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 2019/12/01 09:13:50 UTC

svn commit: r1870664 [2/2] - in /kylin/site: ./ cn/blog/ cn/community/ cn/docs/ cn_blog/2019/05/23/Davinci-Kylin-Insight/ community/ docs/ images/blog/davinci/

Modified: kylin/site/feed.xml
URL: http://svn.apache.org/viewvc/kylin/site/feed.xml?rev=1870664&r1=1870663&r2=1870664&view=diff
==============================================================================
--- kylin/site/feed.xml (original)
+++ kylin/site/feed.xml Sun Dec  1 09:13:50 2019
@@ -19,8 +19,8 @@
     <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>Sun, 17 Nov 2019 05:59:02 -0800</pubDate>
-    <lastBuildDate>Sun, 17 Nov 2019 05:59:02 -0800</lastBuildDate>
+    <pubDate>Sun, 01 Dec 2019 01:05:07 -0800</pubDate>
+    <lastBuildDate>Sun, 01 Dec 2019 01:05:07 -0800</lastBuildDate>
     <generator>Jekyll v2.5.3</generator>
     
       <item>
@@ -1270,58 +1270,72 @@ 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;
-
-&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;
+        <title>你离可视化酷炫大屏只差一套 Kylin + Davinci</title>
+        <description>&lt;p&gt;Kylin 提供与 BI 工具的整合能力，如 Tableau，PowerBI/Excel，MSTR，QlikSense，Hue 和 SuperSet。但就可视化工具而言，Davinci 良好的交互性和个性化的可视化大屏展现效果，使其与 Kylin 的结合能让大部分用户有更好的可视化分析体验。&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;important-features&quot;&gt;Important features&lt;/h1&gt;
+&lt;p&gt;Davinci 是国内开源的大数据可视化平台，是一款基于 web，提供一站式数据可视化解决方案的平台，Java 系。用户只需在可视化 UI 上简单配置即可服务多种数据可视化应用，并支持高级交互/行业分析/模式探索/社交智能等可视化功能。详情请访问其官方网站（https://edp963.github.io/davinci/）。&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;section&quot;&gt;下载与安装&lt;/h3&gt;
+&lt;p&gt;宜信在 2018 å¹´ 4 月发布了 Davinci 的第一个正式版本 V0.1.0，目前为止 Davinci 的正式发布版本是 v0.2.1，其次就是 v0.3 系列的测试版。Davinci 自 0.2.1 版本之后开始支持对 Kylin 的连接。通过对比可以发现，0.2 版本只是简单地实现了数据可视化报表，其功能不全，用户交互性差。但随后的 0.3 版本在不断地完善平台功能，可以说使用过程中体验感良好，功能比较齐全。并且官方在不断地进行版本的更新中，所ä»
 ¥å¯¹äºŽåˆæ¬¡æŽ¥è§¦ Davinci 和想拥有自定义仪表盘和大屏效果的人群，更建议使用最新版 v0.3 系列。&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;p&gt;部署之前，安装环境要包含 JDK，MySQL，Mail Server，PhantomJs。然后，到官网给定的 github 网站上下载最新发布的软件包，解压到自定义的安装目录下，并配置 davinci 的环境变量。同时，修改 bin 目录下 initdb.sh 中数据库信息为要初始化的数据库，运行脚本初始化数据库：sh bin/initdb.sh&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;p&gt;之后，进入到config文件夹下，将 application.yml.example 重命名为 application.yml 后开始配置。如：访问地址和端口号（默认端口号为 8080，可自定义），数据源等配置。详细的配置部署请参考官网说明（https://edp963.github.io/davinci/deployment.html），完成部署后。在  bin 目录下执行 sh start-server.sh 命令启动 Davinci 服务。&lt;/p&gt;
 
