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| <h1 class="title">Migration Guide: SparkR (R on Spark)</h1> |
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| |
| <ul id="markdown-toc"> |
| <li><a href="#upgrading-from-sparkr-31-to-32" id="markdown-toc-upgrading-from-sparkr-31-to-32">Upgrading from SparkR 3.1 to 3.2</a></li> |
| <li><a href="#upgrading-from-sparkr-24-to-30" id="markdown-toc-upgrading-from-sparkr-24-to-30">Upgrading from SparkR 2.4 to 3.0</a></li> |
| <li><a href="#upgrading-from-sparkr-23-to-24" id="markdown-toc-upgrading-from-sparkr-23-to-24">Upgrading from SparkR 2.3 to 2.4</a></li> |
| <li><a href="#upgrading-from-sparkr-23-to-231-and-above" id="markdown-toc-upgrading-from-sparkr-23-to-231-and-above">Upgrading from SparkR 2.3 to 2.3.1 and above</a></li> |
| <li><a href="#upgrading-from-sparkr-22-to-23" id="markdown-toc-upgrading-from-sparkr-22-to-23">Upgrading from SparkR 2.2 to 2.3</a></li> |
| <li><a href="#upgrading-from-sparkr-21-to-22" id="markdown-toc-upgrading-from-sparkr-21-to-22">Upgrading from SparkR 2.1 to 2.2</a></li> |
| <li><a href="#upgrading-from-sparkr-20-to-31" id="markdown-toc-upgrading-from-sparkr-20-to-31">Upgrading from SparkR 2.0 to 3.1</a></li> |
| <li><a href="#upgrading-from-sparkr-16-to-20" id="markdown-toc-upgrading-from-sparkr-16-to-20">Upgrading from SparkR 1.6 to 2.0</a></li> |
| <li><a href="#upgrading-from-sparkr-15-to-16" id="markdown-toc-upgrading-from-sparkr-15-to-16">Upgrading from SparkR 1.5 to 1.6</a></li> |
| </ul> |
| |
| <p>Note that this migration guide describes the items specific to SparkR. |
| Many items of SQL migration can be applied when migrating SparkR to higher versions. |
| Please refer <a href="sql-migration-guide.html">Migration Guide: SQL, Datasets and DataFrame</a>.</p> |
| |
| <h2 id="upgrading-from-sparkr-31-to-32">Upgrading from SparkR 3.1 to 3.2</h2> |
| |
| <ul> |
| <li>Previously, SparkR automatically downloaded and installed the Spark distribution in user’ cache directory to complete SparkR installation when SparkR runs in a plain R shell or Rscript, and the Spark distribution cannot be found. Now, it asks if users want to download and install or not. To restore the previous behavior, set <code class="language-plaintext highlighter-rouge">SPARKR_ASK_INSTALLATION</code> environment variable to <code class="language-plaintext highlighter-rouge">FALSE</code>.</li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-24-to-30">Upgrading from SparkR 2.4 to 3.0</h2> |
| |
| <ul> |
| <li>The deprecated methods <code class="language-plaintext highlighter-rouge">parquetFile</code>, <code class="language-plaintext highlighter-rouge">saveAsParquetFile</code>, <code class="language-plaintext highlighter-rouge">jsonFile</code>, <code class="language-plaintext highlighter-rouge">jsonRDD</code> have been removed. Use <code class="language-plaintext highlighter-rouge">read.parquet</code>, <code class="language-plaintext highlighter-rouge">write.parquet</code>, <code class="language-plaintext highlighter-rouge">read.json</code> instead.</li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-23-to-24">Upgrading from SparkR 2.3 to 2.4</h2> |
| |
| <ul> |
| <li>Previously, we don’t check the validity of the size of the last layer in <code class="language-plaintext highlighter-rouge">spark.mlp</code>. For example, if the training data only has two labels, a <code class="language-plaintext highlighter-rouge">layers</code> param like <code class="language-plaintext highlighter-rouge">c(1, 3)</code> doesn’t cause an error previously, now it does.</li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-23-to-231-and-above">Upgrading from SparkR 2.3 to 2.3.1 and above</h2> |
| |
| <ul> |
