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Posted to commits@kylin.apache.org by li...@apache.org on 2018/07/02 06:08:51 UTC

svn commit: r1834820 [16/48] - in /kylin/site: ./ about/ blog/ blog/2015/01/25/introduce-data-model/ blog/2015/06/10/release-v0.7.1-incubating/ blog/2015/08/13/kylin-dictionary/ blog/2015/08/15/fast-cubing/ blog/2015/09/06/release-v1.0-incubating/ blog...

Added: kylin/site/cn/docs23/tutorial/cube_spark.html
URL: http://svn.apache.org/viewvc/kylin/site/cn/docs23/tutorial/cube_spark.html?rev=1834820&view=auto
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--- kylin/site/cn/docs23/tutorial/cube_spark.html (added)
+++ kylin/site/cn/docs23/tutorial/cube_spark.html Mon Jul  2 06:08:48 2018
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+							<h1 class="post-title">用 Spark 构建 Cube</h1>
+							<article class="post-content" >
+							<p>Kylin v2.0 介绍了 Spark cube engine,在 build cube 步骤中使用 Apache Spark 代替 MapReduce;您可以通过查看 <a href="/blog/2017/02/23/by-layer-spark-cubing/">这篇博客</a> 的图片了解整体情况。当前的文档使用样例 cube 对如何尝试 new engine 进行了演示。</p>
+
+<h2 id="section">准备阶段</h2>
+<p>您需要一个安装了 Kylin v2.1.0 及以上版本的 Hadoop 环境。使用 Hortonworks HDP 2.4 Sandbox VM,其中 Hadoop 组件和 Hive/HBase 已经启动了。</p>
+
+<h2 id="kylin-v210-">安装 Kylin v2.1.0 及以上版本</h2>
+
+<p>从 Kylin 的下载页面下载适用于 HBase 1.x 的 Kylin v2.1.0,然后在 <em>/usr/local/</em> 文件夹中解压 tar 包:</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="kylinenvhadoop-conf-dir">准备 “kylin.env.hadoop-conf-dir”</h2>
+
+<p>为使 Spark 运行在 Yarn 上,需指定 <strong>HADOOP_CONF_DIR</strong> 环境变量,其是一个包含 Hadoop(客户端) 配置文件的目录。许多 Hadoop 分布式的目录设置为 “/etc/hadoop/conf”;但 Kylin 不仅需要访问 HDFS,Yarn 和 Hive,还有 HBase,因此默认的目录可能并未包含所有需要的文件。在此用例中,您需要创建一个新的目录然后拷贝或者连接这些客户端文件 (core-site.xml,hdfs-site.xml,yarn-site.xml,hive-site.xml 和 hbase-site.xml) 到这个目录下。在 HDP 2.4 中,hive-tez 和 Spark 之间有ä
 ¸ªå†²çªï¼Œå› æ­¤å½“为 Kylin 进行复制时,需要将默认的 engine 由 “tez” 换为 “mr”。</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>现在,在 kylin.properties 中设置属性 “kylin.env.hadoop-conf-dir” 好让 Kylin 知道这个目录:</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>如果这个属性没有设置,Kylin 将会使用 “hive-site.xml” 中的默认目录;然而那个文件夹可能并没有 “hbase-site.xml”,会导致 Spark 的 HBase/ZK 连接错误。</p>
+
+<h2 id="spark-">检查 Spark 配置</h2>
+
+<p>Kylin 在 $KYLIN_HOME/spark 中嵌入一个 Spark binary (v2.1.0),所有使用 <em>“kylin.engine.spark-conf.”</em> 作为前缀的 Spark 配置属性都能在 $KYLIN_HOME/conf/kylin.properties 中进行管理。这些属性当运行提交 Spark job 时会被提取并应用;例如,如果您配置 “kylin.engine.spark-conf.spark.executor.memory=4G”,Kylin 将会在执行 “spark-submit” 操作时使用 “–conf spark.executor.memory=4G” 作为参数。</p>
+
+<p>运行 Spark cubing 前,建议查看一下这些配置并根据您集群的情况进行自定义。下面是默认配置,也是 sandbox 最低要求的配置 (1 个 1GB memory 的 executor);通常一个集群,需要更多的 executors 且每一个至少有 4GB memory 和 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>为了在 Hortonworks 平台上运行,需要将 “hdp.version” 指定为 Yarn 容器的 Java 选项,因此请取消 kylin.properties 的最后三行。</p>
+
+<p>除此之外,为了避免重复上传 Spark jar 包到 Yarn,您可以手动上传一次,然后配置 jar 包的 HDFS 路径;请注意,HDFS 路径必须是全限定名。</p>
+
+<div class="highlight"><pre><code class="language-groff" data-lang="groff">jar cv0f spark-libs.jar -C $KYLIN_HOME/spark/jars/ .
