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<table width="100%" summary="page for intersectAll {SparkR}"><tr><td>intersectAll {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table>
<h2>intersectAll</h2>
<h3>Description</h3>
<p>Return a new SparkDataFrame containing rows in both this SparkDataFrame
and another SparkDataFrame while preserving the duplicates.
This is equivalent to <code>INTERSECT ALL</code> in SQL. Also as standard in
SQL, this function resolves columns by position (not by name).
</p>
<h3>Usage</h3>
<pre>
intersectAll(x, y)
## S4 method for signature 'SparkDataFrame,SparkDataFrame'
intersectAll(x, y)
</pre>
<h3>Arguments</h3>
<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
<p>a SparkDataFrame.</p>
</td></tr>
<tr valign="top"><td><code>y</code></td>
<td>
<p>a SparkDataFrame.</p>
</td></tr>
</table>
<h3>Value</h3>
<p>A SparkDataFrame containing the result of the intersect all operation.
</p>
<h3>Note</h3>
<p>intersectAll since 2.4.0
</p>
<h3>See Also</h3>
<p>Other SparkDataFrame functions:
<code><a href="SparkDataFrame.html">SparkDataFrame-class</a></code>,
<code><a href="summarize.html">agg</a>()</code>,
<code><a href="alias.html">alias</a>()</code>,
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</p>
<h3>Examples</h3>
<pre><code class="r">## Not run:
##D sparkR.session()
##D df1 &lt;- read.json(path)
##D df2 &lt;- read.json(path2)
##D intersectAllDF &lt;- intersectAll(df1, df2)
## End(Not run)
</code></pre>
<hr /><div style="text-align: center;">[Package <em>SparkR</em> version 2.4.7 <a href="00Index.html">Index</a>]</div>
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