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svn commit: r1891303 [22/22] - in /kylin/site: ./ blog/ blog/2021/07/
blog/2021/07/02/
blog/2021/07/02/Apache-Kylin4-A-new-storage-and-compute-architecture/
cn/development/ cn/development40/ cn/docs/install/ cn/docs40/
cn/docs40/gettingstarted/ cn/docs...
Modified: kylin/site/feed.xml
URL: http://svn.apache.org/viewvc/kylin/site/feed.xml?rev=1891303&r1=1891302&r2=1891303&view=diff
==============================================================================
--- kylin/site/feed.xml (original)
+++ kylin/site/feed.xml Tue Jul 6 07:50:56 2021
@@ -19,153 +19,100 @@
<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>Wed, 30 Jun 2021 19:37:02 -0700</pubDate>
- <lastBuildDate>Wed, 30 Jun 2021 19:37:02 -0700</lastBuildDate>
+ <pubDate>Tue, 06 Jul 2021 00:25:02 -0700</pubDate>
+ <lastBuildDate>Tue, 06 Jul 2021 00:25:02 -0700</lastBuildDate>
<generator>Jekyll v2.5.3</generator>
<item>
- <title>Why did Youzan choose Kylin4</title>
- <description><p>At the QCon Global Software Developers Conference held on May 29, 2021, Zheng Shengjun, head of Youzanâs data infrastructure platform, shared Youzanâs internal use experience and optimization practice of Kylin 4.0 on the meeting room of open source big data frameworks and applications. <br />
-For many users of Kylin2/3(Kylin on HBase), this is also a chance to learn how and why to upgrade to Kylin 4.</p>
-
-<p>This sharing is mainly divided into the following parts:</p>
-
-<ul>
- <li>The reason for choosing Kylin 4</li>
- <li>Introduction to Kylin 4</li>
- <li>How to optimize performance of Kylin 4</li>
- <li>Practice of Kylin 4 in Youzan</li>
-</ul>
-
-<h2 id="the-reason-for-choosing-kylin-4">01 The reason for choosing Kylin 4</h2>
-
-<h3 id="introduction-to-youzan">Introduction to Youzan</h3>
-<p>China Youzan Co., Ltd (stock code 08083.HK). is an enterprise mainly engaged in retail technology services.<br />
-At present, it owns several tools and solutions to provide SaaS software products and talent services to help merchants operate mobile social e-commerce and new retail channels in an all-round way. <br />
-Currently Youzan has hundreds of millions of consumers and 6 million existing merchants.</p>
-
-<h3 id="history-of-kylin-in-youzan">History of Kylin in Youzan</h3>
-<p><img src="/images/blog/youzan/1 history_of_youzan_OLAP.png" alt="" /></p>
-
-<p>First of all, I would like to share why Youzan chose to upgrade to Kylin 4. Here, let me briefly reviewed the history of Youzan OLAP infra.</p>
-
-<p>In the early days of Youzan, in order to iterate develop process quickly, we chose the method of pre-computation + MySQL; in 2018, Druid was introduced because of query flexibility and development efficiency, but there were problems such as low pre-aggregation, not supporting precisely count distinct measure. In this situation, Youzan introduced Apache Kylin and ClickHouse. Kylin supports high aggregation, precisely count distinct measure and the lowest RT, while ClickHouse is quite flexible in usage(ad hoc query).</p>
-
-<p>From the introduction of Kylin in 2018 to now, Youzan has used Kylin for more than three years. With the continuous enrichment of business scenarios and the continuous accumulation of data volume, Youzan currently has 6 million existing merchants, GMV in 2020 is 107.3 billion, and the daily build data volume is 10 billion +. At present, Kylin has basically covered all the business scenarios of Youzan.</p>
-
-<h3 id="the-challenges-of-kylin-3">The challenges of Kylin 3</h3>
-<p>With Youzanâs rapid development and in-depth use of Kylin, we also encountered some challenges:</p>
-
-<ul>
- <li>First of all, the build performance of Kylin on HBase cannot meet the favorable expectations, and the build performance will affect the userâs failure recovery time and stability experience;</li>
- <li>Secondly, with the access of more large merchants (tens of millions of members in a single store, with hundreds of thousands of goods for each store), it also brings great challenges to our OLAP system. Kylin on HBase is limited by the single-point query of Query Server, and cannot support these complex scenarios well;</li>
