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Posted to commits@kylin.apache.org by li...@apache.org on 2022/01/19 08:32:36 UTC

svn commit: r1897192 [2/2] - in /kylin/site: ./ blog/ blog/2022/ blog/2022/01/ blog/2022/01/12/ blog/2022/01/12/The-Future-Of-Kylin/ cn/blog/ cn_blog/2022/ cn_blog/2022/01/ cn_blog/2022/01/12/ cn_blog/2022/01/12/The-Future-Of-Kylin/ download/ images/blog/

Modified: kylin/site/feed.xml
URL: http://svn.apache.org/viewvc/kylin/site/feed.xml?rev=1897192&r1=1897191&r2=1897192&view=diff
==============================================================================
--- kylin/site/feed.xml (original)
+++ kylin/site/feed.xml Wed Jan 19 08:32:36 2022
@@ -19,11 +19,123 @@
     <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>Thu, 13 Jan 2022 00:29:42 -0800</pubDate>
-    <lastBuildDate>Thu, 13 Jan 2022 00:29:42 -0800</lastBuildDate>
+    <pubDate>Wed, 19 Jan 2022 00:16:41 -0800</pubDate>
+    <lastBuildDate>Wed, 19 Jan 2022 00:16:41 -0800</lastBuildDate>
     <generator>Jekyll v2.5.3</generator>
     
       <item>
+        <title>The future of Apache Kylin：More powerful and easy-to-use OLAP</title>
+        <description>&lt;h2 id=&quot;apache-kylin-today&quot;&gt;01 Apache Kylin Today&lt;/h2&gt;
+
+&lt;p&gt;Currently, the latest release of Apache Kylin is 4.0.1. Apache Kylin 4.0 is a major version update after Kylin 3.x (HBase Storage). Kylin 4.0 uses Parquet to replace HBase as storage engine, so as to improve file scanning performance. At the same time, Kylin 4.0 reimplements the spark based build engine and query engine, making it possible to separate computing and storage, and better adapt to the technology trend of cloud native.&lt;/p&gt;
+
+&lt;p&gt;Kylin 4.0 comprehensively updated the build and query engine, realized the deployment mode without Hadoop dependency, decrease the complexity of deployment. In addition, combined with the feedback of Kylin users and the trend of OLAP technology, Kylin community found that there are still some weaknesses and deficiencies in today’s Apache Kylin, such as the ability of business semantic layer needs to be strengthened and the modification of model/cube is not flexible. With these, we thinking a few things to do::&lt;/p&gt;
+
+&lt;ul&gt;
+  &lt;li&gt;Multi-dimensional query ability friendly to non-technical personnel. Multi-dimensional model is the key to distinguish Kylin from general OLAP engine. The feature is that the model concept based on dimension and measurement is more friendly to non-technical personnel and closer to the goal of “everyone is a data analyst”. The multi-dimensional query capability that non-technical personnel can use should be the new focus of Kylin technology.&lt;/li&gt;
+  &lt;li&gt;Native Engine. The query engine of Kylin still has much room for improvement in vector acceleration and cpu instruction level optimization. The Spark community Kylin relies on also has a strong demand for native engine. It is optimistic that native engine can improve the performance of Kylin by at least three times, which is worthy of investment.&lt;/li&gt;
+  &lt;li&gt;More cloud native capabilities. Kylin 4.0 has only completed the initial cloud deployment and realized the features of rapid deployment and dynamic resource scaling on the cloud, but there are still many cloud native capabilities to be developed.&lt;/li&gt;