+hadoop fs -mkdir -p /kylin/spark/
+hadoop fs -put spark-libs.jar /kylin/spark/</code></pre></div>
+
+<p>然后,要在 kylin.properties 中进行如下配置:</p>
+
+<div class="highlight"><pre><code class="language-groff" data-lang="groff">kylin.engine.spark-conf.spark.yarn.archive=hdfs://sandbox.hortonworks.com:8020/kylin/spark/spark-libs.jar
+kylin.engine.spark-conf.spark.driver.extraJavaOptions=-Dhdp.version=current
+kylin.engine.spark-conf.spark.yarn.am.extraJavaOptions=-Dhdp.version=current
+kylin.engine.spark-conf.spark.executor.extraJavaOptions=-Dhdp.version=current</code></pre></div>
+
+<p>所有 “kylin.engine.spark-conf.*” 参数都可以在 Cube 或 Project 级别进行重写,这为用户提供了极大的灵活性。</p>
+
+<h2 id="cube">创建和修改样例 cube</h2>
+
+<p>运行 sample.sh 创建样例 cube,然后启动 Kylin 服务器:</p>
+
+<div class="highlight"><pre><code class="language-groff" data-lang="groff">$KYLIN_HOME/bin/sample.sh
+$KYLIN_HOME/bin/kylin.sh start</code></pre></div>
+
+<p>Kylin 启动后,访问 Kylin 网站,在 “Advanced Setting” 页,编辑名为 “kylin_sales” 的 cube,将 “Cube Engine” 由 “MapReduce” 换成 “Spark”:</p>
+
+<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/1_cube_engine.png" alt="" /></p>
+
+<p>点击 “Next” 进入 “Configuration Overwrites” 页面,点击 “+Property” 添加属性 “kylin.engine.spark.rdd-partition-cut-mb” 其值为 “500” (理由如下):</p>
+
+<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/2_overwrite_partition.png" alt="" /></p>
+
+<p>样例 cube 有两个耗尽内存的度量: “COUNT DISTINCT” 和 “TOPN(100)”;当源数据较小时,他们的大小估计的不太准确: 预估的大小会比真实的大很多,导致了更多的 RDD partitions 被切分,使得 build 的速度降低。100 对于其是一个较为合理的数字。点击 “Next” 和 “Save” 保存 cube。</p>
+
+<h2 id="spark--cube">用 Spark 构建 Cube</h2>
+
+<p>点击 “Build”,选择当前日期为 end date。Kylin 会在 “Monitor” 页生成一个构建 job,第 7 步是 Spark cubing。Job engine 开始按照顺序执行每一步。</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>当 Kylin 执行这一步时,您可以监视 Yarn 资源管理器里的状态. 点击 “Application Master” 链接将会打开 Spark 的 UI 网页,它会显示每一个 stage 的进度以及详细的信息。</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>所有步骤成功执行后,Cube 的状态变为 “Ready” 且您可以像往常那样进行查询。</p>
+
+<h2 id="section-1">疑难解答</h2>
+
+<p>当出现 error,您可以首先查看 “logs/kylin.log”. 其中包含 Kylin 执行的所有 Spark 命令,例如:</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 &amp;&amp; /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>您可以拷贝 cmd 以便在 shell 中手动执行,然后快速进行参数调整;执行期间,您可以访问 Yarn 资源管理器查看更多的消息。如果 job 已经完成了,您可以在 Spark history server 中查看历史信息。</p>
+
+<p>Kylin 默认将历史信息输出到 “hdfs:///kylin/spark-history”,您需要在该目录下启动 Spark history server,或在 conf/kylin.properties 中使用参数 “kylin.engine.spark-conf.spark.eventLog.dir” 和 “kylin.engine.spark-conf.spark.history.fs.logDirectory” 替换为您已存在的 Spark history server 的事件目录。</p>
+
+<p>下面的命令可以在 Kylin 的输出目录下启动一个 Spark history server 实例,运行前请确保 sandbox 中已存在的 Spark history server 关闭了:</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>浏览器访问 “http://sandbox:18080” 将会显示 job 历史:</p>
+
+<p><img src="/images/tutorial/2.0/Spark-Cubing-Tutorial/9_spark_history.png" alt="" /></p>
+
+<p>点击一个具体的 job,运行时的具体信息将会展示,该信息对疑难解答和性能调整有极大的帮助。</p>
+
+<h2 id="section-2">进一步</h2>
+
+<p>如果您是 Kylin 的管理员但是对于 Spark 是新手,建议您浏览 <a href="https://spark.apache.org/docs/2.1.0/">Spark 文档</a>,别忘记相应地去更新配置。您可以让 Spark 的 <a href="https://spark.apache.org/docs/2.1.0/job-scheduling.html#dynamic-resource-allocation">Dynamic Resource Allocation</a> 生效以便其对于不同的工作负载能自动伸缩。Spark 性能依赖于集群的内存和 CPU 资源,当有复杂数据模型和巨大的数据集一次构建时 Kylin 的 Cube 构建将会是一项繁重的任务。如果您的集群资源不能够执行,Spark executors å°±ä¼
 šæŠ›å‡ºå¦‚ “OutOfMemorry” 这样的错误,因此请合理的使用。对于有 UHC dimension,过多组合 (例如,一个 cube 超过 12 dimensions),或耗尽内存的度量 (Count Distinct,Top-N) 的 Cube,建议您使用 MapReduce engine。如果您的 Cube 模型较为简单,所有的都是 SUM/MIN/MAX/COUNT,源数据规模小至中等,Spark engine 将会是个好的选择。除此之外,Streaming 构建在 engine 中目前还不支持(KYLIN-2484)。</p>
+
+<p>如果您有任何问题,意见,或 bug 修复,欢迎在 dev@kylin.apache.org 中讨论。</p>
+
+							</article>
+						</div>
+					</div>
+				</div>
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+        <a href="/cn/docs23/install/kylin_aws_emr.html" class="list-group-item-lay pjaxlink ">在 AWS EMR 上 安装 Kylin</a>
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+        <a href="/cn/docs23/install/kylin_docker.html" class="list-group-item-lay pjaxlink ">用 Docker 运行 Kylin</a>