- <li>Finally, because HBase is not a cloud-native system, it is difficult to achieve flexible scale up and scale down. With the continuous growth of data volume, this system has peaks and valleys for businesses, which results in the average resource utilization rate is not high enough.</li>
-</ul>
+ <title>Apache Kylin4 â A new storage and compute architecture</title>
+ <description><p>This article will discuss three aspects of Apache Kylin: First, we will briefly introduce query principles of Apache Kylin. Next, we will introduce Apache Parquet Storage, a project our team has been involved in that Kyligence is contributing back to the open source software community by the end of this year (2020). Finally, we will introduce the extensive use of precision count distinct by community users as well as its implementation in Kylin and some extensions.</p>
-<p>Faced with these challenges, Youzan chose to move closer and upgrade to the more cloud-native Apache Kylin 4.</p>
+<h2 id="introduction-to-apache-kylin">01 Introduction to Apache Kylin</h2>
+<p>Apache Kylin is an open source distributed analysis engine that provides SQL query interfaces above Hadoop/Spark and OLAP capabilities to support extremely large data. It was initially developed at eBay Inc. and contributed to the open source software community. It can query massive relational tables with sub-second response times. <br />
+<img src="/images/blog/kylin4/1 apache_kylin_introduction.png" alt="" /></p>
-<h2 id="introduction-to-kylin-4">02 Introduction to Kylin 4</h2>
-<p>First of all, letâs introduce the main advantages of Kylin 4. Apache Kylin 4 completely depends on Spark for cubing job and query. It can make full use of Sparkâs parallelization, quantization(åéå), and global dynamic code generation technologies to improve the efficiency of large queries.<br />
-Here is a brief introduction to the principle of Kylin 4, that is storage engine, build engine and query engine.</p>
+<p>As a SQL acceleration layer, Kylin can connect with various data sources such as Hive and Kafka, and can connect with commonly used BI systems such as Tableau and Power BI. It can also be queried directly (ad hoc) using standard SQL tools.</p>
-<h3 id="storage-engine">Storage engine</h3>
-<p><img src="/images/blog/youzan/2 kylin4_storage.png" alt="" /></p>
+<p>If you find yourself confronted by unhappy BI users for any of the following reasons, you should consider using Apache Kylin: <br />
+- Their batch of queries are too slow <br />
+- Query or user concurrency should be higher <br />
+- Resources usage should be lower <br />
+- The system doesnât fully support SQL syntax <br />
+- The system doesnât seamlessly integrate with their favorite BI tools\</p>
-<p>First of all, letâs take a look at the new storage engine, comparison between Kylin on HBase and Kylin on Parquet. The cuboid data of Kylin on HBase is stored in the table of HBase. Single Segment corresponds to one HBase table. Aggregation is pushed down to HBase coprocessor.</p>
+<h2 id="apache-kylin-rationale">02 Apache Kylin Rationale</h2>
+<p>Kylinâs core idea is the precomputation of result sets, meaning it calculates all possible query results in advance according to the specified dimensions and indicators and uses space for time to speed up OLAP queries with fixed query patterns. <br />
+<img src="/images/blog/kylin4/2 cube_vs_cuboid.png" alt="" /></p>
-<p>But as we know, HBase is not a real Columnar Storage and its throughput is not enough for OLAP System. Kylin 4 replaces HBase with Parquet, all the data is stored in files. Each segment will have a corresponding HDFS directory. All queries and cubing jobs read and write files without HBase . Although there will be a certain loss of performance for simple queries, the improvement brought about by complex queries is more considerable and worthwhile.</p>
+<p>Kylinâs design is based on cube theory. Each combination of dimensions is called a cuboid and the set of all cuboids is a cube. The cuboid composed of all dimensions is called the base cuboid, and the time, item, location, and supplier shown in the figure is an example of this. All cuboids can be calculated from the base cuboid. A cuboid can be understood as a wide table after precomputation. During the query, Kylin will automatically select the most suitable cuboid that meets the query requirements. <br />
