"resolved attribute(s) missing" when performing join on pySpark : <p>I have the following two pySpark dataframe:</p>

<pre><code>&gt; df_lag_pre.columns
['date','sku','name','country','ccy_code','quantity','usd_price','usd_lag','lag_quantity']

&gt; df_unmatched.columns
['alt_sku', 'alt_lag_quantity', 'country', 'ccy_code', 'name', 'usd_price']
</code></pre>

<p>Now I want to join them on common columns, so I try the following:</p>

<pre><code>&gt; df_lag_pre.join(df_unmatched, on=['name','country','ccy_code','usd_price'])
</code></pre>

<p>And I get the following error message:</p>

<pre><code>AnalysisException: u'resolved attribute(s) price#3424 missing from country#3443,month#801,price#808,category#803,subcategory#804,page#805,date#280,link#809,name#806,quantity#807,ccy_code#3439,sku#3004,day#802 in operator !EvaluatePython PythonUDF#&lt;lambda&gt;(ccy_code#3439,price#3424), pythonUDF#811: string;'
</code></pre>

<p>Some of the columns that show up on this error, such as price, were part of another dataframe from which <code>df_lag</code> was built from. I can't find any info on how to interpret this message, so any help would be greatly appreciated.</p>
