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Set up dependencies
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Create Sedona config
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Initiate SedonaContext
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Load data from text files
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Load the raw DataFrame
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Load GeoJSON Data
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Load Shapefile
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<span class="md-ellipsis">
Load GeoParquet
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Load data from STAC catalog
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Load data from JDBC data sources
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Load GeoPackage
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<span class="md-ellipsis">
Load OSM PBF
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<a href="#transform-the-coordinate-reference-system" class="md-nav__link">
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Transform the Coordinate Reference System
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Cluster with DBSCAN
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<a href="#calculate-the-local-outlier-factor-lof" class="md-nav__link">
<span class="md-ellipsis">
Calculate the Local Outlier Factor (LOF)
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<li class="md-nav__item">
<a href="#perform-getis-ord-gi-hot-spot-analysis" class="md-nav__link">
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Perform Getis-Ord Gi(*) Hot Spot Analysis
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Run spatial queries
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Range query
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<span class="md-ellipsis">
KNN query
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Join query
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KNN join query
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Other queries
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Visualize query results
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SedonaPyDeck
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SedonaKepler
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Create a User-Defined Function (UDF)
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Geometry to primitive
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<a href="#geometry-to-geometry" class="md-nav__link">
<span class="md-ellipsis">
Geometry to Geometry
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<a href="#geometry-primitive-to-geometry" class="md-nav__link">
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Geometry, primitive to geometry
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<a href="#geometry-primitive-to-geometry-primitive" class="md-nav__link">
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Geometry, primitive to Geometry, primitive
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Spatial vectorized udfs (Python only)
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Shapely scalar UDF
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GeoSeries UDF
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Save to permanent storage
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Save GeoJSON
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Save GeoParquet
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Save to Postgis
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Convert between DataFrame and SpatialRDD
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DataFrame to SpatialRDD
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SpatialRDD to DataFrame
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SpatialRDD to DataFrame with spatial partitioning
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GeoPandas API on Sedona
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Shapefiles
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Spatial Joins
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Clustering Algorithms
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Map visualization SQL app
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Tune RDD application
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Vector data
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Geometry Functions
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Geography Functions
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<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<a href="https://github.com/apache/sedona/edit/master/docs/tutorial/sql.md" title="Edit this page" class="md-content__button md-icon" rel="edit">
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<h1>Spatial DataFrame / SQL app</h1>
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<p>The page outlines the steps to manage spatial data using SedonaSQL.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Sedona assumes geographic coordinates to be in longitude/latitude order. If your data is lat/lon order, please use <code>ST_FlipCoordinates</code> to swap X and Y.</p>
</div>
<p>SedonaSQL supports SQL/MM Part3 Spatial SQL Standard. It includes four kinds of SQL operators as follows. All these operators can be directly called through:</p>
<div class="tabbed-set tabbed-alternate" data-tabs="1:3"><input checked="checked" id="__tabbed_1_1" name="__tabbed_1" type="radio" /><input id="__tabbed_1_2" name="__tabbed_1" type="radio" /><input id="__tabbed_1_3" name="__tabbed_1" type="radio" /><div class="tabbed-labels"><label for="__tabbed_1_1">Scala</label><label for="__tabbed_1_2">Java</label><label for="__tabbed_1_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">var</span><span class="w"> </span><span class="n">myDataFrame</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">sql</span><span class="p">(</span><span class="s">&quot;YOUR_SQL&quot;</span><span class="p">)</span>
<span class="n">myDataFrame</span><span class="p">.</span><span class="n">createOrReplaceTempView</span><span class="p">(</span><span class="s">&quot;spatialDf&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">myDataFrame</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">sql</span><span class="p">(</span><span class="s">&quot;YOUR_SQL&quot;</span><span class="p">)</span>
<span class="n">myDataFrame</span><span class="p">.</span><span class="na">createOrReplaceTempView</span><span class="p">(</span><span class="s">&quot;spatialDf&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">myDataFrame</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s2">&quot;YOUR_SQL&quot;</span><span class="p">)</span>
<span class="n">myDataFrame</span><span class="o">.</span><span class="n">createOrReplaceTempView</span><span class="p">(</span><span class="s2">&quot;spatialDf&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<p>Detailed SedonaSQL APIs are available here: <a href="../../api/sql/Overview/">SedonaSQL API</a>. You can find example county data (i.e., <code>county_small.tsv</code>) in <a href="https://github.com/apache/sedona/tree/master/spark/common/src/test/resources">Sedona GitHub repo</a>.</p>
<h2 id="set-up-dependencies">Set up dependencies<a class="headerlink" href="#set-up-dependencies" title="Permanent link">&para;</a></h2>
<div class="tabbed-set tabbed-alternate" data-tabs="2:2"><input checked="checked" id="__tabbed_2_1" name="__tabbed_2" type="radio" /><input id="__tabbed_2_2" name="__tabbed_2" type="radio" /><div class="tabbed-labels"><label for="__tabbed_2_1">Scala/Java</label><label for="__tabbed_2_2">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<ol>
<li>Read <a href="../../setup/maven-coordinates/">Sedona Maven Central coordinates</a> and add Sedona dependencies in build.sbt or pom.xml.</li>
<li>Add <a href="https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.11">Apache Spark core</a>, <a href="https://mvnrepository.com/artifact/org.apache.spark/spark-sql">Apache SparkSQL</a> in build.sbt or pom.xml.</li>
<li>Please see <a href="../demo/">SQL example project</a></li>
</ol>
</div>
<div class="tabbed-block">
<ol>
<li>Please read <a href="../../setup/install-python/">Quick start</a> to install Sedona Python.</li>
<li>This tutorial is based on <a href="../jupyter-notebook/">Sedona SQL Jupyter Notebook example</a>.</li>
</ol>
</div>
</div>
</div>
<h2 id="create-sedona-config">Create Sedona config<a class="headerlink" href="#create-sedona-config" title="Permanent link">&para;</a></h2>
<p>Use the following code to create your Sedona config at the beginning. If you already have a SparkSession (usually named <code>spark</code>) created by AWS EMR/Databricks/Microsoft Fabric, please <mark>skip this step</mark>.</p>
<p>You can add additional Spark runtime config to the config builder. For example, <code>SedonaContext.builder().config("spark.sql.autoBroadcastJoinThreshold", "10485760")</code></p>
<div class="tabbed-set tabbed-alternate" data-tabs="3:3"><input checked="checked" id="__tabbed_3_1" name="__tabbed_3" type="radio" /><input id="__tabbed_3_2" name="__tabbed_3" type="radio" /><input id="__tabbed_3_3" name="__tabbed_3" type="radio" /><div class="tabbed-labels"><label for="__tabbed_3_1">Scala</label><label for="__tabbed_3_2">Java</label><label for="__tabbed_3_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<p><div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">sedona</span><span class="p">.</span><span class="nn">spark</span><span class="p">.</span><span class="nc">SedonaContext</span>
<span class="kd">val</span><span class="w"> </span><span class="n">config</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nc">SedonaContext</span><span class="p">.</span><span class="n">builder</span><span class="p">()</span>
<span class="p">.</span><span class="n">master</span><span class="p">(</span><span class="s">&quot;local[*]&quot;</span><span class="p">)</span><span class="w"> </span><span class="c1">// Delete this if run in cluster mode</span>
<span class="p">.</span><span class="n">appName</span><span class="p">(</span><span class="s">&quot;readTestScala&quot;</span><span class="p">)</span><span class="w"> </span><span class="c1">// Change this to a proper name</span>
<span class="p">.</span><span class="n">getOrCreate</span><span class="p">()</span>
</code></pre></div>
If you use SedonaViz together with SedonaSQL, please add the following line after <code>SedonaContext.builder()</code> to enable Sedona Kryo serializer:
<div class="highlight"><pre><span></span><code><span class="p">.</span><span class="n">config</span><span class="p">(</span><span class="s">&quot;spark.kryo.registrator&quot;</span><span class="p">,</span><span class="w"> </span><span class="k">classOf</span><span class="p">[</span><span class="nc">SedonaVizKryoRegistrator</span><span class="p">].</span><span class="n">getName</span><span class="p">)</span><span class="w"> </span><span class="c1">// org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator</span>
</code></pre></div></p>
</div>
<div class="tabbed-block">
<p><div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.sedona.spark.SedonaContext</span><span class="p">;</span>
<span class="n">SparkSession</span><span class="w"> </span><span class="n">config</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">SedonaContext</span><span class="p">.</span><span class="na">builder</span><span class="p">()</span>
<span class="p">.</span><span class="na">master</span><span class="p">(</span><span class="s">&quot;local[*]&quot;</span><span class="p">)</span><span class="w"> </span><span class="c1">// Delete this if run in cluster mode</span>
<span class="p">.</span><span class="na">appName</span><span class="p">(</span><span class="s">&quot;readTestJava&quot;</span><span class="p">)</span><span class="w"> </span><span class="c1">// Change this to a proper name</span>
<span class="p">.</span><span class="na">getOrCreate</span><span class="p">()</span>
</code></pre></div>
If you use SedonaViz together with SedonaSQL, please add the following line after <code>SedonaContext.builder()</code> to enable Sedona Kryo serializer:
<div class="highlight"><pre><span></span><code><span class="p">.</span><span class="na">config</span><span class="p">(</span><span class="s">&quot;spark.kryo.registrator&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">SedonaVizKryoRegistrator</span><span class="p">.</span><span class="na">class</span><span class="p">.</span><span class="na">getName</span><span class="p">())</span><span class="w"> </span><span class="c1">// org.apache.sedona.viz.core.Serde.SedonaVizKryoRegistrator</span>
</code></pre></div></p>
</div>
<div class="tabbed-block">
<p><div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">SedonaContext</span><span class="o">.</span><span class="n">builder</span><span class="p">()</span> <span class="o">.</span>\
<span class="n">config</span><span class="p">(</span><span class="s1">&#39;spark.jars.packages&#39;</span><span class="p">,</span>
<span class="s1">&#39;org.apache.sedona:sedona-spark-shaded-3.3_2.12:1.9.1,&#39;</span>
<span class="s1">&#39;org.datasyslab:geotools-wrapper:1.9.1-33.5&#39;</span><span class="p">)</span><span class="o">.</span> \
<span class="n">getOrCreate</span><span class="p">()</span>
</code></pre></div>
If you are using a different Spark version, please replace the <code>3.3</code> in package name of sedona-spark-shaded with the corresponding major.minor version of Spark, such as <code>sedona-spark-shaded-3.4_2.12:1.9.1</code>.</p>
</div>
</div>
</div>
<h2 id="initiate-sedonacontext">Initiate SedonaContext<a class="headerlink" href="#initiate-sedonacontext" title="Permanent link">&para;</a></h2>
<p>Add the following line after creating Sedona config. If you already have a SparkSession (usually named <code>spark</code>) created by AWS EMR/Databricks/Microsoft Fabric, please call <code>sedona = SedonaContext.create(spark)</code> instead.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="4:3"><input checked="checked" id="__tabbed_4_1" name="__tabbed_4" type="radio" /><input id="__tabbed_4_2" name="__tabbed_4" type="radio" /><input id="__tabbed_4_3" name="__tabbed_4" type="radio" /><div class="tabbed-labels"><label for="__tabbed_4_1">Scala</label><label for="__tabbed_4_2">Java</label><label for="__tabbed_4_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">sedona</span><span class="p">.</span><span class="nn">spark</span><span class="p">.</span><span class="nc">SedonaContext</span>
<span class="kd">val</span><span class="w"> </span><span class="n">sedona</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nc">SedonaContext</span><span class="p">.</span><span class="n">create</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.sedona.spark.SedonaContext</span><span class="p">;</span>
<span class="n">SparkSession</span><span class="w"> </span><span class="n">sedona</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">SedonaContext</span><span class="p">.</span><span class="na">create</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="n">sedona</span> <span class="o">=</span> <span class="n">SedonaContext</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<p>You can also register everything by passing <code>--conf spark.sql.extensions=org.apache.sedona.sql.SedonaSqlExtensions</code> to <code>spark-submit</code> or <code>spark-shell</code>.</p>
<h2 id="load-data-from-text-files">Load data from text files<a class="headerlink" href="#load-data-from-text-files" title="Permanent link">&para;</a></h2>