+&lt;/ul&gt;
+
+&lt;p&gt;More explanations are following.&lt;/p&gt;
+
+&lt;h2 id=&quot;kylin-as-a-multi-dimensional-database&quot;&gt;02 KYLIN AS A MULTI-DIMENSIONAL DATABASE&lt;/h2&gt;
+&lt;p&gt;The core of Kylin is a multi-dimensional database, which is a special OLAP engine. Although Kylin has always had the ability of relational database since its birth, and it is often compared with other relational OLAP engines, what really makes Kylin different is multi-dimensional model and multi-dimensional database ability. Considering the essence of Kylin and its wide range of business uses in the future (not only technical uses), we will clearly position Kylin as a multi-dimensional database. We also hope that through multi-dimensional model and precomputation technology, Apache Kylin can make non-technical people understand and afford big data, and finally realize data democratization.&lt;/p&gt;
+
+&lt;h3 id=&quot;the-semantic-layer&quot;&gt;THE SEMANTIC LAYER&lt;/h3&gt;
+&lt;p&gt;The key difference between multi-dimensional database and relational database is business expression ability. Although SQL has strong expression ability and is the basic skill of data analysts, SQL and relational database are still too difficult for non-technical personnel if we aim at “everyone is a data analyst”. From the perspective of non-technical personnel, the data lake and data warehouse are like a dark room. They know that there is a lot of data, but they can’t see clearly, understand and use this data because they don’t understand database theory and SQL.&lt;br /&gt;
+How to make the Data Lake (and data warehouse) clear to non-technical personnel? This requires introducing a more friendly data model for non-technical personnel —— multi-dimensional data model. While the relational model describes the technical form of data, the multi-dimensional model describes the business form of data. In multi-dimensional database, measurement corresponds to business indicators that everyone understands, and dimension is the perspective of comparing and observing these business indicators. Compare KPI with last month and compare performance between parallel business units, which are concepts understood by every non-technical personnel. By mapping the relational model to the multi-dimensional model, the essence is to enhance the business semantics on the technical data, form a business semantic layer, and help non-technical personnel understand, explore and use the data.&lt;br /&gt;
+In order to enhance Kylin’s ability as the semantic layer of multi-dimensional database, supporting multi-dimensional query language is the key content of Kylin roadmap, such as MDX and DAX. MDX can transform the data model in Kylin into a business friendly language, endow data with business value, and facilitate Kylin’s multi-dimensional analysis with BI tools such as Excel and Tableau.&lt;/p&gt;
+
+&lt;h3 id=&quot;precomputation-and-model-flexibility&quot;&gt;PRECOMPUTATION AND MODEL FLEXIBILITY&lt;/h3&gt;
