You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@kylin.apache.org by li...@apache.org on 2019/06/06 08:35:44 UTC

svn commit: r1860706 [2/2] - in /kylin/site: blog/2019/05/29/ blog/2019/05/29/kylin-2.4.1-to-2.6.1/ blog/2019/05/29/kylin-2.4.1-to-2.6.1/index.html blog/index.html feed.xml

Modified: kylin/site/feed.xml
URL: http://svn.apache.org/viewvc/kylin/site/feed.xml?rev=1860706&r1=1860705&r2=1860706&view=diff
==============================================================================
--- kylin/site/feed.xml (original)
+++ kylin/site/feed.xml Thu Jun  6 08:35:44 2019
@@ -19,11 +19,84 @@
     <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>Mon, 03 Jun 2019 06:59:28 -0700</pubDate>
-    <lastBuildDate>Mon, 03 Jun 2019 06:59:28 -0700</lastBuildDate>
+    <pubDate>Thu, 06 Jun 2019 01:23:11 -0700</pubDate>
+    <lastBuildDate>Thu, 06 Jun 2019 01:23:11 -0700</lastBuildDate>
     <generator>Jekyll v2.5.3</generator>
     
       <item>
+        <title>Kylin upgrade 2.4.1 to 2.6.1</title>
+        <description>&lt;p&gt;At Telecoming we started using Kylin as our main analytics database in a new Business Intelligence project that started by the end of 2017. We moved from a custom report engine based in MySQL and later in AWS Redshift into a fully Hadoop based solution with Kylin as last step prior reporting generation. We started with Kylin 2.2.1, migrated to 2.4.1 by mid 2018 and moved to 2.6.1 last month. Kylin 2.2.1 and our first release but had some annoying bugs and stability problems. It was not only due to less mature release but also because didn’t have experience running Kylin. Some design changes at cube level and optimizations (thank you, Alberto Ramon) helped us a lot in order to improve performance and stability but there were still some issues present that has been solved in later versions.&lt;/p&gt;
+
+&lt;p&gt;This is our experience.&lt;/p&gt;
+
+&lt;h3 id=&quot;what-we-were-looking-for-with-this-upgrade&quot;&gt;What we were looking for with this upgrade:&lt;/h3&gt;
+
+&lt;p&gt;1) Bug fixing. In particular one bug related with views when creating intermediate hive tables.&lt;/p&gt;
+
+&lt;p&gt;2) Performance enhancements. This is always welcomed.&lt;/p&gt;
+
+&lt;p&gt;3) Less dependency at HBase level. HBase is our main source of problems. In part, due to EMR (AWS) distribution that relies in S3 as main storage (now we are in HDP). Table metadata is frequenly accesed and response is not always good. So, we wanted to avoid Hbase storage for metadata table and move to mysql via jdbc.&lt;/p&gt;
+
+&lt;h3 id=&quot;upgrade-plan&quot;&gt;Upgrade plan:&lt;/h3&gt;
+
+&lt;p&gt;1) Minimize downtime. Since our users do intensive use of reporting system, it’s imperative to minimize Kylin downtime.&lt;/p&gt;
+
+&lt;p&gt;2) Easy rollback. Just in case of a unsuccessful upgrade, we need an easy way to rollback.&lt;/p&gt;
+
+&lt;h3 id=&quot;what-we-did&quot;&gt;What we did:&lt;/h3&gt;
+
+&lt;p&gt;1) We prepared a new AWS instance with Kylin 2.6.1, already configured and tuned. Of course, previously we had run a for a few weeks a Kylin 2.6.1 prototype and tweaked all config files in order to run properly. This new instance was stopped when we started the migration. It also had a brand new local mysql 5.7 for metadata storage instead of relying on HBase.&lt;/p&gt;
+
+&lt;p&gt;2) We stopped all cube buildings on Kylin 2.4.1. We waited until those that were in progress had finished. So, at query level everything was working although no more data were added.&lt;/p&gt;
+
+&lt;p&gt;3) We performed a full backup of Kylin metadata (metastore.sh backup) when all builds had finished. It took only 2 minutes.&lt;/p&gt;
+
+&lt;p&gt;4) We performed an Hbase snapshot over all Hbase tables. So, rolling back in case of a total failure to previous version was an easy task: restore metadata + clone snapshot into new table (removing upgraded table). It took only a few seconds since you can run all snapshot commands in a single sentence.&lt;/p&gt;
+
+&lt;p&gt;5) We performed a metadata restore on our new Kylin 2.6.1 instance (metastore.sh restore). In this way, all metadata were automatically migrated from Hbase based storage to jdbc based storage in a few minutes.&lt;/p&gt;
+
+&lt;p&gt;6) We upgraded coprocessor from 2.6.1 (with kylin.sh org.apache.kylin.storage.hbase.util.DeployCoprocessorCLI default all).&lt;/p&gt;
+
+&lt;p&gt;7) We started new kylin version and checked that everything was in place. We could query all cubes and got proper responses.&lt;/p&gt;