<p>Assume we have a WKT file, namely <code>usa-county.tsv</code>, at Path <code>/Download/usa-county.tsv</code> as follows:</p>
<div class="highlight"><pre><span></span><code>POLYGON (..., ...) Cuming County
POLYGON (..., ...) Wahkiakum County
POLYGON (..., ...) De Baca County
POLYGON (..., ...) Lancaster County
</code></pre></div>
<p>The file may have many other columns.</p>
<h3 id="load-the-raw-dataframe">Load the raw DataFrame<a class="headerlink" href="#load-the-raw-dataframe" title="Permanent link">&para;</a></h3>
<p>Use the following code to load the data and create a raw DataFrame:</p>
<div class="tabbed-set tabbed-alternate" data-tabs="5:3"><input checked="checked" id="__tabbed_5_1" name="__tabbed_5" type="radio" /><input id="__tabbed_5_2" name="__tabbed_5" type="radio" /><input id="__tabbed_5_3" name="__tabbed_5" type="radio" /><div class="tabbed-labels"><label for="__tabbed_5_1">Scala</label><label for="__tabbed_5_2">Java</label><label for="__tabbed_5_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">var</span><span class="w"> </span><span class="n">rawDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;csv&quot;</span><span class="p">).</span><span class="n">option</span><span class="p">(</span><span class="s">&quot;delimiter&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;\t&quot;</span><span class="p">).</span><span class="n">option</span><span class="p">(</span><span class="s">&quot;header&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;false&quot;</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;/Download/usa-county.tsv&quot;</span><span class="p">)</span>
<span class="n">rawDf</span><span class="p">.</span><span class="n">createOrReplaceTempView</span><span class="p">(</span><span class="s">&quot;rawdf&quot;</span><span class="p">)</span>
<span class="n">rawDf</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">rawDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">.</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;csv&quot;</span><span class="p">).</span><span class="na">option</span><span class="p">(</span><span class="s">&quot;delimiter&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;\t&quot;</span><span class="p">).</span><span class="na">option</span><span class="p">(</span><span class="s">&quot;header&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;false&quot;</span><span class="p">).</span><span class="na">load</span><span class="p">(</span><span class="s">&quot;/Download/usa-county.tsv&quot;</span><span class="p">)</span>
<span class="n">rawDf</span><span class="p">.</span><span class="na">createOrReplaceTempView</span><span class="p">(</span><span class="s">&quot;rawdf&quot;</span><span class="p">)</span>
<span class="n">rawDf</span><span class="p">.</span><span class="na">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">rawDf</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;csv&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s2">&quot;delimiter&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s2">&quot;header&quot;</span><span class="p">,</span> <span class="s2">&quot;false&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;/Download/usa-county.tsv&quot;</span><span class="p">)</span>
<span class="n">rawDf</span><span class="o">.</span><span class="n">createOrReplaceTempView</span><span class="p">(</span><span class="s2">&quot;rawdf&quot;</span><span class="p">)</span>
<span class="n">rawDf</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>The output will be like this:</p>
<div class="highlight"><pre><span></span><code>| _c0|_c1|_c2| _c3| _c4| _c5| _c6|_c7|_c8| _c9|_c10| _c11|_c12|_c13| _c14| _c15| _c16| _c17|
+--------------------+---+---+--------+-----+-----------+--------------------+---+---+-----+----+-----+----+----+----------+--------+-----------+------------+
|POLYGON ((-97.019...| 31|039|00835841|31039| Cuming| Cuming County| 06| H1|G4020|null| null|null| A|1477895811|10447360|+41.9158651|-096.7885168|
|POLYGON ((-123.43...| 53|069|01513275|53069| Wahkiakum| Wahkiakum County| 06| H1|G4020|null| null|null| A| 682138871|61658258|+46.2946377|-123.4244583|
|POLYGON ((-104.56...| 35|011|00933054|35011| De Baca| De Baca County| 06| H1|G4020|null| null|null| A|6015539696|29159492|+34.3592729|-104.3686961|
|POLYGON ((-96.910...| 31|109|00835876|31109| Lancaster| Lancaster County| 06| H1|G4020| 339|30700|null| A|2169240202|22877180|+40.7835474|-096.6886584|
</code></pre></div>
<h3 id="create-a-geometry-type-column">Create a Geometry type column<a class="headerlink" href="#create-a-geometry-type-column" title="Permanent link">&para;</a></h3>
<p>All geometrical operations in SedonaSQL are on Geometry type objects. Therefore, before any kind of queries, you need to create a Geometry type column on a DataFrame.</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span><span class="w"> </span><span class="n">ST_GeomFromWKT</span><span class="p">(</span><span class="n">_c0</span><span class="p">)</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">countyshape</span><span class="p">,</span><span class="w"> </span><span class="n">_c1</span><span class="p">,</span><span class="w"> </span><span class="n">_c2</span>
</code></pre></div>
<p>You can select many other attributes to compose this <code>spatialdDf</code>. The output will be something like this:</p>
<div class="highlight"><pre><span></span><code>| countyshape|_c1|_c2| _c3| _c4| _c5| _c6|_c7|_c8| _c9|_c10| _c11|_c12|_c13| _c14| _c15| _c16| _c17|
+--------------------+---+---+--------+-----+-----------+--------------------+---+---+-----+----+-----+----+----+----------+--------+-----------+------------+
|POLYGON ((-97.019...| 31|039|00835841|31039| Cuming| Cuming County| 06| H1|G4020|null| null|null| A|1477895811|10447360|+41.9158651|-096.7885168|
|POLYGON ((-123.43...| 53|069|01513275|53069| Wahkiakum| Wahkiakum County| 06| H1|G4020|null| null|null| A| 682138871|61658258|+46.2946377|-123.4244583|
|POLYGON ((-104.56...| 35|011|00933054|35011| De Baca| De Baca County| 06| H1|G4020|null| null|null| A|6015539696|29159492|+34.3592729|-104.3686961|
|POLYGON ((-96.910...| 31|109|00835876|31109| Lancaster| Lancaster County| 06| H1|G4020| 339|30700|null| A|2169240202|22877180|+40.7835474|-096.6886584|
</code></pre></div>
<p>Although it looks same with the input, but actually the type of column countyshape has been changed to <mark>Geometry</mark> type.</p>
<p>To verify this, use the following code to print the schema of the DataFrame:</p>
<div class="highlight"><pre><span></span><code><span class="n">spatialDf</span><span class="p">.</span><span class="n">printSchema</span><span class="p">()</span>
</code></pre></div>
<p>The output will be like this:</p>
<div class="highlight"><pre><span></span><code>root
|-- countyshape: geometry (nullable = false)
|-- _c1: string (nullable = true)
|-- _c2: string (nullable = true)
|-- _c3: string (nullable = true)
|-- _c4: string (nullable = true)
|-- _c5: string (nullable = true)
|-- _c6: string (nullable = true)
|-- _c7: string (nullable = true)
</code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>SedonaSQL provides lots of functions to create a Geometry column, please read <a href="../../api/sql/Geometry-Functions/">SedonaSQL constructor API</a>.</p>
</div>
<h2 id="load-geojson-data">Load GeoJSON Data<a class="headerlink" href="#load-geojson-data" title="Permanent link">&para;</a></h2>
<p>Since <code>v1.6.1</code>, Sedona supports reading GeoJSON files using the <code>geojson</code> data source. It is designed to handle JSON files that use <a href="https://datatracker.ietf.org/doc/html/rfc7946">GeoJSON format</a> for their geometries.</p>
<p>Set the <code>multiLine</code> option to <code>True</code> to read multiline GeoJSON files.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="6:3"><input checked="checked" id="__tabbed_6_1" name="__tabbed_6" type="radio" /><input id="__tabbed_6_2" name="__tabbed_6" type="radio" /><input id="__tabbed_6_3" name="__tabbed_6" type="radio" /><div class="tabbed-labels"><label for="__tabbed_6_1">Python</label><label for="__tabbed_6_2">Scala</label><label for="__tabbed_6_3">Java</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;geojson&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s2">&quot;multiLine&quot;</span><span class="p">,</span> <span class="s2">&quot;true&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;PATH/TO/MYFILE.json&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s2">&quot;explode(features) as features&quot;</span><span class="p">)</span> <span class="c1"># Explode the envelope</span>
<span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;features.*&quot;</span><span class="p">)</span> <span class="c1"># Unpack the features struct</span>
<span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s2">&quot;prop0&quot;</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="n">expr</span><span class="p">(</span><span class="s2">&quot;properties[&#39;prop0&#39;]&quot;</span><span class="p">))</span>
<span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;properties&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;geojson&quot;</span><span class="p">).</span><span class="n">option</span><span class="p">(</span><span class="s">&quot;multiLine&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;true&quot;</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;PATH/TO/MYFILE.json&quot;</span><span class="p">)</span>
<span class="kd">val</span><span class="w"> </span><span class="n">parsedDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">df</span><span class="p">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s">&quot;explode(features) as features&quot;</span><span class="p">).</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;features.*&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s">&quot;prop0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;properties[&#39;prop0&#39;]&quot;</span><span class="p">)).</span><span class="n">drop</span><span class="p">(</span><span class="s">&quot;properties&quot;</span><span class="p">).</span><span class="n">drop</span><span class="p">(</span><span class="s">&quot;type&quot;</span><span class="p">)</span>
<span class="n">parsedDf</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">parsedDf</span><span class="p">.</span><span class="n">printSchema</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">.</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;geojson&quot;</span><span class="p">).</span><span class="na">option</span><span class="p">(</span><span class="s">&quot;multiLine&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;true&quot;</span><span class="p">).</span><span class="na">load</span><span class="p">(</span><span class="s">&quot;PATH/TO/MYFILE.json&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="na">selectExpr</span><span class="p">(</span><span class="s">&quot;explode(features) as features&quot;</span><span class="p">)</span><span class="w"> </span><span class="c1">// Explode the envelope to get one feature per row.</span>
<span class="w"> </span><span class="p">.</span><span class="na">select</span><span class="p">(</span><span class="s">&quot;features.*&quot;</span><span class="p">)</span><span class="w"> </span><span class="c1">// Unpack the features struct.</span>
<span class="w"> </span><span class="p">.</span><span class="na">withColumn</span><span class="p">(</span><span class="s">&quot;prop0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;properties[&#39;prop0&#39;]&quot;</span><span class="p">)).</span><span class="na">drop</span><span class="p">(</span><span class="s">&quot;properties&quot;</span><span class="p">).</span><span class="na">drop</span><span class="p">(</span><span class="s">&quot;type&quot;</span><span class="p">)</span>
<span class="n">df</span><span class="p">.</span><span class="na">show</span><span class="p">();</span>
<span class="n">df</span><span class="p">.</span><span class="na">printSchema</span><span class="p">();</span>
</code></pre></div>
</div>
</div>
</div>
<p>See <a href="../files/geojson-sedona-spark/">this page</a> for more information on loading GeoJSON files.</p>
<h2 id="load-shapefile">Load Shapefile<a class="headerlink" href="#load-shapefile" title="Permanent link">&para;</a></h2>
<p>Since v<code>1.7.0</code>, Sedona supports loading Shapefile as a DataFrame.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="7:3"><input checked="checked" id="__tabbed_7_1" name="__tabbed_7" type="radio" /><input id="__tabbed_7_2" name="__tabbed_7" type="radio" /><input id="__tabbed_7_3" name="__tabbed_7" type="radio" /><div class="tabbed-labels"><label for="__tabbed_7_1">Scala/Java</label><label for="__tabbed_7_2">Java</label><label for="__tabbed_7_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;shapefile&quot;</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;/path/to/shapefile&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">().</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;shapefile&quot;</span><span class="p">).</span><span class="na">load</span><span class="p">(</span><span class="s">&quot;/path/to/shapefile&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;shapefile&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;/path/to/shapefile&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<p>The input path can be a directory containing one or multiple shapefiles, or path to a <code>.shp</code> file.</p>
<p>See <a href="../files/shapefiles-sedona-spark/">this page</a> for more information on loading Shapefiles.</p>
<h2 id="load-geoparquet">Load GeoParquet<a class="headerlink" href="#load-geoparquet" title="Permanent link">&para;</a></h2>
<p>Since v<code>1.3.0</code>, Sedona natively supports loading GeoParquet file. Sedona will infer geometry fields using the "geo" metadata in GeoParquet files.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="8:3"><input checked="checked" id="__tabbed_8_1" name="__tabbed_8" type="radio" /><input id="__tabbed_8_2" name="__tabbed_8" type="radio" /><input id="__tabbed_8_3" name="__tabbed_8" type="radio" /><div class="tabbed-labels"><label for="__tabbed_8_1">Scala/Java</label><label for="__tabbed_8_2">Java</label><label for="__tabbed_8_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;geoparquet&quot;</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="n">geoparquetdatalocation1</span><span class="p">)</span>
<span class="n">df</span><span class="p">.</span><span class="n">printSchema</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">.</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;geoparquet&quot;</span><span class="p">).</span><span class="na">load</span><span class="p">(</span><span class="n">geoparquetdatalocation1</span><span class="p">)</span>
<span class="n">df</span><span class="p">.</span><span class="na">printSchema</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;geoparquet&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">geoparquetdatalocation1</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">printSchema</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>The output will be as follows:</p>
<div class="highlight"><pre><span></span><code>root
|-- pop_est: long (nullable = true)
|-- continent: string (nullable = true)
|-- name: string (nullable = true)
|-- iso_a3: string (nullable = true)
|-- gdp_md_est: double (nullable = true)
|-- geometry: geometry (nullable = true)
</code></pre></div>
<p>Sedona supports spatial predicate push-down for GeoParquet files, please refer to the <a href="../../api/sql/Optimizer/">SedonaSQL query optimizer</a> documentation for details.</p>
<p>GeoParquet file reader can also be used to read legacy Parquet files written by Apache Sedona 1.3.1-incubating or earlier.
