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<a href="https://github.com/apache/sedona/edit/master/docs/tutorial/sql.zh.md" title="编辑此页" class="md-content__button md-icon" rel="edit">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"/></svg>
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<h1>空间 DataFrame / SQL 应用</h1>
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<p>本页介绍如何使用 SedonaSQL 管理空间数据。</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Sedona 假定地理坐标按 lon/lat 顺序排列。如果您的数据是 lat/lon 顺序，请使用 <code>ST_FlipCoordinates</code> 交换 X 与 Y。</p>
</div>
<p>SedonaSQL 支持 SQL/MM Part3 空间 SQL 标准，提供四类 SQL 算子，所有算子都可以直接通过以下方式调用：</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>SedonaSQL 详细 API 说明请参阅 <a href="../../api/sql/Overview/">SedonaSQL API</a>。示例 county 数据（即 <code>county_small.tsv</code>）可在 <a href="https://github.com/apache/sedona/tree/master/spark/common/src/test/resources">Sedona GitHub 仓库</a> 找到。</p>
<h2 id="_1">配置依赖<a class="headerlink" href="#_1" 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>阅读 <a href="../../setup/maven-coordinates/">Sedona Maven Central 坐标</a> 并在 build.sbt 或 pom.xml 中添加 Sedona 依赖。</li>
<li>在 build.sbt 或 pom.xml 中添加 <a href="https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.11">Apache Spark core</a> 与 <a href="https://mvnrepository.com/artifact/org.apache.spark/spark-sql">Apache SparkSQL</a> 依赖。</li>
<li>参考 <a href="../demo/">SQL 示例项目</a>。</li>
</ol>
</div>
<div class="tabbed-block">
<ol>
<li>请阅读 <a href="../../setup/install-python/">快速开始</a> 安装 Sedona Python。</li>
<li>本教程基于 <a href="../jupyter-notebook/">Sedona SQL Jupyter Notebook 示例</a>。</li>
</ol>
</div>
</div>
</div>
<h2 id="sedona">创建 Sedona 配置<a class="headerlink" href="#sedona" title="Permanent link">&para;</a></h2>
<p>在程序起始处使用以下代码创建 Sedona 配置。如果您已经有了由 AWS EMR / Databricks / Microsoft Fabric 创建的 SparkSession（通常名为 <code>spark</code>），请==跳过此步骤==。</p>
<p>可以在 builder 中追加额外的 Spark 运行时配置，例如 <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">// 集群模式下请删除此行</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">// 改成合适的名字</span>
<span class="p">.</span><span class="n">getOrCreate</span><span class="p">()</span>
</code></pre></div>
如果同时使用 SedonaViz 与 SedonaSQL，请在 <code>SedonaContext.builder()</code> 之后追加以下行启用 Sedona Kryo 序列化器：
<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">// 集群模式下请删除此行</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">// 改成合适的名字</span>
<span class="p">.</span><span class="na">getOrCreate</span><span class="p">()</span>
</code></pre></div>
如果同时使用 SedonaViz 与 SedonaSQL，请在 <code>SedonaContext.builder()</code> 之后追加以下行启用 Sedona Kryo 序列化器：
<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>
如使用其他 Spark 版本，请将 sedona-spark-shaded 包名中的 <code>3.3</code> 替换为对应的 Spark major.minor 版本，例如 <code>sedona-spark-shaded-3.4_2.12:1.9.1</code>。</p>
</div>
</div>
</div>
<h2 id="sedonacontext">初始化 SedonaContext<a class="headerlink" href="#sedonacontext" title="Permanent link">&para;</a></h2>
<p>在创建 Sedona 配置之后加上以下代码。如果您已经有了由 AWS EMR / Databricks / Microsoft Fabric 创建的 SparkSession（通常名为 <code>spark</code>），请改为调用 <code>sedona = SedonaContext.create(spark)</code>。</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>也可以通过在 <code>spark-submit</code> 或 <code>spark-shell</code> 中传入 <code>--conf spark.sql.extensions=org.apache.sedona.sql.SedonaSqlExtensions</code> 完成注册。</p>
<h2 id="_2">从文本文件加载数据<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<p>假设有一个 WKT 文件 <code>usa-county.tsv</code>，路径为 <code>/Download/usa-county.tsv</code>，内容如下：</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>文件中可能还有许多其他列。</p>
