diff --git a/doc/source/merging.rst b/doc/source/merging.rst
index c6541a26c72b4..f95987afd4c77 100644
--- a/doc/source/merging.rst
+++ b/doc/source/merging.rst
@@ -692,6 +692,29 @@ either the left or right tables, the values in the joined table will be
    p.plot([left, right], result,
           labels=['left', 'right'], vertical=False);
    plt.close('all');
+   
+Here is another example with duplicate join keys in DataFrames:
+
+.. ipython:: python
+
+   left = pd.DataFrame({'A' : [1,2], 'B' : [2, 2]})
+
+   right = pd.DataFrame({'A' : [4,5,6], 'B': [2,2,2]})
+
+   result = pd.merge(left, right, on='B', how='outer')
+
+.. ipython:: python
+   :suppress:
+
+   @savefig merging_merge_on_key_dup.png
+   p.plot([left, right], result,
+          labels=['left', 'right'], vertical=False);
+   plt.close('all');
+   
+.. warning::
+
+  Joining / merging on duplicate keys can cause a returned frame that is the multiplication of the row dimensions, 
+  may result in memory overflow. It is the user' s responsibility to manage duplicate values in keys before joining large DataFrames.
 
 .. _merging.indicator: