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Posted to commits@kylin.apache.org by li...@apache.org on 2016/03/12 14:18:11 UTC
svn commit: r1734700 [5/14] - in /kylin/site: ./
blog/2016/02/03/streaming-cubing/ blog/2016/02/18/new-aggregation-group/
development/ docs/ docs/gettingstarted/ docs/howto/ docs15/
docs15/gettingstarted/ docs15/howto/ docs15/install/ docs15/tutorial/
Added: kylin/site/docs15/howto/howto_optimize_cubes.html
URL: http://svn.apache.org/viewvc/kylin/site/docs15/howto/howto_optimize_cubes.html?rev=1734700&view=auto
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--- kylin/site/docs15/howto/howto_optimize_cubes.html (added)
+++ kylin/site/docs15/howto/howto_optimize_cubes.html Sat Mar 12 13:18:10 2016
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+ <h1 class="post-title">How to Optimize Cubes</h1>
+
+
+ <article class="post-content" >
+ <h2 id="hierarchies">Hierarchies:</h2>
+
+<p>Theoretically for N dimensions youâll end up with 2^N dimension combinations. However for some group of dimensions there are no need to create so many combinations. For example, if you have three dimensions: continent, country, city (In hierarchies, the âbiggerâ dimension comes first). You will only need the following three combinations of group by when you do drill down analysis:</p>
+
+<p>group by continent<br />
+group by continent, country<br />
+group by continent, country, city</p>
+
+<p>In such cases the combination count is reduced from 2^3=8 to 3, which is a great optimization. The same goes for the YEAR,QUATER,MONTH,DATE case.</p>
+
+<p>If we Donate the hierarchy dimension as H1,H2,H3, typical scenarios would be:</p>
+
+<p>A. Hierarchies on lookup table</p>
+
+<table>
+ <tr>
+ <td align="center">Fact table</td>
+ <td align="center">(joins)</td>
+ <td align="center">Lookup Table</td>
+ </tr>
+ <tr>
+ <td>column1,column2,,,,,, FK</td>
+ <td></td>
+ <td>PK,,H1,H2,H3,,,,</td>
+ </tr>
+</table>
+
+<hr />
+
+<p>B. Hierarchies on fact table</p>
+
+<table>
+ <tr>
+ <td align="center">Fact table</td>
+ </tr>
+ <tr>
+ <td>column1,column2,,,H1,H2,H3,,,,,,, </td>
+ </tr>
+</table>
+
+<hr />
+
+<p>There is a special case for scenario A, where PK on the lookup table is accidentally being part of the hierarchies. For example we have a calendar lookup table where cal_dt is the primary key:</p>
+
+<p>A*. Hierarchies on lookup table over its primary key</p>
+
+<table>
+ <tr>
+ <td align="center">Lookup Table(Calendar)</td>
+ </tr>
+ <tr>
+ <td>cal_dt(PK), week_beg_dt, month_beg_dt, quarter_beg_dt,,,</td>
+ </tr>
+</table>
+
+<hr />
+
+<p>For cases like A* what you need is another optimization called âDerived Columnsâ</p>
+
+<h2 id="derived-columns">Derived Columns:</h2>
+
+<p>Derived column is used when one or more dimensions (They must be dimension on lookup table, these columns are called âDerivedâ) can be deduced from another(Usually it is the corresponding FK, this is called the âhost columnâ)</p>
+
+<p>For example, suppose we have a lookup table where we join fact table and it with âwhere DimA = DimXâ. Notice in Kylin, if you choose FK into a dimension, the corresponding PK will be automatically querable, without any extra cost. The secret is that since FK and PK are always identical, Kylin can apply filters/groupby on the FK first, and transparently replace them to PK. This indicates that if we want the DimA(FK), DimX(PK), DimB, DimC in our cube, we can safely choose DimA,DimB,DimC only.</p>
+
+<table>
+ <tr>
+ <td align="center">Fact table</td>
+ <td align="center">(joins)</td>
+ <td align="center">Lookup Table</td>
+ </tr>
+ <tr>
+ <td>column1,column2,,,,,, DimA(FK) </td>
+ <td></td>
+ <td>DimX(PK),,DimB, DimC</td>
+ </tr>
+</table>
+
+<hr />
+
+<p>Letâs say that DimA(the dimension representing FK/PK) has a special mapping to DimB:</p>
+
+<table>
+ <tr>
+ <th>dimA</th>
+ <th>dimB</th>
+ <th>dimC</th>
+ </tr>
+ <tr>
+ <td>1</td>
+ <td>a</td>
+ <td>?</td>
+ </tr>
+ <tr>
+ <td>2</td>