+<img src="/images/blog/kylin4/3 cuboid_selected_for_query.png" alt="" /></p>
-<h3 id="build-engine">Build engine</h3>
-<p><img src="/images/blog/youzan/3 kylin4_build_engine.png" alt="" /></p>
+<p>For example, the query in the above figure will look for the cuboid (time, item, location). Compared with the calculation from the userâs original table, the calculation from the cuboid can greatly reduce the amount of scanned data and calculation.</p>
-<p>The second is the new build engine. Based on our test, the build speed of Kylin on Parquet has been optimized from 82 minutes to 15 minutes. There are several reasons:</p>
+<h2 id="apache-kylin-basic-query-process">03 Apache Kylin Basic Query Process</h2>
+<p>Letâs look briefly at the rationale of Kylin queries. The first three steps are the routine operations of all query engines. We use the Apache Calcite framework to complete this operation. We will not go into great detail here but, should you wish to learn more, there is plenty of related material online. <br />
+<img src="/images/blog/kylin4/4 apache_kylin_query_process.png" alt="" /></p>
-<ul>
- <li>Kylin 4 removes the encoding of the dimension, eliminating a building step of encoding;</li>
- <li>Removed the HBase File generation step;</li>
- <li>Kylin on Parquet changes the granularity of cubing to cuboid level, which is conducive to further improving parallelism of cubing job.</li>
- <li>Enhanced implementation for global dictionary. In the new algorithm, dictionary and source data are hashed into the same buckets, making it possible for loading only piece of dictionary bucket to encode source data.</li>
-</ul>
-
-<p>As you can see on the right, after upgradation to Kylin 4, cubing job changes from ten steps to two steps, the performance improvement of the construction is very obvious.</p>
+<p>The introduction here focuses on the last two steps: Kylin adaptation and query execution. Why do we need to do Kylin adaptation? Because the query plan we obtained earlier is directly converted according to the userâs query, and so this query plan cannot directly query the precomputed data. Here, a rewrite is needed to create an execution plan so that it can query the precomputed data (i.e. cube data). Letâs look at the following example: <br />
+<img src="/images/blog/kylin4/5 query_using_precomputed_data.png" alt="" /></p>
-<h3 id="query-engine">Query engine</h3>
-<p><img src="/images/blog/youzan/4 kylin4_query.png" alt="" /></p>
-
-<p>Next is the new query engine of Kylin 4. As you can see, the calculation of Kylin on HBase is completely dependent on the coprocessor of HBase and query server process. When the data is read from HBase into query server to do aggregation, sorting, etc, the bottleneck will be restricted by the single point of query server. But Kylin 4 is converted to a fully distributed query mechanism based on Spark, whatâs more, it âs able to do configuration tuning automatically in spark query step !</p>
-
-<h2 id="how-to-optimize-performance-of-kylin-4">03 How to optimize performance of Kylin 4</h2>
-<p>Next, Iâd like to share some performance optimizations made by Youzan in Kylin 4.</p>
-
-<h3 id="optimization-of-query-engine">Optimization of query engine</h3>
-<p>#### 1.Cache Calcite physical plan<br />
-<img src="/images/blog/youzan/5 cache_calcite_plan.png" alt="" /></p>
-
-<p>In Kylin4, SQL will be analyzed, optimized and do code generation in calcite. This step takes up about 150ms for some queries. We have supported PreparedStatementCache in Kylin4 to cache calcite plan, so that the structured SQL donât have to do the same step again. With this optimization it saved about 150ms of time cost.</p>
-
-<h4 id="tunning-spark-configuration">2.Tunning spark configuration</h4>
-<p><img src="/images/blog/youzan/6 tuning_spark_configuration.png" alt="" /></p>
-
-<p>Kylin4 uses spark as query engine. As spark is a distributed engine designed for massive data processing, itâs inevitable to loose some performance for small queries. We have tried to do some tuning to catch up with the latency in Kylin on HBase for small queries.</p>