<h3 id="dataframe">加载原始 DataFrame<a class="headerlink" href="#dataframe" title="Permanent link">&para;</a></h3>
<p>使用以下代码加载数据并创建原始 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>输出大致如下：</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="geometry">创建 Geometry 类型列<a class="headerlink" href="#geometry" title="Permanent link">&para;</a></h3>
<p>SedonaSQL 中所有几何运算都作用在 Geometry 类型对象上。因此在执行任何查询之前，需要在 DataFrame 上构造一列 Geometry 类型列。</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>可以选取更多属性来组成这个 <code>spatialdDf</code>。输出大致如下：</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>虽然外观与输入一致，但 <code>countyshape</code> 列的类型已经变为 <mark>Geometry</mark>。</p>
<p>可以通过打印 schema 进行验证：</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>输出如下：</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 提供了大量构造 Geometry 列的函数，详见 <a href="../../api/sql/Geometry-Functions/">SedonaSQL 构造器 API</a>。</p>
</div>
<h2 id="geojson">加载 GeoJSON 数据<a class="headerlink" href="#geojson" title="Permanent link">&para;</a></h2>
<p>自 <code>v1.6.1</code> 起，Sedona 支持通过 <code>geojson</code> 数据源读取 GeoJSON 文件。它专门用于处理几何对象采用 <a href="https://datatracker.ietf.org/doc/html/rfc7946">GeoJSON 格式</a> 的 JSON 文件。</p>
<p>读取多行 GeoJSON 文件请将 <code>multiLine</code> 选项设为 <code>True</code>。</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"># 展开 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"># 解包 features 结构体</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">// 展开 envelope，每行一个 feature</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">// 解包 features 结构体</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>加载 GeoJSON 文件的更多信息请参阅 <a href="../files/geojson-sedona-spark/">此页</a>。</p>
<h2 id="shapefile">加载 Shapefile<a class="headerlink" href="#shapefile" title="Permanent link">&para;</a></h2>
<p>自 v<code>1.7.0</code> 起，Sedona 支持把 Shapefile 加载为 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>输入路径既可以是包含一个或多个 Shapefile 的目录，也可以是 <code>.shp</code> 文件本身。</p>
<p>加载 Shapefile 的更多信息请参阅 <a href="../files/shapefiles-sedona-spark/">此页</a>。</p>
<h2 id="geoparquet">加载 GeoParquet<a class="headerlink" href="#geoparquet" title="Permanent link">&para;</a></h2>
<p>自 v<code>1.3.0</code> 起，Sedona 原生支持加载 GeoParquet 文件。Sedona 会通过 GeoParquet 文件中的 “geo” 元数据推断几何字段。</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>输出如下：</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 支持 GeoParquet 文件的空间谓词下推，详见 <a href="../../api/sql/Optimizer/">SedonaSQL 查询优化器</a> 文档。</p>
<p>GeoParquet 文件读取器也可用于读取 Apache Sedona 1.3.1-incubating 及更早版本写出的旧版 Parquet 文件，详见 <a href="../../api/sql/Reading-legacy-parquet/">读取旧版 Parquet 文件</a>。</p>
<p>加载 GeoParquet 的更多信息请参阅 <a href="../files/geoparquet-sedona-spark/">此页</a>。</p>
<h2 id="stac-catalog">从 STAC catalog 加载数据<a class="headerlink" href="#stac-catalog" title="Permanent link">&para;</a></h2>
<p>Sedona 的 STAC 数据源允许从 SpatioTemporal Asset Catalog（STAC）API 读取数据，支持读取 STAC items 与 collections。</p>
<p>可以从 S3 上的 collection 文件加载 STAC collection：</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>也可以从 HTTP/HTTPS 端点加载：</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>STAC 数据源支持空间和时间过滤的下推，可以将这些过滤直接下推到底层数据源以减少需要读取的数据量。</p>
<p>加载 STAC 数据的更多信息请参阅 <a href="../files/stac-sedona-spark/">此页</a>。</p>
<h2 id="jdbc">从 JDBC 数据源加载数据<a class="headerlink" href="#jdbc" title="Permanent link">&para;</a></h2>