-&lt;h1 id=&quot;other-enhancements&quot;&gt;Other enhancements&lt;/h1&gt;
+&lt;p&gt;最后，打开浏览器，访问地址：http://{配置的地址}:{配置的端口号}，即可进入 Davinci，新用户进行注册即可使用该服务。&lt;br /&gt;
+&lt;img src=&quot;/images/blog/davinci/login.png&quot; alt=&quot;&quot; /&gt;&lt;/p&gt;
+&lt;center&gt;登陆界面&lt;/center&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;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;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;Clear unnecessary data files in a timely manner&lt;/p&gt;
-
-&lt;p&gt;&lt;strong&gt;Download&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;&lt;strong&gt;Upgrade&lt;/strong&gt;&lt;/p&gt;
+&lt;h3 id=&quot;kylin&quot;&gt;连接 Kylin&lt;/h3&gt;
+&lt;p&gt;Davinci 的官方网站介绍其支持 JDBC 数据源连接，这就为 kylin 的连接提供了可能。Davinci 默认可支持的数据源不包括 kylin，但是提供了自定义数据源配置文件。首先，进入 lib 目录下添加 kylin-jdbc 包，其次，进入config目录下，更改datasource_driver.yml.example文件名为datasource_driver.yml 使其生效，并在文件里配置Kylin 相关信息，如下：&lt;br /&gt;
+&lt;code class=&quot;highlighter-rouge&quot;&gt;
+kylin:
+   name: kylin
+   desc: kylin
+   driver: org.apache.kylin.jdbc.Driver
+   keyword_prefix: \&quot;
+   keyword_suffix: \&quot;
+   alias_prefix: \&quot;
+   alias_suffix: \&quot;
+&lt;/code&gt;&lt;br /&gt;
+重启服务，使配置生效。&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;最后，可做一个简单的数据连接测试来验证是否连接成功。在 Source 部分添加数据源 kylin 并填写相关的用户名，密码，url 地址等信息来进行连接测试，如下图所示：&lt;br /&gt;
+&lt;img src=&quot;/images/blog/davinci/connect.png&quot; alt=&quot;&quot; /&gt;&lt;/p&gt;
+&lt;center&gt;数据源连接&lt;/center&gt;
+&lt;p&gt;连接成功后，接着在 View 层输入查询 SQL 语句，点击右下角的执行按钮即可。如下图：&lt;br /&gt;
+&lt;img src=&quot;/images/blog/davinci/query.png&quot; alt=&quot;&quot; /&gt;&lt;/p&gt;
+
+&lt;h3 id=&quot;section-1&quot;&gt;制作数据仪表盘及大屏展示&lt;/h3&gt;
+&lt;p&gt;Davinci 为用户提供了两种自定义的报表形式，一种是常见的可以自由布局的报表（dashbord），除此之外，还提供了用户可自定制的大屏展现形式（display）。&lt;/p&gt;
+
+&lt;p&gt;我们可以利用 Widget 层丰富的图表来展现 View 层的数据，进而根据需求制作不同展现形式的报表。那么在 Widget 层，我们可以通过拖拽的方式，为不同维度的数据选择适合的图像进行展示。仪表盘（Dashbord）的展现如下图：&lt;br /&gt;
+&lt;img src=&quot;/images/blog/davinci/dashboard.png&quot; alt=&quot;&quot; /&gt;&lt;/p&gt;
+&lt;center&gt;数据仪表盘&lt;/center&gt;
+&lt;p&gt;如果用户需要更加酷炫的大屏展现形式，我们可以使用 Display 来手动定制报表的展现形式，如下图：&lt;br /&gt;
+&lt;img src=&quot;/images/blog/davinci/setting.png&quot; alt=&quot;&quot; /&gt;&lt;/p&gt;
+&lt;center&gt;Display 功能区&lt;/center&gt;
+&lt;p&gt;其中：&lt;br /&gt;
+网格区域：布置画布区域，效果展现区域&lt;br /&gt;
+蓝色区域：添加 Widget 层制作的图表，添加过程中我们可以自定义定时刷新数据；&lt;br /&gt;
+红色区域：添加辅助图形，如：文本编辑框，矩形；&lt;br /&gt;
+绿色区域：画布上不同元素的图层设置；&lt;br /&gt;
+黑色区域：大屏的背景设置区域，包括屏幕的尺寸，缩放规则，背景颜色，添加背景图片，截取封皮。&lt;/p&gt;
 
-&lt;p&gt;&lt;strong&gt;Feedback&lt;/strong&gt;&lt;/p&gt;
+&lt;p&gt;通过这些功能，我们可以轻轻松松地定制出符合场景需求的动态大屏展示效果。如下示例：&lt;br /&gt;
+&lt;img src=&quot;/images/blog/davinci/monitor.png&quot; alt=&quot;&quot; /&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;h3 id=&quot;section-2&quot;&gt;总结&lt;/h3&gt;
+&lt;p&gt;Kylin 本身也提供简单的图表展示，例如：饼图，柱状图等。但并不能满足大多数用户的需求，通过 Kylin+Davinci 的结合，我们可以将 Kylin 快速查询特点与 Davinci 多样化和个性化的展示效果充分的整合起来，从而满足更多用户的需求，做好大数据分析最后一站的服务工作。&lt;/p&gt;
 
-&lt;p&gt;&lt;em&gt;Great thanks to everyone who contributed!&lt;/em&gt;&lt;/p&gt;
+&lt;p&gt;那么本次选择 Davinci 来做数据可视化展现，一是由于其自身丰富的功能和一站式的可视化分析展现。再者，其开源的性质和开发的语言，为大多数开发者提供了更多的可能，如果你喜欢，那么你就可以在其基础上进行二次开发，来满足自己的场景。&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>
+        <pubDate>Thu, 23 May 2019 08:00:00 -0700</pubDate>
+        <link>http://kylin.apache.org/cn_blog/2019/05/23/Davinci-Kylin-Insight/</link>
+        <guid isPermaLink="true">http://kylin.apache.org/cn_blog/2019/05/23/Davinci-Kylin-Insight/</guid>
         
