Added: kylin/site/docs23/tutorial/cube_build_job.html URL: http://svn.apache.org/viewvc/kylin/site/docs23/tutorial/cube_build_job.html?rev=1822508&view=auto ============================================================================== --- kylin/site/docs23/tutorial/cube_build_job.html (added) +++ kylin/site/docs23/tutorial/cube_build_job.html Mon Jan 29 14:05:18 2018 @@ -0,0 +1,4206 @@ +<!-- +* 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. 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+ + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_use_dashboard.html" class="list-group-item-lay pjaxlink" id="navlist">Use Dashboard</a></li> + + + + + + + + + <ul> + </div> +</div> + + + </ul> +</div> + + <div class="col-md-9 col-lg-9 col-xs-14 aside2"> + <div id="container"> + <div id="pjax"> + <h1 class="post-title">Cube Build and Job Monitoring</h1> + + + <article class="post-content" > + <h3 id="cube-build">Cube Build</h3> +<p>First of all, make sure that you have authority of the cube you want to build.</p> + +<ol> + <li> + <p>In <code class="highlighter-rouge">Models</code> page, click the <code class="highlighter-rouge">Action</code> drop down button in the right of a cube column and select operation <code class="highlighter-rouge">Build</code>.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/1 action-build.png" alt="" /></p> + </li> + <li> + <p>There is a pop-up window after the selection, click <code class="highlighter-rouge">END DATE</code> input box to select end date of this incremental cube build.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/3 end-date.png" alt="" /></p> + </li> + <li> + <p>Click <code class="highlighter-rouge">Submit</code> to send the build request. After success, you will see the new job in the <code class="highlighter-rouge">Monitor</code> page.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/4 jobs-page.png" alt="" /></p> + </li> + <li> + <p>The new job is in âpendingâ status; after a while, it will be started to run and you will see the progress by refresh the web page or click the refresh button.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/5 job-progress.png" alt="" /></p> + </li> + <li> + <p>Wait the job to finish. In the between if you want to discard it, click <code class="highlighter-rouge">Actions</code> -> <code class="highlighter-rouge">Discard</code> button.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/6 discard.png" alt="" /></p> + </li> + <li> + <p>After the job is 100% finished, the cubeâs status becomes to âReadyâ, means it is ready to serve SQL queries. In the <code class="highlighter-rouge">Model</code> tab, find the cube, click cube name to expand the section, in the âHBaseâ tab, it will list the cube segments. Each segment has a start/end time; Its underlying HBase table information is also listed.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/10 cube-segment.png" alt="" /></p> + </li> +</ol> + +<p>If you have more source data, repeate the steps above to build them into the cube.</p> + +<h3 id="job-monitoring">Job Monitoring</h3> +<p>In the <code class="highlighter-rouge">Monitor</code> page, click the job detail button to see detail information show in the right side.</p> + +<p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/7 job-steps.png" alt="" /></p> + +<p>The detail information of a job provides a step-by-step record to trace a job. You can hover a step status icon to see the basic status and information.</p> + +<p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/8 hover-step.png" alt="" /></p> + +<p>Click the icon buttons showing in each step to see the details: <code class="highlighter-rouge">Parameters</code>, <code class="highlighter-rouge">Log</code>, <code class="highlighter-rouge">MRJob</code>.</p> + +<ul> + <li> + <p>Parameters</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/9 parameters.png" alt="" /></p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/9 parameters-d.png" alt="" /></p> + </li> + <li> + <p>Log</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/9 log.png" alt="" /></p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/9 log-d.png" alt="" /></p> + </li> + <li> + <p>MRJob(MapReduce Job)</p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/9 mrjob.png" alt="" /></p> + + <p><img src="/images/tutorial/1.5/Kylin-Cube-Build-and-Job-Monitoring-Tutorial/9 mrjob-d.png" alt="" /></p> + </li> +</ul> + + + </article> + </div> + </div> + </div> + </div> + </div> + <!-- +* Licensed to the Apache Software Foundation (ASF) under one +* or more contributor license agreements. 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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 +* limitations under the License. +--> + +<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>. 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Added: kylin/site/docs23/tutorial/cube_build_performance.html URL: http://svn.apache.org/viewvc/kylin/site/docs23/tutorial/cube_build_performance.html?rev=1822508&view=auto ============================================================================== --- kylin/site/docs23/tutorial/cube_build_performance.html (added) +++ kylin/site/docs23/tutorial/cube_build_performance.html Mon Jan 29 14:05:18 2018 @@ -0,0 +1,4382 @@ +<!-- +* 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. 