+<p>The user has a stock of goods. Item and user_id indicate which item has been accessed and the user wants to analyze the Page View (PV) of the goods. The user defines a cube where the dimension is item and the measure is COUNT (user_id). If the user wants to analyze the PV of the goods, he will issue the following SQL:</p>
-<p>Our first optimization is to make more calculations finish in memory. The key is to avoid data spill during aggregation, shuffle and sort. Tuning the following configuration is helpful.</p>
+<div class="highlighter-rouge"><pre class="highlight"><code>SELECT item, COUNT (user_id) FROM stock GROUP BY item;
+</code></pre>
+</div>
-<ul>
- <li>1.set <code class="highlighter-rouge">spark.sql.objectHashAggregate.sortBased.fallbackThreshold</code> to larger value to avoid HashAggregate fall back to Sort Based Aggregate, which really kills performance when happens.</li>
- <li>2.set <code class="highlighter-rouge">spark.shuffle.spill.initialMemoryThreshold</code> to a large value to avoid to many spills during shuffle.</li>
-</ul>
+<p>After this SQL is sent to Kylin, Kylin cannot directly use its original semantics to query our cube data. This is because after the data is precomputed, there will only be one row of data in the key of each item. The rows of the same item key in the original table have been aggregated in advance, generating a new measure column to store how many user_id accesses each item key has, so the rewritten SQL will be similar to this:</p>
-<p>Secondly, we route small queries to Query Server which run spark in local mode. Because the overhead of task schedule, shuffle read and variable broadcast is enlarged for small queries on YARN/Standalone mode.</p>
+<div class="highlighter-rouge"><pre class="highlight"><code> SELECT item, SUM (M_C) FROM stock GROUP BY item;
+</code></pre>
+</div>
-<p>Thirdly, we use RAM disk to enhance shuffle performance. Mount RAM disk as TMPFS and set spark.local.dir to directory using RAM disk.</p>
+<p>Why is there another SUM/GROUP BY operation here instead of directly fetching the data and returning it? Because the cuboid that may be hit by the query is more than one dimension of item, meaning it is not the most accurate cuboid. It needs to be aggregated again from these dimensions, but the amount of partially aggregated data still significantly reduces the amount of data and calculation compared with the data in the userâs original table. If the query hits the cuboid accurately, we can directly skip the process of Agg/GROUP BY, as it is shown in the following figure: <br />
+<img src="/images/blog/kylin4/6 on-site-computation.png" alt="" /></p>
-<p>Lastly, we disabled sparkâs whole stage code generation for small queries, for sparkâs whole stage code generation will cost about 100ms~200ms, whereas itâs not beneficial to small queries which is a simple project.</p>
+<p>The above figure is a scenario without precomputation, which requires on-site calculation. Agg and Join will involve shuffle, so the performance will be poor and more resources will be occupied with large amounts of data, which will affect the concurrency of queries. <br />
+<img src="/images/blog/kylin4/7 on-site-computation.png" alt="" /></p>
-<h4 id="parquet-optimization">3.Parquet optimization</h4>
-<p><img src="/images/blog/youzan/7 parquet_optimization.png" alt="" /></p>
+<p>After the precomputation, the previously most time-consuming two-step operation (Agg/Join) disappeared from the rewritten execution plan, showing a cuboid precise match. Additionally, when defining the cube we can choose to order by column so the Sort operation does not need to be calculated. The whole calculation is a single stage without the expense of a shuffle. The calculation can be completed with only a few tasks therefore improving the concurrency of the query.</p>
-<p>Optimizing parquet is also important for queries.</p>
+<h2 id="apache-kylin-on-hbase">04 Apache Kylin on HBase</h2>
+<p>In the current open source version, the built data is stored in HBase, weâve got a logical execution plan that can query cube data from the above section. Calcite framework will generate the corresponding physical execution plan according to this logical execution plan and, finally, each operator will generate its own executable code through code generation. <br />
