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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/tutorial/Qlik.html" class="list-group-item-lay pjaxlink" id="navlist">Qlik Sense</a></li> + + + + + + + + + <ul> + </div> +</div> + + <li><a href="#titleHow To" data-toggle="collapse" id="navtitle">How To</a></li> + <div class="collapse in"> + <div class="list-group" id="list1"> + <ul style="list-style-type:disc"> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_use_restapi.html" class="list-group-item-lay pjaxlink" id="navlist">Use RESTful API</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_build_cube_with_restapi.html" class="list-group-item-lay pjaxlink" id="navlist">Build Cube with API</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_optimize_cubes.html" class="list-group-item-lay pjaxlink" id="navlist">Optimize Cube Design</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_optimize_build.html" class="list-group-item-lay pjaxlink" id="navlist">Optimize Cube Build</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_backup_metadata.html" class="list-group-item-lay pjaxlink" id="navlist">Backup Metadata</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_cleanup_storage.html" class="list-group-item-lay pjaxlink" id="navlist">Cleanup Storage</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_jdbc.html" class="list-group-item-lay pjaxlink" id="navlist">Kylin JDBC Driver</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_upgrade.html" class="list-group-item-lay pjaxlink" id="navlist">Upgrade From Old Versions</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_ldap_and_sso.html" class="list-group-item-lay pjaxlink" id="navlist">Secure with LDAP and SSO</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_use_beeline.html" class="list-group-item-lay pjaxlink" id="navlist">Use Beeline for Hive</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_update_coprocessor.html" class="list-group-item-lay pjaxlink" id="navlist">Update Coprocessor</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_install_ranger_kylin_plugin.html" class="list-group-item-lay pjaxlink" id="navlist">The Ranger Kylin Plugin Installation Guide</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_enable_zookeeper_acl.html" class="list-group-item-lay pjaxlink" id="navlist">Enable zookeeper acl</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_setup_systemcube.html" class="list-group-item-lay pjaxlink" id="navlist">Set Up System Cube</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_use_cube_planner.html" class="list-group-item-lay pjaxlink" id="navlist">Use Cube Planner</a></li> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + <li><a href="/docs23/howto/howto_use_dashboard.html" class="list-group-item-lay pjaxlink" id="navlist">Use Dashboard</a></li> + + + + + + + + + <ul> + </div> +</div> + + + </ul> +</div> + + <div class="col-md-9 col-lg-9 col-xs-14 aside2"> + <div id="container"> + <div id="pjax"> + <h1 class="post-title">Web Interface</h1> + + + <article class="post-content" > + <blockquote> + <p><strong>Supported Browsers</strong><br /> +Windows: Google Chrome, FireFox<br /> +Mac: Google Chrome, FireFox, Safari</p> +</blockquote> + +<h2 id="access--login">1. Access & Login</h2> +<p>Host to access: http://hostname:7070<br /> +Login with username/password: ADMIN/KYLIN</p> + +<p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/1 login.png" alt="" /></p> + +<h2 id="sync-hive-table-into-kylin">2. Sync Hive Table into Kylin</h2> +<p>Although Kylin will using SQL as query interface and leverage Hive metadata, kylin will not enable user to query all hive tables since itâs a pre-build OLAP (MOLAP) system so far. To enable Table in Kylin, it will be easy to using âSyncâ function to sync up tables from Hive.</p> + +<p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/2 tables.png" alt="" /></p> + +<h2 id="kylin-olap-cube">3. Kylin OLAP Cube</h2> +<p>Kylinâs OLAP Cubes are pre-calculation datasets from star schema tables, Hereâs the web interface for user to explorer, manage all cubes. Go to <code class="highlighter-rouge">Model</code> menu, it will list all cubes available in system:</p> + +<p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/3 cubes.png" alt="" /></p> + +<p>To explore more detail about the Cube</p> + +<ul> + <li> + <p>Form View:</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/4 form-view.png" alt="" /></p> + </li> + <li> + <p>SQL View (Hive Query to read data to generate the cube):</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/5 sql-view.png" alt="" /></p> + </li> + <li> + <p>Access (Grant user/role privileges, grant operation only open to Admin):</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/7 access.png" alt="" /></p> + </li> +</ul> + +<h2 id="write-and-execute-sql-on-web">4. Write and Execute SQL on web</h2> +<p>Kylinâs web offer a simple query tool for user to run SQL to explorer existing cube, verify result and explorer the result set using #5âs Pivot analysis and visualization</p> + +<blockquote> + <p><strong>Query Limit</strong></p> + + <ol> + <li> + <p>Only SELECT query be supported</p> + </li> + <li> + <p>SQL will not be redirect to Hive</p> + </li> + </ol> +</blockquote> + +<p>Go to âInsightâ menu:</p> + +<p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/8 query.png" alt="" /></p> + +<ul> + <li> + <p>Source Tables:</p> + + <p>Browser current available tables (same structure and metadata as Hive):</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/9 query-table.png" alt="" /></p> + </li> + <li> + <p>New Query:</p> + + <p>You can write and execute your query and explorer the result.