow.sparkr.enabled</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">"true"</span><span class="p">))</span><span class="w">
</span><span class="c1"># Converts Spark DataFrame from an R DataFrame</span><span class="w">
</span><span class="n">spark_df</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">mtcars</span><span class="p">)</span><span class="w">
</span><span class="c1"># Converts Spark DataFrame to an R DataFrame</span><span class="w">
</span><span class="n">collect</span><span class="p">(</span><span class="n">spark_df</span><span class="p">)</span><span class="w">
</span><span class="c1"># Apply an R native function to each partition.</span><span class="w">
</span><span class="n">collect</span><span class="p">(</span><span class="n">dapply</span><span class="p">(</span><span class="n">spark_df</span><span class="p">,</span><span class="w"> </span><span class="k">function</span><span class="p">(</span><span class="n">rdf</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="n">rdf</span><span class="o">$</span><span class="n">gear</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="m">1</span><span class="p">)</span><span class="w"> </span><span class="p">},</span><span class="w"> </span><span class="n">structType</span><span class="p">(</span><span class="s2">"gear double"</span><span class="p">)))</span><span class="w">
</span><span class="c1"># Apply an R native function to grouped data.</span><span class="w">
</span><span class="n">collect</span><span class="p">(</span><span class="n">gapply</span><span class="p">(</span><span class="n">spark_df</span><span class="p">,</span><span class="w">
</span><span class="s2">"gear"</span><span class="p">,</span><span class="w">
</span><span class="k">function</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="w"> </span><span class="n">group</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w">
</span><span class="n">data.frame</span><span class="p">(</span><span class="n">gear</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">key</span><span class="p">[[</span><span class="m">1</span><span class="p">]],</span><span class="w"> </span><span class="n">disp</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mean</span><span class="p">(</span><span class="n">group</span><span class="o">$</span><span class="n">disp</span><span class="p">)</span><span class="w"> </span><span class="o">&gt;</span><span class="w"> </span><span class="n">group</span><span class="o">$</span><span class="n">disp</span><span class="p">)</span><span class="w">
</span><span class="p">},</span><span class="w">
</span><span class="n">structType</span><span class="p">(</span><span class="s2">"gear double, disp boolean"</span><span class="p">)))</span></code></pre></figure>
</div>
<p>Note that even with Arrow, <code class="highlighter-rouge">collect(spark_df)</code> results in the collection of all records in the DataFrame to
the driver program and should be done on a small subset of the data. In addition, the specified output schema
in <code class="highlighter-rouge">gapply(...)</code> and <code class="highlighter-rouge">dapply(...)</code> should be matched to the R DataFrame&#8217;s returned by the given function.</p>
<h2 id="supported-sql-types">Supported SQL Types</h2>
<p>Currently, all Spark SQL data types are supported by Arrow-based conversion except <code class="highlighter-rouge">FloatType</code>, <code class="highlighter-rouge">BinaryType</code>, <code class="highlighter-rouge">ArrayType</code>, <code class="highlighter-rouge">StructType</code> and <code class="highlighter-rouge">MapType</code>.</p>
<h1 id="r-function-name-conflicts">R Function Name Conflicts</h1>
<p>When loading and attaching a new package in R, it is possible to have a name <a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/library.html">conflict</a>, where a
function is masking another function.</p>
<p>The following functions are masked by the SparkR package:</p>
<table class="table">
<tr><th>Masked function</th><th>How to Access</th></tr>
<tr>
<td><code>cov</code> in <code>package:stats</code></td>
<td><code><pre>stats::cov(x, y = NULL, use = "everything",
method = c("pearson", "kendall", "spearman"))</pre></code></td>
</tr>
<tr>
<td><code>filter</code> in <code>package:stats</code></td>
<td><code><pre>stats::filter(x, filter, method = c("convolution", "recursive"),
sides = 2, circular = FALSE, init)</pre></code></td>
</tr>
<tr>
<td><code>sample</code> in <code>package:base</code></td>
<td><code>base::sample(x, size, replace = FALSE, prob = NULL)</code></td>
</tr>
</table>
<p>Since part of SparkR is modeled on the <code class="highlighter-rouge">dplyr</code> package, certain functions in SparkR share the same names with those in <code class="highlighter-rouge">dplyr</code>. Depending on the load order of the two packages, some functions from the package loaded first are masked by those in the package loaded after. In such case, prefix such calls with the package name, for instance, <code class="highlighter-rouge">SparkR::cume_dist(x)</code> or <code class="highlighter-rouge">dplyr::cume_dist(x)</code>.</p>
<p>You can inspect the search path in R with <a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/search.html"><code class="highlighter-rouge">search()</code></a></p>
<h1 id="migration-guide">Migration Guide</h1>
<p>The migration guide is now archived <a href="sparkr-migration-guide.html">on this page</a>.</p>
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