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@@ -197,6 +197,15 @@ Apart from these, the following properties are also available, and may be useful
Spark's dependencies and user dependencies. It is currently an experimental feature.
</td>
</tr>
+<tr>
+ <td><code>spark.python.worker.memory</code></td>
+ <td>512m</td>
+ <td>
+ Amount of memory to use per python worker process during aggregation, in the same
+ format as JVM memory strings (e.g. <code>512m</code>, <code>2g</code>). If the memory
+ used during aggregation goes above this amount, it will spill the data into disks.
+ </td>
+</tr>
</table>
#### Shuffle Behavior