Author: lidong
Date: Sun Feb 26 13:59:58 2017
New Revision: 1784444
URL: http://svn.apache.org/viewvc?rev=1784444&view=rev
Log:
add post for spark cubing
Added:
kylin/site/blog/2017/02/23/
kylin/site/blog/2017/02/23/by-layer-spark-cubing/
kylin/site/blog/2017/02/23/by-layer-spark-cubing/index.html
kylin/site/images/blog/spark-cubing-layer.png (with props)
kylin/site/images/blog/spark-dag.png (with props)
kylin/site/images/blog/spark-mr-layer.png (with props)
kylin/site/images/blog/spark-mr-performance.png (with props)
Modified:
kylin/site/blog/index.html
kylin/site/feed.xml
Added: kylin/site/blog/2017/02/23/by-layer-spark-cubing/index.html
URL:
http://svn.apache.org/viewvc/kylin/site/blog/2017/02/23/by-layer-spark-cubing/index.html?rev=1784444&view=auto
==============================================================================
--- kylin/site/blog/2017/02/23/by-layer-spark-cubing/index.html (added)
+++ kylin/site/blog/2017/02/23/by-layer-spark-cubing/index.html Sun Feb 26
13:59:58 2017
@@ -0,0 +1,307 @@
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+ <meta name="description" content="Before v2.0, Apache Kylin uses Hadoop
MapReduce as the framework to build Cubes over huge dataset. The MapReduce
framework is simple, stable and can fulfill ...">
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+ <article class="post-content" >
+ <!--
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you under the Apache License, Version 2.0 (the
+* "License"); you may not use this file except in compliance
+* with the License. You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
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+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
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+
+<div class="post" style=" padding:2em 4em 4em 4em">
+
+ <header class="post-header">
+ <h1 class="post-title">By-layer Spark Cubing</h1>
+ <p class="post-meta" >Feb 23, 2017 ⢠Shaofeng Shi</p>
+ </header>
+
+ <article class="post-content" >
+ <p>Before v2.0, Apache Kylin uses Hadoop MapReduce as the framework to
build Cubes over huge dataset. The MapReduce framework is simple, stable and
can fulfill Kylinâs need very well except the performance. In order to get
better performance, we introduced the âfast cubingâ algorithm in Kylin
v1.5, tries to do as much as possible aggregations at map side within memory,
so to avoid the disk and network I/O; but not all data models can benefit from
it, and it still runs on MR which means on-disk sorting and shuffling.</p>
+
+<p>Now Spark comes; Apache Spark is an open-source cluster-computing
framework, which provides programmers with an application programming interface
centered on a data structure called RDD; it runs in-memory on the cluster, this
makes repeated access to the same data much faster. Spark provides flexible and
fancy APIs. You are not tied to Hadoopâs MapReduce two-stage paradigm.</p>
+
+<p>Before introducing how calculate Cube with Spark, letâs see how Kylin do
that with MR; Figure 1 illustrates how a 4-dimension Cube get calculated with
the classic âby-layerâ algorithm: the first round MR aggregates the base
(4-D) cuboid from source data; the second MR aggregates on the base cuboid to
get the 3-D cuboids; With N+1 round MR all layersâ cuboids get calculated.</p>
+
+<p><img src="/images/blog/spark-mr-layer.png" alt="MapReduce Cubing by Layer"
/></p>
+
+<p>The âby-layerâ Cubing divides a big task into a couple steps, and each
step bases on the previous stepâs output, so it can reuse the previous
calculation and also avoid calculating from very beginning when there is a
failure in between. These makes it as a reliable algorithm. When moving to
Spark, we decide to keep this algorithm, thatâs why we call this feature as
âBy layer Spark Cubingâ.</p>
+