Please refer to <a href="../../api/sql/Reading-legacy-parquet/">Reading Legacy Parquet Files</a> for details.</p>
<p>See <a href="../files/geoparquet-sedona-spark/">this page</a> for more information on loading GeoParquet.</p>
<h2 id="load-data-from-stac-catalog">Load data from STAC catalog<a class="headerlink" href="#load-data-from-stac-catalog" title="Permanent link">&para;</a></h2>
<p>Sedona STAC data source allows you to read data from a SpatioTemporal Asset Catalog (STAC) API. The data source supports reading STAC items and collections.</p>
<p>You can load a STAC collection from a s3 collection file object:</p>
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;stac&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
<span class="s2">&quot;s3a://example.com/stac_bucket/stac_collection.json&quot;</span>
<span class="p">)</span>
</code></pre></div>
<p>You can also load a STAC collection from an HTTP/HTTPS endpoint:</p>
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;stac&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
<span class="s2">&quot;https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a&quot;</span>
<span class="p">)</span>
</code></pre></div>
<p>The STAC data source supports predicate pushdown for spatial and temporal filters. The data source can push down spatial and temporal filters to the underlying data source to reduce the amount of data that needs to be read.</p>
<p>See <a href="../files/stac-sedona-spark/">this page</a> for more information on loading data from STAC.</p>
<h2 id="load-data-from-jdbc-data-sources">Load data from JDBC data sources<a class="headerlink" href="#load-data-from-jdbc-data-sources" title="Permanent link">&para;</a></h2>
<p>The 'query' option in Spark SQL's JDBC data source can be used to convert geometry columns to a format that Sedona can interpret.
This should work for most spatial JDBC data sources.
For Postgis there is no need to add a query to convert geometry types since it's already using EWKB as it's wire format.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="9:3"><input checked="checked" id="__tabbed_9_1" name="__tabbed_9" type="radio" /><input id="__tabbed_9_2" name="__tabbed_9" type="radio" /><input id="__tabbed_9_3" name="__tabbed_9" type="radio" /><div class="tabbed-labels"><label for="__tabbed_9_1">Scala</label><label for="__tabbed_9_2">Java</label><label for="__tabbed_9_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="c1">// For any JDBC data source, including Postgis.</span>
<span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;jdbc&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="c1">// Other options.</span>
<span class="w"> </span><span class="p">.</span><span class="n">option</span><span class="p">(</span><span class="s">&quot;query&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;SELECT id, ST_AsBinary(geom) as geom FROM my_table&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="n">load</span><span class="p">()</span>
<span class="w"> </span><span class="p">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s">&quot;geom&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;ST_GeomFromWKB(geom)&quot;</span><span class="p">))</span>
<span class="c1">// This is a simplified version that works for Postgis.</span>
<span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;jdbc&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="c1">// Other options.</span>
<span class="w"> </span><span class="p">.</span><span class="n">option</span><span class="p">(</span><span class="s">&quot;dbtable&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;my_table&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="n">load</span><span class="p">()</span>
<span class="w"> </span><span class="p">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s">&quot;geom&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;ST_GeomFromWKB(geom)&quot;</span><span class="p">))</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="c1">// For any JDBC data source, including Postgis.</span>
<span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">().</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;jdbc&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="c1">// Other options.</span>
<span class="w"> </span><span class="p">.</span><span class="na">option</span><span class="p">(</span><span class="s">&quot;query&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;SELECT id, ST_AsBinary(geom) as geom FROM my_table&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="na">load</span><span class="p">()</span>
<span class="w"> </span><span class="p">.</span><span class="na">withColumn</span><span class="p">(</span><span class="s">&quot;geom&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;ST_GeomFromWKB(geom)&quot;</span><span class="p">))</span>
<span class="c1">// This is a simplified version that works for Postgis.</span>
<span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">().</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;jdbc&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="c1">// Other options.</span>
<span class="w"> </span><span class="p">.</span><span class="na">option</span><span class="p">(</span><span class="s">&quot;dbtable&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;my_table&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="na">load</span><span class="p">()</span>
<span class="w"> </span><span class="p">.</span><span class="na">withColumn</span><span class="p">(</span><span class="s">&quot;geom&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;ST_GeomFromWKB(geom)&quot;</span><span class="p">))</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="c1"># For any JDBC data source, including Postgis.</span>
<span class="n">df</span> <span class="o">=</span> <span class="p">(</span><span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;jdbc&quot;</span><span class="p">)</span>
<span class="c1"># Other options.</span>
<span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s2">&quot;query&quot;</span><span class="p">,</span> <span class="s2">&quot;SELECT id, ST_AsBinary(geom) as geom FROM my_table&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s2">&quot;geom&quot;</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="n">expr</span><span class="p">(</span><span class="s2">&quot;ST_GeomFromWKB(geom)&quot;</span><span class="p">)))</span>
<span class="c1"># This is a simplified version that works for Postgis.</span>
<span class="n">df</span> <span class="o">=</span> <span class="p">(</span><span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;jdbc&quot;</span><span class="p">)</span>
<span class="c1"># Other options.</span>
<span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s2">&quot;dbtable&quot;</span><span class="p">,</span> <span class="s2">&quot;my_table&quot;</span><span class="p">)</span>
<span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s2">&quot;geom&quot;</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="n">expr</span><span class="p">(</span><span class="s2">&quot;ST_GeomFromWKB(geom)&quot;</span><span class="p">)))</span>
</code></pre></div>
</div>
</div>
</div>
<h2 id="load-geopackage">Load GeoPackage<a class="headerlink" href="#load-geopackage" title="Permanent link">&para;</a></h2>
<p>Since v1.7.0, Sedona supports loading Geopackage file format as a DataFrame.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="10:3"><input checked="checked" id="__tabbed_10_1" name="__tabbed_10" type="radio" /><input id="__tabbed_10_2" name="__tabbed_10" type="radio" /><input id="__tabbed_10_3" name="__tabbed_10" type="radio" /><div class="tabbed-labels"><label for="__tabbed_10_1">Scala/Java</label><label for="__tabbed_10_2">Java</label><label for="__tabbed_10_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;geopackage&quot;</span><span class="p">).</span><span class="n">option</span><span class="p">(</span><span class="s">&quot;tableName&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;tab&quot;</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;/path/to/geopackage&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">().</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;geopackage&quot;</span><span class="p">).</span><span class="na">option</span><span class="p">(</span><span class="s">&quot;tableName&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;tab&quot;</span><span class="p">).</span><span class="na">load</span><span class="p">(</span><span class="s">&quot;/path/to/geopackage&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;geopackage&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">option</span><span class="p">(</span><span class="s2">&quot;tableName&quot;</span><span class="p">,</span> <span class="s2">&quot;tab&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;/path/to/geopackage&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<p>See <a href="../files/geopackage-sedona-spark/">this page</a> for more information on loading GeoPackage.</p>
<h2 id="load-osm-pbf">Load OSM PBF<a class="headerlink" href="#load-osm-pbf" title="Permanent link">&para;</a></h2>
<p>Since v1.7.1, Sedona supports loading OSM PBF file format as a DataFrame.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="11:3"><input checked="checked" id="__tabbed_11_1" name="__tabbed_11" type="radio" /><input id="__tabbed_11_2" name="__tabbed_11" type="radio" /><input id="__tabbed_11_3" name="__tabbed_11" type="radio" /><div class="tabbed-labels"><label for="__tabbed_11_1">Scala/Java</label><label for="__tabbed_11_2">Java</label><label for="__tabbed_11_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">val</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;osmpbf&quot;</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;/path/to/osmpbf&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">sedona</span><span class="p">.</span><span class="na">read</span><span class="p">().</span><span class="na">format</span><span class="p">(</span><span class="s">&quot;osmpbf&quot;</span><span class="p">).</span><span class="na">load</span><span class="p">(</span><span class="s">&quot;/path/to/osmpbf&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">df</span> <span class="o">=</span> <span class="n">sedona</span><span class="o">.</span><span class="n">read</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">&quot;osmpbf&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;/path/to/osmpbf&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<p>OSM PBF files can contain nodes, ways, and relations. Currently Sedona support
Nodes, DenseNodes, Ways and Relations. When you load the data you get a DataFrame with the following schema.</p>
<div class="highlight"><pre><span></span><code>root
|-- id: long (nullable = true)
|-- kind: string (nullable = true)
|-- location: struct (nullable = true)
| |-- longitude: double (nullable = true)
| |-- latitude: double (nullable = true)
|-- tags: map (nullable = true)
| |-- key: string
| |-- value: string (valueContainsNull = true)
|-- refs: array (nullable = true)
| |-- element: long (containsNull = true)
|-- ref_roles: array (nullable = true)
| |-- element: string (containsNull = true)
|-- ref_types: array (nullable = true)
| |-- element: string (containsNull = true)
</code></pre></div>
<p>Where:</p>
<ul>
<li><code>id</code> is the unique identifier of the object.</li>
<li><code>kind</code> is the type of the object, it can be <code>node</code>, <code>way</code> or <code>relation</code>.</li>
<li><code>location</code> is the location of the object, it contains the <code>longitude</code> and <code>latitude</code> of the object.</li>
<li><code>tags</code> is a map of key-value pairs that represent the tags of the object.</li>
<li><code>refs</code> is an array of the references of the object.</li>
<li><code>ref_roles</code> is an array of the roles of the references.</li>
<li><code>ref_types</code> is an array of the types of the references.</li>
</ul>
<p>The dataframe for ways might look like this for nodes:</p>
<div class="highlight"><pre><span></span><code>+---------+----+--------------------+--------------------+----+---------+---------+
| id|kind| location| tags|refs|ref_roles|ref_types|
+---------+----+--------------------+--------------------+----+---------+---------+
|248675410|node|{21.0884952545166...|{tactile_paving -...|NULL| NULL| NULL|
|260821820|node|{21.0191555023193...|{created_by -&gt; JOSM}|NULL| NULL| NULL|
|349189665|node|{22.1437530517578...|{source -&gt; http:/...|NULL| NULL| NULL|
|353366899|node|{22.9787712097167...|{source -&gt; http:/...|NULL| NULL| NULL|
|359460224|node|{22.4816703796386...|{source -&gt; http:/...|NULL| NULL| NULL|
+---------+----+--------------------+--------------------+----+---------+---------+
only showing top 5 rows
</code></pre></div>
<p>and for way</p>
<div class="highlight"><pre><span></span><code>+-------+----+--------+--------------------+--------------------+---------+---------+
| id|kind|location| tags| refs|ref_roles|ref_types|
+-------+----+--------+--------------------+--------------------+---------+---------+
|4307329| way| NULL|{junction -&gt; roun...|[2448759046, 7093...| NULL| NULL|
|4307330| way| NULL|{surface -&gt; aspha...|[26063923, 260639...| NULL| NULL|
|4308966| way| NULL|{sidewalk -&gt; sepa...|[3387797238, 9252...| NULL| NULL|
|4308968| way| NULL|{surface -&gt; pavin...|[26083890, 744724...| NULL| NULL|
|4308969| way| NULL|{cycleway:both -&gt;...|[9526831176, 1218...| NULL| NULL|
+-------+----+--------+--------------------+--------------------+---------+---------+
</code></pre></div>
<p>and for relation</p>
<div class="highlight"><pre><span></span><code>+-----+--------+--------+--------------------+--------------------+--------------------+--------------------+
| id| kind|location| tags| refs| ref_roles| ref_types|
+-----+--------+--------+--------------------+--------------------+--------------------+--------------------+
|28124|relation| NULL|{official_name -&gt;...|[26382394, 26259985]| [inner, outer]| [WAY, WAY]|
|28488|relation| NULL| {type -&gt; junction}|[26409253, 303249...|[roundabout, roun...|[WAY, WAY, WAY, WAY]|
|32939|relation| NULL|{ref -&gt; E 67, rou...|[140673970, 14067...| [, , , , , ]|[WAY, WAY, RELATI...|
|34387|relation| NULL|{note -&gt; rzΔ…d III...|[209161000, 52154...|[main_stream, mai...|[WAY, WAY, WAY, W...|
|34392|relation| NULL|{distance -&gt; 1047...|[150033976, 25076...|[main_stream, mai...|[WAY, WAY, WAY, W...|
+-----+--------+--------+--------------------+--------------------+--------------------+--------------------+
</code></pre></div>
<h2 id="transform-the-coordinate-reference-system">Transform the Coordinate Reference System<a class="headerlink" href="#transform-the-coordinate-reference-system" title="Permanent link">&para;</a></h2>
<p>Sedona doesn't control the coordinate unit (degree-based or meter-based) of all geometries in a Geometry column. The unit of all related distances in SedonaSQL is same as the unit of all geometries in a Geometry column.</p>