+&lt;p&gt;It is kylin’s unchanging mission to continue to reduce the cost of a single query through precomputation technology so that ordinary people can afford big data. If the multi-dimensional model solves the problem that non-technical personnel can understand data, then precomputation can solve the problem that ordinary people can afford data. Both are necessary conditions for data democratization. Through one calculation and multiple use, the data cost can be shared by multiple users to achieve the scale effect that the more users, the cheaper. Precalculation is Kylin’s traditional strength, but it lacks some flexibility in the change of precalculation model. In order to strengthen the ability to change models flexibly of Kylin and bring more optimization room, Kylin community expects to propose a new metadata format in Kylin in the future to make precalculation more flexible, be able to cope with that table format or business requirements may change at any time.&lt;/
 p&gt;
+
+&lt;h3 id=&quot;summary&quot;&gt;SUMMARY&lt;/h3&gt;
+&lt;p&gt;To sum up, we will make it clear that Kylin’s technical position is a multi-dimensional database. Through multi-dimensional model and precomputation technology, ordinary people can understand and afford big data, and finally realize the vision of data democratization. Meanwhile, for today’s users who use Kylin as the SQL acceleration layer, Kylin will continue to maintain a complete SQL interface to ensure that the precomputation technology can be used by both relational model and multi-dimensional model.&lt;br /&gt;
+In the figure below, we can clearly see the direction of Kylin’s attention in the future. The newly added and modified parts are roughly marked in blue and orange.&lt;/p&gt;
+
+&lt;p&gt;&lt;img src=&quot;/images/blog/the_future_of_kylin.png&quot; alt=&quot;&quot; /&gt;&lt;/p&gt;
+
+&lt;h2 id=&quot;the-future-plan&quot;&gt;03 THE FUTURE PLAN&lt;/h2&gt;
+
+&lt;p&gt;Based on Kylin’s positioning as a multi-dimensional database, combined with the existing capabilities of Kylin that need to be strengthened, and in order to support the long-awaited features of users such as schema change, we plan to introduce a new metadata format of DataModel into Kylin : no longer expose Cube to users, but simplify the metadata dependency to ‘Model -&amp;gt; Table’.&lt;br /&gt;
+As metadata is the basis and contract for the subsequent collaborative development of Kylin, the design and development of the new metadata format will be the focus of Kylin community’s work at present and in the next few months. The metadata design and discussion proposal will be released later. You are welcome to participate in the discussion. Not surprisingly, the new metadata format will meet you this year.&lt;br /&gt;
+In addition to metadata format upgrading, the build and query engine which support metadata upgrade, semantic layer capability (MDX), better integration with BI tools and native engine are also the key work that Kylin community has been actively promoting. More like-minded users and developers are welcome to participate in development and promote Kylin community development jointly.&lt;/p&gt;
+
+&lt;p&gt;** Further Reading **&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Data_model&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Semantic_layer&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Multidimensional_analysis&lt;br /&gt;
+- https://en.wikipedia.org/wiki/MultiDimensional_eXpressions&lt;br /&gt;
+- https://en.wikipedia.org/wiki/XML_for_Analysis&lt;br /&gt;
+- https://en.wikipedia.org/wiki/SIMD&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Cloud_native_computing&lt;br /&gt;