+<p>As we know, RDD (Resilient Distributed Dataset) is a basic concept in
Spark. A collection of N-Dimension cuboids can be well described as an RDD, a
N-Dimension Cube will have N+1 RDD. These RDDs have the parent/child
relationship as the parent can be used to generate the children. With the
parent RDD cached in memory, the child RDDâs generation can be much efficient
than reading from disk. Figure 2 describes this process.</p>
+
+<p><img src="/images/blog/spark-cubing-layer.png" alt="Spark Cubing by Layer"
/></p>
+
+<p>Figure 3 is the DAG of Cubing in Spark, it illustrates the process in
detail: In âStage 5â, Kylin uses a HiveContext to read the intermediate
Hive table, and then do a âmapâ operation, which is an one to one map, to
encode the origin values into K-V bytes. On complete Kylin gets an intermediate
encoded RDD. In âStage 6â, the intermediate RDD is aggregated with a
âreduceByKeyâ operation to get RDD-1, which is the base cuboid. Nextly, do
an âflatMapâ (one to many map) on RDD-1, because the base cuboid has N
children cuboids. And so on, all levelsâ RDDs get calculated. These RDDs will
be persisted to distributed file system on complete, but be cached in memory
for next levelâs calculation. When child be generated, it will be removed
from cache.</p>
+
+<p><img src="/images/blog/spark-dag.png" alt="DAG of Spark Cubing" /></p>
+
+<p>We did a test to see how much performance improvement can gain from
Spark:</p>
+
+<p>Environment</p>
+
+<ul>
+ <li>4 nodes Hadoop cluster; each node has 28 GB RAM and 12 cores;</li>
+ <li>YRAN has 48GB RAM and 30 cores in total;</li>
+ <li>CDH 5.8, Apache Kylin 2.0 beta.</li>
+</ul>
+
+<p>Spark</p>
+
+<ul>
+ <li>Spark 1.6.3 on YARN</li>
+ <li>6 executors, each has 4 cores, 4GB +1GB (overhead) memory</li>
+</ul>
+
+<p>Test Data</p>
+
+<ul>
+ <li>Airline data, total 160 million rows</li>
+ <li>Cube: 10 dimensions, 5 measures (SUM)</li>
+</ul>
+
+<p>Test Scenarios</p>
+
+<ul>
+ <li>Build the cube at different source data level: 3 million, 50 million and
160 million source rows; Compare the build time with MapReduce (by layer) and
Spark. No compression enabled.<br />
+The time only cover the building cube step, not including data preparations
and subsequent steps.</li>
+</ul>
+
+<p><img src="/images/blog/spark-mr-performance.png" alt="Spark vs MR
performance" /></p>
+
+<p>Spark is faster than MR in all the 3 scenarios, and overall it can reduce
about half time in the cubing.</p>
+
+<p>Now you can download a 2.0.0 beta build from Kylinâs download page, and
then follow this <a
href="https://kylin.apache.org/blog/2017/02/25/v2.0.0-beta-ready/";>post</a> to
build a cube with Spark engine. If you have any comments or inputs, please
discuss in the community.</p>
+
+
+ </article>
+
+</div>
+
+
+
+
+
+ </article>
+ </div>
+ </div>
+ <!--
+* Licensed to the Apache Software Foundation (ASF) under one
+* or more contributor license agreements. See the NOTICE file
+* distributed with this work for additional information
+* regarding copyright ownership. The ASF licenses this file
+* to you under the Apache License, Version 2.0 (the
+* "License"); you may not use this file except in compliance
+* with the License. You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
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+-->
+
+<footer id="underfooter">
+ <div class="container">
+ <div class="row">
+ <div class="col-md-12 widget">
+ <div class="widget-body" style="text-align:center">
+ <a href="http://www.apache.org";>
+ <img id="asf-logo" alt="Apache Software Foundation"
src="/assets/images/feather-small.gif">
+ </a>
+
+ <div>
+ The contents of this website are © 2015 Apache
Software Foundation under the terms of the <a
+ href="http://www.apache.org/licenses/LICENSE-2.0";>
Apache License v2 </a>. Apache Kylin and
+ its logo are trademarks of the Apache Software
Foundation.