+
+&lt;p&gt;8) After all test were finished, we pointed our DNS entries to new kylin 2.6.1 instance and resumed the building processes from it. Of course, kylin 2.4.1 was shutdown. Some days later, we removed hbase snapshots in order to free up storage space.&lt;/p&gt;
+
+&lt;p&gt;As you can imagine, our downtime was near zero at query level since all kylin queries were run at 2.4.1 until 2.6.1 was ready to serve requests.&lt;/p&gt;
+
+&lt;h3 id=&quot;rollback-plan-not-needed&quot;&gt;Rollback plan (not needed)&lt;/h3&gt;
+
+&lt;p&gt;Rollback plan was very simple. In case of problem, we would switch to previous version and we would restore all Hbase tables from previously taken snapshots. This snapshots included Hbase metadata table, so, no need to restore metadata.&lt;/p&gt;
+
+&lt;h3 id=&quot;what-we-have-got&quot;&gt;What we have got&lt;/h3&gt;
+&lt;ul&gt;
+  &lt;li&gt;Kylin 2.6.1 is more stable than 2.4.1. Only problems we have found at server level has been due to memory issues at platform level. Now, many steps are spark based and take some extra memory when launching jobs. We solved easily by adjusting JVM (-Xmx, -Xms) parameters for kylin 2.6.1.&lt;/li&gt;
+  &lt;li&gt;Cube builds are faster due to improvements at cube build level.&lt;/li&gt;
+  &lt;li&gt;User interface is really fast. Previously browsing cube build list took up to 10 seconds. Now, it almost instantaneous.&lt;/li&gt;
+  &lt;li&gt;Some bugs are not present anymore. Two of them were in particular very problematic:
+    &lt;ul&gt;
+      &lt;li&gt;Hive intermediate tables from views didn’t add an uuid suffix. So, if the same table was used for other builds, it could be accidentally deleted by previous job at cleaning phase. So, build failed.&lt;/li&gt;
+      &lt;li&gt;Param “kylin.job.cube-auto-ready-enabled” was not working. So, after building a segment it was automatically enabled and not always we wanted this.&lt;/li&gt;
+    &lt;/ul&gt;
+  &lt;/li&gt;
+&lt;/ul&gt;
+
+&lt;h3 id=&quot;final-thoughts&quot;&gt;Final thoughts&lt;/h3&gt;
+
+&lt;p&gt;In general, upgrade procedure has been easy and fast. However, a lot of previous work had to be done in order to test that upgrade would not be a problem. So, we built a parallel environment in order to check that all new functionalities were working and bugs were solved. We adapted our config files to new Kylin requirements, for example by replacing metadata storage from hbase to jdbc.&lt;br /&gt;
+Upgrading a Kylin version to a new one is, in general, a simple task. However, it’s imperative to read carefully documentation because sometimes it require extra steps, and of course, check parameters in order to detect new features or changes that has to be taken in consideration in the new version. Having a backup and a rollback plan (tested) to come back as fast as possible is also very important.&lt;br /&gt;
+From our users side, the most successful problem solved is the interface response. They are really happy how kylin interface is working right now. And from support side dealing with a MySQL database instead of an Hbase is also a great improvement. Editing a parameter inside a json on hbase can be hard since there are not many applications for editing a modifying a Hbase table.&lt;/p&gt;
+
+</description>
+        <pubDate>Wed, 29 May 2019 05:00:00 -0700</pubDate>
+        <link>http://kylin.apache.org/blog/2019/05/29/kylin-2.4.1-to-2.6.1/</link>
+        <guid isPermaLink="true">http://kylin.apache.org/blog/2019/05/29/kylin-2.4.1-to-2.6.1/</guid>
+        
+        
+        <category>blog</category>
+        
+      </item>
+    
+      <item>
         <title>Saiku + Kylin 搭建多维 OLAP 平台</title>
         <description>&lt;p&gt;随着数据量的激增，传统的 OLTP 平台已无法满足用户的分析需求。OLAP 平台应运而生，OLAP 平台通常由 OLAP Engine 与用户操作分析页面组成。本文将会以 Saiku + Kylin 的组合方式讲解多维 OLAP 平台的搭建。&lt;/p&gt;
 
@@ -169,55 +242,55 @@ Security: (depend on your security setti
       </item>
     
       <item>
-        <title>Apache Kylin v3.0.0-alpha Release Announcement</title>
-        <description>&lt;p&gt;The Apache Kylin community is pleased to announce the release of Apache Kylin v3.0.0-alpha.&lt;/p&gt;
+        <title>Apache Kylin v3.0.0-alpha 发布</title>
+        <description>&lt;p&gt;近日 Apache Kylin 社区很高兴地宣布，Apache Kylin v3.0.0-alpha 正式发布。&lt;/p&gt;
 
-&lt;p&gt;Apache Kylin is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Big Data supporting extremely large datasets.&lt;/p&gt;
+&lt;p&gt;Apache Kylin 是一个开源的分布式分析引擎，旨在为极大数据集提供 SQL 接口和多维分析（OLAP）的能力。&lt;/p&gt;
 