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Before do a real tunning, please get an overall understanding about Cube build process from <a href="/docs20/howto/howto_optimize_build.html">Optimize Cube Build</a></p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/01.png" alt="" /></p> + +<p>The baseline is:</p> + +<ul> + <li>One Measure: Balance, calculate always Max, Min and Count</li> + <li>All Dim_date (10 items) will be used as dimensions</li> + <li>Input is a Hive CSV external table</li> + <li>Output is a Cube in HBase without compression</li> +</ul> + +<p>With this configuration, the results are: 13 min to build a cube of 20 Mb (Cube_01)</p> + +<h3 id="cube02-reduce-combinations">Cube_02: Reduce combinations</h3> +<p>To make the first improvement, use Joint and Hierarchy on Dimensions to reduce the combinations (number of cuboids).</p> + +<p>Put together all ID and Text of: Month, Week, Weekday and Quarter using Joint Dimension</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/02.png" alt="" /></p> + +<p>Define Id_date and Year as a Hierarchy Dimension</p> + +<p>This reduces the size down to 0.72MB and time to 5 min</p> + +<p><a href="https://issues.apache.org/jira/browse/KYLIN-2149";>Kylin 2149</a>, ideally, these Hierarchies can be defined also:<br /> +* Id_weekday > Id_date<br /> +* Id_Month > Id_date<br /> +* Id_Quarter > Id_date<br /> +* Id_week > Id_date</p> + +<p>But for now, it impossible to use Joint and Hierarchy together for one dimension.</p> + +<h3 id="cube03-compress-output">Cube_03: Compress output</h3> +<p>To make the next improvement, compress HBase Cube with Snappy:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/03.png" alt="alt text" /></p> + +<p>Another option is Gzip:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/04.png" alt="alt text" /></p> + +<p>The results of compression output are:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/05.png" alt="alt text" /></p> + +<p>The difference between Snappy and Ggzip in time is less than 1% but in size it is 18%</p> + +<h3 id="cube04-compress-hive-table">Cube_04: Compress Hive table</h3> +<p>The time distribution is like this:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/06.png" alt="" /></p> + +<p>Group detailed times by concepts :</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/07.png" alt="" /></p> + +<p>67 % is used to build / process flat table and respect 30% to build the cube</p> + +<p>A lot of time is used in the first steps.</p> + +<p>This time distribution is typical in a cube with few measures and few dim (or very optimized)</p> + +<p>Try to use ORC Format and compression on Hive input table (Snappy):</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/08.png" alt="" /></p> + +<p>The time in the first three stree steps (Flat Table) has been improved by half.</p> + +<p>Other columnar formats can be tested:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/19.png" alt="" /></p> + +<ul> + <li>ORC</li> + <li>ORC compressed with Snappy</li> +</ul> + +<p>But the results are worse than when using Sequence file.</p> + +<p>See comments about this here: <a href="http://apache-kylin.74782.x6.nabble.com/Kylin-Performance-td6713.html#a6767";>Shaofengshi in MailList</a></p> + +<p>The second strep is to redistribute Flat Hive table:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/20.png" alt="" /></p> + +<p>Is a simple row count, two approximations can be made<br /> +* If it doesnât need to be accurate, the rows of the fact table can be countedâ this can be performed in parallel with Step 1 (and 99% of the time it will be accurate)</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/21.png" alt="" /></p> + +<ul> + <li>In the future versions (KYLIN-2165 v2.0), this steps will be implemented using Hive table statistics.</li> +</ul> + +<h3 id="cube05-partition-hive-table-fail">Cube_05: Partition Hive table (fail)</h3> +<p>The distribution of rows is:</p> + +<table> + <thead> + <tr> + <th>Table</th> + <th>Rows</th> + </tr> + </thead> + <tbody> + <tr> + <td>Fact Table</td> + <td>3.900.00</td> + </tr> + <tr> + <td>Dim Date</td> + <td>2.100</td> + </tr> + </tbody> +</table> + +<p>And the query (the simplified version) to build the flat table is:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">```sql +SELECT +,DIM_DATE.X +,DIM_DATE.y +,FACT_POSICIONES.BALANCE +FROM FACT_POSICIONES INNER JOIN DIM_DATE + ON ID_FECHA = .ID_FECHA +WHERE (ID_DATE >= '2016-12-08' AND ID_DATE < '2016-12-23') +```</code></pre></div> + +<p>The problem here, is that, Hive in only using 1 Map to create Flat Table. It is important to lets go to change this behavior. The solution is to partition DIM and FACT in the same columns</p> + +<ul> + <li>Option 1: Use id_date as a partition column on Hive table. This has a big problem: the Hive metastore is meant for few a hundred of partitions and not thousands (In <a href="https://issues.apache.org/jira/browse/HIVE-9452";>Hive 9452</a> there is an idea to solve this but it isnât finished yet)</li> + <li>Option 2: Generate a new column for this purpose like Monthslot.