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Posted to commits@spark.apache.org by td...@apache.org on 2016/01/08 02:37:50 UTC
spark git commit: [SPARK-12507][STREAMING][DOCUMENT] Expose
closeFileAfterWrite and allowBatching configurations for Streaming
Repository: spark
Updated Branches:
refs/heads/master 5a4021998 -> c94199e97
[SPARK-12507][STREAMING][DOCUMENT] Expose closeFileAfterWrite and allowBatching configurations for Streaming
/cc tdas brkyvz
Author: Shixiong Zhu <sh...@databricks.com>
Closes #10453 from zsxwing/streaming-conf.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/c94199e9
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/c94199e9
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/c94199e9
Branch: refs/heads/master
Commit: c94199e977279d9b4658297e8108b46bdf30157b
Parents: 5a40219
Author: Shixiong Zhu <sh...@databricks.com>
Authored: Thu Jan 7 17:37:46 2016 -0800
Committer: Tathagata Das <ta...@gmail.com>
Committed: Thu Jan 7 17:37:46 2016 -0800
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docs/configuration.md | 18 ++++++++++++++++++
docs/streaming-programming-guide.md | 12 +++++-------
2 files changed, 23 insertions(+), 7 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/c94199e9/docs/configuration.md
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diff --git a/docs/configuration.md b/docs/configuration.md
index 6bd0658..08392c3 100644
--- a/docs/configuration.md
+++ b/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
http://git-wip-us.apache.org/repos/asf/spark/blob/c94199e9/docs/streaming-programming-guide.md
----------------------------------------------------------------------
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 3b071c7..1edc0fe 100644
--- a/docs/streaming-programming-guide.md
+++ b/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`.
-
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