-&lt;p&gt;This is the first release of the new generation v3.x, the main feature introduced is the Real-time OLAP. All of the changes can be found in the &lt;a href=&quot;/docs/release_notes.html&quot;&gt;release notes&lt;/a&gt;. Here we just highlight the main features.&lt;/p&gt;
+&lt;p&gt;这是 Kylin 下一代 v3.x 的第一个发布版本，用于早期预览，主要的功能是实时 （Real-time） OLAP。完整的改动列表请参见&lt;a href=&quot;/docs/release_notes.html&quot;&gt;release notes&lt;/a&gt;；这里挑一些主要改进做说明。&lt;/p&gt;
 
-&lt;h1 id=&quot;important-features&quot;&gt;Important features&lt;/h1&gt;
+&lt;h1 id=&quot;section&quot;&gt;重要新功能&lt;/h1&gt;
 
-&lt;h3 id=&quot;kylin-3654---real-time-olap&quot;&gt;KYLIN-3654 - Real-time OLAP&lt;/h3&gt;
-&lt;p&gt;With the newly introduced Kylin real-time receiver and coordinator components, Kylin can implement a millisecond-level data preparation delay for streaming data from sources like Apache Kafka. This means since v3.0 on,  Kylin can support sub-second level OLAP over historical batch data, near real-time streaming as well as real-time streaming. The user can use one OLAP platform to serve different scenarios. This solution has been deployed and verified in early adopters like eBay since 2018. For how to enable it, please refer to &lt;a href=&quot;/docs30/tutorial/realtime_olap.html&quot;&gt;this tutorial&lt;/a&gt;.&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3654----olap&quot;&gt;KYLIN-3654 - 实时 OLAP&lt;/h3&gt;
+&lt;p&gt;随着引入新的 real-time receiver 和 coordinator 组件，Kylin 能够实现毫秒级别的数据准备延迟，数据源来自流式数据如 Apache Kafka。这意味着，从 v3.0 开始，Kylin 既能够支持历史批量数据的 OLAP，也支持对流式数据的准实时（Near real-time）以及完全实时(real-time)分析。用户可以使用一个 OLAP 平台来服务不同的使用场景。此方案已经在早期用户如 eBay 得到部署和验证。关于如何使用此功能，请参考&lt;a href=&quot;/docs30/tutorial/realtime_olap.html&quot;&gt;æ­¤æ•
 ™ç¨‹&lt;/a&gt;。&lt;/p&gt;
 
-&lt;h3 id=&quot;kylin-3795---submit-spark-jobs-via-apache-livy&quot;&gt;KYLIN-3795 - Submit Spark jobs via Apache Livy&lt;/h3&gt;
-&lt;p&gt;This feature allows the administrator to configure Kylin to integrate with Apache Livy (incubating) for Spark job submissions. The Spark job is submitted to the Livy Server through Livy’s REST API, instead of starting the Spark Driver process in local, which facilitates the management and monitoring of the Spark resources, and also releases the pressure of the nodes where the Kylin job server is running.&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3795----apache-livy--spark-&quot;&gt;KYLIN-3795 - 通过 Apache Livy 递交 Spark 任务&lt;/h3&gt;
+&lt;p&gt;这个功能允许管理员为 Kylin 配置使用 Apache Livy (incubating) 来完成任务的递交。Spark 作业的提交通过 Livy 的 REST API 来提交，而无需在本地启动 Spark Driver 进程，从而方便对 Spark 资源的管理监控，同时也降低对 Kylin 任务进程所在节点的压力。&lt;/p&gt;
 
-&lt;h3 id=&quot;kylin-3820---a-curator-based-job-scheduler&quot;&gt;KYLIN-3820 - A curator-based job scheduler&lt;/h3&gt;
-&lt;p&gt;A new job scheduler is added to automatically discover the Kylin nodes and do an automatic leader selection among them (only the leader will submit jobs). With this feature, you can easily deploy and scale out Kylin nodes without manually update the node address in &lt;code class=&quot;highlighter-rouge&quot;&gt;kylin.properties&lt;/code&gt; and restart Kylin to take effective.&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3820----curator-&quot;&gt;KYLIN-3820 - 基于 Curator 的任务节点分配和服务发现&lt;/h3&gt;
+&lt;p&gt;新增一种基于Apache Zookeeper 和 Curator作业调度器，可以自动发现 Kylin 节点，并自动分配一个节点来进行任务的管理以及故障恢复。有了这个功能后，管理员可以更加容易地部署和扩展 Kylin 节点，而不再需要在 &lt;code class=&quot;highlighter-rouge&quot;&gt;kylin.properties&lt;/code&gt; 中配置每个 Kylin 节点的地址并重启 Kylin 以使之生效。&lt;/p&gt;
 
-&lt;h1 id=&quot;other-enhancements&quot;&gt;Other enhancements&lt;/h1&gt;
+&lt;h1 id=&quot;section-1&quot;&gt;其它改进&lt;/h1&gt;
 
