Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/10453#discussion_r49127420
--- Diff: docs/configuration.md ---
@@ -1600,6 +1600,34 @@ Apart from these, the following properties are also
available, and may be useful
How many batches the Spark Streaming UI and status APIs remember
before garbage collecting.
</td>
</tr>
+<tr>
+
<td><code>spark.streaming.driver.writeAheadLog.closeFileAfterWrite</code></td>
+ <td>false</td>
+ <td>
+ Whether to close the file after writing a write ahead log record on
the driver. Because S3 doesn't
+ support flushing of data, when using S3 for checkpointing, you should
enable it to achieve read
+ after write consistency.
+ </td>
+</tr>
+<tr>
+
<td><code>spark.streaming.receiver.writeAheadLog.closeFileAfterWrite</code></td>
+ <td>false</td>
+ <td>
+ Whether to close the file after writing a write ahead log record on
the receivers. Because S3
+ doesn't support flushing of data, when using S3 for checkpointing, you
should enable it to
+ achieve read after write consistency.
+ </td>
+</tr>
+<tr>
+ <td><code>spark.streaming.driver.writeAheadLog.allowBatching</code></td>
+ <td>true</td>
+ <td>
+ Whether to batch write ahead logs on the driver to write. When using
S3 for checkpointing, write
+ operations on the driver usually take too long. Enabling batching
write ahead logs will improve
--- End diff --
Whether to batch writes to the metadata WAL in the driver. This is useful
from improving performance for file systems like S3 where the write latency is
high and / or scenarios with large number of receivers.
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