+- https://blogs.gartner.com/carlie-idoine/2018/05/13/citizen-data-scientists-and-why-they-matter/&lt;/p&gt;
+
+</description>
+        <pubDate>Wed, 12 Jan 2022 03:00:00 -0800</pubDate>
+        <link>http://kylin.apache.org/blog/2022/01/12/The-Future-Of-Kylin/</link>
+        <guid isPermaLink="true">http://kylin.apache.org/blog/2022/01/12/The-Future-Of-Kylin/</guid>
+        
+        
+        <category>blog</category>
+        
+      </item>
+    
+      <item>
+        <title>下一代 Kylin：更强大和易用的 OLAP</title>
+        <description>&lt;h2 id=&quot;apache-kylin-&quot;&gt;01 Apache Kylin 的今天&lt;/h2&gt;
+&lt;p&gt;目前，Apache Kylin 的最新发布版本是 4.0.1。 Apache Kylin 4.0 是 Kylin 3.x（HBase Storage）版本后的一次重大版本更新，Kylin 4 使用 Parquet 这种真正的列式存储来代替 HBase 存储，从而提升文件扫描性能；同时，Kylin 4 重新实现了基于 Spark 的构建引擎和查询引擎，使得计算和存储的分离变为可能，更加适应云原生的技术趋势。&lt;br /&gt;
+Kylin 4.0 对构建和查询引擎做了全面更新，实现了去 Hadoop 部署，解决了初步上云的问题。除此之外，结合社区用户的反馈以及 OLAP 技术发展的趋势，Kylin 社区发现当前的 Kylin 仍然存在一些弱势与不足，比如业务语义层能力有待加强、预计算模型变更不够灵活等，基于这些不足可以将后续需要进行的工作总结为以下几个方面：&lt;/p&gt;
+
+&lt;ul&gt;
+  &lt;li&gt;对非技术人员友好的多维查询能力。多维模型是 Kylin 区别于一般 OLAP 引擎的关键。特点在于，以维度、度量为基础的模型概念对非技术人员更友好，更接近 “人人都是数据分析师” 的目标。非技术人员能用的多维查询能力，应该是 Kylin 技术后续的新重心。&lt;/li&gt;
+  &lt;li&gt;Native Engine。Kylin 引擎在向量加速、指令级优化方面尚有很大的提升空间。Kylin 依赖的 Spark 社区也有很强的 Native Engine 需求，乐观估计，Native Engine 可以至少提升目前的 Kylin 3 倍以上性能，值得投入。&lt;/li&gt;
+  &lt;li&gt;更多云原生能力。Kylin 4.0 只完成了初步上云，实现了云上的快速部署、动态资源伸缩等功能，但仍有很多云原生的能力还有待开发。&lt;/li&gt;
+&lt;/ul&gt;
+
+&lt;h2 id=&quot;apache-kylin---&quot;&gt;02 Apache Kylin 的定位 —— 多维数据库&lt;/h2&gt;
+&lt;p&gt;Kylin 的核心是一个多维数据库，是一种特殊的 OLAP 引擎。虽然从诞生以来，Kylin 一直都有关系数据库的能力，也常常与其他关系型 OLAP 引擎做对比，但真正让 Kylin 与众不同的是它的多维模型和多维数据库能力。考虑到 Kylin 的本质和未来广泛的业务用途（不仅是技术用途），我们将明确定位 Kylin 为一个多维数据库。我们也期望通过多维模型和预计算技术，Apache Kylin 能让普通人看得懂和用得起大
 数据，最终实现数据民主化。&lt;/p&gt;
+
+&lt;h3 id=&quot;section&quot;&gt;语义层&lt;/h3&gt;
+&lt;p&gt;多维数据库与关系型数据库的 关键区别在于业务表达能力。尽管 SQL 表达能力很强，是数据分析师的基本技能，但如果以 “人人都是分析师” 为目标，SQL 和关系数据库对非技术人员还是太难了。从非技术人员的视角，数据湖和数据仓库就好似一个黑暗的房间，知道其中有很多数据，却因为不懂数据库理论和 SQL，无法看清、理解、和使用这些数据。&lt;br /&gt;
+如何让数据湖（和数据仓库）对非技术人员也 “清澈见底”？这就需要引入一个对非技术人员更加友好的数据模型 – 多维数据模型。如果说关系模型描述了数据的技术形态，那么多维模型则描述了数据的业务形态。在多维数据库中，度量对应了每个人都懂的业务指标，维度则是比较、观察这些业务指标的角度。要与上个月比较 KPI，要在平行事业部之间比较绩效，这些是每个非
 技术人员都理解的概念。通过将关系模型映射到多维模型，本质是在技术数据之上增强了业务语义，形成业务语义层，帮助非技术人员也能看懂、探索、使用数据。&lt;br /&gt;
+为了增强 Kylin 作为多维数据库的语义层能力，支持多维查询语言是 Kylin Roadmap 上的重点内容，比如 MDX 和 DAX。通过 MDX 可以将 Kylin 中的数据模型转换为业务友好的语言，赋予数据业务价值，方便对接 Excel、Tableau 等 BI 工具进行多维分析。&lt;/p&gt;
+
+&lt;h3 id=&quot;section-1&quot;&gt;预计算和灵活的模型&lt;/h3&gt;
+&lt;p&gt;继续通过预计算技术降低单查询成本，让普通人用得起大数据，也是 Kylin 不变的使命。如果说多维模型解决了非技术人员看得懂数据的问题，那么预计算则能解决普通人用得起数据的问题，两者都是数据民主化的必备条件。通过一次计算多次使用，数据成本可以被多个用户分摊，达到用户越多越便宜的规模效应。预计算是 Kylin 的传统强项，但是在预计算模型的变更方面缺乏一å
 ®šçš„灵活性，为了加强 Kylin 的模型的灵活变更能力，并带来更多可优化的空间，Kylin 社区预计在未来的 Kylin 中提出全新的元数据结构，使预计算更灵活，能够应对随时可能发生变化的表结构或者业务需求。&lt;/p&gt;
+
+&lt;h3 id=&quot;section-2&quot;&gt;总结&lt;/h3&gt;
+&lt;p&gt;综上，我们将明确 Kylin 的技术定位是一个多维数据库，通过多维模型和预计算技术，让普通人看得懂和用得起大数据，最终实现数据民主化的美好愿景。同时，对于今天将 Kylin 用作 SQL 加速层的用户，Kylin 将继续保有完备的 SQL 接口，保证预计算技术可以同时被关系模型和多维模型使用。&lt;br /&gt;
+在下图中，我们能清晰地看到未来 Kylin 关注的方向，新增和修改的部分大致使用蓝色和橙色标示出来。&lt;/p&gt;
+
+&lt;p&gt;&lt;img src=&quot;/images/blog/the_future_of_kylin.png&quot; alt=&quot;&quot; /&gt;&lt;/p&gt;
+
+&lt;h2 id=&quot;apache-kylin--1&quot;&gt;03 Apache Kylin 升级计划&lt;/h2&gt;