-&lt;h3 id=&quot;kylin-3716---fastthreadlocal-replaces-threadlocal&quot;&gt;KYLIN-3716 - FastThreadLocal replaces ThreadLocal&lt;/h3&gt;
-&lt;p&gt;Using FastThreadLocal instead of ThreadLocal can improve Kylin’s overall performance to some extent.&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3716---fastthreadlocal--threadlocal&quot;&gt;KYLIN-3716 - FastThreadLocal 替换 ThreadLocal&lt;/h3&gt;
+&lt;p&gt;使用 Netty 中的 FastThreadLocal 替代 JDK 原生的 ThreadLocal，可以一定程度上提升 Kylin 在高并发下的性能。&lt;/p&gt;
 
 &lt;h3 id=&quot;kylin-3867---enable-jdbc-to-use-key-store--trust-store-for-https-connection&quot;&gt;KYLIN-3867 - Enable JDBC to use key store &amp;amp; trust store for https connection&lt;/h3&gt;
-&lt;p&gt;By using HTTPS, the authentication information used by JDBC is protected, making Kylin more secure.&lt;/p&gt;
+&lt;p&gt;通过使用HTTPS，保护了JDBC使用的身份验证信息，使得Kylin更加安全&lt;/p&gt;
 
 &lt;h3 id=&quot;kylin-3905---enable-shrunken-dictionary-default&quot;&gt;KYLIN-3905 - Enable shrunken dictionary default&lt;/h3&gt;
-&lt;p&gt;By default, the shrunken dictionary is enabled, and the precise counting scene for high cardinal dimensions can significantly reduce the build time.&lt;/p&gt;
+&lt;p&gt;默认开启 shrunken dictionary，针对高基维进行精确去重的场景，可以显著减少构建用时。&lt;/p&gt;
 
 &lt;h3 id=&quot;kylin-3839---storage-clean-up-after-the-refreshing-and-deleting-a-segment&quot;&gt;KYLIN-3839 - Storage clean up after the refreshing and deleting a segment&lt;/h3&gt;
-&lt;p&gt;Clear unnecessary data files in a timely manner&lt;/p&gt;
+&lt;p&gt;更加及时地清除不必要的数据文件&lt;/p&gt;
 
-&lt;p&gt;&lt;strong&gt;Download&lt;/strong&gt;&lt;/p&gt;
+&lt;p&gt;&lt;strong&gt;下载&lt;/strong&gt;&lt;/p&gt;
 
-&lt;p&gt;To download Apache Kylin v3.0.0-alpha source code or binary package, visit the &lt;a href=&quot;http://kylin.apache.org/download&quot;&gt;download&lt;/a&gt; page.&lt;/p&gt;
+&lt;p&gt;要下载Apache Kylin 源代码或二进制包，请访问&lt;a href=&quot;/download&quot;&gt;下载页面&lt;/a&gt; page.&lt;/p&gt;
 
-&lt;p&gt;&lt;strong&gt;Upgrade&lt;/strong&gt;&lt;/p&gt;
+&lt;p&gt;&lt;strong&gt;升级&lt;/strong&gt;&lt;/p&gt;
 
-&lt;p&gt;Follow the &lt;a href=&quot;/docs/howto/howto_upgrade.html&quot;&gt;upgrade guide&lt;/a&gt;.&lt;/p&gt;
+&lt;p&gt;参考&lt;a href=&quot;/docs/howto/howto_upgrade.html&quot;&gt;升级指南&lt;/a&gt;.&lt;/p&gt;
 
-&lt;p&gt;&lt;strong&gt;Feedback&lt;/strong&gt;&lt;/p&gt;
+&lt;p&gt;&lt;strong&gt;反馈&lt;/strong&gt;&lt;/p&gt;
 
-&lt;p&gt;If you face issue or question, please send mail to Apache Kylin dev or user mailing list: dev@kylin.apache.org , user@kylin.apache.org; Before sending, please make sure you have subscribed the mailing list by dropping an email to dev-subscribe@kylin.apache.org or user-subscribe@kylin.apache.org.&lt;/p&gt;
+&lt;p&gt;如果您遇到问题或疑问，请发送邮件至 Apache Kylin dev 或 user 邮件列表：dev@kylin.apache.org，user@kylin.apache.org; 在发送之前，请确保您已通过发送电子邮件至 dev-subscribe@kylin.apache.org 或 user-subscribe@kylin.apache.org 订阅了邮件列表。&lt;/p&gt;
 
-&lt;p&gt;&lt;em&gt;Great thanks to everyone who contributed!&lt;/em&gt;&lt;/p&gt;
+&lt;p&gt;&lt;em&gt;非常感谢所有贡献Apache Kylin的朋友!&lt;/em&gt;&lt;/p&gt;
 </description>
         <pubDate>Fri, 19 Apr 2019 13:00:00 -0700</pubDate>
-        <link>http://kylin.apache.org/blog/2019/04/19/release-v3.0.0-alpha/</link>
-        <guid isPermaLink="true">http://kylin.apache.org/blog/2019/04/19/release-v3.0.0-alpha/</guid>
+        <link>http://kylin.apache.org/cn/blog/2019/04/19/release-v3.0.0-alpha/</link>
+        <guid isPermaLink="true">http://kylin.apache.org/cn/blog/2019/04/19/release-v3.0.0-alpha/</guid>
         