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/10 query-result.png" alt="" /></p> + </li> + <li> + <p>Saved Query (only work after enable LDAP security):</p> + + <p>Associate with user account, you can get saved query from different browsers even machines.<br /> + Click âSaveâ in Result area, it will popup for name and description to save current query:</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/11 save-query.png" alt="" /></p> + + <p>Click âSaved Queriesâ to browser all your saved queries, you could direct submit it or remove it.</p> + </li> + <li> + <p>Query History:</p> + + <p>Only keep the current userâs query history in current bowser, it will require cookie enabled and will lost if you clean up bowserâs cache. Click âQuery Historyâ tab, you could directly resubmit any of them to execute again.</p> + </li> +</ul> + +<h2 id="pivot-analysis-and-visualization">5. Pivot Analysis and Visualization</h2> +<p>Thereâs one simple pivot and visualization analysis tool in Kylinâs web for user to explore their query result:</p> + +<ul> + <li> + <p>General Information:</p> + + <p>When the query execute success, it will present a success indictor and also a cubeâs name which be hit. <br /> + Also it will present how long this query be executed in backend engine (not cover network traffic from Kylin server to browser):</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/12 general.png" alt="" /></p> + </li> + <li> + <p>Query Result:</p> + + <p>Itâs easy to order on one column.</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/13 results.png" alt="" /></p> + </li> + <li> + <p>Export to CSV File</p> + + <p>Click âExportâ button to save current result as CSV file.</p> + </li> + <li> + <p>Pivot Table:</p> + + <p>Drag and drop one or more columns into the header, the result will grouping by such columnâs value:</p> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/14 drag.png" alt="" /></p> + </li> + <li> + <p>Visualization:</p> + + <p>Also, the result set will be easy to show with different charts in âVisualizationâ:</p> + + <p>note: line chart only available when thereâs at least one dimension with real âDateâ data type of column from Hive Table.</p> + + <ul> + <li>Bar Chart:</li> + </ul> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/15 bar-chart.png" alt="" /></p> + + <ul> + <li>Pie Chart:</li> + </ul> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/16 pie-chart.png" alt="" /></p> + + <ul> + <li>Line Chart</li> + </ul> + + <p><img src="/images/tutorial/1.5/Kylin-Web-Tutorial/17 line-chart.png" alt="" /></p> + </li> +</ul> + + + </article> + </div> + </div> + </div> + </div> + </div> + <!-- +* Licensed to the Apache Software Foundation (ASF) under one +* or more contributor license agreements. See the NOTICE file +* distributed with this work for additional information +* regarding copyright ownership. The ASF licenses this file +* to you under the Apache License, Version 2.0 (the +* "License"); you may not use this file except in compliance +* with the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +--> + +<footer id="underfooter"> + <div class="container"> + <div class="row"> + <div class="col-md-12 widget"> + <div class="widget-body" style="text-align:center"> + <a href="http://www.apache.org";> + <img id="asf-logo" alt="Apache Software Foundation" src="/assets/images/feather-small.gif"> + </a> + + <div> + The contents of this website are © 2015 Apache Software Foundation under the terms of the <a + href="http://www.apache.org/licenses/LICENSE-2.0";> Apache License v2 </a>. Apache Kylin and + its logo are trademarks of the Apache Software Foundation. + </div> + + </div> + </div> + </div> + <!-- /row of widgets --> + + </div> + <div></div> + +</footer> + + <script src="/assets/js/jquery-1.9.1.min.js"></script> + <script src="/assets/js/bootstrap.min.js"></script> + <script src="/assets/js/main.js"></script> + </body> +</html>
Modified: kylin/site/feed.xml URL: http://svn.apache.org/viewvc/kylin/site/feed.xml?rev=1822508&r1=1822507&r2=1822508&view=diff ============================================================================== --- kylin/site/feed.xml (original) +++ kylin/site/feed.xml Mon Jan 29 14:05:18 2018 @@ -19,11 +19,280 @@ <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>Fri, 26 Jan 2018 05:59:17 -0800</pubDate> - <lastBuildDate>Fri, 26 Jan 2018 05:59:17 -0800</lastBuildDate> + <pubDate>Mon, 29 Jan 2018 06:00:48 -0800</pubDate> + <lastBuildDate>Mon, 29 Jan 2018 06:00:48 -0800</lastBuildDate> <generator>Jekyll v2.5.3</generator> <item> + <title>Get Your Interactive Analytics Superpower, with Apache Kylin and Apache Superset</title> + <description><h2 id="challenge-of-big-data">Challenge of Big Data</h2> + +<p>In the big data era, all enterprisesâ face the growing demand and challenge of processing large volumes of dataâworkloads that traditional legacy systems can no longer satisfy. With the emergence of Artificial Intelligence (AI) and Internet-of-Things (IoT) technology, it has become mission-critical for businesses to accelerate their pace of discovering valuable insights from their massive and ever-growing datasets. Thus, large companies are constantly searching for a solution, often turning to open source technologies. We will introduce two open source technologies that, when combined together, can meet these pressing big data demands for large enterprises.