+&lt;p&gt;基于 Kylin 作为一个多维数据库的定位，结合当前 Kylin 存在的有待加强的能力，同时为了支持 Schema Change 等用户期待已久的功能，我们计划在未来的 Kylin 中引入新的 DataModel 的元数据结构，不再向用户暴露 Cube 的元数据，将元数据依赖关系简化为 Model -&amp;gt; Table 。&lt;br /&gt;
+由于元数据是社区后续协作开发的基础和契约，全新元数据结构的设计开发将会是当前以及今后几个月内 Kylin 社区工作的重点，元数据设计以及讨论文档会在一个月内发布，欢迎大家踊跃参与讨论，不出意外地话 2022 年新的元数据结构就会与大家见面，敬请期待。&lt;br /&gt;
+除了元数据结构升级以外，和元数据升级配套的构建和查询引擎、语义层能力（MDX）、与 BI 工具更好集成、Native Engine 等也是 Kylin 社区一直在积极推进的重点工作，欢迎更多志同道合的小伙伴参与进来，共创社区。&lt;/p&gt;
+
+&lt;p&gt;** Further Reading **&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Data_model&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Semantic_layer&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Multidimensional_analysis&lt;br /&gt;
+- https://en.wikipedia.org/wiki/MultiDimensional_eXpressions&lt;br /&gt;
+- https://en.wikipedia.org/wiki/XML_for_Analysis&lt;br /&gt;
+- https://en.wikipedia.org/wiki/SIMD&lt;br /&gt;
+- https://en.wikipedia.org/wiki/Cloud_native_computing&lt;br /&gt;
+- https://blogs.gartner.com/carlie-idoine/2018/05/13/citizen-data-scientists-and-why-they-matter/&lt;/p&gt;
+</description>
+        <pubDate>Wed, 12 Jan 2022 03:00:00 -0800</pubDate>
+        <link>http://kylin.apache.org/cn_blog/2022/01/12/The-Future-Of-Kylin/</link>
+        <guid isPermaLink="true">http://kylin.apache.org/cn_blog/2022/01/12/The-Future-Of-Kylin/</guid>
+        
+        
+        <category>cn_blog</category>
+        
+      </item>
+    
+      <item>
         <title>Kylin4 云上性能优化：本地缓存和软亲和性调度</title>
         <description>&lt;h2 id=&quot;section&quot;&gt;01 背景介绍&lt;/h2&gt;
 &lt;p&gt;日前，Apache Kylin 社区发布了全新架构的 Kylin 4.0。Kylin 4.0 的架构支持存储和计算分离，这使得 kylin 用户可以采取更加灵活、计算资源可以弹性伸缩的云上部署方式来运行 Kylin 4.0。借助云上的基础设施，用户可以选择使用便宜且可靠的对象存储来储存 cube 数据，比如 S3 等。不过在存储与计算分离的架构下，我们需要考虑到，计算节点通过网络从远端存储读取数据仍然是一个代价较大的操作，往å¾
 €ä¼šå¸¦æ¥æ€§èƒ½çš„损耗。&lt;br /&gt;
@@ -1032,155 +1144,6 @@ Here is a brief introduction to the prin
         
         
         <category>blog</category>
-        
-      </item>
-    
-      <item>
-        <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;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;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;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;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;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&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;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;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;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;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, 29 Nov 2019 07:00:00 -0800</pubDate>
-        <link>http://kylin.apache.org/cn_blog/2019/11/29/Davinci-Kylin-Insight/</link>
-        <guid isPermaLink="true">http://kylin.apache.org/cn_blog/2019/11/29/Davinci-Kylin-Insight/</guid>
-        
-        
-        <category>cn_blog</category>
-        
-      </item>
-    
-      <item>
-        <title>Connecting Tableau Desktop and Tableau Server with Apache Kylin</title>
-        <description>&lt;h2 id=&quot;background&quot;&gt;Background&lt;/h2&gt;
-
-&lt;p&gt;This document describes how to connect Tableau to Apache Kylin OLAP server, particularly (but not only) in live mode to use both reporting and analytics features of Tableau together with Apache Kylin’s fast query processing engine. The configuration is platform independent - it works for both Windows and Linux installations of Tableau Server.&lt;/p&gt;