         
         <category>blog</category>
@@ -225,55 +298,55 @@ Security: (depend on your security setti
       </item>
     
       <item>
-        <title>Apache Kylin v3.0.0-alpha 发布</title>
-        <description>&lt;p&gt;近日 Apache Kylin 社区很高兴地宣布，Apache Kylin v3.0.0-alpha 正式发布。&lt;/p&gt;
+        <title>Apache Kylin v3.0.0-alpha Release Announcement</title>
+        <description>&lt;p&gt;The Apache Kylin community is pleased to announce the release of Apache Kylin v3.0.0-alpha.&lt;/p&gt;
 
-&lt;p&gt;Apache Kylin 是一个开源的分布式分析引擎，旨在为极大数据集提供 SQL 接口和多维分析（OLAP）的能力。&lt;/p&gt;
+&lt;p&gt;Apache Kylin is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Big Data supporting extremely large datasets.&lt;/p&gt;
 
-&lt;p&gt;这是 Kylin 下一代 v3.x 的第一个发布版本，用于早期预览，主要的功能是实时 （Real-time） OLAP。完整的改动列表请参见&lt;a href=&quot;/docs/release_notes.html&quot;&gt;release notes&lt;/a&gt;；这里挑一些主要改进做说明。&lt;/p&gt;
+&lt;p&gt;This is the first release of the new generation v3.x, the main feature introduced is the Real-time OLAP. All of the changes can be found in the &lt;a href=&quot;/docs/release_notes.html&quot;&gt;release notes&lt;/a&gt;. Here we just highlight the main features.&lt;/p&gt;
 
-&lt;h1 id=&quot;section&quot;&gt;重要新功能&lt;/h1&gt;
+&lt;h1 id=&quot;important-features&quot;&gt;Important features&lt;/h1&gt;
 
-&lt;h3 id=&quot;kylin-3654----olap&quot;&gt;KYLIN-3654 - 实时 OLAP&lt;/h3&gt;
-&lt;p&gt;随着引入新的 real-time receiver 和 coordinator 组件，Kylin 能够实现毫秒级别的数据准备延迟，数据源来自流式数据如 Apache Kafka。这意味着，从 v3.0 开始，Kylin 既能够支持历史批量数据的 OLAP，也支持对流式数据的准实时（Near real-time）以及完全实时(real-time)分析。用户可以使用一个 OLAP 平台来服务不同的使用场景。此方案已经在早期用户如 eBay 得到部署和验证。关于如何使用此功能，请参考&lt;a href=&quot;/docs30/tutorial/realtime_olap.html&quot;&gt;æ­¤æ•
 ™ç¨‹&lt;/a&gt;。&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3654---real-time-olap&quot;&gt;KYLIN-3654 - Real-time OLAP&lt;/h3&gt;
+&lt;p&gt;With the newly introduced Kylin real-time receiver and coordinator components, Kylin can implement a millisecond-level data preparation delay for streaming data from sources like Apache Kafka. This means since v3.0 on,  Kylin can support sub-second level OLAP over historical batch data, near real-time streaming as well as real-time streaming. The user can use one OLAP platform to serve different scenarios. This solution has been deployed and verified in early adopters like eBay since 2018. For how to enable it, please refer to &lt;a href=&quot;/docs30/tutorial/realtime_olap.html&quot;&gt;this tutorial&lt;/a&gt;.&lt;/p&gt;
 
-&lt;h3 id=&quot;kylin-3795----apache-livy--spark-&quot;&gt;KYLIN-3795 - 通过 Apache Livy 递交 Spark 任务&lt;/h3&gt;
-&lt;p&gt;这个功能允许管理员为 Kylin 配置使用 Apache Livy (incubating) 来完成任务的递交。Spark 作业的提交通过 Livy 的 REST API 来提交，而无需在本地启动 Spark Driver 进程，从而方便对 Spark 资源的管理监控，同时也降低对 Kylin 任务进程所在节点的压力。&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3795---submit-spark-jobs-via-apache-livy&quot;&gt;KYLIN-3795 - Submit Spark jobs via Apache Livy&lt;/h3&gt;
+&lt;p&gt;This feature allows the administrator to configure Kylin to integrate with Apache Livy (incubating) for Spark job submissions. The Spark job is submitted to the Livy Server through Livy’s REST API, instead of starting the Spark Driver process in local, which facilitates the management and monitoring of the Spark resources, and also releases the pressure of the nodes where the Kylin job server is running.&lt;/p&gt;
 
-&lt;h3 id=&quot;kylin-3820----curator-&quot;&gt;KYLIN-3820 - 基于 Curator 的任务节点分配和服务发现&lt;/h3&gt;
-&lt;p&gt;新增一种基于Apache Zookeeper 和 Curator作业调度器，可以自动发现 Kylin 节点，并自动分配一个节点来进行任务的管理以及故障恢复。有了这个功能后，管理员可以更加容易地部署和扩展 Kylin 节点，而不再需要在 &lt;code class=&quot;highlighter-rouge&quot;&gt;kylin.properties&lt;/code&gt; 中配置每个 Kylin 节点的地址并重启 Kylin 以使之生效。&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3820---a-curator-based-job-scheduler&quot;&gt;KYLIN-3820 - A curator-based job scheduler&lt;/h3&gt;
+&lt;p&gt;A new job scheduler is added to automatically discover the Kylin nodes and do an automatic leader selection among them (only the leader will submit jobs). With this feature, you can easily deploy and scale out Kylin nodes without manually update the node address in &lt;code class=&quot;highlighter-rouge&quot;&gt;kylin.properties&lt;/code&gt; and restart Kylin to take effective.&lt;/p&gt;
 
