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authorReynold Xin <rxin@databricks.com>2016-10-15 22:31:37 -0700
committerReynold Xin <rxin@databricks.com>2016-10-15 22:31:37 -0700
commit72a6e7a57a63aba69f26c84bf68a5fb213d2a521 (patch)
treeeed796602ea7d8e479754612c06a006b9757fec9 /docs
parented1463341455830b8867b721a1b34f291139baf3 (diff)
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Revert "[SPARK-17637][SCHEDULER] Packed scheduling for Spark tasks across executors"
This reverts commit ed1463341455830b8867b721a1b34f291139baf3. The patch merged had obvious quality and documentation issue. The idea is useful, and we should work towards improving its quality and merging it in again.
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diff --git a/docs/configuration.md b/docs/configuration.md
index 6f3fbeb76c..373e22d71a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1334,17 +1334,6 @@ Apart from these, the following properties are also available, and may be useful
Should be greater than or equal to 1. Number of allowed retries = this value - 1.
</td>
</tr>
-<tr>
- <td><code>spark.task.assigner</code></td>
- <td>org.apache.spark.scheduler.RoundRobinAssigner</td>
- <td>
- The strategy of how to allocate tasks among workers with free cores.
- By default, round robin with randomness is used.
- org.apache.spark.scheduler.BalancedAssigner tries to balance the task across all workers (allocating tasks to
- workers with most free cores). org.apache.spark.scheduler.PackedAssigner tries to allocate tasks to workers
- with the least free cores, which may help releasing the resources when dynamic allocation is enabled.
- </td>
-</tr>
</table>
#### Dynamic Allocation