<p>By default, this function uses lon/lat order since <code>v1.5.0</code>. Before, it used lat/lon order. You can use <mark>ST_FlipCoordinates</mark> to swap X and Y.</p>
<p>For more details, please read the <code>ST_Transform</code> section in Sedona API References.</p>
<p>To convert Coordinate Reference System of the Geometry column created before, use the following code:</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span><span class="w"> </span><span class="n">ST_Transform</span><span class="p">(</span><span class="n">countyshape</span><span class="p">,</span><span class="w"> </span><span class="ss">&quot;epsg:4326&quot;</span><span class="p">,</span><span class="w"> </span><span class="ss">&quot;epsg:3857&quot;</span><span class="p">)</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">newcountyshape</span><span class="p">,</span><span class="w"> </span><span class="n">_c1</span><span class="p">,</span><span class="w"> </span><span class="n">_c2</span><span class="p">,</span><span class="w"> </span><span class="n">_c3</span><span class="p">,</span><span class="w"> </span><span class="n">_c4</span><span class="p">,</span><span class="w"> </span><span class="n">_c5</span><span class="p">,</span><span class="w"> </span><span class="n">_c6</span><span class="p">,</span><span class="w"> </span><span class="n">_c7</span>
<span class="k">FROM</span><span class="w"> </span><span class="n">spatialdf</span>
</code></pre></div>
<p>The first EPSG code EPSG:4326 in <code>ST_Transform</code> is the source CRS of the geometries. It is WGS84, the most common degree-based CRS.</p>
<p>The second EPSG code EPSG:3857 in <code>ST_Transform</code> is the target CRS of the geometries. It is the most common meter-based CRS.</p>
<p>This <code>ST_Transform</code> transform the CRS of these geometries from EPSG:4326 to EPSG:3857. The details CRS information can be found on <a href="https://epsg.io/">EPSG.io</a></p>
<p>The coordinates of polygons have been changed. The output will be like this:</p>
<div class="highlight"><pre><span></span><code>+--------------------+---+---+--------+-----+-----------+--------------------+---+
| newcountyshape|_c1|_c2| _c3| _c4| _c5| _c6|_c7|
+--------------------+---+---+--------+-----+-----------+--------------------+---+
|POLYGON ((-108001...| 31|039|00835841|31039| Cuming| Cuming County| 06|
|POLYGON ((-137408...| 53|069|01513275|53069| Wahkiakum| Wahkiakum County| 06|
|POLYGON ((-116403...| 35|011|00933054|35011| De Baca| De Baca County| 06|
|POLYGON ((-107880...| 31|109|00835876|31109| Lancaster| Lancaster County| 06|
</code></pre></div>
<h2 id="cluster-with-dbscan">Cluster with DBSCAN<a class="headerlink" href="#cluster-with-dbscan" title="Permanent link">&para;</a></h2>
<p>Sedona provides an implementation of the <a href="https://en.wikipedia.org/wiki/Dbscan">DBSCAN</a> algorithm to cluster spatial data.</p>
<p>The algorithm is available as a Scala and Python function called on a spatial dataframe. The returned dataframe has an additional column added containing the unique identifier of the cluster that record is a member of and a boolean column indicating if the record is a core point.</p>
<p>The first parameter is the dataframe, the next two are the epsilon and min_points parameters of the DBSCAN algorithm.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="12:3"><input checked="checked" id="__tabbed_12_1" name="__tabbed_12" type="radio" /><input id="__tabbed_12_2" name="__tabbed_12" type="radio" /><input id="__tabbed_12_3" name="__tabbed_12" type="radio" /><div class="tabbed-labels"><label for="__tabbed_12_1">Scala</label><label for="__tabbed_12_2">Java</label><label for="__tabbed_12_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">sedona</span><span class="p">.</span><span class="nn">stats</span><span class="p">.</span><span class="nn">clustering</span><span class="p">.</span><span class="nc">DBSCAN</span><span class="p">.</span><span class="n">dbscan</span>
<span class="n">dbscan</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="mf">0.1</span><span class="p">,</span><span class="w"> </span><span class="mi">5</span><span class="p">).</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.sedona.stats.clustering.DBSCAN</span><span class="p">;</span>
<span class="n">DBSCAN</span><span class="p">.</span><span class="na">dbscan</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="mf">0.1</span><span class="p">,</span><span class="w"> </span><span class="mi">5</span><span class="p">).</span><span class="na">show</span><span class="p">();</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">dbscan</span>
<span class="n">dbscan</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>The output will look like this:</p>
<div class="highlight"><pre><span></span><code>+----------------+---+------+-------+
| geometry| id|isCore|cluster|
+----------------+---+------+-------+
| POINT (2.5 4)| 3| false| 1|
| POINT (3 4)| 2| false| 1|
| POINT (3 5)| 5| false| 1|
| POINT (1 3)| 9| true| 0|
| POINT (2.5 4.5)| 7| true| 1|
| POINT (1 2)| 1| true| 0|
| POINT (1.5 2.5)| 4| true| 0|
| POINT (1.2 2.5)| 8| true| 0|
| POINT (1 2.5)| 11| true| 0|
| POINT (1 5)| 10| false| -1|
| POINT (5 6)| 12| false| -1|
|POINT (12.8 4.5)| 6| false| -1|
| POINT (4 3)| 13| false| -1|
+----------------+---+------+-------+
</code></pre></div>
<p>See <a href="../concepts/clustering-algorithms/">this page</a> for more information on the DBSCAN algorithm.</p>
<h2 id="calculate-the-local-outlier-factor-lof">Calculate the Local Outlier Factor (LOF)<a class="headerlink" href="#calculate-the-local-outlier-factor-lof" title="Permanent link">&para;</a></h2>
<p>Sedona provides an implementation of the <a href="https://en.wikipedia.org/wiki/Local_outlier_factor">Local Outlier Factor</a> algorithm to identify anomalous data.</p>
<p>The algorithm is available as a Scala and Python function called on a spatial dataframe. The returned dataframe has an additional column added containing the local outlier factor.</p>
<p>The first parameter is the dataframe, the next is the number of nearest neighbors to consider use in calculating the score.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="13:3"><input checked="checked" id="__tabbed_13_1" name="__tabbed_13" type="radio" /><input id="__tabbed_13_2" name="__tabbed_13" type="radio" /><input id="__tabbed_13_3" name="__tabbed_13" type="radio" /><div class="tabbed-labels"><label for="__tabbed_13_1">Scala</label><label for="__tabbed_13_2">Java</label><label for="__tabbed_13_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">sedona</span><span class="p">.</span><span class="nn">stats</span><span class="p">.</span><span class="nn">outlierDetection</span><span class="p">.</span><span class="nc">LocalOutlierFactor</span><span class="p">.</span><span class="n">localOutlierFactor</span>
<span class="n">localOutlierFactor</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="mi">20</span><span class="p">).</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.sedona.stats.outlierDetection.LocalOutlierFactor</span><span class="p">;</span>
<span class="n">LocalOutlierFactor</span><span class="p">.</span><span class="na">localOutlierFactor</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="mi">20</span><span class="p">).</span><span class="na">show</span><span class="p">();</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">local_outlier_factor</span>
<span class="n">local_outlier_factor</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>The output will look like this:</p>
<div class="highlight"><pre><span></span><code>+--------------------+------------------+
| geometry| lof|
+--------------------+------------------+
|POINT (-2.0231305...| 0.952098153363662|
|POINT (-2.0346944...|0.9975325496668104|
|POINT (-2.2040074...|1.0825843906411081|
|POINT (1.61573501...|1.7367129352162634|
|POINT (-2.1176324...|1.5714144683150393|
|POINT (-2.2349759...|0.9167275845938276|
|POINT (1.65470192...| 1.046231536764447|
|POINT (0.62624112...|1.1988700676990034|
|POINT (2.01746261...|1.1060219481067417|
|POINT (-2.0483857...|1.0775553430145446|
|POINT (2.43969463...|1.1129132178576646|
|POINT (-2.2425480...| 1.104108012697006|
|POINT (-2.7859235...| 2.86371824574529|
|POINT (-1.9738858...|1.0398822680356794|
|POINT (2.00153403...| 0.927409656346015|
|POINT (2.06422812...|0.9222203762264445|
|POINT (-1.7533819...|1.0273650471626696|
|POINT (-2.2030766...| 0.964744555830738|
|POINT (-1.8509857...|1.0375927869698574|
|POINT (2.10849080...|1.0753419197322656|
+--------------------+------------------+
</code></pre></div>
<h2 id="perform-getis-ord-gi-hot-spot-analysis">Perform Getis-Ord Gi(*) Hot Spot Analysis<a class="headerlink" href="#perform-getis-ord-gi-hot-spot-analysis" title="Permanent link">&para;</a></h2>
<p>Sedona provides an implementation of the <a href="https://en.wikipedia.org/wiki/Getis%E2%80%93Ord_statistics">Gi and Gi*</a> algorithms to identify local hotspots in spatial data</p>
<p>The algorithm is available as a Scala and Python function called on a spatial dataframe. The returned dataframe has additional columns added containing G statistic, E[G], V[G], the Z score, and the p-value.</p>
<p>Using Gi involves first generating the neighbors list for each record, then calling the g_local function.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="14:3"><input checked="checked" id="__tabbed_14_1" name="__tabbed_14" type="radio" /><input id="__tabbed_14_2" name="__tabbed_14" type="radio" /><input id="__tabbed_14_3" name="__tabbed_14" type="radio" /><div class="tabbed-labels"><label for="__tabbed_14_1">Scala</label><label for="__tabbed_14_2">Java</label><label for="__tabbed_14_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">sedona</span><span class="p">.</span><span class="nn">stats</span><span class="p">.</span><span class="nc">Weighting</span><span class="p">.</span><span class="n">addBinaryDistanceBandColumn</span>
<span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">sedona</span><span class="p">.</span><span class="nn">stats</span><span class="p">.</span><span class="nn">hotspotDetection</span><span class="p">.</span><span class="nc">GetisOrd</span><span class="p">.</span><span class="n">gLocal</span>
<span class="kd">val</span><span class="w"> </span><span class="n">distanceRadius</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">1.0</span>
<span class="kd">val</span><span class="w"> </span><span class="n">weightedDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">addBinaryDistanceBandColumn</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">distanceRadius</span><span class="p">)</span>
<span class="n">gLocal</span><span class="p">(</span><span class="n">weightedDf</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;val&quot;</span><span class="p">).</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.sedona.stats.Weighting</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.sedona.stats.hotspotDetection.GetisOrd</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.DataFrame</span><span class="p">;</span>
<span class="kt">double</span><span class="w"> </span><span class="n">distanceRadius</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mf">1.0</span><span class="p">;</span>
<span class="n">DataFrame</span><span class="w"> </span><span class="n">weightedDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Weighting</span><span class="p">.</span><span class="na">addBinaryDistanceBandColumn</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">distanceRadius</span><span class="p">);</span>
<span class="n">GetisOrd</span><span class="p">.</span><span class="na">gLocal</span><span class="p">(</span><span class="n">weightedDf</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;val&quot;</span><span class="p">).</span><span class="na">show</span><span class="p">();</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">add_binary_distance_band_column</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">g_local</span>
<span class="n">distance_radius</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">weighted_df</span> <span class="o">=</span> <span class="n">addBinaryDistanceBandColumn</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">distance_radius</span><span class="p">)</span>
<span class="n">g_local</span><span class="p">(</span><span class="n">weightedDf</span><span class="p">,</span> <span class="s2">&quot;val&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>The output will look like this:</p>
<pre>
<code>
+-----------+---+--------------------+-------------------+-------------------+--------------------+--------------------+--------------------+
| geometry|val| weights| G| EG| VG| Z| P|
+-----------+---+--------------------+-------------------+-------------------+--------------------+--------------------+--------------------+
|POINT (2 2)|0.9|[&#123;&#123;POINT (2 3), 1...| 0.4488188976377953|0.45454545454545453| 0.00356321373799772|-0.09593402008347063| 0.4617864875295957|
|POINT (2 3)|1.2|[&#123;&#123;POINT (2 2), 0...|0.35433070866141736|0.36363636363636365|0.003325666155464539|-0.16136436037034918| 0.4359032175415549|
|POINT (3 3)|1.2|[&#123;&#123;POINT (2 3), 1...|0.28346456692913385| 0.2727272727272727|0.002850570990398176| 0.20110780337013057| 0.42030714022155924|
|POINT (3 2)|1.2|[&#123;&#123;POINT (2 2), 0...| 0.4488188976377953|0.45454545454545453| 0.00356321373799772|-0.09593402008347063| 0.4617864875295957|
|POINT (3 1)|1.2|[&#123;&#123;POINT (3 2), 3...| 0.3622047244094489| 0.2727272727272727|0.002850570990398176| 1.6758983614177538| 0.04687905137429871|
|POINT (2 1)|2.2|[&#123;&#123;POINT (2 2), 0...| 0.4330708661417323|0.36363636363636365|0.003325666155464539| 1.2040263812249166| 0.11428969105925013|
|POINT (1 1)|1.2|[&#123;&#123;POINT (2 1), 5...| 0.2834645669291339| 0.2727272727272727|0.002850570990398176| 0.2011078033701316| 0.4203071402215588|
|POINT (1 2)|0.2|[&#123;&#123;POINT (2 2), 0...|0.35433070866141736|0.45454545454545453| 0.00356321373799772| -1.67884535146075|0.046591093685710794|
|POINT (1 3)|1.2|[&#123;&#123;POINT (2 3), 1...| 0.2047244094488189| 0.2727272727272727|0.002850570990398176| -1.2736827546774914| 0.10138793530151635|
|POINT (0 2)|1.0|[&#123;&#123;POINT (1 2), 7...|0.09448818897637795|0.18181818181818182|0.002137928242798632| -1.8887168824332323|0.029464887612748458|
|POINT (4 2)|1.2|[&#123;&#123;POINT (3 2), 3...| 0.1889763779527559|0.18181818181818182|0.002137928242798632| 0.15481285921583854| 0.43848442662481324|
+-----------+---+--------------------+-------------------+-------------------+--------------------+--------------------+--------------------+
</code>
</pre>
<h2 id="run-spatial-queries">Run spatial queries<a class="headerlink" href="#run-spatial-queries" title="Permanent link">&para;</a></h2>
<p>After creating a Geometry type column, you are able to run spatial queries.</p>
<h3 id="range-query">Range query<a class="headerlink" href="#range-query" title="Permanent link">&para;</a></h3>
<p>Use <mark>ST_Contains</mark>, <mark>ST_Intersects</mark>, <mark>ST_Within</mark> to run a range query over a single column.</p>