-&lt;h1 id=&quot;section-1&quot;&gt;其它改进&lt;/h1&gt;
+&lt;h1 id=&quot;other-enhancements&quot;&gt;Other enhancements&lt;/h1&gt;
 
-&lt;h3 id=&quot;kylin-3716---fastthreadlocal--threadlocal&quot;&gt;KYLIN-3716 - FastThreadLocal 替换 ThreadLocal&lt;/h3&gt;
-&lt;p&gt;使用 Netty 中的 FastThreadLocal 替代 JDK 原生的 ThreadLocal，可以一定程度上提升 Kylin 在高并发下的性能。&lt;/p&gt;
+&lt;h3 id=&quot;kylin-3716---fastthreadlocal-replaces-threadlocal&quot;&gt;KYLIN-3716 - FastThreadLocal replaces ThreadLocal&lt;/h3&gt;
+&lt;p&gt;Using FastThreadLocal instead of ThreadLocal can improve Kylin’s overall performance to some extent.&lt;/p&gt;
 
 &lt;h3 id=&quot;kylin-3867---enable-jdbc-to-use-key-store--trust-store-for-https-connection&quot;&gt;KYLIN-3867 - Enable JDBC to use key store &amp;amp; trust store for https connection&lt;/h3&gt;
-&lt;p&gt;通过使用HTTPS，保护了JDBC使用的身份验证信息，使得Kylin更加安全&lt;/p&gt;
+&lt;p&gt;By using HTTPS, the authentication information used by JDBC is protected, making Kylin more secure.&lt;/p&gt;
 
 &lt;h3 id=&quot;kylin-3905---enable-shrunken-dictionary-default&quot;&gt;KYLIN-3905 - Enable shrunken dictionary default&lt;/h3&gt;
-&lt;p&gt;默认开启 shrunken dictionary，针对高基维进行精确去重的场景，可以显著减少构建用时。&lt;/p&gt;
+&lt;p&gt;By default, the shrunken dictionary is enabled, and the precise counting scene for high cardinal dimensions can significantly reduce the build time.&lt;/p&gt;
 
 &lt;h3 id=&quot;kylin-3839---storage-clean-up-after-the-refreshing-and-deleting-a-segment&quot;&gt;KYLIN-3839 - Storage clean up after the refreshing and deleting a segment&lt;/h3&gt;
-&lt;p&gt;更加及时地清除不必要的数据文件&lt;/p&gt;
+&lt;p&gt;Clear unnecessary data files in a timely manner&lt;/p&gt;
 
-&lt;p&gt;&lt;strong&gt;下载&lt;/strong&gt;&lt;/p&gt;
+&lt;p&gt;&lt;strong&gt;Download&lt;/strong&gt;&lt;/p&gt;
 
-&lt;p&gt;要下载Apache Kylin 源代码或二进制包，请访问&lt;a href=&quot;/download&quot;&gt;下载页面&lt;/a&gt; page.&lt;/p&gt;
+&lt;p&gt;To download Apache Kylin v3.0.0-alpha source code or binary package, visit the &lt;a href=&quot;http://kylin.apache.org/download&quot;&gt;download&lt;/a&gt; page.&lt;/p&gt;
 
-&lt;p&gt;&lt;strong&gt;升级&lt;/strong&gt;&lt;/p&gt;
+&lt;p&gt;&lt;strong&gt;Upgrade&lt;/strong&gt;&lt;/p&gt;
 
-&lt;p&gt;参考&lt;a href=&quot;/docs/howto/howto_upgrade.html&quot;&gt;升级指南&lt;/a&gt;.&lt;/p&gt;
+&lt;p&gt;Follow the &lt;a href=&quot;/docs/howto/howto_upgrade.html&quot;&gt;upgrade guide&lt;/a&gt;.&lt;/p&gt;
 
-&lt;p&gt;&lt;strong&gt;反馈&lt;/strong&gt;&lt;/p&gt;
+&lt;p&gt;&lt;strong&gt;Feedback&lt;/strong&gt;&lt;/p&gt;
 
-&lt;p&gt;如果您遇到问题或疑问，请发送邮件至 Apache Kylin dev 或 user 邮件列表：dev@kylin.apache.org，user@kylin.apache.org; 在发送之前，请确保您已通过发送电子邮件至 dev-subscribe@kylin.apache.org 或 user-subscribe@kylin.apache.org 订阅了邮件列表。&lt;/p&gt;
+&lt;p&gt;If you face issue or question, please send mail to Apache Kylin dev or user mailing list: dev@kylin.apache.org , user@kylin.apache.org; Before sending, please make sure you have subscribed the mailing list by dropping an email to dev-subscribe@kylin.apache.org or user-subscribe@kylin.apache.org.&lt;/p&gt;
 
