Author: lidong
Date: Tue Mar 7 01:19:49 2017
New Revision: 1785789
URL: http://svn.apache.org/viewvc?rev=1785789&view=rev
Log:
fix doc typo
Modified:
kylin/site/docs16/tutorial/cube_spark.html
kylin/site/feed.xml
Modified: kylin/site/docs16/tutorial/cube_spark.html
URL:
http://svn.apache.org/viewvc/kylin/site/docs16/tutorial/cube_spark.html?rev=1785789&r1=1785788&r2=1785789&view=diff
==============================================================================
--- kylin/site/docs16/tutorial/cube_spark.html (original)
+++ kylin/site/docs16/tutorial/cube_spark.html Tue Mar 7 01:19:49 2017
@@ -2513,13 +2513,13 @@ $KYLIN_HOME/bin/kylin.sh start</code></p
<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/9_spark_history.png"
alt="" /></p>
-<p>Click a specific job, there you will see the detail runtime information,
that is very helpful for trouble shooting and performance tunning.</p>
+<p>Click a specific job, there you will see the detail runtime information,
that is very helpful for trouble shooting and performance tuning.</p>
<h2 id="go-further">Go further</h2>
-<p>If youâre a Kylin administrator but new to Spark, suggest you go through
<a href="https://spark.apache.org/docs/1.6.3/";>Spark document</a>, and donât
forget to update the configurations accordingly. Sparkâs performance relies
on Clusterâs memory and CPU resource, while Kylinâs Cube build is a heavy
task when having a complex data model and a huge dataset to build at one time.
If your cluster can not match the requirement, kinds of error like
âOutOfMemorryâ will be thrown in executors, so please use the new engine
properly. For Cube which has many combinations (e.g, a full cube with more than
12 dimensions), UHC, or memory hungry measures (Count Distinct, Top-N), suggest
to keep using the MapReduce engine. If your Cube model is simple, all measures
are SUM/MIN/MAX/COUNT, source data is small to medium scale, Spark engine would
be a good choice. Besides, Streaming build isnât supported in Spark engine so
far (KYLIN-2484).</p>
+<p>If youâre a Kylin administrator but new to Spark, suggest you go through
<a href="https://spark.apache.org/docs/1.6.3/";>Spark documents</a>, and donât
forget to update the configurations accordingly. Sparkâs performance relies
on Clusterâs memory and CPU resource, while Kylinâs Cube build is a heavy
task when having a complex data model and a huge dataset to build at one time.
If your cluster resource couldnât fulfill, errors like âOutOfMemorryâ
will be thrown in Spark executors, so please use it properly. For Cube which
has UHC dimension, many combinations (e.g, a full cube with more than 12
dimensions), or memory hungry measures (Count Distinct, Top-N), suggest to use
the MapReduce engine. If your Cube model is simple, all measures are
SUM/MIN/MAX/COUNT, source data is small to medium scale, Spark engine would be
a good choice. Besides, Streaming build isnât supported in this engine so far
(KYLIN-2484).</p>
-<p>Now this engine is in public beta; If you have any question, comment, or
bug fixe, welcome to discuss in [email protected].</p>
+<p>Now the Spark engine is in public beta; If you have any question, comment,
or bug fix, welcome to discuss in [email protected].</p>
</article>
</div>
Modified: kylin/site/feed.xml
URL:
http://svn.apache.org/viewvc/kylin/site/feed.xml?rev=1785789&r1=1785788&r2=1785789&view=diff
==============================================================================
--- kylin/site/feed.xml (original)
+++ kylin/site/feed.xml Tue Mar 7 01:19:49 2017
@@ -19,8 +19,8 @@
<description>Apache Kylin Home</description>
<link>http://kylin.apache.org/</link>
<atom:link href="http://kylin.apache.org/feed.xml"; rel="self"
type="application/rss+xml"/>
- <pubDate>Mon, 06 Mar 2017 15:54:11 -0800</pubDate>
- <lastBuildDate>Mon, 06 Mar 2017 15:54:11 -0800</lastBuildDate>
+ <pubDate>Mon, 06 Mar 2017 17:18:54 -0800</pubDate>
+ <lastBuildDate>Mon, 06 Mar 2017 17:18:54 -0800</lastBuildDate>
<generator>Jekyll v2.5.3</generator>
<item>