+ </div>
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+
+
Modified: kylin/site/blog/index.html
URL:
http://svn.apache.org/viewvc/kylin/site/blog/index.html?rev=1784444&r1=1784443&r2=1784444&view=diff
==============================================================================
--- kylin/site/blog/index.html (original)
+++ kylin/site/blog/index.html Sun Feb 26 13:59:58 2017
@@ -186,6 +186,12 @@
<li>
<h2 align="left" style="margin:0px">
+ <a class="post-link"
href="/blog/2017/02/23/by-layer-spark-cubing/">By-layer Spark
Cubing</a></h2><div align="left" class="post-meta">posted: Feb 23, 2017</div>
+
+ </li>
+
+ <li>
+ <h2 align="left" style="margin:0px">
<a class="post-link"
href="/cn/blog/2016/12/04/release-v1.6.0/">Apache Kylin v1.6.0
æ£å¼åå¸</a></h2><div align="left" class="post-meta">posted: Dec 4,
2016</div>
</li>
@@ -300,13 +306,13 @@
<li>
<h2 align="left" style="margin:0px">
- <a class="post-link" href="/blog/2016/03/16/release-v1.3.0/">Apache
Kylin v1.3.0 Release Announcement</a></h2><div align="left"
class="post-meta">posted: Mar 16, 2016</div>
+ <a class="post-link"
href="/cn/blog/2016/03/16/release-v1.3.0/">Apache Kylin v1.3.0
æ£å¼åå¸</a></h2><div align="left" class="post-meta">posted: Mar 16,
2016</div>
</li>
<li>
<h2 align="left" style="margin:0px">
- <a class="post-link"
href="/cn/blog/2016/03/16/release-v1.3.0/">Apache Kylin v1.3.0
æ£å¼åå¸</a></h2><div align="left" class="post-meta">posted: Mar 16,
2016</div>
+ <a class="post-link" href="/blog/2016/03/16/release-v1.3.0/">Apache
Kylin v1.3.0 Release Announcement</a></h2><div align="left"
class="post-meta">posted: Mar 16, 2016</div>
</li>
@@ -324,13 +330,13 @@
<li>
<h2 align="left" style="margin:0px">
- <a class="post-link"
href="/cn/blog/2015/12/25/support-powerbi-tableau9/">Apache Kylinå¢å
对Tableau 9å微软Excel, Power BIçæ¯æ</a></h2><div align="left"
class="post-meta">posted: Dec 25, 2015</div>
+ <a class="post-link"
href="/blog/2015/12/25/support-powerbi-tableau9/">Apache Kylin supports Tableau
9 and MS Excel, Power BI now</a></h2><div align="left"
class="post-meta">posted: Dec 25, 2015</div>
</li>
<li>
<h2 align="left" style="margin:0px">
- <a class="post-link"
href="/blog/2015/12/25/support-powerbi-tableau9/">Apache Kylin supports Tableau
9 and MS Excel, Power BI now</a></h2><div align="left"
class="post-meta">posted: Dec 25, 2015</div>
+ <a class="post-link"
href="/cn/blog/2015/12/25/support-powerbi-tableau9/">Apache Kylinå¢å
对Tableau 9å微软Excel, Power BIçæ¯æ</a></h2><div align="left"
class="post-meta">posted: Dec 25, 2015</div>
</li>
Modified: kylin/site/feed.xml
URL:
http://svn.apache.org/viewvc/kylin/site/feed.xml?rev=1784444&r1=1784443&r2=1784444&view=diff
==============================================================================
--- kylin/site/feed.xml (original)
+++ kylin/site/feed.xml Sun Feb 26 13:59:58 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>Sat, 25 Feb 2017 05:55:03 -0800</pubDate>
- <lastBuildDate>Sat, 25 Feb 2017 05:55:03 -0800</lastBuildDate>
+ <pubDate>Sun, 26 Feb 2017 05:59:06 -0800</pubDate>
+ <lastBuildDate>Sun, 26 Feb 2017 05:59:06 -0800</lastBuildDate>
<generator>Jekyll v2.5.3</generator>
<item>
@@ -156,6 +156,73 @@
</item>
<item>
+ <title>By-layer Spark Cubing</title>
+ <description><p>Before v2.0, Apache Kylin uses Hadoop MapReduce
as the framework to build Cubes over huge dataset. The MapReduce framework is
simple, stable and can fulfill Kylinâs need very well except the performance.