<p>可以使用 Spark SQL JDBC 数据源的 'query' 选项把几何列转换成 Sedona 能识别的格式。该方式适用于大多数支持空间的 JDBC 数据源。Postgis 由于使用 EWKB 作为其传输格式，无需添加查询进行类型转换。</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">// 适用于任意 JDBC 数据源（包括 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">// 其他选项</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">// Postgis 上的简化写法</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">// 其他选项</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">// 适用于任意 JDBC 数据源（包括 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">// 其他选项</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">// Postgis 上的简化写法</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">// 其他选项</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"># 适用于任意 JDBC 数据源（包括 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"># 其他选项</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"># Postgis 上的简化写法</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"># 其他选项</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="geopackage">加载 GeoPackage<a class="headerlink" href="#geopackage" title="Permanent link">&para;</a></h2>
<p>自 v1.7.0 起，Sedona 支持把 GeoPackage 文件加载为 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>加载 GeoPackage 的更多信息请参阅 <a href="../files/geopackage-sedona-spark/">此页</a>。</p>
<h2 id="osm-pbf">加载 OSM PBF<a class="headerlink" href="#osm-pbf" title="Permanent link">&para;</a></h2>
<p>自 v1.7.1 起，Sedona 支持把 OSM PBF 文件加载为 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 文件可以包含 nodes、ways 与 relations。Sedona 目前支持 Nodes、DenseNodes、Ways 与 Relations。加载后的 DataFrame schema 如下：</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>各字段含义：</p>
<ul>
<li><code>id</code>：对象的唯一标识。</li>
<li><code>kind</code>：对象类型，可能为 <code>node</code>、<code>way</code> 或 <code>relation</code>。</li>
<li><code>location</code>：对象的位置，包含 <code>longitude</code> 与 <code>latitude</code>。</li>
<li><code>tags</code>：键值对的 map，表示对象的 tags。</li>
<li><code>refs</code>：对象引用的数组。</li>
<li><code>ref_roles</code>：引用对应的角色数组。</li>
<li><code>ref_types</code>：引用对应的类型数组。</li>
</ul>
<p>nodes 的 DataFrame 大致如下：</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>ways 的大致样子：</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>relations 的大致样子：</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; rząd 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="_3">转换坐标参考系<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h2>
<p>Sedona 不会自动管理一列 Geometry 中所有几何对象的坐标单位（基于度还是基于米）。SedonaSQL 中所有相关距离的单位与 Geometry 列中几何对象的单位保持一致。</p>
<p>自 <code>v1.5.0</code> 起，该函数默认使用 lon/lat 顺序（之前为 lat/lon 顺序）。可以使用 <mark>ST_FlipCoordinates</mark> 交换 X 与 Y。</p>
<p>更多细节请参阅 Sedona API 参考中的 <code>ST_Transform</code> 章节。</p>
<p>转换前述 Geometry 列的坐标参考系：</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><code>ST_Transform</code> 第一个 EPSG 代码 EPSG:4326 是源 CRS——也就是最常见的基于度的 CRS（WGS84）。</p>
<p>第二个 EPSG 代码 EPSG:3857 是目标 CRS——最常见的基于米的 CRS。</p>
<p><code>ST_Transform</code> 会把这些几何对象的 CRS 从 EPSG:4326 转换到 EPSG:3857。详细 CRS 信息可在 <a href="https://epsg.io/">EPSG.io</a> 找到。</p>
<p>多边形坐标已发生变化。输出大致如下：</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="dbscan">使用 DBSCAN 进行聚类<a class="headerlink" href="#dbscan" title="Permanent link">&para;</a></h2>
<p>Sedona 提供 <a href="https://en.wikipedia.org/wiki/Dbscan">DBSCAN</a> 算法的实现，用于对空间数据进行聚类。</p>
<p>该算法以 Scala 与 Python 函数的形式提供，作用在空间 DataFrame 上。返回的 DataFrame 中会增加两列：所属簇的唯一标识，以及一个表示该记录是否为核心点的布尔列。</p>
<p>第一个参数是 DataFrame，后两个是 DBSCAN 的 epsilon 与 min_points 参数。</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>输出大致如下：</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>DBSCAN 算法的更多内容请参阅 <a href="../concepts/clustering-algorithms/">此页</a>。</p>