+<img src="/images/blog/kylin4/8 on-site-computation.png" alt="" /></p>
-<p>The first principal is that weâd better always include shard by column in our filter condition, for parquet files are shard by shard-by-column, filter using shard by column reduces the data files to read.</p>
+<p>This process is an iterator model. Data flows from the lowest TableScan operator to the upstream operator. The whole process is like a volcanic eruption, so it is also called Volcano Iterator Mode. The code generated by this TableScan will fetch cube data from HBase, and when the data is returned to Kylin Query Server, it will be consumed layer by layer by the upper operator.</p>
-<p>Then look into parquet files, data within files are sorted by rowkey columns, that is to say, prefix match in query is as important as Kylin on HBase. When a query condition satisfies prefix match, it can filter row groups with columnâs max/min index. Furthermore, we can reduce row group size to make finer index granularity, but be aware that the compression rate will be lower if we set row group size smaller.</p>
+<h2 id="bottlenecks-with-kylin-on-hbase">05 Bottlenecks with Kylin on HBase</h2>
+<p>This scenario is not a big problem with simple SQL because, in the case of a precise matching cuboid, minimal computing will be done on Kylin Query Server after retrieving the data from HBase. However, for some more complex queries, Kylin Query Server will not only pull back a large amount of data from HBase but also compute very resource-intensive operations such as Joins and Aggregates. <br />
+<img src="/images/blog/kylin4/9 diagram_of_bottleneck_on_HBase.png" alt="" /><br />
+For example, a query joins two subqueries, each subquery hits its own cube and then does some more complicated aggregate operations at the outermost layer such as COUNT DISTINCT. When the amount of data becomes large, Kylin Query Server may be out of memory (OOM). The solution is to simply increase the memory of the Kylin Query Server.</p>
-<h4 id="dynamic-elimination-of-partitioning-dimensions">4.Dynamic elimination of partitioning dimensions</h4>
-<p>Kylin4 have a new ability that the older version is not capable of, which is able to reduce dozens of times of data reading and computing for some big queries. Itâs offen the case that partition column is used to filter data but not used as group dimension. For those cases Kylin would always choose cuboid with partition column, but now it is able to use different cuboid in that query to reduce IO read and computing.</p>
+<p>However, this is a vertical expansion process that becomes a bottleneck. We know from experience that bottlenecks in big data can be difficult to diagnose and can lead to the abandonment of a critical technology when selecting an architecture. In addition, there are many other limitations when using this system. For example, the operation and maintenance of HBase is notoriously difficult. It is safe to assume that once the performance of HBase is not good, the performance of Kylin will also suffer.</p>
-<p>The key of this optimization is to split a query into two parts, one of the part uses all segmentâs data so that partition column doesnât have to be included in cuboid, the other part that uses part of segments data will choose cuboid with partition dimension to do the data filter.</p>
-
-<p>We have tested that in some situations the response time reduced from 20s to 6s, 10s to 3s.</p>
-
-<p><img src="/images/blog/youzan/8 Dynamic_elimination_of_partitioning_dimensions.png" alt="" /></p>
-
-<h3 id="optimization-of-build-engine">Optimization of build engine</h3>
-<p>#### 1.cache parent dataset<br />
-<img src="/images/blog/youzan/9 cache_parent_dataset.png" alt="" /></p>
+<p>The resource isolation capabilities of HBase are also relatively weak. When there is a large load at a given moment, other applications using HBase will also be affected. This may cause Kylin to have unstable query performance which can be difficult to troubleshoot. All data stored in HBase are encoded Byte Array types and the overhead of serialization and deserialization cannot be ignored.</p>