In order to get better performance, we introduced the âfast cubingâ
algorithm in Kylin v1.5, tries to do as much as possible aggregations at map
side within memory, so to avoid the disk and network I/O; but not all data
models can benefit from it, and it still runs on MR which means on-disk sorting
and shuffling.</p>
+
+<p>Now Spark comes; Apache Spark is an open-source cluster-computing
framework, which provides programmers with an application programming interface
centered on a data structure called RDD; it runs in-memory on the cluster, this
makes repeated access to the same data much faster. Spark provides flexible and
fancy APIs. You are not tied to Hadoopâs MapReduce two-stage
paradigm.</p>
+
+<p>Before introducing how calculate Cube with Spark, letâs see how
Kylin do that with MR; Figure 1 illustrates how a 4-dimension Cube get
calculated with the classic âby-layerâ algorithm: the first round MR
aggregates the base (4-D) cuboid from source data; the second MR aggregates on
the base cuboid to get the 3-D cuboids; With N+1 round MR all layersâ cuboids
get calculated.</p>
+
+<p><img src="/images/blog/spark-mr-layer.png"
alt="MapReduce Cubing by Layer" /></p>
+
+<p>The âby-layerâ Cubing divides a big task into a couple steps, and
each step bases on the previous stepâs output, so it can reuse the previous
calculation and also avoid calculating from very beginning when there is a
failure in between. These makes it as a reliable algorithm. When moving to
Spark, we decide to keep this algorithm, thatâs why we call this feature as
âBy layer Spark Cubingâ.</p>
+
+<p>As we know, RDD (Resilient Distributed Dataset) is a basic concept in
Spark. A collection of N-Dimension cuboids can be well described as an RDD, a
N-Dimension Cube will have N+1 RDD. These RDDs have the parent/child
relationship as the parent can be used to generate the children. With the
parent RDD cached in memory, the child RDDâs generation can be much efficient
than reading from disk. Figure 2 describes this process.</p>
+
+<p><img src="/images/blog/spark-cubing-layer.png"
alt="Spark Cubing by Layer" /></p>
+
+<p>Figure 3 is the DAG of Cubing in Spark, it illustrates the process in
detail: In âStage 5â, Kylin uses a HiveContext to read the intermediate
Hive table, and then do a âmapâ operation, which is an one to one map, to
encode the origin values into K-V bytes. On complete Kylin gets an intermediate
encoded RDD. In âStage 6â, the intermediate RDD is aggregated with a
âreduceByKeyâ operation to get RDD-1, which is the base cuboid. Nextly, do