<p>The following example finds all counties that are within the given polygon:</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span><span class="w"> </span><span class="o">*</span>
<span class="k">FROM</span><span class="w"> </span><span class="n">spatialdf</span>
<span class="k">WHERE</span><span class="w"> </span><span class="n">ST_Contains</span><span class="w"> </span><span class="p">(</span><span class="n">ST_PolygonFromEnvelope</span><span class="p">(</span><span class="mi">1</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="mi">1100</span><span class="p">.</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="n">newcountyshape</span><span class="p">)</span>
</code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Read <a href="../../api/sql/Geometry-Functions/">SedonaSQL constructor API</a> to learn how to create a Geometry type query window</p>
</div>
<h3 id="knn-query">KNN query<a class="headerlink" href="#knn-query" title="Permanent link">&para;</a></h3>
<p>Use <mark>ST_Distance</mark> to calculate the distance and rank the distance.</p>
<p>The following code returns the 5 nearest neighbor of the given polygon.</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span><span class="w"> </span><span class="n">countyname</span><span class="p">,</span><span class="w"> </span><span class="n">ST_Distance</span><span class="p">(</span><span class="n">ST_PolygonFromEnvelope</span><span class="p">(</span><span class="mi">1</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">.</span><span class="mi">0</span><span class="p">,</span><span class="mi">1100</span><span class="p">.</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="n">newcountyshape</span><span class="p">)</span><span class="w"> </span><span class="k">AS</span><span class="w"> </span><span class="n">distance</span>
<span class="k">FROM</span><span class="w"> </span><span class="n">spatialdf</span>
<span class="k">ORDER</span><span class="w"> </span><span class="k">BY</span><span class="w"> </span><span class="n">distance</span><span class="w"> </span><span class="k">DESC</span>
<span class="k">LIMIT</span><span class="w"> </span><span class="mi">5</span>
</code></pre></div>
<h3 id="join-query">Join query<a class="headerlink" href="#join-query" title="Permanent link">&para;</a></h3>
<p>The details of a join query is available here <a href="../../api/sql/Optimizer/">Join query</a>.</p>
<h3 id="knn-join-query">KNN join query<a class="headerlink" href="#knn-join-query" title="Permanent link">&para;</a></h3>
<p>The details of a KNN join query is available here <a href="../../api/sql/NearestNeighbourSearching/">KNN join query</a>.</p>
<h3 id="other-queries">Other queries<a class="headerlink" href="#other-queries" title="Permanent link">&para;</a></h3>
<p>There are lots of other functions can be combined with these queries. Please read <a href="../../api/sql/Overview/">SedonaSQL functions</a> and <a href="../../api/sql/Geometry-Functions/#aggregate-functions">SedonaSQL aggregate functions</a>.</p>
<h2 id="visualize-query-results">Visualize query results<a class="headerlink" href="#visualize-query-results" title="Permanent link">&para;</a></h2>
<p>Sedona provides <code>SedonaPyDeck</code> and <code>SedonaKepler</code> wrappers, both of which expose APIs to create interactive map visualizations from SedonaDataFrames in a Jupyter environment.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Both SedonaPyDeck and SedonaKepler expect the default geometry order to be lon-lat. If your dataframe has geometries in the lat-lon order, please check out <a href="../../api/sql/Geometry-Editors/ST_FlipCoordinates/">ST_FlipCoordinates</a></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Both SedonaPyDeck and SedonaKepler are designed to work with SedonaDataFrames containing only 1 geometry column. Passing dataframes with multiple geometry columns will cause errors.</p>
</div>
<h3 id="sedonapydeck">SedonaPyDeck<a class="headerlink" href="#sedonapydeck" title="Permanent link">&para;</a></h3>
<p>Spatial query results can be visualized in a Jupyter lab/notebook environment using SedonaPyDeck.</p>
<p>SedonaPyDeck exposes APIs to create interactive map visualizations using <a href="https://deckgl.readthedocs.io">pydeck</a> based on <a href="https://deck.gl/">deck.gl</a></p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To use SedonaPyDeck, install sedona with the <code>pydeck-map</code> extra:
<div class="highlight"><pre><span></span><code>pip install apache-sedona[pydeck-map]
</code></pre></div></p>
</div>
<p>The following tutorial showcases the various maps that can be created using SedonaPyDeck, the datasets used to create these maps are publicly available.</p>
<p>Each API exposed by SedonaPyDeck offers customization via optional arguments, details on all possible arguments can be found in the <a href="../../api/sql/Visualization-SedonaPyDeck/">API docs of SedonaPyDeck</a>.</p>
<h4 id="creating-a-choropleth-map-using-sedonapydeck">Creating a Choropleth map using SedonaPyDeck<a class="headerlink" href="#creating-a-choropleth-map-using-sedonapydeck" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck exposes a <code>create_choropleth_map</code> API which can be used to visualize a choropleth map out of the passed SedonaDataFrame containing polygons with an observation:</p>
<p>Example:</p>
<div class="highlight"><pre><span></span><code><span class="n">SedonaPyDeck</span><span class="o">.</span><span class="n">create_choropleth_map</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">groupedresult</span><span class="p">,</span> <span class="n">plot_col</span><span class="o">=</span><span class="s2">&quot;AirportCount&quot;</span><span class="p">)</span>
</code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>plot_col</code> is a required argument informing SedonaPyDeck of the column name used to render the choropleth effect.</p>
</div>
<p><img alt="Creating a Choropleth map using SedonaPyDeck" src="../../image/choropleth.gif" /></p>
<p>The dataset used is available <a href="https://github.com/apache/sedona/tree/b66e768155866a38ba2e3404f1151cac14fad5ea/docs/usecases/data/ne_50m_airports">here</a> and
can also be found in the example notebook available <a href="https://github.com/apache/sedona/blob/master/docs/usecases/legacy/ApacheSedonaSQL_SpatialJoin_AirportsPerCountry.ipynb">here</a></p>
<h4 id="creating-a-geometry-map-using-sedonapydeck">Creating a Geometry map using SedonaPyDeck<a class="headerlink" href="#creating-a-geometry-map-using-sedonapydeck" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck exposes a create_geometry_map API which can be used to visualize a passed SedonaDataFrame containing any type of geometries:</p>
<p>Example:</p>
<div class="highlight"><pre><span></span><code><span class="n">SedonaPyDeck</span><span class="o">.</span><span class="n">create_geometry_map</span><span class="p">(</span><span class="n">df_building</span><span class="p">,</span> <span class="n">elevation_col</span><span class="o">=</span><span class="s2">&quot;height&quot;</span><span class="p">)</span>
</code></pre></div>
<p><img alt="Creating a Geometry map using SedonaPyDeck" src="../../image/buildings.gif" /></p>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p><code>elevation_col</code> is an optional argument which can be used to render a 3D map. Pass the column with 'elevation' values for the geometries here.</p>
</div>
<h4 id="creating-a-scatterplot-map-using-sedonapydeck">Creating a Scatterplot map using SedonaPyDeck<a class="headerlink" href="#creating-a-scatterplot-map-using-sedonapydeck" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck exposes a create_scatterplot_map API which can be used to visualize a scatterplot out of the passed SedonaDataFrame containing points:</p>
<p>Example:</p>
<div class="highlight"><pre><span></span><code><span class="n">SedonaPyDeck</span><span class="o">.</span><span class="n">create_scatterplot_map</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">crimes_df</span><span class="p">)</span>
</code></pre></div>
<p><img alt="Creating a Scatterplot map using SedonaPyDeck" src="../../image/points.gif" /></p>
<p>The dataset used here is the Chicago crimes dataset, available <a href="https://github.com/apache/sedona/blob/sedona-1.5.0/spark/common/src/test/resources/Chicago_Crimes.csv">here</a></p>
<h4 id="creating-a-heatmap-using-sedonapydeck">Creating a heatmap using SedonaPyDeck<a class="headerlink" href="#creating-a-heatmap-using-sedonapydeck" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck exposes a create_heatmap API which can be used to visualize a heatmap out of the passed SedonaDataFrame containing points:</p>
<p>Example:</p>
<div class="highlight"><pre><span></span><code><span class="n">SedonaPyDeck</span><span class="o">.</span><span class="n">create_heatmap</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">crimes_df</span><span class="p">)</span>
</code></pre></div>
<p><img alt="Creating a heatmap using SedonaPyDeck" src="../../image/heatmap.gif" /></p>
<p>The dataset used here is the Chicago crimes dataset, available <a href="https://github.com/apache/sedona/blob/sedona-1.5.0/spark/common/src/test/resources/Chicago_Crimes.csv">here</a></p>
<h3 id="sedonakepler">SedonaKepler<a class="headerlink" href="#sedonakepler" title="Permanent link">&para;</a></h3>
<p>Spatial query results can be visualized in a Jupyter lab/notebook environment using SedonaKepler.</p>
<p>SedonaKepler exposes APIs to create interactive and customizable map visualizations using <a href="https://kepler.gl/">KeplerGl</a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To use SedonaKepler, install sedona with the <code>kepler-map</code> extra:
<div class="highlight"><pre><span></span><code>pip install apache-sedona[kepler-map]
</code></pre></div></p>
</div>
<p>This tutorial showcases how simple it is to instantly visualize geospatial data using SedonaKepler.</p>
<p>Example (referenced from an example notebook via the binder):</p>
<div class="highlight"><pre><span></span><code><span class="n">SedonaKepler</span><span class="o">.</span><span class="n">create_map</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">groupedresult</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;AirportCount&quot;</span><span class="p">)</span>
</code></pre></div>
<p><img alt="Visualize geospatial data using SedonaKepler" src="../../image/sedona_customization.gif" /></p>
<p>The dataset used is available <a href="https://github.com/apache/sedona/tree/b66e768155866a38ba2e3404f1151cac14fad5ea/docs/usecases/data/ne_50m_airports">here</a> and
can also be found in the example notebook available <a href="https://github.com/apache/sedona/blob/master/docs/usecases/legacy/ApacheSedonaSQL_SpatialJoin_AirportsPerCountry.ipynb">here</a></p>
<p>Details on all the APIs available by SedonaKepler are listed in the <a href="../../api/sql/Visualization-SedonaKepler/">SedonaKepler API docs</a></p>
<h2 id="create-a-user-defined-function-udf">Create a User-Defined Function (UDF)<a class="headerlink" href="#create-a-user-defined-function-udf" title="Permanent link">&para;</a></h2>
<p>User-Defined Functions (UDFs) are user-created procedures that can perform operations on a single row of information. To cover almost all use cases, we will showcase 4 types of UDFs for a better understanding of how to use geometry with UDFs. Sedona's serializer deserializes the SQL geometry type to <a href="https://locationtech.github.io/jts/javadoc-1.18.0/org/locationtech/jts/geom/Geometry.html">JTS Geometry</a> (Scala/Java) or <a href="https://shapely.readthedocs.io/en/stable/geometry.html">Shapely Geometry</a> (Python). You can implement any custom logic using the rich ecosystem around these two libraries.</p>
<h3 id="geometry-to-primitive">Geometry to primitive<a class="headerlink" href="#geometry-to-primitive" title="Permanent link">&para;</a></h3>
<p>This UDF example takes a geometry type input and returns a primitive type output:</p>
<div class="tabbed-set tabbed-alternate" data-tabs="15:3"><input checked="checked" id="__tabbed_15_1" name="__tabbed_15" type="radio" /><input id="__tabbed_15_2" name="__tabbed_15" type="radio" /><input id="__tabbed_15_3" name="__tabbed_15" type="radio" /><div class="tabbed-labels"><label for="__tabbed_15_1">Scala</label><label for="__tabbed_15_2">Java</label><label for="__tabbed_15_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">locationtech</span><span class="p">.</span><span class="nn">jts</span><span class="p">.</span><span class="nn">geom</span><span class="p">.</span><span class="nc">Geometry</span>
<span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">spark</span><span class="p">.</span><span class="nn">sql</span><span class="p">.</span><span class="nn">types</span><span class="p">.</span><span class="n">_</span>
<span class="k">def</span><span class="w"> </span><span class="nf">lengthPoly</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span><span class="w"> </span><span class="nc">Geometry</span><span class="p">):</span><span class="w"> </span><span class="nc">Double</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">geom</span><span class="p">.</span><span class="n">getLength</span>
<span class="p">}</span>
<span class="n">sedona</span><span class="p">.</span><span class="n">udf</span><span class="p">.</span><span class="n">register</span><span class="p">(</span><span class="s">&quot;udf_lengthPoly&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">lengthPoly</span><span class="w"> </span><span class="n">_</span><span class="p">)</span>
<span class="n">df</span><span class="p">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s">&quot;udf_lengthPoly(geom)&quot;</span><span class="p">).</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.api.java.UDF1</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.types.DataTypes</span><span class="p">;</span>
<span class="c1">// using lambda function to register the UDF</span>
<span class="n">sparkSession</span><span class="p">.</span><span class="na">udf</span><span class="p">().</span><span class="na">register</span><span class="p">(</span>
<span class="w"> </span><span class="s">&quot;udf_lengthPoly&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="p">(</span><span class="n">UDF1</span><span class="o">&lt;</span><span class="n">Geometry</span><span class="p">,</span><span class="w"> </span><span class="n">Double</span><span class="o">&gt;</span><span class="p">)</span><span class="w"> </span><span class="n">Geometry</span><span class="p">::</span><span class="n">getLength</span><span class="p">,</span>
<span class="w"> </span><span class="n">DataTypes</span><span class="p">.</span><span class="na">DoubleType</span><span class="p">);</span>
<span class="n">df</span><span class="p">.</span><span class="na">selectExpr</span><span class="p">(</span><span class="s">&quot;udf_lengthPoly(geom)&quot;</span><span class="p">).</span><span class="na">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark.sql.types</span><span class="w"> </span><span class="kn">import</span> <span class="n">GeometryType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.types</span><span class="w"> </span><span class="kn">import</span> <span class="n">DoubleType</span>