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+        <a href="/cn/docs23/tutorial/kylin_sample.html" class="list-group-item-lay pjaxlink ">样例 Cube 快速入门</a>
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+        <a href="/cn/docs23/tutorial/cube_spark.html" class="list-group-item-lay pjaxlink ">用 Spark 构建 Cube</a>
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+        <a href="/cn/docs23/tutorial/cube_streaming.html" class="list-group-item-lay pjaxlink ">从 Kafka 流构建 Cube</a>
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/cube_build_performance.html" class="list-group-item-lay pjaxlink ">优化 Cube 构建</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/kylin_client_tool.html" class="list-group-item-lay pjaxlink ">Python 客户端</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/setup_systemcube.html" class="list-group-item-lay pjaxlink ">建立系统 Cube</a>
+      </li>      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/use_cube_planner.html" class="list-group-item-lay pjaxlink ">使用 Cube Planner</a>
+      </li>      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/use_dashboard.html" class="list-group-item-lay pjaxlink ">使用 Dashboard</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/setup_jdbc_datasource.html" class="list-group-item-lay pjaxlink ">建立 JDBC 数据源</a>
+      </li>      
+      
+
+
+
+
+
+
+
+        <ul>
+  </div>
+</div>
+    
+    <li><a href="#title工具集成" data-toggle="collapse" class="navtitle">工具集成</a></li>
+    <div class="collapse in">
+  	<div class="list-group" id="list1">
+    <ul>
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/odbc.html" class="list-group-item-lay pjaxlink ">ODBC 驱动</a>
+      </li>      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/jdbc.html" class="list-group-item-lay pjaxlink ">JDBC 驱动</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/tableau.html" class="list-group-item-lay pjaxlink ">Tableau 8</a>
+      </li>      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/tableau_91.html" class="list-group-item-lay pjaxlink ">Tableau 9</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/powerbi.html" class="list-group-item-lay pjaxlink ">Excel 及 Power BI 教程</a>
+      </li>      
+      
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+  
+    
+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/squirrel.html" class="list-group-item-lay pjaxlink ">SQuirreL</a>
+      </li>      
+      
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+    
+      <li class="navlist">
+        <a href="/cn/docs23/tutorial/Qlik.html" class="list-group-item-lay pjaxlink ">Qlik Sense 集成</a>
+      </li>      
+      
+
+
+
+
+
+
+
+        <ul>
+  </div>
+</div>
+    
+    <li><a href="#title帮助" data-toggle="collapse" class="navtitle">帮助</a></li>
+    <div class="collapse in">
+  	<div class="list-group" id="list1">
+    <ul>
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+      <li class="navlist">
+        <a href="/cn/docs23/howto/howto_use_restapi.html" class="list-group-item-lay pjaxlink ">Use RESTful API</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/howto/howto_build_cube_with_restapi.html" class="list-group-item-lay pjaxlink ">用 API 构建 Cube</a>
+      </li>      
+      
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+    
+      <li class="navlist">
+        <a href="/cn/docs23/howto/howto_optimize_cubes.html" class="list-group-item-lay pjaxlink ">优化 Cube 设计</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/howto/howto_optimize_build.html" class="list-group-item-lay pjaxlink ">优化 Cube 构建</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/howto/howto_backup_metadata.html" class="list-group-item-lay pjaxlink ">备份元数据</a>
+      </li>      
+      
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+      <li class="navlist">
+        <a href="/cn/docs23/howto/howto_cleanup_storage.html" class="list-group-item-lay pjaxlink ">清理存储</a>
+      </li>      
+      
+
+
+
+
+
+
+
+        <ul>
+  </div>
+</div>
+    
+
+    </ul>
+</div>
+			</div>
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