an âflatMapâ (one to many map) on RDD-1, because the base cuboid has N
children cuboids. And so on, all levelsâ RDDs get calculated. These RDDs will
be persisted to distributed file system on complete, but be cached in memory
for next levelâs calculation. When child be generated, it will be removed
from cache.</p>
+
+<p><img src="/images/blog/spark-dag.png" alt="DAG of
Spark Cubing" /></p>
+
+<p>We did a test to see how much performance improvement can gain from
Spark:</p>
+
+<p>Environment</p>
+
+<ul>
+ <li>4 nodes Hadoop cluster; each node has 28 GB RAM and 12
cores;</li>
+ <li>YRAN has 48GB RAM and 30 cores in total;</li>
+ <li>CDH 5.8, Apache Kylin 2.0 beta.</li>
+</ul>
+
+<p>Spark</p>
+
+<ul>
+ <li>Spark 1.6.3 on YARN</li>
+ <li>6 executors, each has 4 cores, 4GB +1GB (overhead)
memory</li>
+</ul>
+
+<p>Test Data</p>
+
+<ul>
+ <li>Airline data, total 160 million rows</li>
+ <li>Cube: 10 dimensions, 5 measures (SUM)</li>
+</ul>
+
+<p>Test Scenarios</p>
+
+<ul>
+ <li>Build the cube at different source data level: 3 million, 50
million and 160 million source rows; Compare the build time with MapReduce (by
layer) and Spark. No compression enabled.<br />
+The time only cover the building cube step, not including data preparations
and subsequent steps.</li>
+</ul>
+
+<p><img src="/images/blog/spark-mr-performance.png"
alt="Spark vs MR performance" /></p>
+
+<p>Spark is faster than MR in all the 3 scenarios, and overall it can
reduce about half time in the cubing.</p>
+
+<p>Now you can download a 2.0.0 beta build from Kylinâs download page,
and then follow this <a
href="https://kylin.apache.org/blog/2017/02/25/v2.0.0-beta-ready/">post</a>
to build a cube with Spark engine. If you have any comments or inputs, please
discuss in the community.</p>
+
+</description>
+ <pubDate>Thu, 23 Feb 2017 09:30:00 -0800</pubDate>
+
<link>http://kylin.apache.org/blog/2017/02/23/by-layer-spark-cubing/</link>
+ <guid
isPermaLink="true">http://kylin.apache.org/blog/2017/02/23/by-layer-spark-cubing/</guid>
+
+
+ <category>blog</category>
+
+ </item>
+
+ <item>
<title>Apache Kylin v1.6.0 æ£å¼åå¸</title>
<description><p>Apache Kylin社åºé常é«å
´å®£å¸Apache Kylin
v1.6.0æ£å¼åå¸ã</p>
@@ -721,57 +788,6 @@ kylin.job.mr.config.override.mapreduce.m
<category>blog</category>
-
- </item>
-
- <item>
- <title>Apache Kylin v1.5.3 æ£å¼åå¸</title>
- <description><p>Apache Kylin社åºé常é«å
´å®£å¸Apache Kylin
v1.5.3æ£å¼åå¸ã</p>
-
-<p>Apache
Kylinæ¯ä¸ä¸ªå¼æºçåå¸å¼åæå¼æï¼æä¾Hadoopä¹ä¸çSQLæ¥è¯¢æ¥å£åå¤ç»´åæï¼OLAPï¼è½å以æ¯æè¶
大è§æ¨¡æ°æ®ï¼æåç±eBay Inc.
