Added: kylin/site/docs23/tutorial/cube_spark.html URL: http://svn.apache.org/viewvc/kylin/site/docs23/tutorial/cube_spark.html?rev=1822508&view=auto ============================================================================== --- kylin/site/docs23/tutorial/cube_spark.html (added) +++ kylin/site/docs23/tutorial/cube_spark.html Mon Jan 29 14:05:18 2018 @@ -0,0 +1,4264 @@ +<!-- +* 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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The current document uses the sample cube to demo how to try the new engine.</p> + +<h2 id="preparation">Preparation</h2> +<p>To finish this tutorial, you need a Hadoop environment which has Kylin v2.1.0 or above installed. Here we will use Hortonworks HDP 2.4 Sandbox VM, the Hadoop components as well as Hive/HBase has already been started.</p> + +<h2 id="install-kylin-v210-or-above">Install Kylin v2.1.0 or above</h2> + +<p>Download the Kylin v2.1.0 for HBase 1.x from Kylinâs download page, and then uncompress the tar ball into <em>/usr/local/</em> folder:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">wget http://www-us.apache.org/dist/kylin/apache-kylin-2.1.0/apache-kylin-2.1.0-bin-hbase1x.tar.gz -P /tmp + +tar -zxvf /tmp/apache-kylin-2.1.0-bin-hbase1x.tar.gz -C /usr/local/ + +export KYLIN_HOME=/usr/local/apache-kylin-2.1.0-bin-hbase1x</code></pre></div> + +<h2 id="prepare-kylinenvhadoop-conf-dir">Prepare âkylin.env.hadoop-conf-dirâ</h2> + +<p>To run Spark on Yarn, need specify <strong>HADOOP_CONF_DIR</strong> environment variable, which is the directory that contains the (client side) configuration files for Hadoop. In many Hadoop distributions the directory is â/etc/hadoop/confâ; But Kylin not only need access HDFS, Yarn and Hive, but also HBase, so the default directory might not have all necessary files. In this case, you need create a new directory and then copying or linking those client files (core-site.xml, hdfs-site.xml, yarn-site.xml, hive-site.xml and hbase-site.xml) there. In HDP 2.4, there is a conflict between hive-tez and Spark, so need change the default engine from âtezâ to âmrâ when copy for Kylin.</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">mkdir $KYLIN_HOME/hadoop-conf +ln -s /etc/hadoop/conf/core-site.xml $KYLIN_HOME/hadoop-conf/core-site.xml +ln -s /etc/hadoop/conf/hdfs-site.xml $KYLIN_HOME/hadoop-conf/hdfs-site.xml +ln -s /etc/hadoop/conf/yarn-site.xml $KYLIN_HOME/hadoop-conf/yarn-site.xml +ln -s /etc/hbase/2.4.0.0-169/0/hbase-site.xml $KYLIN_HOME/hadoop-conf/hbase-site.xml +cp /etc/hive/2.4.0.0-169/0/hive-site.xml $KYLIN_HOME/hadoop-conf/hive-site.xml +vi $KYLIN_HOME/hadoop-conf/hive-site.xml (change "hive.execution.engine" value from "tez" to "mr")</code></pre></div> + +<p>Now, let Kylin know this directory with property âkylin.env.hadoop-conf-dirâ in kylin.properties:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">kylin.env.hadoop-conf-dir=/usr/local/apache-kylin-2.1.0-bin-hbase1x/hadoop-conf</code></pre></div> + +<p>If this property isnât set, Kylin will use the directory that âhive-site.xmlâ locates in; while that folder may have no âhbase-site.xmlâ, will get HBase/ZK connection error in Spark.</p> + +<h2 id="check-spark-configuration">Check Spark configuration</h2> + +<p>Kylin embedes a Spark binary (v2.1.0) in $KYLIN_HOME/spark, all the Spark configurations can be managed in $KYLIN_HOME/conf/kylin.properties with prefix <em>âkylin.engine.spark-conf.â</em>. These properties will be extracted and applied when runs submit Spark job; E.g, if you configure âkylin.engine.spark-conf.spark.executor.memory=4Gâ, Kylin will use ââconf spark.executor.memory=4Gâ as parameter when execute âspark-submitâ.