-<p>Kylin build cube layer by layer. For a parent layer with multi cuboids to build, we can choose to cache parent dataset by setting kylin.engine.spark.parent-dataset.max.persist.count to a number greater than 0. But notice that if you set this value too small, it will affect the parallelism of build job, as the build granularity is at cuboid level.</p>
+<h2 id="apache-kylin-with-spark--parquet">06 Apache Kylin with Spark + Parquet</h2>
+<p>Due to the limitations of the Kylin-on-HBase solution mentioned above, Kyligence has developed a new generation of Spark + Parquet-based solutions for the commercial version of Kylin. This was done early on to update and enhance the open source software solution for enterprise use.</p>
-<h2 id="practice-of-kylin-4-in-youzan">04 Practice of Kylin 4 in Youzan</h2>
-<p>After introducing Youzanâs experience of performance optimization, letâs share the optimization effect. That is, Kylin 4âs practice in Youzan includes the upgrade process and the performance of online system.</p>
+<p>The following is an introduction to the overall framework of this new system. <br />
+<img src="/images/blog/kylin4/10 spark_parquet_solution.png" alt="" /></p>
-<h3 id="upgrade-metadata-to-adapt-to-kylin-4">Upgrade metadata to adapt to Kylin 4</h3>
-<p>First of all, for metadata for Kylin 3 which stored on HBase, we have developed a tool for seamless upgrading of metadata. First of all, our metadata in Kylin on HBase is stored in HBase. We export the metadata in HBase into local files, and then use tools to transform and write back the new metadata into MySQL. We also updated the operation documents and general principles in the official wiki of Apache Kylin. For more details, you can refer to: <a href="https://wiki.apache.org/confluence/display/KYLIN/How+to+migrate+metadata+to+Kylin+4">How to migrate metadata to Kylin 4</a>.</p>
+<p>In fact, the new design is very simple. The visitor mode is used to traverse the previously generated logical execution plan tree that can query cube data. The nodes of the execution plan tree represent an operator, which actually stores nothing more than some information such as which table to scan, which columns to filter/project, etc. Each operator will be translated into a Spark operation on Dataframe on the original tree, each upstream node asks its downstream node for a DF up to the most downstream TableScan node after it has finished processing. After it generates the initial DF, which can be simply understood as cuboidDF = spark.read.parquet (path). After obtaining the initial DF, it returns to its upstream. The upstream node applies its own operation on the downstream DF and returns to its upstream. Finally, the top node collects the DF to trigger the whole calculation process.</p>
-<p>Letâs give a general introduction to some compatibility in the whole process. The project metadata, tables metadata, permission-related metadata, and model metadata do not need be modified. What needs to be modified is the cube metadata, including the type of storage and query used by Cube. After updating these two fields, you need to recalculate the Cube signature. The function of this signature is designed internally by Kylin to avoid some problems caused by Cube after Cube is determined.</p>
+<h2 id="advantages-of-the-sparkparquet-architecture">07 Advantages of the Spark/Parquet Architecture</h2>
+<p>This Kylin on Parquet plan relies on Spark. All calculations are distributed and there is no single point where performance can bottleneck. The computing power of the system can be improved through horizontal expansion (scale-out). There are various schemes for resource scheduling such as Yarn, K8S, or Mesos to meet the needs of enterprises for resource isolation. Sparkâs performance efforts can be naturally enjoyed. The overhead of serialization and deserialization of Kylin on HBase mentioned above can be optimized by Sparkâs Tungsten project. <br />
+<img src="/images/blog/kylin4/11 spark_parquet_architecture.png" alt="" /></p>
-<h3 id="performance-of-kylin-4-on-youzan-online-system">Performance of Kylin 4 on Youzan online system</h3>
-<p><img src="/images/blog/youzan/10 commodity_insight.png" alt="" /></p>
+<p>Reducing the dependence upon HBase simplifies operation and maintenance. All upstream and downstream dependencies can be handled by Spark for us, reducing our dependence and facilitating cloud access.</p>