+ <td>b</td>
+ <td>?</td>
+ </tr>
+ <tr>
+ <td>3</td>
+ <td>c</td>
+ <td>?</td>
+ </tr>
+ <tr>
+ <td>4</td>
+ <td>a</td>
+ <td>?</td>
+ </tr>
+</table>
+
+<p>in this case, given a value in DimA, the value of DimB is determined, so we say dimB can be derived from DimA. When we build a cube that contains both DimA and DimB, we simple include DimA, and marking DimB as derived. Derived column(DimB) does not participant in cuboids generation:</p>
+
+<p>original combinations:<br />
+ABC,AB,AC,BC,A,B,C</p>
+
+<p>combinations when driving B from A:<br />
+AC,A,C</p>
+
+<p>at Runtime, in case queries like âselect count(*) from fact_table inner join looup1 group by looup1 .dimBâ, it is expecting cuboid containing DimB to answer the query. However, DimB will appear in NONE of the cuboids due to derived optimization. In this case, we modify the execution plan to make it group by DimA(its host column) first, weâll get intermediate answer like:</p>
+
+<table>
+ <tr>
+ <th>DimA</th>
+ <th>count(*)</th>
+ </tr>
+ <tr>
+ <td>1</td>
+ <td>1</td>
+ </tr>
+ <tr>
+ <td>2</td>
+ <td>1</td>
+ </tr>
+ <tr>
+ <td>3</td>
+ <td>1</td>
+ </tr>
+ <tr>
+ <td>4</td>
+ <td>1</td>
+ </tr>
+</table>
+
+<p>Afterwards, Kylin will replace DimA values with DimB values(since both of their values are in lookup table, Kylin can load the whole lookup table into memory and build a mapping for them), and the intermediate result becomes:</p>
+
+<table>
+ <tr>
+ <th>DimB</th>
+ <th>count(*)</th>
+ </tr>
+ <tr>
+ <td>a</td>
+ <td>1</td>
+ </tr>
+ <tr>
+ <td>b</td>
+ <td>1</td>
+ </tr>
+ <tr>
+ <td>c</td>
+ <td>1</td>
+ </tr>
+ <tr>
+ <td>a</td>
+ <td>1</td>
+ </tr>
+</table>
+
+<p>After this, the runtime SQL engine(calcite) will further aggregate the intermediate result to:</p>
+
+<table>
+ <tr>
+ <th>DimB</th>
+ <th>count(*)</th>
+ </tr>
+ <tr>
+ <td>a</td>
+ <td>2</td>
+ </tr>
+ <tr>
+ <td>b</td>
+ <td>1</td>
+ </tr>
+ <tr>
+ <td>c</td>
+ <td>1</td>
+ </tr>
+</table>
+
+<p>this step happens at query runtime, this is what it means âat the cost of extra runtime aggregationâ</p>
+
+ </article>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
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+
+ <li><a href="#titleHow To" data-toggle="collapse" id="navtitle">How To</a></li>
+ <div class="collapse in">
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+</div>
+
+ <div class="col-md-9 col-lg-9 col-xs-14 aside2">
+ <div id="container">
+ <div id="pjax">
+ <h1 class="post-title">How to Upgrade</h1>
+
+
+ <article class="post-content" >
+ <h2 id="upgrade-from-v13-to-v15">Upgrade from v1.3 to v1.5</h2>
+
+<p>From v1.3 to v1.5, Kylinâs cube data is backward compatible, but metadata has been refactored as new schema, to support new features on cubing and query enhancement. So if you want to deploy v1.5 on your v1.3 base, you need to upgrade the metadata as following steps:</p>
+
+<h4 id="backup-metadata-on-v13">1. Backup metadata on v1.3</h4>
+<p>To avoid data loss during the upgrade, a backup at the very beginning is always suggested. In case of upgrade failure, you can roll back to original state with the backup.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$KYLIN_HOME/bin/metastore.sh backup
+</code></pre>
+</div>
+<p>It will print the backup folder, take it down and make sure it will not be deleted before the upgrade finished. If there is no âmetastore.shâ, you can use HBaseâs snapshot command to do backup:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>hbase shell
+snapshot 'kylin_metadata', 'kylin_metadata_backup20160101'
+</code></pre>
+</div>
+<p>Here âkylin_metadataâ is the default kylin metadata table name, replace it with the right table name of your Kylin metastore.</p>
+
+<h4 id="stop-kylin-v13-instance">2. Stop Kylin v1.3 instance</h4>
+<p>Before deploying Kylin v1.5 instance, you need to stop the old instance. Note that end users cannot access kylin service from this point.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$KYLIN_HOME/bin/kylin.sh stop