</p> + +<p>Before you run Spark cubing, suggest take a look on these configurations and do customization according to your cluster. Below is the default configurations, which is also the minimal config for a sandbox (1 executor with 1GB memory); usually in a normal cluster, need much more executors and each has at least 4GB memory and 2 cores:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">kylin.engine.spark-conf.spark.master=yarn +kylin.engine.spark-conf.spark.submit.deployMode=cluster +kylin.engine.spark-conf.spark.yarn.queue=default +kylin.engine.spark-conf.spark.executor.memory=1G +kylin.engine.spark-conf.spark.executor.cores=2 +kylin.engine.spark-conf.spark.executor.instances=1 +kylin.engine.spark-conf.spark.eventLog.enabled=true +kylin.engine.spark-conf.spark.eventLog.dir=hdfs\:///kylin/spark-history +kylin.engine.spark-conf.spark.history.fs.logDirectory=hdfs\:///kylin/spark-history + +#kylin.engine.spark-conf.spark.io.compression.codec=org.apache.spark.io.SnappyCompressionCodec + +## uncomment for HDP +#kylin.engine.spark-conf.spark.driver.extraJavaOptions=-Dhdp.version=current +#kylin.engine.spark-conf.spark.yarn.am.extraJavaOptions=-Dhdp.version=current +#kylin.engine.spark-conf.spark.executor.extraJavaOptions=-Dhdp.version=current</code></pre></div> + +<p>For running on Hortonworks platform, need specify âhdp.versionâ as Java options for Yarn containers, so please uncommment the last three lines in kylin.properties.</p> + +<p>Besides, in order to avoid repeatedly uploading Spark jars to Yarn, you can manually do that once, and then configure the jarâs HDFS location; Please note, the HDFS location need be full qualified name.</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">jar cv0f spark-libs.jar -C $KYLIN_HOME/spark/jars/ . +hadoop fs -mkdir -p /kylin/spark/ +hadoop fs -put spark-libs.jar /kylin/spark/</code></pre></div> + +<p>After do that, the config in kylin.properties will be:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">kylin.engine.spark-conf.spark.yarn.archive=hdfs://sandbox.hortonworks.com:8020/kylin/spark/spark-libs.jar +kylin.engine.spark-conf.spark.driver.extraJavaOptions=-Dhdp.version=current +kylin.engine.spark-conf.spark.yarn.am.extraJavaOptions=-Dhdp.version=current +kylin.engine.spark-conf.spark.executor.extraJavaOptions=-Dhdp.version=current</code></pre></div> + +<p>All the âkylin.engine.spark-conf.*â parameters can be overwritten at Cube or Project level, this gives more flexibility to the user.</p> + +<h2 id="create-and-modify-sample-cube">Create and modify sample cube</h2> + +<p>Run the sample.sh to create the sample cube, and then start Kylin server:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">$KYLIN_HOME/bin/sample.sh +$KYLIN_HOME/bin/kylin.sh start</code></pre></div> + +<p>After Kylin is started, access Kylin web, edit the âkylin_salesâ cube, in the âAdvanced Settingâ page, change the âCube Engineâ from âMapReduceâ to âSparkâ:</p> + +<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/1_cube_engine.png" alt="" /></p> + +<p>Click âNextâ to the âConfiguration Overwritesâ page, click â+Propertyâ to add property âkylin.engine.spark.rdd-partition-cut-mbâ with value â500â (reasons below):</p> + +<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/2_overwrite_partition.png" alt="" /></p> + +<p>The sample cube has two memory hungry measures: a âCOUNT DISTINCTâ and a âTOPN(100)â; Their size estimation can be inaccurate when the source data is small: the estimized size is much larger than the real size, that causes much more RDD partitions be splitted, which slows down the build. Here 100 is a more reasonable number for it. Click âNextâ and âSaveâ to save the cube.</p> + +<h2 id="build-cube-with-spark">Build Cube with Spark</h2> + +<p>Click âBuildâ, select current date as the build end date. Kylin generates a build job in the âMonitorâ page, in which the 7th step is the Spark cubing. The job engine starts to execute the steps in sequence.</p> + +<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/2_job_with_spark.png" alt="" /></p> + +<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/3_spark_cubing_step.png" alt="" /></p> + +<p>When Kylin executes this step, you can monitor the status in Yarn resource manager. Click the âApplication Masterâ link will open Spark web UI, it shows the progress of each stage and the detailed information.