</p> + +<h2 id="apache-kylin-a-leading-opensource-olap-on-hadoop">Apache Kylin: a Leading OpenSource OLAP-on-Hadoop</h2> +<p>Modern organizations have had a long history of applying Online Analytical Processing (OLAP) technology to analyze data and uncover business insights. These insights help businesses make informed decisions and improve their service and product. With the emergence of the Hadoop ecosystem, OLAP has also embraced new technologies in the big data era.</p> + +<p>Apache Kylin is one such technology that directly addresses the challenge of conducting analytical workloads on massive datasets. It is already widely adopted by enterprises around the world. With powerful pre-calculation technology, Apache Kylin enables sub-second query latency over petabyte-scale datasets. The innovative and intricate design of Apache Kylin allows it to seamlessly consume data from any Hadoop-based data source, as well as other relational database management system (RDBMS). Analysts can use Apache Kylin using standard SQL through ODBC, JDBC, and Restful API, which enables the platform to integrate with any third-party applications.<br /> +<img src="/images/Kylin-and-Superset/png/1. kylin_diagram.png" alt="" /><br /> +Figure 1: Apache Kylin Architecture</p> + +<p>In a fast-paced and rapidly-changing business environment, business users and analysts are expected to uncover insights with speed of thoughts. They can meet this expectation with Apache Kylin, and no longer subjected to the predicament of waiting for hours for one single query to return results. Such a powerful data processing engine empowers the data scientists, engineers, and business analysts of any enterprise to find insights to help reach critical business decisions. However, business decisions cannot be made without rich data visualization. To address this last-mile challenge of big data analytics, Apache Superset comes into the picture.</p> + +<h2 id="apache-superset-modern-enterprise-ready-business-intelligence-platform">Apache Superset: Modern, Enterprise-ready Business Intelligence Platform</h2> + +<p>Apache Superset is a data exploration and visualization platform designed to be visual, intuitive, and interactive. A user can access data in the following two ways:</p> + +<ol> + <li> + <p>Access data from the following commonly used data sources one table at a time: Kylin, Presto, Hive, Impala, SparkSQL, MySQL, Postgres, Oracle, Redshift, SQL Server, Druid.</p> + </li> + <li> + <p>Use a rich SQL Interactive Development Environment (IDE) called SQL Lab that is designed for power users with the ability to write SQL queries to analyze multiple tables.</p> + </li> +</ol> + +<p>Users can immediately analyze and visualize their query results using Apache Superset âs rich visualization and reporting features.</p> + +<p><img src="/images/Kylin-and-Superset/png/2. superset_logo.png" alt="" /><br /> +Figure 2</p> + +<p><img src="/images/Kylin-and-Superset/png/3. Superset_screen_shot.png" alt="" /><br /> +Figure 3: Apache Superset Visualization Interface</p> + +<h2 id="integrating-apache-kylin-and-apache-superset-to-boost-your-productivity">Integrating Apache Kylin and Apache Superset to Boost Your Productivity</h2> + +<p>Both Apache Kylin and Apache Superset are built to provide fast and interactive analytics for their users. The combination of these two open source projects can bring that goal to reality on petabyte-scale datasets, thanks to pre-calculated Kylin Cube.</p> + +<p>The Kyligence Data Science team has recently open sourced kylinpy, a project that makes this combination possible. Kylinpy is a Python-based Apache Kylin client library. Any application that uses SQLAlchemy can now query Apache Kylin with this library installed, specifically Apache Superset. Below is a brief tutorial that shows how to integrate Apache Kylin and Apache Superset.</p> + +<h2 id="prerequisite">Prerequisite</h2> +<ol> + <li>Install Apache Kylin<br /> +Please refer to this installation tutorial.</li> + <li>Apache Kylin provides a script for you to create a sample Cube. After you successfully installed Apache Kylin, you can run the below script under Apache Kylin installation directory to generate sample project and Cube. <br /> + ./${KYLIN_HOME}/bin/sample.sh</li> + <li> + <p>When the script finishes running, log onto Apache Kylin web with default user ADMIN/KYLIN; in the system page click âReload Metadata,â then you will see a sample project called âLearn Kylin.â</p> + </li> + <li>Select the sample cube âkylin_sales_cubeâ, click âActionsâ -&gt; âBuildâ, pick a date later than 2014-01-01 (to cover all 10000 sample records);</li> +</ol> + +<p><img src="/images/Kylin-and-Superset/png/4. build_cube.png" alt="" /><br /> +Figure 4: Build Cube in Apache Kylin</p> + +<ol> + <li>Check the build progress in âMonitorâ tab until it reaches 100%;</li> + <li>Execute SQL in the âInsightâ tab, for example:</li> +</ol> + +<div class="highlighter-rouge"><pre class="highlight"><code> select part_dtï¼ + sum(price) as total_selledï¼ + count(distinct seller_id) as sellers + from kylin_sales + group by part_dt + order by part_dt +-- #This query will hit on the newly built Cube âKylin_sales_cubeâ. +</code></pre> +</div> + +<ol> + <li>Next, we will install Apache Superset and initialize it.