</li> +</ul> + +<p><img src="/images/tutorial/2.0/cube_build_performance/09.png" alt="" /></p> + +<p>Add the same column to dim and fact tables</p> + +<p>Now, upgrade the the data model with this new condition to join tables</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/10.png" alt="" /></p> + +<p>The new query to generate flat table will be similar to:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">```sql +SELECT * + FROM FACT_POSICIONES **INNER JOIN** DIM_DATE + ON ID_FECHA = .ID_FECHA AND MONTHSLOT=MONTHSLOT +```</code></pre></div> + +<p>Rebuild the new cube with this data model</p> + +<p>As a result, the performance has worsened :( . After tried several attempts, there hasnât been a solution</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/11.png" alt="" /></p> + +<p>The problem is that partitions were not used to generate several Mappers</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/12.png" alt="" /></p> + +<p>(I checked this issue with ShaoFeng Shi. He thinks the problem is that there are few many rows and we are not working with a real Hadoop cluster. See this <a href="http://kylin.apache.org/docs16/howto/howto_optimize_build.html";>tech note</a>).</p> + +<h3 id="resume-of-results">Resume of results</h3> + +<p><img src="/images/tutorial/2.0/cube_build_performance/13.png" alt="" /></p> + +<p>The tunning process has been:<br /> +* Hive Input tables compressed<br /> +* HBase Output compressed<br /> +* Apply techniques of reduction of cardinality (Joint, Derived, Hierarchy and Mandatory)<br /> +* Personalize Dim encoder for each Dim and choose the best order of Dim in Row Key</p> + +<p>Now, there are three types of cubes:<br /> +* Cubes with low cardinality in their dimensions (Like cube 4, most of time is usend in flat table steps)<br /> +* Cubes with high cardinality in their dimensions (Like cube 6,most of time is usend on Build cube, the flat table steps are lower than 10%)<br /> +* The third type, ultra high cardinality (UHC) which is outside the scope of this article</p> + +<h3 id="cube-6-cube-with-high-cardinality-dimensions">Cube 6: Cube with high cardinality Dimensions</h3> + +<p><img src="/images/tutorial/2.0/cube_build_performance/22.png" alt="" /></p> + +<p>In this case the <strong>72%</strong> of the time is used to build Cube</p> + +<p>This step is a MapReduce task, you can see the YARN log of these steps on <img src="/images/tutorial/2.0/cube_build_performance/23.png" alt="alt text" /> > <img src="/images/tutorial/2.0/cube_build_performance/24.png" alt="alt text" /></p> + +<p>How can the performance of Map â Reduce be improved? The easy way is to increase the numbers of Mappers and Reduces (= Increase parallelism).</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/25.png" alt="" /></p> + +<p><strong>NOTE:</strong> YARN / MapReduce have a lot parameters to configure and adapt to theyour system. The focus here is only on small parts.</p> + +<p>(In my system I can assign 12 â 14 GB and 8 cores to YARN Resources):</p> + +<ul> + <li>yarn.nodemanager.resource.memory-mb = 15 GB</li> + <li>yarn.scheduler.maximum-allocation-mb = 8 GB</li> + <li>yarn.nodemanager.resource.cpu-vcores = 8 cores<br /> +With this config our max theoreticaleorical grade of parallelismelist is 8. However, but this has a problem: âTimed out after 3600 secsâ</li> +</ul> + +<p><img src="/images/tutorial/2.0/cube_build_performance/26.png" alt="" /></p> + +<p>The parameter mapreduce.task.timeout (1 hour by default) define max time that Application Master (AM) can happen with out ACK of Yarn Container. Once this time passes, AM kill the container and retry the same 4 times (with the same result)</p> + +<p>Where is the problem? The problem is that 4 mappers started, but each mapper needed more than 4 GB to finish</p> + +<ul> + <li>The solution 1: add more RAM to YARN</li> + <li>The solution 2: increase vCores number used in Mapper step to reduce the RAM used</li> + <li>The solution 3: you can play with max RAM to YARN by node (yarn.nodemanager.resource.memory-mb) and experiment with mimin RAM perto container (yarn.scheduler.minimum-allocation-mb). If you increase minimum RAM per container, YARN will reduce the numbers of Mappers</li> +</ul> + +<p><img src="/images/tutorial/2.0/cube_build_performance/27.png" alt="" /></p> + +<p>In the last two cases the results are the same: reduce the level of parallelism ==> <br /> +* Now we only start 3 mappers start at the same time, the fourth must be wait for a free slot<br /> +* The three first mappers distribute spread the ram among themselves, and as a result they will have enough ram to finish the task</p> + +<p>During a normal âBuild Cubeâ step you will see similars messages on YARN log:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/28.png" alt="" /></p> + +<p>If you donât see this periodically, perhaps you have a bottleneck in the memory.</p> + +<h3 id="cube-7-improve-cube-response-time">Cube 7: Improve cube response time</h3> +<p>We can try to use different aggregations groups to improve the query performance of some very important Dim or a Dim with high cardinality.</p> + +<p>In our case we define 3 Aggregations Groups: <br /> +1. âNormal cubeâ<br /> +2. Cube with Date Dim and Currency (as mandatory)<br /> +3. Cube with Date Dim and Carteras_Desc (as mandatory)</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/29.png" alt="" /></p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/30.png" alt="" /></p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/31.png" alt="" /></p> + +<p>Compare without / with AGGs:</p> + +<p><img src="/images/tutorial/2.0/cube_build_performance/32.png" alt="" /></p> + +<p>Now it uses 3% more of time to build the cube and 0.6% of space, but queries by currency or Carteras_Desc will be much faster.</p> + + + </article> + </div> + </div> + </div> + </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. 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