| <li>In SparkR 2.3.0 and earlier, the <code class="language-plaintext highlighter-rouge">start</code> parameter of <code class="language-plaintext highlighter-rouge">substr</code> method was wrongly subtracted by one and considered as 0-based. This can lead to inconsistent substring results and also does not match with the behaviour with <code class="language-plaintext highlighter-rouge">substr</code> in R. In version 2.3.1 and later, it has been fixed so the <code class="language-plaintext highlighter-rouge">start</code> parameter of <code class="language-plaintext highlighter-rouge">substr</code> method is now 1-based. As an example, <code class="language-plaintext highlighter-rouge">substr(lit('abcdef'), 2, 4))</code> would result to <code class="language-plaintext highlighter-rouge">abc</code> in SparkR 2.3.0, and the result would be <code class="language-plaintext highlighter-rouge">bcd</code> in SparkR 2.3.1.</li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-22-to-23">Upgrading from SparkR 2.2 to 2.3</h2> |
| |
| <ul> |
| <li>The <code class="language-plaintext highlighter-rouge">stringsAsFactors</code> parameter was previously ignored with <code class="language-plaintext highlighter-rouge">collect</code>, for example, in <code class="language-plaintext highlighter-rouge">collect(createDataFrame(iris), stringsAsFactors = TRUE))</code>. It has been corrected.</li> |
| <li>For <code class="language-plaintext highlighter-rouge">summary</code>, option for statistics to compute has been added. Its output is changed from that from <code class="language-plaintext highlighter-rouge">describe</code>.</li> |
| <li>A warning can be raised if versions of SparkR package and the Spark JVM do not match.</li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-21-to-22">Upgrading from SparkR 2.1 to 2.2</h2> |
| |
| <ul> |
| <li>A <code class="language-plaintext highlighter-rouge">numPartitions</code> parameter has been added to <code class="language-plaintext highlighter-rouge">createDataFrame</code> and <code class="language-plaintext highlighter-rouge">as.DataFrame</code>. When splitting the data, the partition position calculation has been made to match the one in Scala.</li> |
| <li>The method <code class="language-plaintext highlighter-rouge">createExternalTable</code> has been deprecated to be replaced by <code class="language-plaintext highlighter-rouge">createTable</code>. Either methods can be called to create external or managed table. Additional catalog methods have also been added.</li> |
| <li>By default, derby.log is now saved to <code class="language-plaintext highlighter-rouge">tempdir()</code>. This will be created when instantiating the SparkSession with <code class="language-plaintext highlighter-rouge">enableHiveSupport</code> set to <code class="language-plaintext highlighter-rouge">TRUE</code>.</li> |
| <li><code class="language-plaintext highlighter-rouge">spark.lda</code> was not setting the optimizer correctly. It has been corrected.</li> |
| <li>Several model summary outputs are updated to have <code class="language-plaintext highlighter-rouge">coefficients</code> as <code class="language-plaintext highlighter-rouge">matrix</code>. This includes <code class="language-plaintext highlighter-rouge">spark.logit</code>, <code class="language-plaintext highlighter-rouge">spark.kmeans</code>, <code class="language-plaintext highlighter-rouge">spark.glm</code>. Model summary outputs for <code class="language-plaintext highlighter-rouge">spark.gaussianMixture</code> have added log-likelihood as <code class="language-plaintext highlighter-rouge">loglik</code>.</li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-20-to-31">Upgrading from SparkR 2.0 to 3.1</h2> |
| |
| <ul> |
| <li><code class="language-plaintext highlighter-rouge">join</code> no longer performs Cartesian Product by default, use <code class="language-plaintext highlighter-rouge">crossJoin</code> instead.</li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-16-to-20">Upgrading from SparkR 1.6 to 2.0</h2> |
| |
| <ul> |
| <li>The method <code class="language-plaintext highlighter-rouge">table</code> has been removed and replaced by <code class="language-plaintext highlighter-rouge">tableToDF</code>.</li> |