<br /> + You may refer to Apache Superset official website instruction to install and initialize.</li> + <li>Install kylinpy</li> +</ol> + +<div class="highlighter-rouge"><pre class="highlight"><code> $ pip install kylinpy +</code></pre> +</div> + +<ol> + <li>Verify your installation, if everything goes well, Apache Superset daemon should be up and running.</li> +</ol> + +<div class="highlighter-rouge"><pre class="highlight"><code>$ superset runserver -d +Starting server with command: +gunicorn -w 2 --timeout 60 -b 0.0.0.0:8088 --limit-request-line 0 --limit-request-field_size 0 superset:app + +[2018-01-03 15:54:03 +0800] [73673] [INFO] Starting gunicorn 19.7.1 +[2018-01-03 15:54:03 +0800] [73673] [INFO] Listening at: http://0.0.0.0:8088 (73673) +[2018-01-03 15:54:03 +0800] [73673] [INFO] Using worker: sync +[2018-01-03 15:54:03 +0800] [73676] [INFO] Booting worker with pid: 73676 +[2018-01-03 15:54:03 +0800] [73679] [INFO] Booting worker with pid: 73679 +</code></pre> +</div> + +<h2 id="connect-apache-kylin-from-apachesuperset">Connect Apache Kylin from ApacheSuperset</h2> + +<p>Now everything you need is installed and ready to go. Letâs try to create an Apache Kylin data source in Apache Superset.<br /> +1. Open up http://localhost:8088 in your web browser with the credential you set during Apache Superset installation.<br /> + <img src="/images/Kylin-and-Superset/png/5. superset_1.png" alt="" /><br /> + Figure 5: Apache Superset Login Page</p> + +<ol> + <li>Go to Source -&gt; Datasource to configure a new data source. + <ul> + <li>SQLAlchemy URI pattern is : kylin://<username>:<password>@<hostname>:<port>/<project name=""></project></port></hostname></password></username></li> + <li>Check âExpose in SQL Labâ if you want to expose this data source in SQL Lab.</li> + <li>Click âTest Connectionâ to see if the URI is working properly.</li> + </ul> + </li> +</ol> + +<p><img src="/images/Kylin-and-Superset/png/6. superset_2.png" alt="" /><br /> + Figure 6: Create an Apache Kylin data source</p> + +<p><img src="/images/Kylin-and-Superset/png/7. superset_3.png" alt="" /><br /> + Figure 7: Test Connection to Apache Kylin</p> + +<p>If the connection to Apache Kylin is successful, you will see all the tables from Learn_kylin project show up at the bottom of the connection page.</p> + +<p><img src="/images/Kylin-and-Superset/png/8. superset_4.png" alt="" /><br /> +Figure 8: Tables will show up if connection is successful</p> + +<h3 id="query-kylin-table">Query Kylin Table</h3> +<ol> + <li>Go to Source -&gt; Tables to add a new table, type in a table name from âLearn_kylinâ project, for example, âKylin_salesâ.</li> +</ol> + +<p><img src="/images/Kylin-and-Superset/png/9. superset_5.png" alt="" /><br /> +Figure 9 Add Kylin Table in Apache Superset</p> + +<ol> + <li>Click on the table you created. Now you are ready to analyze your data from Apache Kylin.</li> +</ol> + +<p><img src="/images/Kylin-and-Superset/png/10. superset_6.png" alt="" /><br /> +Figure 10 Query single table from Apache Kylin</p> + +<h3 id="query-multiple-tables-from-kylin-using-sql-lab">Query Multiple Tables from Kylin Using SQL Lab.</h3> +<p>Kylin Cube is usually based on a data model joined by multiples tables. Thus, it is quite common to query multiple tables at the same time using Apache Kylin. In Apache Superset, you can use SQL Lab to join your data across tables by composing SQL queries. We will use a query that can hit on the sample cube âkylin_sales_cubeâ as an example. <br /> +When you run your query in SQL Lab, the result will come from the data source, in this case, Apache Kylin.</p> + +<p><img src="/images/Kylin-and-Superset/png/11. SQL_Lab.png" alt="" /><br /> +Figure 11 Query multiple tables from Apache Kylin using SQL Lab</p> + +<p>When the query returns results, you may immediately visualize them by clicking on the âVisualizeâ button.<br /> +<img src="/images/Kylin-and-Superset/png/12. SQL_Lab_2.png" alt="" /><br /> +Figure 12 Define your query and visualize it immediately</p> + +<p>You may copy the entire SQL below to experience how you can query Kylin Cube in SQL Lab. <br /> +<code class="highlighter-rouge"> +select +YEAR_BEG_DT, +MONTH_BEG_DTï¼ +WEEK_BEG_DTï¼ +META_CATEG_NAMEï¼ +CATEG_LVL2_NAME, +CATEG_LVL3_NAME, +OPS_REGION, +NAME as BUYER_COUNTRY_NAME, +sum(PRICE) as GMV, +sum(ACCOUNT_BUYER_LEVEL) ACCOUNT_BUYER_LEVEL, +count(*) as CNT +from KYLIN_SALES +join KYLIN_CAL_DT on CAL_DT=PART_DT +join KYLIN_CATEGORY_GROUPINGS on SITE_ID=LSTG_SITE_ID and KYLIN_CATEGORY_GROUPINGS.LEAF_CATEG_ID=KYLIN_SALES.LEAF_CATEG_ID +join KYLIN_ACCOUNT on ACCOUNT_ID=BUYER_ID +join KYLIN_COUNTRY on ACCOUNT_COUNTRY=COUNTRY +group by YEAR_BEG_DT, MONTH_BEG_DTï¼WEEK_BEG_DTï¼META_CATEG_NAMEï¼CATEG_LVL2_NAME, CATEG_LVL3_NAME, OPS_REGION, NAME +</code><br /> +## Experience All Features in Apache Superset with Apache Kylin</p> + +<p>Most of the common reporting features are available in Apache Superset. Now letâs see how we can use those features to analyze data from Apache Kylin.