         
-        <category>blog</category>
+        <category>cn_blog</category>
         
       </item>
     
@@ -1382,6 +1396,62 @@ 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;
+
+&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;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;important-features&quot;&gt;Important features&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-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---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;other-enhancements&quot;&gt;Other enhancements&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-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;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;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;&lt;strong&gt;Download&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;&lt;strong&gt;Upgrade&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;&lt;strong&gt;Feedback&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;&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/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>
+        
+      </item>
+    
+      <item>
         <title>Real-time Streaming Design in Apache Kylin</title>
         <description>&lt;h2 id=&quot;why-build-real-time-streaming-in-kylin&quot;&gt;Why Build Real-time Streaming in Kylin&lt;/h2&gt;
 &lt;p&gt;The real-time streaming feature is contributed by eBay big data team in Kylin 3.0, the purpose we build real-time streaming is:&lt;/p&gt;
@@ -1530,84 +1600,6 @@ The checkpoint info is the smallest part
         
         
         <category>blog</category>
-        
-      </item>
-    
-      <item>
-        <title>Apache Kylin v2.6.0 Release Announcement</title>
-        <description>&lt;p&gt;The Apache Kylin community is pleased to announce the release of Apache Kylin v2.6.0.&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;This is a major release after 2.5.0, including many enhancements. All of the changes can be found in the &lt;a href=&quot;https://kylin.apache.org/docs/release_notes.html&quot;&gt;release notes&lt;/a&gt;. Here just highlight the major ones:&lt;/p&gt;
-
-&lt;h3 id=&quot;sdk-for-jdbc-sources&quot;&gt;SDK for JDBC sources&lt;/h3&gt;
-&lt;p&gt;Apache Kylin has already supported several data sources like Amazon Redshift, SQL Server through JDBC. &lt;br /&gt;
-To help developers handle SQL dialect differences and easily implement a new data source through JDBC, Kylin provides a new data source SDK with APIs for:&lt;br /&gt;
-* Synchronize metadata and data from JDBC source&lt;br /&gt;
-* Build cube from JDBC source&lt;br /&gt;
-* Query pushdown to JDBC source engine when cube is unmatched&lt;/p&gt;
-
-&lt;p&gt;Check KYLIN-3552 for more.&lt;/p&gt;
-
-&lt;h3 id=&quot;memcached-as-distributed-cache&quot;&gt;Memcached as distributed cache&lt;/h3&gt;
-&lt;p&gt;In the past, query caches are not efficiently used in Kylin due to two aspects: aggressive cache expiration strategy and local cache. &lt;br /&gt;
-Because of the aggressive cache expiration strategy, useful caches are often cleaned up unnecessarily. &lt;br /&gt;
-Because query caches are stored in local servers, they cannot be shared between servers. &lt;br /&gt;
-And because of the size limitation of local cache, not all useful query results can be cached.&lt;/p&gt;
-
-&lt;p&gt;To deal with these shortcomings, we change the query cache expiration strategy by signature checking and introduce the memcached as Kylin’s distributed cache so that Kylin servers are able to share cache between servers. &lt;br /&gt;
-And it’s easy to add memcached servers to scale out distributed cache. With enough memcached servers, we can cached things as much as possible. &lt;br /&gt;
-Then we also introduce segment level query cache which can not only speed up query but also reduce the rpcs to HBase. &lt;br /&gt;
-The related tasks are KYLIN-2895, KYLIN-2894, KYLIN-2896, KYLIN-2897, KYLIN-2898, KYLIN-2899.&lt;/p&gt;
-
-&lt;h3 id=&quot;forkjoinpool-for-fast-cubing&quot;&gt;ForkJoinPool for fast cubing&lt;/h3&gt;
-&lt;p&gt;In the past, fast cubing uses split threads, task threads and main thread to do the cube building, there is complex join and error handling logic.&lt;/p&gt;
-
-&lt;p&gt;The new implement leverages the ForkJoinPool from JDK, the event split logic is handled in&lt;br /&gt;
-main thread. Cuboid task and sub-tasks are handled in fork join pool, cube results are collected&lt;br /&gt;
-async and can be write to output earlier. Check KYLIN-2932 for more.&lt;/p&gt;
-
-&lt;h3 id=&quot;improve-hllcounter-performance&quot;&gt;Improve HLLCounter performance&lt;/h3&gt;
-&lt;p&gt;In the past, the way to create HLLCounter and to compute harmonic mean are not efficient.&lt;/p&gt;
-
-&lt;p&gt;The new implement improve the HLLCounter creation by copy register from another HLLCounter instead of merge. To compute harmonic mean in the HLLCSnapshot, it does the enhancement by &lt;br /&gt;
-* using table to cache all 1/2^r  without computing on the fly&lt;br /&gt;
-* remove floating addition by using integer addition in the bigger loop&lt;br /&gt;
-* remove branch, e.g. needn’t checking whether registers[i] is zero or not, although this is minor improvement.&lt;/p&gt;
-
-&lt;p&gt;Check KYLIN-3656 for more.&lt;/p&gt;
-
-&lt;h3 id=&quot;improve-cuboid-recommendation-algorithm&quot;&gt;Improve Cuboid Recommendation Algorithm&lt;/h3&gt;
-&lt;p&gt;In the past, to add cuboids which are not prebuilt, the cube planner turns to mandatory cuboids which are selected if its rollup row count is above some threshold. &lt;br /&gt;
-There are two shortcomings:&lt;br /&gt;
-* The way to estimate the rollup row count is not good&lt;br /&gt;
-* It’s hard to determine the threshold of rollup row count for recommending mandatory cuboids&lt;/p&gt;
-
-&lt;p&gt;The new implement improves the way to estimate the row count of un-prebuilt cuboids by rollup ratio rather than exact rollup row count. &lt;br /&gt;
-With better estimated row counts for un-prebuilt cuboids, the cost-based cube planner algorithm will decide which cuboid to be built or not and the threshold for previous mandatory cuboids is not needed. &lt;br /&gt;
-By this improvement, we don’t need the threshold for mandatory cuboids recommendation, and mandatory cuboids can only be manually set and will not be recommended. Check KYLIN-3540 for more.&lt;/p&gt;
-
-&lt;p&gt;&lt;strong&gt;Download&lt;/strong&gt;&lt;/p&gt;
-
-&lt;p&gt;To download Apache Kylin v2.6.0 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;Upgrade&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;&lt;strong&gt;Feedback&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;&lt;em&gt;Great thanks to everyone who contributed!&lt;/em&gt;&lt;/p&gt;
-</description>
-        <pubDate>Fri, 18 Jan 2019 12:00:00 -0800</pubDate>
-        <link>http://kylin.apache.org/blog/2019/01/18/release-v2.6.0/</link>
-        <guid isPermaLink="true">http://kylin.apache.org/blog/2019/01/18/release-v2.6.0/</guid>
-        
-        
-        <category>blog</category>
         