-&lt;p&gt;&lt;em&gt;非常感谢所有贡献Apache Kylin的朋友!&lt;/em&gt;&lt;/p&gt;
+&lt;p&gt;&lt;em&gt;Great thanks to everyone who contributed!&lt;/em&gt;&lt;/p&gt;
 </description>
         <pubDate>Fri, 19 Apr 2019 13:00:00 -0700</pubDate>
-        <link>http://kylin.apache.org/cn/blog/2019/04/19/release-v3.0.0-alpha/</link>
-        <guid isPermaLink="true">http://kylin.apache.org/cn/blog/2019/04/19/release-v3.0.0-alpha/</guid>
+        <link>http://kylin.apache.org/blog/2019/04/19/release-v3.0.0-alpha/</link>
+        <guid isPermaLink="true">http://kylin.apache.org/blog/2019/04/19/release-v3.0.0-alpha/</guid>
         
         
         <category>blog</category>
@@ -1107,70 +1180,6 @@ Graphic 10 Process of Querying Cube&lt;/
         
         
         <category>blog</category>
-        
-      </item>
-    
-      <item>
-        <title>Apache Kylin v2.5.0 正式发布</title>
-        <description>&lt;p&gt;近日Apache Kylin 社区很高兴地宣布，Apache Kylin 2.5.0 正式发布。&lt;/p&gt;
-
-&lt;p&gt;Apache Kylin 是一个开源的分布式分析引擎，旨在为极大数据集提供 SQL 接口和多维分析（OLAP）的能力。&lt;/p&gt;
-
-&lt;p&gt;这是继2.4.0 后的一个新功能版本。该版本引入了很多有价值的改进，完整的改动列表请参见&lt;a href=&quot;https://kylin.apache.org/docs/release_notes.html&quot;&gt;release notes&lt;/a&gt;；这里挑一些主要改进做说明：&lt;/p&gt;
-
-&lt;h3 id=&quot;all-in-spark--cubing-&quot;&gt;All-in-Spark 的 Cubing 引擎&lt;/h3&gt;
-&lt;p&gt;Kylin 的 Spark 引擎将使用 Spark 运行 cube 计算中的所有分布式作业，包括获取各个维度的不同值，将 cuboid 文件转换为 HBase HFile，合并 segment，合并词典等。默认的 Spark 配置也经过优化，使得用户可以获得开箱即用的体验。相关开发任务是 KYLIN-3427, KYLIN-3441, KYLIN-3442.&lt;/p&gt;
-
-&lt;p&gt;Spark 任务管理也有所改进：一旦 Spark 任务开始运行，您就可以在Web控制台上获得作业链接；如果您丢弃该作业，Kylin 将立刻终止 Spark 作业以及时释放资源；如果重新启动 Kylin，它可以从上一个作业恢复，而不是重新提交新作业.&lt;/p&gt;
-
-&lt;h3 id=&quot;mysql--kylin-&quot;&gt;MySQL 做 Kylin 元数据的存储&lt;/h3&gt;
-&lt;p&gt;在过去，HBase 是 Kylin 元数据存储的唯一选择。 在某些情况下 HBase不适用，例如使用多个 HBase 集群来为 Kylin 提供跨区域的高可用，这里复制的 HBase 集群是只读的，所以不能做元数据存储。现在我们引入了 MySQL Metastore 以满足这种需求。此功能现在处于测试阶段。更多内容参见 KYLIN-3488。&lt;/p&gt;
-
-&lt;h3 id=&quot;hybrid-model-&quot;&gt;Hybrid model 图形界面&lt;/h3&gt;
-&lt;p&gt;Hybrid 是一种用于组装多个 cube 的高级模型。 它可用于满足 cube 的 schema 要发生改变的情况。这个功能过去没有图形界面，因此只有一小部分用户知道它。现在我们在 Web 界面上开启了它，以便更多用户可以尝试。&lt;/p&gt;
-
-&lt;h3 id=&quot;cube-planner&quot;&gt;默认开启 Cube planner&lt;/h3&gt;
-&lt;p&gt;Cube planner 可以极大地优化 cube 结构，减少构建的 cuboid 数量，从而节省计算/存储资源并提高查询性能。它是在v2.3中引入的，但默认情况下没有开启。为了让更多用户看到并尝试它，我们默认在v2.5中启用它。 算法将在第一次构建 segment 的时候，根据数据统计自动优化 cuboid 集合.&lt;/p&gt;
-
-&lt;h3 id=&quot;segment-&quot;&gt;改进的 Segment 剪枝&lt;/h3&gt;
-&lt;p&gt;Segment（分区）修剪可以有效地减少磁盘和网络I / O，因此大大提高了查询性能。 过去，Kylin 只按分区列 (partition date column) 的值进行 segment 的修剪。 如果查询中没有将分区列作为过滤条件，那么修剪将不起作用，会扫描所有segment。.&lt;br /&gt;