</p> + +<h3 id="sorting">Sorting</h3> +<p>You may sort by a measure regardless of how it is visualized.</p> + +<p>You may specify a âSort Byâ measure or sort the measure on the visualization after the query returns.</p> + +<p><img src="/images/Kylin-and-Superset/png/13. sort.png" alt="" /><br /> +Figure 13 Sort by</p> + +<h3 id="filtering">Filtering</h3> +<p>There are multiple ways you may filter data from Apache Kylin.<br /> +1. Date Filter<br /> + You may filter date and time dimension with the calendar filter. <br /> + <img src="/images/Kylin-and-Superset/png/14. time_filter.png" alt="" /><br /> + Figure 14 Filtering time</p> + +<ol> + <li> + <p>Dimension Filter<br /> + For other dimensions, you may filter it with SQL conditions like âin, not in, equal to, not equal to, greater than and equal to, smaller than and equal to, greater than, smaller than, likeâ.<br /> + <img src="/images/Kylin-and-Superset/png/15. dimension_filter.png" alt="" /><br /> + Figure 15 Filtering dimension</p> + </li> + <li> + <p>Search Box<br /> + In some visualizations, it is also possible to further narrow down your result set after the query is returned from the data source using the âSearch Boxâ. <br /> + <img src="/images/Kylin-and-Superset/png/16. search_box.png" alt="" /><br /> + Figure 16 Search Box</p> + </li> + <li> + <p>Filtering the measure<br /> + Apache Superset allows you to write a âhaving clauseâ to filtering the measure. <br /> + <img src="/images/Kylin-and-Superset/png/17. having.png" alt="" /><br /> + Figure 17 Filtering measure</p> + </li> + <li> + <p>Filter Box<br /> + The filter box visualization allows you to create a drop-down style filter that can filter all slices on a dashboard dynamically <br /> + As the screenshot below shows, if you filter the CATE_LVL2_NAME dimension from the filter box, all the visualizations on this dashboard will be filtered based on your selection. <br /> + <img src="/images/Kylin-and-Superset/png/18. filter_box.png" alt="" /><br /> + Figure 18 The filter box visualization</p> + </li> +</ol> + +<h3 id="top-n">Top-N</h3> +<p>To provide higher performance in query time for Top N query, Apache Kylin provides approximate Top N measure to pre-calculate the top records. In Apache Superset, you may use both âSort Byâ and âRow Limitâ feature to make sure your query can utilize the Top N pre-calculation from Kylin Cube. <br /> + <img src="/images/Kylin-and-Superset/png/19. top10.png" alt="" /><br /> + Figure 19 use both âSort Byâ and âRow Limitâ to get Top 10</p> + +<h3 id="page-length">Page Length</h3> +<p>Apache Kylin users usually need to deal with high cardinality dimension. When displaying a high cardinality dimension, the visualization will display too many distinct values, taking a long time to render. In that case, it is nice that Apache Superset provides the page length feature to limit the number of rows per page. This way the up-front rendering effort can be reduced. <br /> + <img src="/images/Kylin-and-Superset/png/20. page_length.png" alt="" /><br /> + Figure 20 Limit page length</p> + +<h3 id="visualizations">Visualizations</h3> +<p>Apache Superset provides a rich and extensive set of visualizations. From basic charts like a pie chart, bar chart, line chart to advanced visualizations, like a sunburst, heatmap, world map, Sankey diagram. <br /> + <img src="/images/Kylin-and-Superset/png/21. viz.png" alt="" /><br /> + Figure 21</p> + +<p><img src="/images/Kylin-and-Superset/png/22. viz_2.png" alt="" /><br /> + Figure 22</p> + +<p><img src="/images/Kylin-and-Superset/png/23. map.png" alt="" /><br /> + Figure 23 World map visualization</p> + +<p><img src="/images/Kylin-and-Superset/png/24. bubble.png" alt="" /><br /> + Figure 24 bubble chart</p> + +<h3 id="other-functionalities">Other functionalities</h3> +<p>Apache Superset also supports exporting to CSV, sharing, and viewing SQL query.</p> + +<h2 id="summary">Summary</h2> +<p>With the right technical synergy of open source projects, you can achieve amazing results, more than the sum of its parts. The pre-calculation technology of Apache Kylin accelerates visualization performance. The rich functionality of Apache Superset enables all Kylin Cube features to be fully utilized. When you marry the two, you get the superpower of accelerated interactive analytics.</p> + +<h2 id="references">References</h2> + +<ol> + <li><a href="http://kylin.apache.org">Apache Kylin</a></li> + <li><a href="https://github.com/Kyligence/kylinpy">kylinpy on Github</a></li> + <li><a href="https://medium.com/airbnb-engineering/caravel-airbnb-s-data-exploration-platform-15a72aa610e5">Superset:Airbnbâs data exploration platform</a></li> + <li><a href="https://github.com/apache/incubator-superset">Apache Superset on Github</a></li> +</ol> + +</description> + <pubDate>Mon, 01 Jan 2018 04:28:00 -0800</pubDate> + <link>http://kylin.apache.org/blog/2018/01/01/kylin-and-superset/</link> + <guid isPermaLink="true">http://kylin.apache.org/blog/2018/01/01/kylin-and-superset/</guid> + + + <category>blog</category> + + </item> + + <item> <title>Improving Spark Cubing in Kylin 2.0</title> <description><p>Apache Kylin is a OALP Engine that speeding up query by Cube precomputation. The Cube is multi-dimensional dataset which contain precomputed all measures in all dimension combinations. Before v2.0, Kylin uses MapReduce to build Cube. In order to get better performance, Kylin 2.0 introduced the Spark Cubing. About the principle of Spark Cubing, please refer to the article <a href="http://kylin.apache.org/blog/2017/02/23/by-layer-spark-cubing/">By-layer Spark Cubing</a>.