<span class="k">def</span><span class="w"> </span><span class="nf">lengthPoly</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span> <span class="n">GeometryType</span><span class="p">()):</span>
<span class="k">return</span> <span class="n">geom</span><span class="o">.</span><span class="n">length</span>
<span class="n">sedona</span><span class="o">.</span><span class="n">udf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">&quot;udf_lengthPoly&quot;</span><span class="p">,</span> <span class="n">lengthPoly</span><span class="p">,</span> <span class="n">DoubleType</span><span class="p">())</span>
<span class="n">df</span><span class="o">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s2">&quot;udf_lengthPoly(geom)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>Output:</p>
<div class="highlight"><pre><span></span><code>+--------------------+
|udf_lengthPoly(geom)|
+--------------------+
| 3.414213562373095|
+--------------------+
</code></pre></div>
<h3 id="geometry-to-geometry">Geometry to Geometry<a class="headerlink" href="#geometry-to-geometry" title="Permanent link">&para;</a></h3>
<p>This UDF example takes a geometry type input and returns a geometry type output:</p>
<div class="tabbed-set tabbed-alternate" data-tabs="16:3"><input checked="checked" id="__tabbed_16_1" name="__tabbed_16" type="radio" /><input id="__tabbed_16_2" name="__tabbed_16" type="radio" /><input id="__tabbed_16_3" name="__tabbed_16" type="radio" /><div class="tabbed-labels"><label for="__tabbed_16_1">Scala</label><label for="__tabbed_16_2">Java</label><label for="__tabbed_16_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">locationtech</span><span class="p">.</span><span class="nn">jts</span><span class="p">.</span><span class="nn">geom</span><span class="p">.</span><span class="nc">Geometry</span>
<span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">spark</span><span class="p">.</span><span class="nn">sql</span><span class="p">.</span><span class="nn">types</span><span class="p">.</span><span class="n">_</span>
<span class="k">def</span><span class="w"> </span><span class="nf">bufferFixed</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span><span class="w"> </span><span class="nc">Geometry</span><span class="p">):</span><span class="w"> </span><span class="nc">Geometry</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">geom</span><span class="p">.</span><span class="n">buffer</span><span class="p">(</span><span class="mf">5.5</span><span class="p">)</span>
<span class="p">}</span>
<span class="n">sedona</span><span class="p">.</span><span class="n">udf</span><span class="p">.</span><span class="n">register</span><span class="p">(</span><span class="s">&quot;udf_bufferFixed&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bufferFixed</span><span class="w"> </span><span class="n">_</span><span class="p">)</span>
<span class="n">df</span><span class="p">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s">&quot;udf_bufferFixed(geom)&quot;</span><span class="p">).</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.api.java.UDF1</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.types.DataTypes</span><span class="p">;</span>
<span class="c1">// using lambda function to register the UDF</span>
<span class="n">sparkSession</span><span class="p">.</span><span class="na">udf</span><span class="p">().</span><span class="na">register</span><span class="p">(</span>
<span class="w"> </span><span class="s">&quot;udf_bufferFixed&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="p">(</span><span class="n">UDF1</span><span class="o">&lt;</span><span class="n">Geometry</span><span class="p">,</span><span class="w"> </span><span class="n">Geometry</span><span class="o">&gt;</span><span class="p">)</span><span class="w"> </span><span class="n">geom</span><span class="w"> </span><span class="o">-&gt;</span>
<span class="w"> </span><span class="n">geom</span><span class="p">.</span><span class="na">buffer</span><span class="p">(</span><span class="mf">5.5</span><span class="p">),</span>
<span class="w"> </span><span class="k">new</span><span class="w"> </span><span class="n">GeometryUDT</span><span class="p">());</span>
<span class="n">df</span><span class="p">.</span><span class="na">selectExpr</span><span class="p">(</span><span class="s">&quot;udf_bufferFixed(geom)&quot;</span><span class="p">).</span><span class="na">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">GeometryType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.types</span><span class="w"> </span><span class="kn">import</span> <span class="n">DoubleType</span>
<span class="k">def</span><span class="w"> </span><span class="nf">bufferFixed</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span> <span class="n">GeometryType</span><span class="p">()):</span>
<span class="k">return</span> <span class="n">geom</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mf">5.5</span><span class="p">)</span>
<span class="n">sedona</span><span class="o">.</span><span class="n">udf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">&quot;udf_bufferFixed&quot;</span><span class="p">,</span> <span class="n">bufferFixed</span><span class="p">,</span> <span class="n">GeometryType</span><span class="p">())</span>
<span class="n">df</span><span class="o">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s2">&quot;udf_bufferFixed(geom)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>Output:</p>
<div class="highlight"><pre><span></span><code>+--------------------------------------------------+
| udf_bufferFixed(geom)|
+--------------------------------------------------+
|POLYGON ((1 -4.5, -0.0729967710887076 -4.394319...|
+--------------------------------------------------+
</code></pre></div>
<h3 id="geometry-primitive-to-geometry">Geometry, primitive to geometry<a class="headerlink" href="#geometry-primitive-to-geometry" title="Permanent link">&para;</a></h3>
<p>This UDF example takes a geometry type input and a primitive type input and returns a geometry type output:</p>
<div class="tabbed-set tabbed-alternate" data-tabs="17:3"><input checked="checked" id="__tabbed_17_1" name="__tabbed_17" type="radio" /><input id="__tabbed_17_2" name="__tabbed_17" type="radio" /><input id="__tabbed_17_3" name="__tabbed_17" type="radio" /><div class="tabbed-labels"><label for="__tabbed_17_1">Scala</label><label for="__tabbed_17_2">Java</label><label for="__tabbed_17_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">locationtech</span><span class="p">.</span><span class="nn">jts</span><span class="p">.</span><span class="nn">geom</span><span class="p">.</span><span class="nc">Geometry</span>
<span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">spark</span><span class="p">.</span><span class="nn">sql</span><span class="p">.</span><span class="nn">types</span><span class="p">.</span><span class="n">_</span>
<span class="k">def</span><span class="w"> </span><span class="nf">bufferIt</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span><span class="w"> </span><span class="nc">Geometry</span><span class="p">,</span><span class="w"> </span><span class="n">distance</span><span class="p">:</span><span class="w"> </span><span class="nc">Double</span><span class="p">):</span><span class="w"> </span><span class="nc">Geometry</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">geom</span><span class="p">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">distance</span><span class="p">)</span>
<span class="p">}</span>
<span class="n">sedona</span><span class="p">.</span><span class="n">udf</span><span class="p">.</span><span class="n">register</span><span class="p">(</span><span class="s">&quot;udf_buffer&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">bufferIt</span><span class="w"> </span><span class="n">_</span><span class="p">)</span>
<span class="n">df</span><span class="p">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s">&quot;udf_buffer(geom, distance)&quot;</span><span class="p">).</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.api.java.UDF2</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.types.DataTypes</span><span class="p">;</span>
<span class="c1">// using lambda function to register the UDF</span>
<span class="n">sparkSession</span><span class="p">.</span><span class="na">udf</span><span class="p">().</span><span class="na">register</span><span class="p">(</span>
<span class="w"> </span><span class="s">&quot;udf_buffer&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="p">(</span><span class="n">UDF2</span><span class="o">&lt;</span><span class="n">Geometry</span><span class="p">,</span><span class="w"> </span><span class="n">Double</span><span class="p">,</span><span class="w"> </span><span class="n">Geometry</span><span class="o">&gt;</span><span class="p">)</span><span class="w"> </span><span class="n">Geometry</span><span class="p">::</span><span class="n">buffer</span><span class="p">,</span>
<span class="w"> </span><span class="k">new</span><span class="w"> </span><span class="n">GeometryUDT</span><span class="p">());</span>
<span class="n">df</span><span class="p">.</span><span class="na">selectExpr</span><span class="p">(</span><span class="s">&quot;udf_buffer(geom, distance)&quot;</span><span class="p">).</span><span class="na">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">GeometryType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.types</span><span class="w"> </span><span class="kn">import</span> <span class="n">DoubleType</span>
<span class="k">def</span><span class="w"> </span><span class="nf">bufferIt</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span> <span class="n">GeometryType</span><span class="p">(),</span> <span class="n">distance</span><span class="p">:</span> <span class="n">DoubleType</span><span class="p">()):</span>
<span class="k">return</span> <span class="n">geom</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">distance</span><span class="p">)</span>
<span class="n">sedona</span><span class="o">.</span><span class="n">udf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">&quot;udf_buffer&quot;</span><span class="p">,</span> <span class="n">bufferIt</span><span class="p">,</span> <span class="n">GeometryType</span><span class="p">())</span>
<span class="n">df</span><span class="o">.</span><span class="n">selectExpr</span><span class="p">(</span><span class="s2">&quot;udf_buffer(geom, distance)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>Output:</p>
<div class="highlight"><pre><span></span><code>+--------------------------------------------------+
| udf_buffer(geom, distance)|
+--------------------------------------------------+
|POLYGON ((1 -9, -0.9509032201612866 -8.80785280...|
+--------------------------------------------------+
</code></pre></div>
<h3 id="geometry-primitive-to-geometry-primitive">Geometry, primitive to Geometry, primitive<a class="headerlink" href="#geometry-primitive-to-geometry-primitive" title="Permanent link">&para;</a></h3>
<p>This UDF example takes a geometry type input and a primitive type input and returns a geometry type and a primitive type output:</p>
<div class="tabbed-set tabbed-alternate" data-tabs="18:3"><input checked="checked" id="__tabbed_18_1" name="__tabbed_18" type="radio" /><input id="__tabbed_18_2" name="__tabbed_18" type="radio" /><input id="__tabbed_18_3" name="__tabbed_18" type="radio" /><div class="tabbed-labels"><label for="__tabbed_18_1">Scala</label><label for="__tabbed_18_2">Java</label><label for="__tabbed_18_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">locationtech</span><span class="p">.</span><span class="nn">jts</span><span class="p">.</span><span class="nn">geom</span><span class="p">.</span><span class="nc">Geometry</span>
<span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">spark</span><span class="p">.</span><span class="nn">sql</span><span class="p">.</span><span class="nn">types</span><span class="p">.</span><span class="n">_</span>
<span class="k">import</span><span class="w"> </span><span class="nn">org</span><span class="p">.</span><span class="nn">apache</span><span class="p">.</span><span class="nn">spark</span><span class="p">.</span><span class="nn">sql</span><span class="p">.</span><span class="nn">api</span><span class="p">.</span><span class="nn">java</span><span class="p">.</span><span class="nc">UDF2</span>
<span class="kd">val</span><span class="w"> </span><span class="n">schemaUDF</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nc">StructType</span><span class="p">(</span><span class="nc">Array</span><span class="p">(</span>
<span class="w"> </span><span class="nc">StructField</span><span class="p">(</span><span class="s">&quot;buffed&quot;</span><span class="p">,</span><span class="w"> </span><span class="nc">GeometryUDT</span><span class="p">),</span>
<span class="w"> </span><span class="nc">StructField</span><span class="p">(</span><span class="s">&quot;length&quot;</span><span class="p">,</span><span class="w"> </span><span class="nc">DoubleType</span><span class="p">)</span>
<span class="p">))</span>
<span class="kd">val</span><span class="w"> </span><span class="n">udf_bufferLength</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">udf</span><span class="p">(</span>
<span class="w"> </span><span class="k">new</span><span class="w"> </span><span class="nc">UDF2</span><span class="p">[</span><span class="nc">Geometry</span><span class="p">,</span><span class="w"> </span><span class="nc">Double</span><span class="p">,</span><span class="w"> </span><span class="p">(</span><span class="nc">Geometry</span><span class="p">,</span><span class="w"> </span><span class="nc">Double</span><span class="p">)]</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">def</span><span class="w"> </span><span class="nf">call</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span><span class="w"> </span><span class="nc">Geometry</span><span class="p">,</span><span class="w"> </span><span class="n">distance</span><span class="p">:</span><span class="w"> </span><span class="nc">Double</span><span class="p">):</span><span class="w"> </span><span class="p">(</span><span class="nc">Geometry</span><span class="p">,</span><span class="w"> </span><span class="nc">Double</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="kd">val</span><span class="w"> </span><span class="n">buffed</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">geom</span><span class="p">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">distance</span><span class="p">)</span>
<span class="w"> </span><span class="kd">val</span><span class="w"> </span><span class="n">length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">geom</span><span class="p">.</span><span class="n">getLength</span>