-现在从v2.5开始，Kylin 将在 segment 级别记录每个维度的最小/最大值。 在扫描 segment 之前，会将查询的条件与最小/最大索引进行比较。 如果不匹配，将跳过该 segment。 检查KYLIN-3370了解更多信息。&lt;/p&gt;
-
-&lt;h3 id=&quot;yarn-&quot;&gt;在 YARN 上合并字典&lt;/h3&gt;
-&lt;p&gt;当 segment 合并时，它们的词典也需要合并。在过去，字典合并发生在 Kylin 的 JVM 中，这需要使用大量的本地内存和 CPU 资源。 在极端情况下（如果有几个并发作业），可能会导致 Kylin 进程崩溃。 因此，一些用户不得不为 Kylin 任务节点分配更多内存，或运行多个任务节点以平衡工作负载。&lt;br /&gt;
-现在从v2.5开始，Kylin 将把这项任务提交给 Hadoop MapReduce 和 Spark，这样就可以解决这个瓶颈问题。 查看KYLIN-3471了解更多信息.&lt;/p&gt;
-
-&lt;h3 id=&quot;cube-&quot;&gt;改进使用全局字典的 cube 构建性能&lt;/h3&gt;
-&lt;p&gt;全局字典 (Global Dictionary) 是 bitmap 精确去重计数的必要条件。如果去重列具有非常高的基数，则 GD 可能非常大。在 cube 构建阶段，Kylin 需要通过 GD 将非整数值转换为整数。尽管 GD 已被分成多个切片，可以分开加载到内存，但是由于去重列的值是乱序的。Kylin 需要反复载入和载出(swap in/out)切片，这会导致构建任务非常缓慢。&lt;br /&gt;
-该增强功能引入了一个新步骤，为每个数据块从全局字典中构建一个缩小的字典。 随后每个任务只需要加载缩小的字典，从而避免频繁的载入和载出。性能可以比以前快3倍。查看 KYLIN-3491 了解更多信息.&lt;/p&gt;
-
-&lt;h3 id=&quot;topn-count-distinct--cube-&quot;&gt;改进含 TOPN, COUNT DISTINCT 的 cube 大小的估计&lt;/h3&gt;
-&lt;p&gt;Cube 的大小在构建时是预先估计的，并被后续几个步骤使用，例如决定 MR / Spark 作业的分区数，计算 HBase region 切割等。它的准确与否会对构建性能产生很大影响。 当存在 COUNT DISTINCT，TOPN 的度量时候，因为它们的大小是灵活的，因此估计值可能跟真实值有很大偏差。 在过去，用户需要调整若干个参数以使尺寸估计更接近实际尺寸，这对普通用户有点困难。&lt;br /&gt;
-现在，Kylin 将根据收集的统计信息自动调整大小估计。这可以使估计值与实际大小更接近。查看 KYLIN-3453 了解更多信息。&lt;/p&gt;
-
-&lt;h3 id=&quot;hadoop-30hbase-20&quot;&gt;支持Hadoop 3.0/HBase 2.0&lt;/h3&gt;
-&lt;p&gt;Hadoop 3和 HBase 2开始被许多用户采用。现在 Kylin 提供使用新的 Hadoop 和 HBase API 编译的新二进制包。我们已经在 Hortonworks HDP 3.0 和 Cloudera CDH 6.0 上进行了测试&lt;/p&gt;
-
-&lt;p&gt;&lt;strong&gt;下载&lt;/strong&gt;&lt;/p&gt;
-
-&lt;p&gt;要下载Apache Kylin v2.5.0源代码或二进制包，请访问&lt;a href=&quot;http://kylin.apache.org/download&quot;&gt;下载页面&lt;/a&gt; .&lt;/p&gt;
-
-&lt;p&gt;&lt;strong&gt;升级&lt;/strong&gt;&lt;/p&gt;
-
-&lt;p&gt;参考&lt;a href=&quot;/docs/howto/howto_upgrade.html&quot;&gt;升级指南&lt;/a&gt;.&lt;/p&gt;
-
-&lt;p&gt;&lt;strong&gt;反馈&lt;/strong&gt;&lt;/p&gt;
-
-&lt;p&gt;如果您遇到问题或疑问，请发送邮件至 Apache Kylin dev 或 user 邮件列表：dev@kylin.apache.org，user@kylin.apache.org; 在发送之前，请确保您已通过发送电子邮件至 dev-subscribe@kylin.apache.org 或 user-subscribe@kylin.apache.org订阅了邮件列表。&lt;/p&gt;
-
-&lt;p&gt;&lt;em&gt;非常感谢所有贡献Apache Kylin的朋友!&lt;/em&gt;&lt;/p&gt;
-</description>
-        <pubDate>Thu, 20 Sep 2018 13:00:00 -0700</pubDate>
-        <link>http://kylin.apache.org/cn/blog/2018/09/20/release-v2.5.0/</link>
-        <guid isPermaLink="true">http://kylin.apache.org/cn/blog/2018/09/20/release-v2.5.0/</guid>
-        
-        
-        <category>blog</category>
         
       </item>