<h2 id="local-outlier-factorlof">计算 Local Outlier Factor（LOF）<a class="headerlink" href="#local-outlier-factorlof" title="Permanent link">&para;</a></h2>
<p>Sedona 提供 <a href="https://en.wikipedia.org/wiki/Local_outlier_factor">Local Outlier Factor</a> 算法的实现，用于识别空间数据中的异常点。</p>
<p>该算法以 Scala 与 Python 函数的形式提供，作用在空间 DataFrame 上。返回的 DataFrame 中会增加一列保存 local outlier factor。</p>
<p>第一个参数是 DataFrame，第二个参数是计算评分时考虑的最近邻数量。</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>输出大致如下：</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="getis-ord-gi">执行 Getis-Ord Gi(*) 热点分析<a class="headerlink" href="#getis-ord-gi" title="Permanent link">&para;</a></h2>
<p>Sedona 提供 <a href="https://en.wikipedia.org/wiki/Getis%E2%80%93Ord_statistics">Gi 与 Gi*</a> 算法的实现，用于识别空间数据中的局部热点。</p>
<p>该算法以 Scala 与 Python 函数的形式提供，作用在空间 DataFrame 上。返回的 DataFrame 中会增加 G 统计量、E[G]、V[G]、Z 分数与 p 值等列。</p>
<p>使用 Gi 时，先为每条记录生成邻居列表，再调用 g_local 函数。</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>输出大致如下：</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="_4">执行空间查询<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h2>
<p>构造完 Geometry 类型列后，即可执行各类空间查询。</p>
<h3 id="_5">范围查询<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h3>
<p>使用 <mark>ST_Contains</mark>、<mark>ST_Intersects</mark>、<mark>ST_Within</mark> 在单列上执行范围查询。</p>
<p>下例查找位于给定多边形内的所有 county：</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>了解如何创建 Geometry 类型的查询窗口请参阅 <a href="../../api/sql/Geometry-Functions/">SedonaSQL 构造器 API</a>。</p>
</div>
<h3 id="knn">KNN 查询<a class="headerlink" href="#knn" title="Permanent link">&para;</a></h3>
<p>使用 <mark>ST_Distance</mark> 计算距离并排序。</p>
<p>下面的代码返回距离给定多边形最近的 5 个对象：</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="_6">连接查询<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h3>
<p>连接查询的细节请参阅 <a href="../../api/sql/Optimizer/">Join query</a>。</p>
<h3 id="knn_1">KNN 连接查询<a class="headerlink" href="#knn_1" title="Permanent link">&para;</a></h3>
<p>KNN 连接查询的细节请参阅 <a href="../../api/sql/NearestNeighbourSearching/">KNN join query</a>。</p>
<h3 id="_7">其他查询<a class="headerlink" href="#_7" title="Permanent link">&para;</a></h3>
<p>还有许多函数可以与上述查询组合使用，详见 <a href="../../api/sql/Overview/">SedonaSQL 函数</a> 与 <a href="../../api/sql/Geometry-Functions/#aggregate-functions">SedonaSQL 聚合函数</a>。</p>
<h2 id="_8">可视化查询结果<a class="headerlink" href="#_8" title="Permanent link">&para;</a></h2>
<p>Sedona 提供 <code>SedonaPyDeck</code> 与 <code>SedonaKepler</code> 两种封装，都提供了在 Jupyter 环境中基于 SedonaDataFrame 创建交互式地图可视化的 API。</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>SedonaPyDeck</code> 与 <code>SedonaKepler</code> 默认要求几何对象按 lon-lat 顺序排列。如果您的 DataFrame 是 lat-lon 顺序，请使用 <a href="../../api/sql/Geometry-Editors/ST_FlipCoordinates/">ST_FlipCoordinates</a>。</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code>SedonaPyDeck</code> 与 <code>SedonaKepler</code> 设计上仅处理只含 1 个几何列的 SedonaDataFrame，传入包含多个几何列的 DataFrame 会出错。</p>
</div>
<h3 id="sedonapydeck">SedonaPyDeck<a class="headerlink" href="#sedonapydeck" title="Permanent link">&para;</a></h3>
<p>可在 Jupyter Lab/Notebook 中通过 SedonaPyDeck 可视化空间查询结果。</p>
<p>SedonaPyDeck 基于 <a href="https://deckgl.readthedocs.io">pydeck</a>（构建于 <a href="https://deck.gl/">deck.gl</a> 之上）提供创建交互式地图的 API。</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>使用 SedonaPyDeck 需要安装 sedona 的 <code>pydeck-map</code> 附加项：
<div class="highlight"><pre><span></span><code>pip install apache-sedona[pydeck-map]