| <li>The class <code class="language-plaintext highlighter-rouge">DataFrame</code> has been renamed to <code class="language-plaintext highlighter-rouge">SparkDataFrame</code> to avoid name conflicts.</li> |
| <li>Spark’s <code class="language-plaintext highlighter-rouge">SQLContext</code> and <code class="language-plaintext highlighter-rouge">HiveContext</code> have been deprecated to be replaced by <code class="language-plaintext highlighter-rouge">SparkSession</code>. Instead of <code class="language-plaintext highlighter-rouge">sparkR.init()</code>, call <code class="language-plaintext highlighter-rouge">sparkR.session()</code> in its place to instantiate the SparkSession. Once that is done, that currently active SparkSession will be used for SparkDataFrame operations.</li> |
| <li>The parameter <code class="language-plaintext highlighter-rouge">sparkExecutorEnv</code> is not supported by <code class="language-plaintext highlighter-rouge">sparkR.session</code>. To set environment for the executors, set Spark config properties with the prefix “spark.executorEnv.VAR_NAME”, for example, “spark.executorEnv.PATH”</li> |
| <li>The <code class="language-plaintext highlighter-rouge">sqlContext</code> parameter is no longer required for these functions: <code class="language-plaintext highlighter-rouge">createDataFrame</code>, <code class="language-plaintext highlighter-rouge">as.DataFrame</code>, <code class="language-plaintext highlighter-rouge">read.json</code>, <code class="language-plaintext highlighter-rouge">jsonFile</code>, <code class="language-plaintext highlighter-rouge">read.parquet</code>, <code class="language-plaintext highlighter-rouge">parquetFile</code>, <code class="language-plaintext highlighter-rouge">read.text</code>, <code class="language-plaintext highlighter-rouge">sql</code>, <code class="language-plaintext highlighter-rouge">tables</code>, <code class="language-plaintext highlighter-rouge">tableNames</code>, <code class="language-plaintext highlighter-rouge">cacheTable</code>, <code class="language-plaintext highlighter-rouge">uncacheTable</code>, <code class="language-plaintext highlighter-rouge">clearCache</code>, <code class="language-plaintext highlighter-rouge">dropTempTable</code>, <code class="language-plaintext highlighter-rouge">read.df</code>, <code class="language-plaintext highlighter-rouge">loadDF</code>, <code class="language-plaintext highlighter-rouge">createExternalTable</code>.</li> |
| <li>The method <code class="language-plaintext highlighter-rouge">registerTempTable</code> has been deprecated to be replaced by <code class="language-plaintext highlighter-rouge">createOrReplaceTempView</code>.</li> |
| <li>The method <code class="language-plaintext highlighter-rouge">dropTempTable</code> has been deprecated to be replaced by <code class="language-plaintext highlighter-rouge">dropTempView</code>.</li> |
| <li>The <code class="language-plaintext highlighter-rouge">sc</code> SparkContext parameter is no longer required for these functions: <code class="language-plaintext highlighter-rouge">setJobGroup</code>, <code class="language-plaintext highlighter-rouge">clearJobGroup</code>, <code class="language-plaintext highlighter-rouge">cancelJobGroup</code></li> |
| </ul> |
| |
| <h2 id="upgrading-from-sparkr-15-to-16">Upgrading from SparkR 1.5 to 1.6</h2> |
| |
| <ul> |
| <li>Before Spark 1.6.0, the default mode for writes was <code class="language-plaintext highlighter-rouge">append</code>. It was changed in Spark 1.6.0 to <code class="language-plaintext highlighter-rouge">error</code> to match the Scala API.</li> |
| <li>SparkSQL converts <code class="language-plaintext highlighter-rouge">NA</code> in R to <code class="language-plaintext highlighter-rouge">null</code> and vice-versa.</li> |
| <li>Since 1.6.1, withColumn method in SparkR supports adding a new column to or replacing existing columns |
| of the same name of a DataFrame.</li> |
| </ul> |
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