<span class="w"> </span><span class="p">(</span><span class="n">buffed</span><span class="p">,</span><span class="w"> </span><span class="n">length</span><span class="p">)</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">},</span><span class="w"> </span><span class="n">schemaUDF</span><span class="p">)</span>
<span class="n">sedona</span><span class="p">.</span><span class="n">udf</span><span class="p">.</span><span class="n">register</span><span class="p">(</span><span class="s">&quot;udf_bufferLength&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">udf_bufferLength</span><span class="p">)</span>
<span class="n">data</span><span class="p">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s">&quot;bufferLength&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">expr</span><span class="p">(</span><span class="s">&quot;udf_bufferLengths(geom, distance)&quot;</span><span class="p">))</span>
<span class="w"> </span><span class="p">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;geom&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;distance&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;bufferLength.*&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.api.java.UDF2</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.types.DataTypes</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">org.apache.spark.sql.types.StructType</span><span class="p">;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">scala.Tuple2</span><span class="p">;</span>
<span class="n">StructType</span><span class="w"> </span><span class="n">schemaUDF</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">new</span><span class="w"> </span><span class="n">StructType</span><span class="p">()</span>
<span class="w"> </span><span class="p">.</span><span class="na">add</span><span class="p">(</span><span class="s">&quot;buffedGeom&quot;</span><span class="p">,</span><span class="w"> </span><span class="k">new</span><span class="w"> </span><span class="n">GeometryUDT</span><span class="p">())</span>
<span class="w"> </span><span class="p">.</span><span class="na">add</span><span class="p">(</span><span class="s">&quot;length&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">DataTypes</span><span class="p">.</span><span class="na">DoubleType</span><span class="p">);</span>
<span class="c1">// using lambda function to register the UDF</span>
<span class="n">sparkSession</span><span class="p">.</span><span class="na">udf</span><span class="p">().</span><span class="na">register</span><span class="p">(</span><span class="s">&quot;udf_bufferLength&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="p">(</span><span class="n">UDF2</span><span class="o">&lt;</span><span class="n">Geometry</span><span class="p">,</span><span class="w"> </span><span class="n">Double</span><span class="p">,</span><span class="w"> </span><span class="n">Tuple2</span><span class="o">&lt;</span><span class="n">Geometry</span><span class="p">,</span><span class="w"> </span><span class="n">Double</span><span class="o">&gt;&gt;</span><span class="p">)</span><span class="w"> </span><span class="p">(</span><span class="n">geom</span><span class="p">,</span><span class="w"> </span><span class="n">distance</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">Geometry</span><span class="w"> </span><span class="n">buffed</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">geom</span><span class="p">.</span><span class="na">buffer</span><span class="p">(</span><span class="n">distance</span><span class="p">);</span>
<span class="w"> </span><span class="n">Double</span><span class="w"> </span><span class="n">length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">buffed</span><span class="p">.</span><span class="na">getLength</span><span class="p">();</span>
<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="k">new</span><span class="w"> </span><span class="n">Tuple2</span><span class="o">&lt;&gt;</span><span class="p">(</span><span class="n">buffed</span><span class="p">,</span><span class="w"> </span><span class="n">length</span><span class="p">);</span>
<span class="w"> </span><span class="p">},</span>
<span class="w"> </span><span class="n">schemaUDF</span><span class="p">);</span>
<span class="n">df</span><span class="p">.</span><span class="na">withColumn</span><span class="p">(</span><span class="s">&quot;bufferLength&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">functions</span><span class="p">.</span><span class="na">expr</span><span class="p">(</span><span class="s">&quot;udf_bufferLength(geom, distance)&quot;</span><span class="p">))</span>
<span class="w"> </span><span class="p">.</span><span class="na">select</span><span class="p">(</span><span class="s">&quot;geom&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;distance&quot;</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;bufferLength.*&quot;</span><span class="p">)</span>
<span class="w"> </span><span class="p">.</span><span class="na">show</span><span class="p">();</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">GeometryType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pyspark.sql.types</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="n">schemaUDF</span> <span class="o">=</span> <span class="n">StructType</span><span class="p">([</span>
<span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;buffed&quot;</span><span class="p">,</span> <span class="n">GeometryType</span><span class="p">()),</span>
<span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;length&quot;</span><span class="p">,</span> <span class="n">DoubleType</span><span class="p">())</span>
<span class="p">])</span>
<span class="k">def</span><span class="w"> </span><span class="nf">bufferAndLength</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span> <span class="n">GeometryType</span><span class="p">(),</span> <span class="n">distance</span><span class="p">:</span> <span class="n">DoubleType</span><span class="p">()):</span>
<span class="n">buffed</span> <span class="o">=</span> <span class="n">geom</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">distance</span><span class="p">)</span>
<span class="n">length</span> <span class="o">=</span> <span class="n">buffed</span><span class="o">.</span><span class="n">length</span>
<span class="k">return</span> <span class="p">[</span><span class="n">buffed</span><span class="p">,</span> <span class="n">length</span><span class="p">]</span>
<span class="n">sedona</span><span class="o">.</span><span class="n">udf</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="s2">&quot;udf_bufferLength&quot;</span><span class="p">,</span> <span class="n">bufferAndLength</span><span class="p">,</span> <span class="n">schemaUDF</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s2">&quot;bufferLength&quot;</span><span class="p">,</span> <span class="n">expr</span><span class="p">(</span><span class="s2">&quot;udf_bufferLength(geom, buffer)&quot;</span><span class="p">))</span> \
<span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;geom&quot;</span><span class="p">,</span> <span class="s2">&quot;buffer&quot;</span><span class="p">,</span> <span class="s2">&quot;bufferLength.*&quot;</span><span class="p">)</span> \
<span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
</div>
</div>
</div>
<p>Output:</p>
<div class="highlight"><pre><span></span><code>+------------------------------+--------+--------------------------------------------------+-----------------+
| geom|distance| buffedGeom| length|
+------------------------------+--------+--------------------------------------------------+-----------------+
|POLYGON ((1 1, 1 2, 2 1, 1 1))| 10.0|POLYGON ((1 -9, -0.9509032201612866 -8.80785280...|66.14518337329191|
+------------------------------+--------+--------------------------------------------------+-----------------+
</code></pre></div>
<h2 id="spatial-vectorized-udfs-python-only">Spatial vectorized udfs (Python only)<a class="headerlink" href="#spatial-vectorized-udfs-python-only" title="Permanent link">&para;</a></h2>
<p>By default when you create the user defined functions in Python, the UDFs are not vectorized.
This means that the UDFs are called row by row which can be slow.
To speed up the UDFs, you can use the <code>vectorized</code> UDF which will be called in a batch mode
using Apache Arrow.</p>
<p>To create a vectorized UDF please use the decorator sedona_vectorized_udf.
Currently supports only the scalar UDFs. Vectorized UDFs are way faster than
the normal UDFs. It might be even 2x faster than the normal UDFs.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When you use geometry as an input type, please include the BaseGeometry type,
like Point from shapely or geopandas GeoSeries, when you use GEO_SERIESΒ vectorizedΒ udf.
That's how Sedona infers the type and knows if the data should be cast.</p>
</div>
<p>Decorator signature looks as follows:</p>
<div class="highlight"><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">sedona_vectorized_udf</span><span class="p">(</span>
<span class="n">udf_type</span><span class="p">:</span> <span class="n">SedonaUDFType</span> <span class="o">=</span> <span class="n">SedonaUDFType</span><span class="o">.</span><span class="n">SHAPELY_SCALAR</span><span class="p">,</span> <span class="n">return_type</span><span class="p">:</span> <span class="n">DataType</span>
<span class="p">):</span> <span class="o">...</span>
</code></pre></div>
<p>where udf_type is the type of the UDF function, currently supported are:</p>
<ul>
<li>SHAPELY_SCALAR</li>
<li>GEO_SERIES</li>
</ul>
<p>The main difference is what input data you get in the function
Let's analyze the two examples below, that creates buffers from
a given geometry.</p>
<h3 id="shapely-scalar-udf">Shapely scalar UDF<a class="headerlink" href="#shapely-scalar-udf" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">shapely.geometry.base</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">b</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">sedona_vectorized_udf</span>
<span class="nd">@sedona_vectorized_udf</span><span class="p">(</span><span class="n">return_type</span><span class="o">=</span><span class="n">GeometryType</span><span class="p">())</span>
<span class="k">def</span><span class="w"> </span><span class="nf">vectorized_buffer</span><span class="p">(</span><span class="n">geom</span><span class="p">:</span> <span class="n">b</span><span class="o">.</span><span class="n">BaseGeometry</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">b</span><span class="o">.</span><span class="n">BaseGeometry</span><span class="p">:</span>
<span class="k">return</span> <span class="n">geom</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
</code></pre></div>
<h3 id="geoseries-udf">GeoSeries UDF<a class="headerlink" href="#geoseries-udf" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">geopandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">gpd</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">sedona_vectorized_udf</span><span class="p">,</span> <span class="n">SedonaUDFType</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">GeometryType</span>
<span class="nd">@sedona_vectorized_udf</span><span class="p">(</span><span class="n">udf_type</span><span class="o">=</span><span class="n">SedonaUDFType</span><span class="o">.</span><span class="n">GEO_SERIES</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="n">GeometryType</span><span class="p">())</span>
<span class="k">def</span><span class="w"> </span><span class="nf">vectorized_geo_series_buffer</span><span class="p">(</span><span class="n">series</span><span class="p">:</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">:</span>
<span class="n">buffered</span> <span class="o">=</span> <span class="n">series</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">buffered</span>
</code></pre></div>
<p>To call the UDFs you can use the following code:</p>
<div class="highlight"><pre><span></span><code><span class="c1"># Shapely scalar UDF</span>
<span class="n">df</span><span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s2">&quot;buffered&quot;</span><span class="p">,</span> <span class="n">vectorized_buffer</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">geom</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># GeoSeries UDF</span>
<span class="n">df</span><span class="o">.</span><span class="n">withColumn</span><span class="p">(</span><span class="s2">&quot;buffered&quot;</span><span class="p">,</span> <span class="n">vectorized_geo_series_buffer</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">geom</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></div>
<h2 id="save-to-permanent-storage">Save to permanent storage<a class="headerlink" href="#save-to-permanent-storage" title="Permanent link">&para;</a></h2>
<p>To save a Spatial DataFrame to some permanent storage such as Hive tables and HDFS, you can simply convert each geometry in the Geometry type column back to a plain String and save the plain DataFrame to wherever you want.</p>
<p>Use the following code to convert the Geometry column in a DataFrame back to a WKT string column:</p>
<div class="highlight"><pre><span></span><code><span class="k">SELECT</span><span class="w"> </span><span class="n">ST_AsText</span><span class="p">(</span><span class="n">countyshape</span><span class="p">)</span>
<span class="k">FROM</span><span class="w"> </span><span class="n">polygondf</span>
</code></pre></div>
<h2 id="save-geojson">Save GeoJSON<a class="headerlink" href="#save-geojson" title="Permanent link">&para;</a></h2>
<p>Since <code>v1.6.1</code>, the GeoJSON data source in Sedona can be used to save a Spatial DataFrame to a single-line JSON file, with geometries written in GeoJSON format.</p>
<div class="highlight"><pre><span></span><code>df.write.format(&quot;geojson&quot;).save(&quot;YOUR/PATH.json&quot;)
</code></pre></div>
<p>See <a href="../files/geojson-sedona-spark/">this page</a> for more information on writing to GeoJSON.</p>
<h2 id="save-geoparquet">Save GeoParquet<a class="headerlink" href="#save-geoparquet" title="Permanent link">&para;</a></h2>
<p>Since v<code>1.3.0</code>, Sedona natively supports writing GeoParquet file. GeoParquet can be saved as follows:</p>
<div class="highlight"><pre><span></span><code><span class="n">df</span><span class="p">.</span><span class="n">write</span><span class="p">.</span><span class="n">format</span><span class="p">(</span><span class="s">&quot;geoparquet&quot;</span><span class="p">).</span><span class="n">save</span><span class="p">(</span><span class="n">geoparquetoutputlocation</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="s">&quot;/GeoParquet_File_Name.parquet&quot;</span><span class="p">)</span>
</code></pre></div>
<p>See <a href="../files/geoparquet-sedona-spark/">this page</a> for more information on writing to GeoParquet.</p>
<h2 id="save-to-postgis">Save to Postgis<a class="headerlink" href="#save-to-postgis" title="Permanent link">&para;</a></h2>
<p>Unfortunately, the Spark SQL JDBC data source doesn't support creating geometry types in PostGIS using the 'createTableColumnTypes' option.
