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/

[lidong]

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åŠ
 é€‚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å‡
 ä¸ªæ–¹é¢ï¼š&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å…
³é”®ã€‚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æ 
‡ã€‚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å¾
…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å…
³ç³»æ•°æ®åº“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 å…
³é”®åŒºåˆ«åœ¨äºŽä¸šåŠ¡è¡¨è¾¾èƒ½åŠ›ã€‚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å…
³ç³»æ•°æ®åº“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å
…¶ä¸­æœ‰å¾ˆå¤šæ•°æ®ï¼Œå´å› ä¸ºä¸æ‡‚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å…
³ç³»æ¨¡åž‹æè¿°äº†æ•°æ®çš„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æ
 ‡ï¼Œç»´åº¦åˆ™æ˜¯æ¯”较、观察这些业务指æ 
‡çš„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è€
…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å…
ƒæ•°æ®ç»“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è€
…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å…
³ç³»æ¨¡åž‹å’Œå¤šç»´æ¨¡åž‹ä½¿ç”¨ã€‚&lt;br /&gt;
+在下图中，我们能清晰地看到未来 Kylin å…
³æ³¨çš„方向，新增和修改的部分大致使用蓝色和橙色æ 
‡ç¤ºå‡ºæ¥ã€‚&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å…
ƒæ•°æ®ä¾èµ–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å…
ƒæ•°æ®ç»“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å…
ƒæ•°æ®ç»“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é…
å¥—的构建和查询引擎、语义层能力（MDX）、与 BI å·¥å…
·æ›´å¥½é›†æˆã€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，å
…±åˆ›ç¤¾åŒºã€‚&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é…
ç½®å³å¯æœåŠ¡å¤šç§æ•°æ®å¯è§†åŒ–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æƒ
…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 年 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å…
¨ï¼Œç”¨æˆ·äº¤äº’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å
…¨ã€‚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é…
ç½®ã€‚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é…
ç½®éƒ¨ç½²è¯·å‚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}:{é…
ç½®çš„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é…
ç½®æ–‡ä»¶ã€‚首先，进入 lib 目录下添加 kylin-jdbc 包，其次，进å…
¥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é…
·ç‚«çš„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å…
¶è‡ªèº«ä¸°å¯Œçš„功能和一站式的可视化分析展现。再者，å…
¶å¼€æºçš„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å…
¶åŸºç¡€ä¸Šè¿›è¡ŒäºŒæ¬¡å¼€å‘，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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