+</code></pre>
+</div>
+<p>#### 3. Install Kylin v1.5 and copy back âconfâ<br />
+Download the new Kylin v1.5 binary package from Kylinâs download page; Extract it to a different folder other than current KYLIN_HOME; Before copy back the âconfâ folder, do a compare and merge between the old and new kylin.properties to ensure newly introduced property will be kept.</p>
+
+<h4 id="optional-4-upgrading-metadata-will-not-bring-new-features-of-v15-to-existing-cube-built-with-v13-engine-if-you-want-to-leverage-those-features-please-refer-to-highlight-part">(Optional) 4. Upgrading metadata will not bring new features of v1.5 to existing cube built with v1.3 engine. If you want to leverage those features, please refer to <a href="">Highlight</a> part.</h4>
+
+<h4 id="automaticly-upgrade-metadata">5. Automaticly upgrade metadata</h4>
+<p>Kylin v1.5 package provides a script for metadata automaticly upgrade. In this upgrade, empty cubes will be updated to v1.5 version and all new features are enabled for them. But those non-empty cubes are not able to use those new features.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>export KYLIN_HOME="<path_of_new_installation>"
+$KYLIN_HOME/bin/upgrade_v2.sh
+</code></pre>
+</div>
+<p>After this, the metadata in hbase table has been applied with new metadata schema.</p>
+
+<h4 id="start-kylin-v15-instance">6. Start Kylin v1.5 instance</h4>
+<div class="highlighter-rouge"><pre class="highlight"><code>$KYLIN_HOME/bin/kylin.sh start
+</code></pre>
+</div>
+<p>Check the log and open web UI to see if the upgrade succeeded.</p>
+
+<h2 id="rollback-if-the-upgrade-is-failed">Rollback if the upgrade is failed</h2>
+<p>If the new version couldnât startup normally, you need to roll back to orignal v1.3 version. The steps are as followed:</p>
+
+<h4 id="stop-kylin-v15-instance">1. Stop Kylin v1.5 instance</h4>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$KYLIN_HOME/bin/kylin.sh stop
+</code></pre>
+</div>
+<p>#### 2. Restore 1.3 metadata from backup folder</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>export KYLIN_HOME="<path_of_1.3_installation>"
+$KYLIN_HOME/bin/metastore.sh restore <backup_folder>
+</code></pre>
+</div>
+<p>#### 3. Deploy coprocessor of v1.3<br />
+Since coprocessor of used HTable are upgraded as v1.5, you need to manually downgrade them with this command.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$KYLIN_HOME/bin/kylin.sh org.apache.kylin.job.tools.DeployCoprocessorCLI $KYLIN_HOME/lib/kylin-coprocessor*.jar -all
+</code></pre>
+</div>
+
+<h4 id="start-kylin-v13-instance">4. Start Kylin v1.3 instance</h4>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$KYLIN_HOME/bin/kylin.sh start
+</code></pre>
+</div>
+
+<h2 id="highlights">Highlights</h2>
+<p>Since old cubes built with v1.3 cannot leverage new features of v1.5. But if you must have them on your cubes, you can choose one of these solutions:<br />
+#### 1. Rebuilt cubes<br />
+If the cost of rebuilding is acceptable, if you purge the cube before Step 4(Running Upgrade Scripts). After upgrade done, you need to manually rebuilt those segments by yourself.<br />
+#### 2. Use hybrid model<br />
+If you donât want to rebuild any cube, but want to leverage new features for new data. You can use hybrid model, which contains not only your old cube, but also an empty cube which has same model with the old one. For the empty cube, you can do incremental building with v2 features. For the old cube, you can refresh existing segments only.</p>
+
+<p>Here is the command to create hybrid model:</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>export KYLIN_HOME="<path_of_v1.5_installation>"
+$KYLIN_HOME/bin/kylin.sh org.apache.kylin.storage.hbase.util.ExtendCubeToHybridCLI <project_name> <cube_name>
+</code></pre>
+</div>
+
+ </article>
+ </div>
+ </div>
+ </div>
+ </div>
+ </div>
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