</code></pre></div></p>
</div>
<p>下面的教程展示了 SedonaPyDeck 可创建的多种地图，所用数据集均为公开数据。</p>
<p>每个 SedonaPyDeck API 都通过可选参数提供定制能力，所有可用参数详见 <a href="../../api/sql/Visualization-SedonaPyDeck/">SedonaPyDeck API 文档</a>。</p>
<h4 id="sedonapydeck-choropleth">使用 SedonaPyDeck 创建 Choropleth 地图<a class="headerlink" href="#sedonapydeck-choropleth" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck 的 <code>create_choropleth_map</code> API 可基于包含多边形与观测值的 SedonaDataFrame 创建 choropleth 地图：</p>
<p>示例：</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> 是必填参数，用于告诉 SedonaPyDeck 渲染 choropleth 时使用哪一列。</p>
</div>
<p><img alt="使用 SedonaPyDeck 创建 Choropleth 地图" src="../../../image/choropleth.gif" /></p>
<p>所用数据集见 <a href="https://github.com/apache/sedona/tree/b66e768155866a38ba2e3404f1151cac14fad5ea/docs/usecases/data/ne_50m_airports">此处</a>，对应示例 notebook 见 <a href="https://github.com/apache/sedona/blob/master/docs/usecases/legacy/ApacheSedonaSQL_SpatialJoin_AirportsPerCountry.ipynb">此处</a>。</p>
<h4 id="sedonapydeck-geometry">使用 SedonaPyDeck 创建 Geometry 地图<a class="headerlink" href="#sedonapydeck-geometry" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck 的 <code>create_geometry_map</code> API 可可视化任意类型几何对象的 SedonaDataFrame：</p>
<p>示例：</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="使用 SedonaPyDeck 创建 Geometry 地图" src="../../../image/buildings.gif" /></p>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p><code>elevation_col</code> 是可选参数，用于渲染 3D 地图。请把对应几何对象 “高度” 信息所在的列名传入此参数。</p>
</div>
<h4 id="sedonapydeck-scatterplot">使用 SedonaPyDeck 创建 Scatterplot 地图<a class="headerlink" href="#sedonapydeck-scatterplot" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck 的 <code>create_scatterplot_map</code> API 可基于包含点的 SedonaDataFrame 创建散点地图：</p>
<p>示例：</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="使用 SedonaPyDeck 创建 Scatterplot 地图" src="../../../image/points.gif" /></p>
<p>所用的是 Chicago crimes 数据集，见 <a href="https://github.com/apache/sedona/blob/sedona-1.5.0/spark/common/src/test/resources/Chicago_Crimes.csv">此处</a>。</p>
<h4 id="sedonapydeck_1">使用 SedonaPyDeck 创建热力图<a class="headerlink" href="#sedonapydeck_1" title="Permanent link">&para;</a></h4>
<p>SedonaPyDeck 的 <code>create_heatmap</code> API 可基于包含点的 SedonaDataFrame 创建热力图：</p>
<p>示例：</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="使用 SedonaPyDeck 创建热力图" src="../../../image/heatmap.gif" /></p>
<p>所用同样是 Chicago crimes 数据集，见 <a href="https://github.com/apache/sedona/blob/sedona-1.5.0/spark/common/src/test/resources/Chicago_Crimes.csv">此处</a>。</p>
<h3 id="sedonakepler">SedonaKepler<a class="headerlink" href="#sedonakepler" title="Permanent link">&para;</a></h3>
<p>可在 Jupyter Lab/Notebook 中通过 SedonaKepler 可视化空间查询结果。</p>
<p>SedonaKepler 提供基于 <a href="https://kepler.gl/">KeplerGl</a> 的交互式、可定制地图可视化 API。</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>使用 SedonaKepler 需要安装 sedona 的 <code>kepler-map</code> 附加项：
<div class="highlight"><pre><span></span><code>pip install apache-sedona[kepler-map]
</code></pre></div></p>
</div>
<p>下面演示如何使用 SedonaKepler 立即可视化地理空间数据。</p>
<p>示例（取自 binder 提供的示例 notebook）：</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="使用 SedonaKepler 可视化地理空间数据" src="../../../image/sedona_customization.gif" /></p>
<p>所用数据集见 <a href="https://github.com/apache/sedona/tree/b66e768155866a38ba2e3404f1151cac14fad5ea/docs/usecases/data/ne_50m_airports">此处</a>，示例 notebook 见 <a href="https://github.com/apache/sedona/blob/master/docs/usecases/legacy/ApacheSedonaSQL_SpatialJoin_AirportsPerCountry.ipynb">此处</a>。</p>
