[SPARK-12507][STREAMING][DOCUMENT] Expose closeFileAfterWrite and...

[SPARK-12507][STREAMING][DOCUMENT] Expose closeFileAfterWrite and allowBatching configurations for Streaming

/cc tdas brkyvz

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10453 from zsxwing/streaming-conf.

docs/configuration.md

@@ -1574,6 +1574,24 @@ 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. Set this to 'true'
+    when you want to use S3 (or any file system that does not support flushing) for the metadata WAL
+    on the driver.
+  </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. Set this to 'true'
+    when you want to use S3 (or any file system that does not support flushing) for the data WAL
+    on the receivers.
+  </td>
+</tr>
 </table>
 
 #### SparkR

docs/streaming-programming-guide.md

@@ -1985,7 +1985,11 @@ To run a Spark Streaming applications, you need to have the following.
   to increase aggregate throughput. Additionally, it is recommended that the replication of the
   received data within Spark be disabled when the write ahead log is enabled as the log is already
   stored in a replicated storage system. This can be done by setting the storage level for the
-  input stream to `StorageLevel.MEMORY_AND_DISK_SER`.
+  input stream to `StorageLevel.MEMORY_AND_DISK_SER`. While using S3 (or any file system that
+  does not support flushing) for _write ahead logs_, please remember to enable
+  `spark.streaming.driver.writeAheadLog.closeFileAfterWrite` and
+  `spark.streaming.receiver.writeAheadLog.closeFileAfterWrite`. See
+  [Spark Streaming Configuration](configuration.html#spark-streaming) for more details.
 
 - *Setting the max receiving rate* - If the cluster resources is not large enough for the streaming
   application to process data as fast as it is being received, the receivers can be rate limited
@@ -2023,12 +2027,6 @@ contains serialized Scala/Java/Python objects and trying to deserialize objects
 modified classes may lead to errors. In this case, either start the upgraded app with a different
 checkpoint directory, or delete the previous checkpoint directory.
 
-### Other Considerations
-{:.no_toc}
-If the data is being received by the receivers faster than what can be processed,
-you can limit the rate by setting the [configuration parameter](configuration.html#spark-streaming)
-`spark.streaming.receiver.maxRate`.
-
 ***
 
 ## Monitoring Applications