Only the Spark built-in types are recognized.
This means that you'll need to manage your PostGIS schema separately from Spark.
One way to do this is to create the table with the correct geometry column before writing data to it with Spark.
Alternatively, you can write your data to the table using Spark and then manually alter the column to be a geometry type afterward.</p>
<p>Postgis uses EWKB to serialize geometries.
If you convert your geometries to EWKB format in Sedona you don't have to do any additional conversion in Postgis.</p>
<div class="highlight"><pre><span></span><code>my_postgis_db# create table my_table (id int8, geom geometry);
df.withColumn(&quot;geom&quot;, expr(&quot;ST_AsEWKB(geom)&quot;)
.write.format(&quot;jdbc&quot;)
.option(&quot;truncate&quot;,&quot;true&quot;) // Don&#39;t let Spark recreate the table.
// Other options.
.save()
// If you didn&#39;t create the table before writing you can change the type afterward.
my_postgis_db# alter table my_table alter column geom type geometry;
</code></pre></div>
<h2 id="convert-between-dataframe-and-spatialrdd">Convert between DataFrame and SpatialRDD<a class="headerlink" href="#convert-between-dataframe-and-spatialrdd" title="Permanent link">&para;</a></h2>
<h3 id="dataframe-to-spatialrdd">DataFrame to SpatialRDD<a class="headerlink" href="#dataframe-to-spatialrdd" title="Permanent link">&para;</a></h3>
<p>Use SedonaSQL DataFrame-RDD Adapter to convert a DataFrame to an SpatialRDD.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="19:3"><input checked="checked" id="__tabbed_19_1" name="__tabbed_19" type="radio" /><input id="__tabbed_19_2" name="__tabbed_19" type="radio" /><input id="__tabbed_19_3" name="__tabbed_19" type="radio" /><div class="tabbed-labels"><label for="__tabbed_19_1">Scala</label><label for="__tabbed_19_2">Java</label><label for="__tabbed_19_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">var</span><span class="w"> </span><span class="n">spatialRDD</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nc">StructuredAdapter</span><span class="p">.</span><span class="n">toSpatialRdd</span><span class="p">(</span><span class="n">spatialDf</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;usacounty&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">SpatialRDD</span><span class="w"> </span><span class="n">spatialRDD</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">StructuredAdapter</span><span class="p">.</span><span class="na">toSpatialRdd</span><span class="p">(</span><span class="n">spatialDf</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;usacounty&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">StructuredAdapter</span>
<span class="n">spatialRDD</span> <span class="o">=</span> <span class="n">StructuredAdapter</span><span class="o">.</span><span class="n">toSpatialRdd</span><span class="p">(</span><span class="n">spatialDf</span><span class="p">,</span> <span class="s2">&quot;usacounty&quot;</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<p>"usacounty" is the name of the geometry column. It is an optional parameter. If you don't provide it, the first geometry column will be used.</p>
<h3 id="spatialrdd-to-dataframe">SpatialRDD to DataFrame<a class="headerlink" href="#spatialrdd-to-dataframe" title="Permanent link">&para;</a></h3>
<p>Use SedonaSQL DataFrame-RDD Adapter to convert a DataFrame to an SpatialRDD. Please read <a href="../api/javadoc/sql/org/apache/sedona/sql/utils/index.html">Adapter Scaladoc</a></p>
<div class="tabbed-set tabbed-alternate" data-tabs="20:3"><input checked="checked" id="__tabbed_20_1" name="__tabbed_20" type="radio" /><input id="__tabbed_20_2" name="__tabbed_20" type="radio" /><input id="__tabbed_20_3" name="__tabbed_20" type="radio" /><div class="tabbed-labels"><label for="__tabbed_20_1">Scala</label><label for="__tabbed_20_2">Java</label><label for="__tabbed_20_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">var</span><span class="w"> </span><span class="n">spatialDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nc">StructuredAdapter</span><span class="p">.</span><span class="n">toDf</span><span class="p">(</span><span class="n">spatialRDD</span><span class="p">,</span><span class="w"> </span><span class="n">sedona</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">spatialDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">StructuredAdapter</span><span class="p">.</span><span class="na">toDf</span><span class="p">(</span><span class="n">spatialRDD</span><span class="p">,</span><span class="w"> </span><span class="n">sedona</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">StructuredAdapter</span>
<span class="n">spatialDf</span> <span class="o">=</span> <span class="n">StructuredAdapter</span><span class="o">.</span><span class="n">toDf</span><span class="p">(</span><span class="n">spatialRDD</span><span class="p">,</span> <span class="n">sedona</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<h3 id="spatialrdd-to-dataframe-with-spatial-partitioning">SpatialRDD to DataFrame with spatial partitioning<a class="headerlink" href="#spatialrdd-to-dataframe-with-spatial-partitioning" title="Permanent link">&para;</a></h3>
<p>By default, <code>StructuredAdapter.toDf()</code> does not preserve spatial partitions because doing so
may introduce duplicate features for most types of spatial data. These duplicates
are introduced on purpose to ensure correctness when performing a spatial join;
however, when using Sedona to prepare a dataset for distribution this is not typically
desired.</p>
<p>You can use <code>StructuredAdapter</code> and the <code>spatialRDD.spatialPartitioningWithoutDuplicates</code> function to obtain a Sedona DataFrame that is spatially partitioned without duplicates. This is especially useful for generating balanced GeoParquet files while preserving spatial proximity within files, which is crucial for optimizing filter pushdown performance in GeoParquet files.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="21:3"><input checked="checked" id="__tabbed_21_1" name="__tabbed_21" type="radio" /><input id="__tabbed_21_2" name="__tabbed_21" type="radio" /><input id="__tabbed_21_3" name="__tabbed_21" type="radio" /><div class="tabbed-labels"><label for="__tabbed_21_1">Scala</label><label for="__tabbed_21_2">Java</label><label for="__tabbed_21_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">spatialRDD</span><span class="p">.</span><span class="n">spatialPartitioningWithoutDuplicates</span><span class="p">(</span><span class="nc">GridType</span><span class="p">.</span><span class="nc">KDBTREE</span><span class="p">)</span>
<span class="c1">// Specify the desired number of partitions as 10, though the actual number may vary</span>
<span class="c1">// spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE, 10)</span>
<span class="kd">var</span><span class="w"> </span><span class="n">spatialDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nc">StructuredAdapter</span><span class="p">.</span><span class="n">toSpatialPartitionedDf</span><span class="p">(</span><span class="n">spatialRDD</span><span class="p">,</span><span class="w"> </span><span class="n">sedona</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">spatialRDD</span><span class="p">.</span><span class="na">spatialPartitioningWithoutDuplicates</span><span class="p">(</span><span class="n">GridType</span><span class="p">.</span><span class="na">KDBTREE</span><span class="p">)</span>
<span class="c1">// Specify the desired number of partitions as 10, though the actual number may vary</span>
<span class="c1">// spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE, 10)</span>
<span class="n">Dataset</span><span class="o">&lt;</span><span class="n">Row</span><span class="o">&gt;</span><span class="w"> </span><span class="n">spatialDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">StructuredAdapter</span><span class="p">.</span><span class="na">toSpatialPartitionedDf</span><span class="p">(</span><span class="n">spatialRDD</span><span class="p">,</span><span class="w"> </span><span class="n">sedona</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">StructuredAdapter</span>
<span class="n">spatialRDD</span><span class="o">.</span><span class="n">spatialPartitioningWithoutDuplicates</span><span class="p">(</span><span class="n">GridType</span><span class="o">.</span><span class="n">KDBTREE</span><span class="p">)</span>
<span class="c1"># Specify the desired number of partitions as 10, though the actual number may vary</span>
<span class="c1"># spatialRDD.spatialPartitioningWithoutDuplicates(GridType.KDBTREE, 10)</span>
<span class="n">spatialDf</span> <span class="o">=</span> <span class="n">StructuredAdapter</span><span class="o">.</span><span class="n">toSpatialPartitionedDf</span><span class="p">(</span><span class="n">spatialRDD</span><span class="p">,</span> <span class="n">sedona</span><span class="p">)</span>
</code></pre></div>
</div>
</div>
</div>
<h3 id="spatialpairrdd-to-dataframe">SpatialPairRDD to DataFrame<a class="headerlink" href="#spatialpairrdd-to-dataframe" title="Permanent link">&para;</a></h3>
<p>PairRDD is the result of a spatial join query or distance join query. SedonaSQL DataFrame-RDD Adapter can convert the result to a DataFrame. But you need to provide the schema of the left and right RDDs.</p>
<div class="tabbed-set tabbed-alternate" data-tabs="22:3"><input checked="checked" id="__tabbed_22_1" name="__tabbed_22" type="radio" /><input id="__tabbed_22_2" name="__tabbed_22" type="radio" /><input id="__tabbed_22_3" name="__tabbed_22" type="radio" /><div class="tabbed-labels"><label for="__tabbed_22_1">Scala</label><label for="__tabbed_22_2">Java</label><label for="__tabbed_22_3">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kd">var</span><span class="w"> </span><span class="n">joinResultDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nc">StructuredAdapter</span><span class="p">.</span><span class="n">toDf</span><span class="p">(</span><span class="n">joinResultPairRDD</span><span class="p">,</span><span class="w"> </span><span class="n">leftDf</span><span class="p">.</span><span class="n">schema</span><span class="p">,</span><span class="w"> </span><span class="n">rightDf</span><span class="p">.</span><span class="n">schema</span><span class="p">,</span><span class="w"> </span><span class="n">sedona</span><span class="p">)</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="n">Dataset</span><span class="w"> </span><span class="n">joinResultDf</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">StructuredAdapter</span><span class="p">.</span><span class="na">toDf</span><span class="p">(</span><span class="n">joinResultPairRDD</span><span class="p">,</span><span class="w"> </span><span class="n">leftDf</span><span class="p">.</span><span class="na">schema</span><span class="p">,</span><span class="w"> </span><span class="n">rightDf</span><span class="p">.</span><span class="na">schema</span><span class="p">,</span><span class="w"> </span><span class="n">sedona</span><span class="p">);</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">sedona.spark</span><span class="w"> </span><span class="kn">import</span> <span class="n">StructuredAdapter</span>
<span class="n">joinResultDf</span> <span class="o">=</span> <span class="n">StructuredAdapter</span><span class="o">.</span><span class="n">pairRddToDf</span><span class="p">(</span><span class="n">result_pair_rdd</span><span class="p">,</span> <span class="n">leftDf</span><span class="o">.</span><span class="n">schema</span><span class="p">,</span> <span class="n">rightDf</span><span class="o">.</span><span class="n">schema</span><span class="p">,</span> <span class="n">spark</span><span class="p">)</span>
</code></pre></div>
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