<p>SedonaKepler 提供的所有 API 详见 <a href="../../api/sql/Visualization-SedonaKepler/">SedonaKepler API 文档</a>。</p>
<h2 id="udf">创建用户自定义函数（UDF）<a class="headerlink" href="#udf" title="Permanent link">&para;</a></h2>
<p>用户自定义函数（UDF）是用户编写的函数，可以对单行数据执行操作。为了覆盖几乎所有的使用场景，下面给出 4 种不同形式的 UDF 示例，演示如何在 UDF 中使用几何对象。Sedona 的序列化器会把 SQL 几何类型反序列化为 <a href="https://locationtech.github.io/jts/javadoc-1.18.0/org/locationtech/jts/geom/Geometry.html">JTS Geometry</a>（Scala/Java）或 <a href="https://shapely.readthedocs.io/en/stable/geometry.html">Shapely Geometry</a>（Python），可以充分利用这两个生态实现自定义逻辑。</p>
<h3 id="geometry-primitive">Geometry 转 primitive<a class="headerlink" href="#geometry-primitive" title="Permanent link">&para;</a></h3>
<p>下例 UDF 接收 Geometry 类型输入，返回基本类型输出：</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">// 使用 lambda 注册 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>输出：</p>
<div class="highlight"><pre><span></span><code>+--------------------+
|udf_lengthPoly(geom)|
+--------------------+
| 3.414213562373095|
+--------------------+
</code></pre></div>
<h3 id="geometry-geometry">Geometry 转 Geometry<a class="headerlink" href="#geometry-geometry" title="Permanent link">&para;</a></h3>
<p>下例 UDF 接收 Geometry 类型输入，返回 Geometry 类型输出：</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">// 使用 lambda 注册 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>输出：</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-geometry">Geometry + primitive 转 Geometry<a class="headerlink" href="#geometry-primitive-geometry" title="Permanent link">&para;</a></h3>
<p>下例 UDF 接收 Geometry 与基本类型两个输入，返回 Geometry 输出：</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">// 使用 lambda 注册 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>输出：</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-geometry-primitive">Geometry + primitive 转 Geometry + primitive<a class="headerlink" href="#geometry-primitive-geometry-primitive" title="Permanent link">&para;</a></h3>
<p>下例 UDF 接收 Geometry 与基本类型两个输入，同时返回 Geometry 与基本类型两个输出：</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">// 使用 lambda 注册 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>输出：</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="udf-python">空间向量化 UDF（仅 Python）<a class="headerlink" href="#udf-python" title="Permanent link">&para;</a></h2>
<p>默认情况下，您在 Python 中创建的 UDF 不是向量化的，会逐行调用，速度较慢。可以使用 <code>vectorized</code> UDF，通过 Apache Arrow 以批模式执行，从而加速。</p>
<p>要创建向量化 UDF，请使用 <code>sedona_vectorized_udf</code> 装饰器。当前仅支持标量 UDF，向量化 UDF 通常比普通 UDF 快得多，可能达到 2 倍。</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>当输入类型为几何对象时，请在使用 GEO_SERIES 向量化 UDF 时显式包含 BaseGeometry 类型（例如 shapely 的 Point 或 GeoPandas 的 GeoSeries），Sedona 会借此推断类型并判断数据是否需要转换。</p>
</div>
<p>装饰器签名如下：</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>其中 <code>udf_type</code> 是 UDF 函数的类型，目前支持：</p>
<ul>
<li>SHAPELY_SCALAR</li>
<li>GEO_SERIES</li>
</ul>
<p>主要差异在于函数所接收的输入数据形式不同。下面通过两个示例对几何对象执行 buffer 操作来说明。</p>
<h3 id="shapely-udf">Shapely 标量 UDF<a class="headerlink" href="#shapely-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>调用 UDF 的方式如下：</p>
<div class="highlight"><pre><span></span><code><span class="c1"># Shapely 标量 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="_9">保存到永久存储<a class="headerlink" href="#_9" title="Permanent link">&para;</a></h2>
<p>要把空间 DataFrame 保存到 Hive 表、HDFS 等永久存储，最简单的做法是把 Geometry 列里的每个几何对象转换回普通字符串，再把得到的 DataFrame 保存到任意位置。</p>