</p> @@ -222,54 +491,54 @@ kylin.engine.spark.rdd-partition-cut-mb= </item> <item> - <title>Apache Kylin v2.0.0 beta åå¸</title> - <description><p>Apache Kylin社åºé常é«å ´å°å®£å¸ <a href="http://kylin.apache.org/cn/download/">v2.0.0 beta package</a> å·²ç»å¯ä»¥ä¸è½½å¹¶æµè¯äºã</p> + <title>Apache Kylin v2.0.0 Beta Announcement</title> + <description><p>The Apache Kylin community is pleased to announce the <a href="http://kylin.apache.org/download/">v2.0.0 beta package</a> is ready for download and test.</p> <ul> - <li>ä¸è½½é¾æ¥: <a href="http://kylin.apache.org/cn/download/">http://kylin.apache.org/cn/download/</a></li> - <li>æºä»£ç : https://github.com/apache/kylin/tree/kylin-2.0.0-beta</li> + <li>Download link: <a href="http://kylin.apache.org/download/">http://kylin.apache.org/download/</a></li> + <li>Source code: https://github.com/apache/kylin/tree/kylin-2.0.0-beta</li> </ul> -<p>èªä»v1.6.0çæ¬åå¸å·²ç»2个å¤æäºãè¿æ®µæ¶é´éï¼æ´ä¸ªç¤¾åºååå¼åå®æäºä¸ç³»åé大çåè½ï¼å¸æè½å°Apache Kylinæåå°ä¸ä¸ªæ°çé«åº¦ã</p> +<p>It has been more than 2 month since the v1.6.0 release. The community has been working hard to deliver some long wanted features, hoping to move Apache Kylin to the next level.</p> <ul> - <li>æ¯æéªè±æ¨¡å (<a href="https://issues.apache.org/jira/browse/KYLIN-1875">KYLIN-1875</a>)</li> - <li>æ¯æ TPC-H æ¥è¯¢ (<a href="https://issues.apache.org/jira/browse/KYLIN-2467">KYLIN-2467</a>)</li> - <li>Spark æ建å¼æ (<a href="https://issues.apache.org/jira/browse/KYLIN-2331">KYLIN-2331</a>)</li> - <li>Job Engine é«å¯ç¨æ§ (<a href="https://issues.apache.org/jira/browse/KYLIN-2006">KYLIN-2006</a>)</li> - <li>Percentile 度é (<a href="https://issues.apache.org/jira/browse/KYLIN-2396">KYLIN-2396</a>)</li> - <li>å¨ Cloud ä¸éè¿æµè¯ (<a href="https://issues.apache.org/jira/browse/KYLIN-2351">KYLIN-2351</a>)</li> + <li>Support snowflake data model (<a href="https://issues.apache.org/jira/browse/KYLIN-1875">KYLIN-1875</a>)</li> + <li>Support TPC-H queries (<a href="https://issues.apache.org/jira/browse/KYLIN-2467">KYLIN-2467</a>)</li> + <li>Spark cubing engine (<a href="https://issues.apache.org/jira/browse/KYLIN-2331">KYLIN-2331</a>)</li> + <li>Job engine HA (<a href="https://issues.apache.org/jira/browse/KYLIN-2006">KYLIN-2006</a>)</li> + <li>Percentile measure (<a href="https://issues.apache.org/jira/browse/KYLIN-2396">KYLIN-2396</a>)</li> + <li>Cloud tested (<a href="https://issues.apache.org/jira/browse/KYLIN-2351">KYLIN-2351</a>)</li> </ul> -<p>é常欢è¿å¤§å®¶ä¸è½½å¹¶æµè¯ v2.0.0 betaãæ¨çåé¦å¯¹æ们é常éè¦ï¼è¯·åé®ä»¶å° <a href="&#109;&#097;&#105;&#108;&#116;&#111;:&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;">&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;</a>ã</p> +<p>You are very welcome to give the v2.0.0 beta a try, and please do send feedbacks to <a href="&#109;&#097;&#105;&#108;&#116;&#111;:&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;">&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;</a>.</p> <hr /> -<h2 id="section">å®è£ </h2> +<h2 id="install">Install</h2> -<p>ææ¶ v2.0.0 beta æ æ³ä» v1.6.0 ç´æ¥å级ï¼å¿ éå ¨æ°å®è£ ãè¿æ¯ç±äºæ°çæ¬çå æ°æ®å¹¶ä¸ååå ¼å®¹ãå¥½å¨ Cube æ°æ®æ¯ååå ¼å®¹çï¼å æ¤åªéè¦å¼åä¸ä¸ªå æ°æ®è½¬æ¢å·¥å ·ï¼å°±è½å¨ä¸ä¹ çå°æ¥å®ç°å¹³æ»å级ãæ们æ£å¨ä¸ºæ¤åªåã</p> +<p>The v2.0.0 beta requires a refresh install at the moment. It cannot be upgraded from v1.6.0 due to the incompatible metadata. However the underlying cube is backward compatible. We are working on an upgrade tool to transform the metadata, so that a smooth upgrade will be possible.</p> <hr /> -<h2 id="tpc-h-">è¿è¡ TPC-H åºåæµè¯</h2> +<h2 id="run-tpc-h-benchmark">Run TPC-H Benchmark</h2> -<p>å¨ Apache Kylin ä¸è¿è¡ TPC-H çå ·ä½æ¥éª¤: <a href="https://github.com/Kyligence/kylin-tpch">https://github.com/Kyligence/kylin-tpch</a></p> +<p>Steps to run TPC-H benchmark on Apache Kylin can be found here: <a href="https://github.com/Kyligence/kylin-tpch">https://github.com/Kyligence/kylin-tpch</a></p> <hr /> -<h2 id="spark-">Spark æ建å¼æ</h2> +<h2 id="spark-cubing-engine">Spark Cubing Engine</h2> -<p>Apache Kylin v2.0.0 å¼å ¥äºä¸ä¸ªå ¨æ°çåºäº Apache Spark çæ建å¼æãå®å¯ç¨äºæ¿æ¢åæç MapReduce æ建å¼æãåæ¥æµè¯æ¾ç¤º Cube çæ建æ¶é´ä¸è¬è½ç¼©çå°åå ç 50% å·¦å³ã</p> +<p>Apache Kylin v2.0.0 introduced a new cubing engine based on Apache Spark that can be selected to replace the original MR engine. Initial tests showed that the spark engine could cut the build time to 50% in most cases.</p> -<p>å¯ç¨ Spark æ建å¼æï¼è¯·åè<a href="/docs16/tutorial/cube_spark.html">è¿ç¯ææ¡£</a>.</p> +<p>To enable the Spark cubing engine, check <a href="/docs16/tutorial/cube_spark.html">this tutorial</a>.</p> <hr /> -<p><em>æè°¢æ¯ä¸ä½æåçåä¸åè´¡ç®!</em></p> +<p><em>Great thanks to everyone who contributed!</em></p> </description> <pubDate>Sat, 25 Feb 2017 12:00:00 -0800</pubDate> - <link>http://kylin.apache.org/cn/blog/2017/02/25/v2.0.0-beta-ready/</link> - <guid isPermaLink="true">http://kylin.apache.org/cn/blog/2017/02/25/v2.0.0-beta-ready/</guid> + <link>http://kylin.apache.org/blog/2017/02/25/v2.0.0-beta-ready/</link> + <guid isPermaLink="true">http://kylin.apache.org/blog/2017/02/25/v2.0.0-beta-ready/</guid> <category>blog</category> @@ -277,54 +546,54 @@ kylin.engine.spark.rdd-partition-cut-mb= </item> <item> - <title>Apache Kylin v2.0.0 Beta Announcement</title> - <description><p>The Apache Kylin community is pleased to announce the <a href="http://kylin.apache.org/download/">v2.0.0 beta package</a> is ready for download and test.