å¼å并贡ç®è³å¼æºç¤¾åºã</p>
-
-<p>ä¸è½½Apache Kylin v1.5.3æºä»£ç åäºè¿å¶å®è£
å
ï¼<br />
-请访é®<a
href="http://kylin.apache.org/cn/download/">ä¸è½½</a>页é¢.</p>
-
-<p>è¿æ¯ä¸ä¸ªä¸»è¦ççæ¬åå¸å¸¦æ¥äºæ´ç¨³å®ï¼å¥å£®åæ´å¥½ç®¡çççæ¬ï¼Apache
Kylin社åºè§£å³äº84个issueï¼å
æ¬Bugä¿®å¤ï¼åè½å¢å¼ºåä¸äºæ°ç¹æ§çã</p>
-
-<h2 id="section">主è¦åå</h2>
-
-<ul>
- <li>éç¨æ åAPIè·åHadoopä»»å¡çç¶æ <a
href="https://issues.apache.org/jira/browse/KYLIN-1319">KYLIN-1319</a></li>
- <li>å
¨å±çï¼æ©å±æ§æ´å¥½çï¼åå
¸ç¼ç æ¹æ³ <a
href="https://issues.apache.org/jira/browse/KYLIN-1705">KYLIN-1705</a></li>
- <li>æ´ç¨³å®ç精确å»é(count distinct)度é <a
href="https://issues.apache.org/jira/browse/KYLIN-1379">KYLIN-1379</a></li>
-
<li>éè¿æå®æ¯ä¸ªMapperå¤ç纪å½çæ°éï¼ä»èæé«Cubeæ建æ§è½
<a
href="https://issues.apache.org/jira/browse/KYLIN-1656">KYLIN-1656</a></li>
- <li>å¨å建Hive平表æ¶ææäºåï¼UHCï¼åæ¥åæ£æ°æ®
<a
href="https://issues.apache.org/jira/browse/KYLIN-1677">KYLIN-1677</a></li>
- <li>å
许å¨Cube级å«è¦çMRä»»å¡çå±æ§ <a
href="https://issues.apache.org/jira/browse/KYLIN-1706">KYLIN-1706</a></li>
- <li>å
许é管çåç¨æ·ç¼è¾ä¿®æ¹Cubeå导çâé«çº§è®¾ç½®â页 <a
href="https://issues.apache.org/jira/browse/KYLIN-1731">KYLIN-1731</a></li>
- <li>计ç®å
¨0ç»åï¼mandantory维度é¤å¤ï¼ cuboids <a
href="https://issues.apache.org/jira/browse/KYLIN-1747">KYLIN-1747</a></li>
- <li>å
许å
¨é¨ç»´åº¦é½æ¯mandatory <a
href="https://issues.apache.org/jira/browse/KYLIN-1749">KYLIN-1749</a></li>
-
<li>ä¿®å¤âå½è¿æ¥ç±»åæ¶inneræ¶ä¸è½ä½¿ç¨viewå维度表âçé®é¢
<a
href="https://issues.apache.org/jira/browse/KYLIN-1789">KYLIN-1789</a></li>
- <li>HBase coprocessoråºéæ¶å°Exceptionä¼ åæ¥è¯¢çº¿ç¨ <a
href="https://issues.apache.org/jira/browse/KYLIN-1645">KYLIN-1645</a></li>
- <li>ç²¾ç®JDBC driverçä¾èµ <a
href="https://issues.apache.org/jira/browse/KYLIN-1846">KYLIN-1846</a></li>
- <li>TopN度éæ¯æ使ç¨éåå
¸çç¼ç æ¹å¼ <a
href="https://issues.apache.org/jira/browse/KYLIN-1478">KYLIN-1478</a></li>
-</ul>
-
-<p><strong>å级</strong></p>
-
-<p>åè§<a
href="/docs15/howto/howto_upgrade.html">å级æå</a>.</p>
-
-<p><strong>æ¯æ</strong></p>
-
-<p>å级å使ç¨è¿ç¨ä¸æä»»ä½é®é¢ï¼è¯·ï¼<br />
-æ交è³KylinçJIRA: <a
href="https://issues.apache.org/jira/browse/KYLIN/">https://issues.apache.org/jira/browse/KYLIN/</a><br
/>
-æè
<br />
-åéé®ä»¶å°Apache Kyliné®ä»¶å表: <a
href="&#109;&#097;&#105;&#108;&#116;&#111;:&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;">&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;</a></p>
-
-<p><em>æè°¢æ¯ä¸ä½æåçåä¸åè´¡ç®!</em></p>
-</description>
- <pubDate>Thu, 28 Jul 2016 14:00:00 -0700</pubDate>
- <link>http://kylin.apache.org/cn/blog/2016/07/28/release-v1.5.3/</link>
- <guid
isPermaLink="true">http://kylin.apache.org/cn/blog/2016/07/28/release-v1.5.3/</guid>
-
-
- <category>blog</category>
</item>
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