<p>把 DataFrame 中的 Geometry 列还原为 WKT 字符串列：</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="geojson_1">保存为 GeoJSON<a class="headerlink" href="#geojson_1" title="Permanent link">&para;</a></h2>
<p>自 <code>v1.6.1</code> 起，Sedona 中的 GeoJSON 数据源可把空间 DataFrame 保存为单行 JSON 文件，几何对象按 GeoJSON 格式写出：</p>
<div class="highlight"><pre><span></span><code>df.write.format(&quot;geojson&quot;).save(&quot;YOUR/PATH.json&quot;)
</code></pre></div>
<p>写出 GeoJSON 的更多信息请参阅 <a href="../files/geojson-sedona-spark/">此页</a>。</p>
<h2 id="geoparquet_1">保存为 GeoParquet<a class="headerlink" href="#geoparquet_1" title="Permanent link">&para;</a></h2>
<p>自 v<code>1.3.0</code> 起，Sedona 原生支持写出 GeoParquet 文件：</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>写出 GeoParquet 的更多信息请参阅 <a href="../files/geoparquet-sedona-spark/">此页</a>。</p>
<h2 id="postgis">保存到 Postgis<a class="headerlink" href="#postgis" title="Permanent link">&para;</a></h2>
<p>遗憾的是，Spark SQL JDBC 数据源的 'createTableColumnTypes' 选项不支持在 PostGIS 中创建几何类型，仅识别 Spark 内置类型。这意味着您需要在 Spark 之外单独管理 PostGIS 的 schema。一种做法是先在 PostGIS 中创建好包含几何列的表，再用 Spark 写入数据；也可以先用 Spark 写入数据，然后再手动把列类型改为 geometry。</p>
<p>PostGIS 使用 EWKB 序列化几何对象。如果您在 Sedona 中把几何对象转换为 EWKB 格式，PostGIS 端就无需再做额外转换。</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;) // 不让 Spark 重建表
// 其他选项
.save()
// 如果写入前没有创建表，可以在写入后修改列类型
my_postgis_db# alter table my_table alter column geom type geometry;
</code></pre></div>
<h2 id="dataframe-spatialrdd">在 DataFrame 与 SpatialRDD 之间互转<a class="headerlink" href="#dataframe-spatialrdd" title="Permanent link">&para;</a></h2>
<h3 id="dataframe-spatialrdd_1">DataFrame 转 SpatialRDD<a class="headerlink" href="#dataframe-spatialrdd_1" title="Permanent link">&para;</a></h3>
<p>使用 SedonaSQL 的 DataFrame-RDD Adapter 将 DataFrame 转换为 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><code>"usacounty"</code> 是几何列的列名，可选参数。如果不提供，将使用第一个几何列。</p>
<h3 id="spatialrdd-dataframe">SpatialRDD 转 DataFrame<a class="headerlink" href="#spatialrdd-dataframe" title="Permanent link">&para;</a></h3>
<p>使用 SedonaSQL 的 DataFrame-RDD Adapter 将 SpatialRDD 转换为 DataFrame。详见 <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-dataframe_1">保留空间分区的 SpatialRDD 转 DataFrame<a class="headerlink" href="#spatialrdd-dataframe_1" title="Permanent link">&para;</a></h3>
<p><code>StructuredAdapter.toDf()</code> 默认不保留空间分区，因为对于大多数空间数据保留空间分区会引入重复要素。这些重复是为了在执行空间连接时保证正确性而有意引入的；但当使用 Sedona 准备数据用于分发时，通常不希望出现这些重复。</p>
<p>可以使用 <code>StructuredAdapter</code> 与 <code>spatialRDD.spatialPartitioningWithoutDuplicates</code> 函数得到不含重复的、按空间分区的 Sedona DataFrame。这在生成均衡的 GeoParquet 文件时特别有用，可以在文件内保留空间邻近性，从而最大化 GeoParquet 的过滤下推性能。</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">// 期望分区数为 10（实际数量可能略有不同）</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">// 期望分区数为 10（实际数量可能略有不同）</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"># 期望分区数为 10（实际数量可能略有不同）</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-dataframe">SpatialPairRDD 转 DataFrame<a class="headerlink" href="#spatialpairrdd-dataframe" title="Permanent link">&para;</a></h3>
<p>PairRDD 是空间连接查询或距离连接查询的结果。SedonaSQL 的 DataFrame-RDD Adapter 可以把它转换为 DataFrame，但需要提供左、右 RDD 的 schema。</p>
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<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>
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<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>
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<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>
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