-
-&lt;p&gt;For the time of writing this guide we tested that it works with Kylin 3.0.0 and Tableau Server 2019.1.&lt;/p&gt;
-
-&lt;h2 id=&quot;prerequisites&quot;&gt;Prerequisites&lt;/h2&gt;
-
-&lt;h3 id=&quot;apache-kylin-jdbc-driver&quot;&gt;Apache Kylin JDBC Driver&lt;/h3&gt;
-
-&lt;p&gt;First we need to get Apache Kylin JDBC Driver - kylin-jdbc-X.Y.Z.jar file. You can either get it from the compiled package available on the download page http://kylin.apache.org/download/ from &lt;code class=&quot;highlighter-rouge&quot;&gt;lib&lt;/code&gt; folder or compile it on your own using instructions below.&lt;/p&gt;
-
-&lt;p&gt;&lt;em&gt;Note&lt;/em&gt;: To make JDBC driver work properly, there has been a fix recently https://github.com/apache/kylin/pull/739 that upgraded one of the libraries used by the driver. The fix was applied for version 3, so if for some reason you need a jar for earlier version, you have to apply the fix on the lower version’s codebase and compile yourself.&lt;/p&gt;
-
-&lt;h4 id=&quot;compiling-apache-kylin-jdbc-driver&quot;&gt;Compiling Apache Kylin JDBC Driver&lt;/h4&gt;
-
-&lt;div class=&quot;highlighter-rouge&quot;&gt;&lt;pre class=&quot;highlight&quot;&gt;&lt;code&gt;git clone https://github.com/apache/kylin.git 
-cd kylin
-mvn clean package -DskipTests -am -pl jdbc
-&lt;/code&gt;&lt;/pre&gt;
-&lt;/div&gt;
-
-&lt;p&gt;The compiled jar is located in the following location: &lt;code class=&quot;highlighter-rouge&quot;&gt;jdbc/target/kylin-jdbc-X.Y.Z.jar&lt;/code&gt;&lt;/p&gt;
-
-&lt;h3 id=&quot;tableau-server-on-linux&quot;&gt;Tableau Server on Linux&lt;/h3&gt;
-
-&lt;p&gt;If you have installed Tableau Server in a Linux box, e.g. CentOS, copy the driver’s jar file to the following location: &lt;code class=&quot;highlighter-rouge&quot;&gt;/opt/tableau/tableau_driver/jdbc/&lt;/code&gt; and restart Tableau Server. &lt;br /&gt;
-The server is now ready to create and refresh data from Apache Kylin.&lt;/p&gt;
-
-&lt;h3 id=&quot;tableau-server-and-tableau-desktop-on-windows&quot;&gt;Tableau Server and Tableau Desktop on Windows&lt;/h3&gt;
-
-&lt;p&gt;For either Tableau Server or Tableau Desktop that is installed on a Windows machine, copy the driver’s jar file to the following location &lt;code class=&quot;highlighter-rouge&quot;&gt;C:\Program Files\Tableau\Drivers&lt;/code&gt; and restart Tableau Server or reopen Tableau Desktop.&lt;/p&gt;
-
-&lt;p&gt;Some more details regarding jdbc connection from Tableau are well described in Tableau’s documentation: https://onlinehelp.tableau.com/current/pro/desktop/en-us/examples_otherdatabases_jdbc.htm.&lt;/p&gt;
-
-&lt;h2 id=&quot;creating-report-in-tableau-desktop---connecting-to-apache-kylin&quot;&gt;Creating report in Tableau Desktop - connecting to Apache Kylin&lt;/h2&gt;
-
-&lt;p&gt;To create report follow the steps:&lt;br /&gt;
-1. Open Tableau Desktop&lt;br /&gt;
-2. Use “Other Databases (JDBC)” to create connection for the data source&lt;br /&gt;
-&lt;img src=&quot;/images/blog/kylin-tableau/tableau_other_databases_jdbc.jpg&quot; alt=&quot;Other Databases (JDBC)&quot; /&gt;&lt;br /&gt;
-3. Configure the connection in the following way:&lt;br /&gt;