       </item>
     

Added: kylin/site/images/blog/davinci/connect.png
URL: http://svn.apache.org/viewvc/kylin/site/images/blog/davinci/connect.png?rev=1870664&view=auto
==============================================================================
Binary file - no diff available.

Propchange: kylin/site/images/blog/davinci/connect.png
------------------------------------------------------------------------------
    svn:mime-type = application/octet-stream

Added: kylin/site/images/blog/davinci/dashboard.png
URL: http://svn.apache.org/viewvc/kylin/site/images/blog/davinci/dashboard.png?rev=1870664&view=auto
==============================================================================
Binary file - no diff available.

Propchange: kylin/site/images/blog/davinci/dashboard.png
------------------------------------------------------------------------------
    svn:mime-type = application/octet-stream

Added: kylin/site/images/blog/davinci/login.png
URL: http://svn.apache.org/viewvc/kylin/site/images/blog/davinci/login.png?rev=1870664&view=auto
==============================================================================
Binary file - no diff available.

Propchange: kylin/site/images/blog/davinci/login.png
------------------------------------------------------------------------------
    svn:mime-type = application/octet-stream

Added: kylin/site/images/blog/davinci/monitor.png
URL: http://svn.apache.org/viewvc/kylin/site/images/blog/davinci/monitor.png?rev=1870664&view=auto
==============================================================================
Binary file - no diff available.

Propchange: kylin/site/images/blog/davinci/monitor.png
------------------------------------------------------------------------------
    svn:mime-type = application/octet-stream

Added: kylin/site/images/blog/davinci/query.png
URL: http://svn.apache.org/viewvc/kylin/site/images/blog/davinci/query.png?rev=1870664&view=auto
==============================================================================
Binary file - no diff available.

Propchange: kylin/site/images/blog/davinci/query.png
------------------------------------------------------------------------------
    svn:mime-type = application/octet-stream

Added: kylin/site/images/blog/davinci/setting.png
URL: http://svn.apache.org/viewvc/kylin/site/images/blog/davinci/setting.png?rev=1870664&view=auto
==============================================================================
Binary file - no diff available.

Propchange: kylin/site/images/blog/davinci/setting.png
------------------------------------------------------------------------------
    svn:mime-type = application/octet-stream