</p> + <title>Apache Kylin v2.0.0 beta åå¸</title> + <description><p>Apache Kylin社åºé常é«å ´å°å®£å¸ <a href="http://kylin.apache.org/cn/download/">v2.0.0 beta package</a> å·²ç»å¯ä»¥ä¸è½½å¹¶æµè¯äºã</p> <ul> - <li>Download link: <a href="http://kylin.apache.org/download/">http://kylin.apache.org/download/</a></li> - <li>Source code: https://github.com/apache/kylin/tree/kylin-2.0.0-beta</li> + <li>ä¸è½½é¾æ¥: <a href="http://kylin.apache.org/cn/download/">http://kylin.apache.org/cn/download/</a></li> + <li>æºä»£ç : https://github.com/apache/kylin/tree/kylin-2.0.0-beta</li> </ul> -<p>It has been more than 2 month since the v1.6.0 release. The community has been working hard to deliver some long wanted features, hoping to move Apache Kylin to the next level.</p> +<p>èªä»v1.6.0çæ¬åå¸å·²ç»2个å¤æäºãè¿æ®µæ¶é´éï¼æ´ä¸ªç¤¾åºååå¼åå®æäºä¸ç³»åé大çåè½ï¼å¸æè½å°Apache Kylinæåå°ä¸ä¸ªæ°çé«åº¦ã</p> <ul> - <li>Support snowflake data model (<a href="https://issues.apache.org/jira/browse/KYLIN-1875">KYLIN-1875</a>)</li> - <li>Support TPC-H queries (<a href="https://issues.apache.org/jira/browse/KYLIN-2467">KYLIN-2467</a>)</li> - <li>Spark cubing engine (<a href="https://issues.apache.org/jira/browse/KYLIN-2331">KYLIN-2331</a>)</li> - <li>Job engine HA (<a href="https://issues.apache.org/jira/browse/KYLIN-2006">KYLIN-2006</a>)</li> - <li>Percentile measure (<a href="https://issues.apache.org/jira/browse/KYLIN-2396">KYLIN-2396</a>)</li> - <li>Cloud tested (<a href="https://issues.apache.org/jira/browse/KYLIN-2351">KYLIN-2351</a>)</li> + <li>æ¯æéªè±æ¨¡å (<a href="https://issues.apache.org/jira/browse/KYLIN-1875">KYLIN-1875</a>)</li> + <li>æ¯æ TPC-H æ¥è¯¢ (<a href="https://issues.apache.org/jira/browse/KYLIN-2467">KYLIN-2467</a>)</li> + <li>Spark æ建å¼æ (<a href="https://issues.apache.org/jira/browse/KYLIN-2331">KYLIN-2331</a>)</li> + <li>Job Engine é«å¯ç¨æ§ (<a href="https://issues.apache.org/jira/browse/KYLIN-2006">KYLIN-2006</a>)</li> + <li>Percentile 度é (<a href="https://issues.apache.org/jira/browse/KYLIN-2396">KYLIN-2396</a>)</li> + <li>å¨ Cloud ä¸éè¿æµè¯ (<a href="https://issues.apache.org/jira/browse/KYLIN-2351">KYLIN-2351</a>)</li> </ul> -<p>You are very welcome to give the v2.0.0 beta a try, and please do send feedbacks to <a href="&#109;&#097;&#105;&#108;&#116;&#111;:&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;">&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;</a>.</p> +<p>é常欢è¿å¤§å®¶ä¸è½½å¹¶æµè¯ v2.0.0 betaãæ¨çåé¦å¯¹æ们é常éè¦ï¼è¯·åé®ä»¶å° <a href="&#109;&#097;&#105;&#108;&#116;&#111;:&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;">&#100;&#101;&#118;&#064;&#107;&#121;&#108;&#105;&#110;&#046;&#097;&#112;&#097;&#099;&#104;&#101;&#046;&#111;&#114;&#103;</a>ã</p> <hr /> -<h2 id="install">Install</h2> +<h2 id="section">å®è£ </h2> -<p>The v2.0.0 beta requires a refresh install at the moment. It cannot be upgraded from v1.6.0 due to the incompatible metadata. However the underlying cube is backward compatible. We are working on an upgrade tool to transform the metadata, so that a smooth upgrade will be possible.</p> +<p>ææ¶ v2.0.0 beta æ æ³ä» v1.6.0 ç´æ¥å级ï¼å¿ éå ¨æ°å®è£ ãè¿æ¯ç±äºæ°çæ¬çå æ°æ®å¹¶ä¸ååå ¼å®¹ãå¥½å¨ Cube æ°æ®æ¯ååå ¼å®¹çï¼å æ¤åªéè¦å¼åä¸ä¸ªå æ°æ®è½¬æ¢å·¥å ·ï¼å°±è½å¨ä¸ä¹ çå°æ¥å®ç°å¹³æ»å级ãæ们æ£å¨ä¸ºæ¤åªåã</p> <hr /> -<h2 id="run-tpc-h-benchmark">Run TPC-H Benchmark</h2> +<h2 id="tpc-h-">è¿è¡ TPC-H åºåæµè¯</h2> -<p>Steps to run TPC-H benchmark on Apache Kylin can be found here: <a href="https://github.com/Kyligence/kylin-tpch">https://github.com/Kyligence/kylin-tpch</a></p> +<p>å¨ Apache Kylin ä¸è¿è¡ TPC-H çå ·ä½æ¥éª¤: <a href="https://github.com/Kyligence/kylin-tpch">https://github.com/Kyligence/kylin-tpch</a></p> <hr /> -<h2 id="spark-cubing-engine">Spark Cubing Engine</h2> +<h2 id="spark-">Spark æ建å¼æ</h2> -<p>Apache Kylin v2.0.0 introduced a new cubing engine based on Apache Spark that can be selected to replace the original MR engine. Initial tests showed that the spark engine could cut the build time to 50% in most cases.</p> +<p>Apache Kylin v2.0.0 å¼å ¥äºä¸ä¸ªå ¨æ°çåºäº Apache Spark çæ建å¼æãå®å¯ç¨äºæ¿æ¢åæç MapReduce æ建å¼æãåæ¥æµè¯æ¾ç¤º Cube çæ建æ¶é´ä¸è¬è½ç¼©çå°åå ç 50% å·¦å³ã</p> -<p>To enable the Spark cubing engine, check <a href="/docs16/tutorial/cube_spark.html">this tutorial</a>.</p> +<p>å¯ç¨ Spark æ建å¼æï¼è¯·åè<a href="/docs16/tutorial/cube_spark.html">è¿ç¯ææ¡£</a>.</p> <hr /> -<p><em>Great thanks to everyone who contributed!</em></p> +<p><em>æè°¢æ¯ä¸ä½æåçåä¸åè´¡ç®!</em></p> </description> <pubDate>Sat, 25 Feb 2017 12:00:00 -0800</pubDate> - <link>http://kylin.apache.org/blog/2017/02/25/v2.0.0-beta-ready/</link> - <guid isPermaLink="true">http://kylin.apache.org/blog/2017/02/25/v2.0.0-beta-ready/</guid> + <link>http://kylin.apache.org/cn/blog/2017/02/25/v2.0.0-beta-ready/</link> + <guid isPermaLink="true">http://kylin.apache.org/cn/blog/2017/02/25/v2.0.0-beta-ready/</guid> <category>blog</category> @@ -673,80 +942,6 @@ group by grouping sets((dim1, dim2), (di <category>blog</category> - - </item> - - <item> - <title>New NRT Streaming in Apache Kylin</title> - <description><p>In 1.5.0 Apache Kylin introduces the Streaming Cubing feature, which can consume data from Kafka topic directly. This <a href="/blog/2016/02/03/streaming-cubing/">blog</a> introduces how that be implemented, and this <a href="/docs15/tutorial/cube_streaming.html">tutorial</a> introduces how to use it.