-- URL: &lt;code class=&quot;highlighter-rouge&quot;&gt;jdbc:kylin://&amp;lt;kylin-server-name&amp;gt;:&amp;lt;kylin-port&amp;gt;/&amp;lt;project&amp;gt;&lt;/code&gt;&lt;br /&gt;
-- Dialect: &lt;code class=&quot;highlighter-rouge&quot;&gt;SQL92&lt;/code&gt;&lt;br /&gt;
-&lt;img src=&quot;/images/blog/kylin-tableau/tableau_kylin_connection.jpg&quot; alt=&quot;Datasource connection&quot; /&gt;&lt;br /&gt;
-4. Configure data source as follows:&lt;br /&gt;
-- Database: &lt;code class=&quot;highlighter-rouge&quot;&gt;defaultCatalog&lt;/code&gt;&lt;br /&gt;
-- Schema: &lt;code class=&quot;highlighter-rouge&quot;&gt;DEFAULT&lt;/code&gt;&lt;br /&gt;
-You should be able to see the tables/cubes in the Apache Kylin’s project&lt;br /&gt;
-&lt;img src=&quot;/images/blog/kylin-tableau/kylin_jdbc_tableau_working.jpg&quot; alt=&quot;Data source&quot; /&gt;&lt;br /&gt;
-&lt;strong&gt;Important&lt;/strong&gt;: Decide if you want the data source be in &lt;code class=&quot;highlighter-rouge&quot;&gt;live&lt;/code&gt; or &lt;code class=&quot;highlighter-rouge&quot;&gt;extract&lt;/code&gt; mode. Some of the functions might not work in &lt;code class=&quot;highlighter-rouge&quot;&gt;live&lt;/code&gt; mode as for the other data sources - it’s just how Tableau works. Recommendation is to start with &lt;code class=&quot;highlighter-rouge&quot;&gt;live&lt;/code&gt; mode to utilize performance of Apache Kylin. If you’re forced to switch to &lt;code class=&quot;highlighter-rouge&quot;&gt;extract&lt;/code&gt; mode - consider creating a custom query against Apache Kylin’s cubes to retrieve as small amount of data as possible as it will help the report to perform well.&lt;br /&gt;
-5. Finish designing your data source and then switch to worksheets, dashboards&lt;/p&gt;
-
-&lt;p&gt;&lt;img src=&quot;/images/blog/kylin-tableau/kylin_jdbc_tableau_working_sheet.jpg&quot; alt=&quot;Tableau Desktop&quot; /&gt;&lt;/p&gt;
-
-&lt;h2 id=&quot;publishing-reports-from-tableau-desktop-to-tableau-server&quot;&gt;Publishing reports from Tableau Desktop to Tableau Server&lt;/h2&gt;
-
-&lt;p&gt;To publish the data source and the report follow these steps:&lt;br /&gt;
-1. In Tableau Desktop from top menu select Server -&amp;gt; Publish&lt;br /&gt;
-2. Choose the settings for publishing like Project, select sheets&lt;br /&gt;
-3. &lt;strong&gt;Important&lt;/strong&gt;: For data source Authentication set &lt;code class=&quot;highlighter-rouge&quot;&gt;Embedded&lt;/code&gt; - this is very important for data refresh to work, however keep in mind that the credentials will be embeded in the report then&lt;br /&gt;
-4. Publish the report&lt;br /&gt;
-5. Pop up should be displayed with the preview of the report rendered by the server&lt;/p&gt;
-
-&lt;p&gt;&lt;img src=&quot;/images/blog/kylin-tableau/kylin_jdbc_tableau_server.jpg&quot; alt=&quot;Tableau Server&quot; /&gt;&lt;/p&gt;
-
-&lt;p&gt;Verify if the report is displaying properly and can connect to Apache Kylin correctly by opening it directly in Tableau Server web application.&lt;/p&gt;
-</description>
-        <pubDate>Sun, 22 Sep 2019 13:30:00 -0700</pubDate>
-        <link>http://kylin.apache.org/blog/2019/09/22/kylin-tableau/</link>
-        <guid isPermaLink="true">http://kylin.apache.org/blog/2019/09/22/kylin-tableau/</guid>
-        
-        
-        <category>blog</category>
         
       </item>
     

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