</p> + +<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/4_job_on_rm.png" alt="" /></p> + +<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/5_spark_web_gui.png" alt="" /></p> + +<p>After all steps be successfully executed, the Cube becomes âReadyâ and you can query it as normal.</p> + +<h2 id="troubleshooting">Troubleshooting</h2> + +<p>When getting error, you should check âlogs/kylin.logâ firstly. There has the full Spark command that Kylin executes, e.g:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">2017-03-06 14:44:38,574 INFO [Job 2d5c1178-c6f6-4b50-8937-8e5e3b39227e-306] spark.SparkExecutable:121 : cmd:export HADOOP_CONF_DIR=/usr/local/apache-kylin-2.1.0-bin-hbase1x/hadoop-conf && /usr/local/apache-kylin-2.1.0-bin-hbase1x/spark/bin/spark-submit --class org.apache.kylin.common.util.SparkEntry --conf spark.executor.instances=1 --conf spark.yarn.queue=default --conf spark.yarn.am.extraJavaOptions=-Dhdp.version=current --conf spark.history.fs.logDirectory=hdfs:///kylin/spark-history --conf spark.driver.extraJavaOptions=-Dhdp.version=current --conf spark.master=yarn --conf spark.executor.extraJavaOptions=-Dhdp.version=current --conf spark.executor.memory=1G --conf spark.eventLog.enabled=true --conf spark.eventLog.dir=hdfs:///kylin/spark-history --conf spark.executor.cores=2 --conf spark.submit.deployMode=cluster --files /etc/hbase/2.4.0.0-169/0/hbase-site.xml --jars /usr/hdp/2.4.0.0-16 9/hbase/lib/htrace-core-3.1.0-incubating.jar,/usr/hdp/2.4.0.0-169/hbase/lib/hbase-client-1.1.2.2.4.0.0-169.jar,/usr/hdp/2.4.0.0-169/hbase/lib/hbase-common-1.1.2.2.4.0.0-169.jar,/usr/hdp/2.4.0.0-169/hbase/lib/hbase-protocol-1.1.2.2.4.0.0-169.jar,/usr/hdp/2.4.0.0-169/hbase/lib/metrics-core-2.2.0.jar,/usr/hdp/2.4.0.0-169/hbase/lib/guava-12.0.1.jar, /usr/local/apache-kylin-2.1.0-bin-hbase1x/lib/kylin-job-2.1.0.jar -className org.apache.kylin.engine.spark.SparkCubingByLayer -hiveTable kylin_intermediate_kylin_sales_cube_555c4d32_40bb_457d_909a_1bb017bf2d9e -segmentId 555c4d32-40bb-457d-909a-1bb017bf2d9e -confPath /usr/local/apache-kylin-2.1.0-bin-hbase1x/conf -output hdfs:///kylin/kylin_metadata/kylin-2d5c1178-c6f6-4b50-8937-8e5e3b39227e/kylin_sales_cube/cuboid/ -cubename kylin_sales_cube</code></pre></div> + +<p>You can copy the cmd to execute manually in shell and then tunning the parameters quickly; During the execution, you can access Yarn resource manager to check more. If the job has already finished, you can check the history info in Spark history server.</p> + +<p>By default Kylin outputs the history to âhdfs:///kylin/spark-historyâ, you need start Spark history server on that directory, or change to use your existing Spark history serverâs event directory in conf/kylin.properties with parameter âkylin.engine.spark-conf.spark.eventLog.dirâ and âkylin.engine.spark-conf.spark.history.fs.logDirectoryâ.</p> + +<p>The following command will start a Spark history server instance on Kylinâs output directory, before run it making sure you have stopped the existing Spark history server in sandbox:</p> + +<div class="highlight"><pre><code class="language-groff" data-lang="groff">$KYLIN_HOME/spark/sbin/start-history-server.sh hdfs://sandbox.hortonworks.com:8020/kylin/spark-history</code></pre></div> + +<p>In web browser, access âhttp://sandbox:18080â it shows the job history:</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 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/2.1.0/";>Spark documents</a>, and donât forget to update the configurations accordingly. You can enable Spark <a href="https://spark.apache.org/docs/2.1.0/job-scheduling.html#dynamic-resource-allocation";>Dynamic Resource Allocation</a> so that it can auto scale/shrink for different work load. 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 S UM/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>If you have any question, comment, or bug fix, welcome to discuss in d...@kylin.apache.org.</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. 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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