</p> - -<p>While, that implementation was marked as âexperimentalâ because it has the following limitations:</p> - -<ul> - <li> - <p>Not scalable: it starts a Java process for a micro-batch cube building, instead of leveraging any computing framework; If too many messages arrive at one time, the build may fail with OutOfMemory error;</p> - </li> - <li> - <p>May loss data: it uses a time window to seek the approximate start/end offsets on Kafka topic, which means too late/early arrived messages will be skipped; Then the query couldnât ensure 100% accuracy.</p> - </li> - <li> - <p>Difficult to monitor: the streaming cubing is out of the Job engineâs scope, user can not monitor the jobs with Web GUI or REST API.</p> - </li> - <li> - <p>Others: hard to recover from accident, difficult to maintain the code, etc.</p> - </li> -</ul> - -<p>To overcome these limitations, the Apache Kylin team developed the new streaming (<a href="https://issues.apache.org/jira/browse/KYLIN-1726">KYLIN-1726</a>) with Kafka 0.10, it has been tested internally for some time, will release to public soon.</p> - -<p>The new design is a perfect implementation under Kylin 1.5âs âplug-inâ architecture: treat Kafka topic as a âData Sourceâ like Hive table, using an adapter to extract the data to HDFS; the next steps are almost the same as other cubes. Figure 1 is a high level architecture of the new design.</p> - -<p><img src="/images/blog/new-streaming.png" alt="Kylin New Streaming Framework Architecture" /></p> - -<p>The adapter to read Kafka messages is modified from <a href="https://github.com/amient/kafka-hadoop-loader">kafka-hadoop-loader</a>, the author Michal Harish open sourced it under Apache License V2.0; it starts a mapper for each Kafka partition, reading and then saving the messages to HDFS; so Kylin will be able to leverage existing framework like MR to do the processing, this makes the solution scalable and fault-tolerant.</p> - -<p>To overcome the âdata lossâ limitation, Kylin adds the start/end offset information on each Cube segment, and then use the offsets as the partition value (no overlap allowed); this ensures no data be lost and 1 message be consumed at most once. To let the late/early message can be queried, Cube segments allow overlap for the partition time dimension: each segment has a âminâ date/time and a âmaxâ date/time; Kylin will scan all segments which matched with the queried time scope. Figure 2 illurates this.</p> - -<p><img src="/images/blog/offset-as-partition-value.png" alt="Use Offset to Cut Segments" /></p> - -<p>Other changes/enhancements are made in the new streaming:</p> - -<ul> - <li>Allow multiple segments being built/merged concurrently</li> - <li>Automatically seek start/end offsets (if user doesnât specify) from previous segment or Kafka</li> - <li>Support embeded properties in JSON message</li> - <li>Add REST API to trigger streaming cubeâs building</li> - <li>Add REST API to check and fill the segment holes</li> -</ul> - -<p>The integration test result is promising:</p> - -<ul> - <li>Scalability: it can easily process up to hundreds of million records in one build;</li> - <li>Flexibility: you can trigger the build at any time, with the frequency you want; for example: every 5 minutes in day time but every hour in night time, and even pause when you need do a maintenance; Kylin manages the offsets so it can automatically continue from the last position;</li> - <li>Stability: pretty stable, no OutOfMemoryError;</li> - <li>Management: user can check all jobsâ status through Kylinâs âMonitorâ page or REST API;</li> - <li>Build Performance: in a testing cluster (8 AWS instances to consume Twitter streams), 10 thousands arrives per second, define a 9-dimension cube with 3 measures; when build interval is 2 mintues, the job finishes in around 3 minutes; if change interval to 5 mintues, build finishes in around 4 minutes;</li> -</ul> - -<p>Here are a couple of screenshots in this test, we may compose it as a step-by-step tutorial in the future:<br /> -<img src="/images/blog/streaming-monitor.png" alt="Streaming Job Monitoring" /></p> - -<p><img src="/images/blog/streaming-adapter.png" alt="Streaming Adapter" /></p> - -<p><img src="/images/blog/streaming-twitter.png" alt="Streaming Twitter Sample" /></p> - -<p>In short, this is a more robust Near Real Time Streaming OLAP solution compared with the previous version.</p> - -<p>Now you can download a 1.6.0-SNAPSHOT build from Kylinâs download page, and then follow this <a href="/docs16/tutorial/cube_streaming.html">tutorial</a> to build a sample streaming cube.</p> - -</description> - <pubDate>Tue, 18 Oct 2016 10:30:00 -0700</pubDate> - <link>http://kylin.apache.org/blog/2016/10/18/new-nrt-streaming/</link> - <guid isPermaLink="true">http://kylin.apache.org/blog/2016/10/18/new-nrt-streaming/</guid> - - - <category>blog</category> </item> Added: kylin/site/images/Kylin-and-Superset/gif/11. 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