title: spark 'join with condition' versus 'join with filter', which is better?
body: <p>I want to join two dataframes based on some condition. <strong>Which of the two approaches has better performance characteristics?</strong></p>
  <p><strong>first approach</strong></p>
  <pre class="lang-py prettyprint-override"><code>df1.join(df2, (df1(&quot;numer&quot;) === df2(&quot;numer&quot;)), &quot;full&quot;)
  </code></pre>
  <p><strong>second approach</strong></p>
  <pre class="lang-py prettyprint-override"><code>df1.join(df2, how=&quot;full&quot;).filter(df1(&quot;numer&quot;) === df2(&quot;numer&quot;))
  </code></pre>