-<p>After the migration of metadata to Kylin4, letâs share the qualitative changes and substantial performance improvements brought about by some of the promising scenarios. First of all, in a scenario like Commodity Insight, there is a large store with several hundred thousand of commodities. We have to analyze its transactions and traffic, etc. There are more than a dozen precise precisely count distinct measures in single cube. Precisely count distinct measure is actually very inefficient if it is not optimized through pre-calculation and Bitmap. Kylin currently uses Bitmap to support precisely count distinct measure. In a scene that requires complex queries to sort hundreds of thousands of commodities in various UV(precisely count distinct measure), the RT of Kylin 2 is 27 seconds, while the RT of Kylin 4 is reduced from 27 seconds to less than 2 seconds.</p>
+<p>For developers, the DF generated by each operator can be collected directly to observe whether there is any problem with the data at this level, and Spark + Parquet is currently a very popular SQL on Hadoop scheme. The open source committers at Kyligence are also familiar with these two projects and maintain their own Spark and Parquet branch. A lot of performance optimization and stability improvements have been done in this area for our specific scenarios.</p>
-<p>What I find most appealing to me about Kylin 4 is that itâs like a manual transmission car, you can control its query concurrency at your will, whereas you canât change query concurrency in Kylin on HBase freely, because its concurrency is completely tied to the number of regions.</p>
-
-<h3 id="plan-for-kylin-4-in-youzan">Plan for Kylin 4 in Youzan</h3>
-<p>We have made full test, fixed several bugs and improved apache KYLIN4 for several months. Now we are migrating cubes from older version to newer version. For the cubes already migrated to KYLIN4, its small queriesâ performance meet our expectations, its complex query and build performance did bring us a big surprise. We are planning to migrate all cubes from older version to Kylin4.</p>
+<h2 id="summary">08 Summary</h2>
+<p>Apache Kylin has over 1,000 users worldwide. But, in order for the project to ensure its future position as a vital, Cloud-Native technology for enterprise analytics, the Kylin community must periodically evaluate and update the key architectural assumptions being made to accomplish that goal. The removal of legacy connections to the Hadoop ecosystem in favor of Spark and Parquet is an important next step to realizing the dream of pervasive analytics based on open source technology for organizations of all sizes around the world.</p>
</description>
- <pubDate>Thu, 17 Jun 2021 08:00:00 -0700</pubDate>
- <link>http://kylin.apache.org/blog/2021/06/17/Why-did-Youzan-choose-Kylin4/</link>
- <guid isPermaLink="true">http://kylin.apache.org/blog/2021/06/17/Why-did-Youzan-choose-Kylin4/</guid>
+ <pubDate>Fri, 02 Jul 2021 08:00:00 -0700</pubDate>
+ <link>http://kylin.apache.org/blog/2021/07/02/Apache-Kylin4-A-new-storage-and-compute-architecture/</link>
+ <guid isPermaLink="true">http://kylin.apache.org/blog/2021/07/02/Apache-Kylin4-A-new-storage-and-compute-architecture/</guid>
<category>blog</category>
@@ -332,6 +279,155 @@ Here is a brief introduction to the prin
</item>
<item>
+ <title>Why did Youzan choose Kylin4</title>
+ <description><p>At the QCon Global Software Developers Conference held on May 29, 2021, Zheng Shengjun, head of Youzanâs data infrastructure platform, shared Youzanâs internal use experience and optimization practice of Kylin 4.0 on the meeting room of open source big data frameworks and applications. <br />
+For many users of Kylin2/3(Kylin on HBase), this is also a chance to learn how and why to upgrade to Kylin 4.</p>
+
+<p>This sharing is mainly divided into the following parts:</p>
+
+<ul>
+ <li>The reason for choosing Kylin 4</li>
+ <li>Introduction to Kylin 4</li>
+ <li>How to optimize performance of Kylin 4</li>
+ <li>Practice of Kylin 4 in Youzan</li>
+</ul>
+
+<h2 id="the-reason-for-choosing-kylin-4">01 The reason for choosing Kylin 4</h2>
+
+<h3 id="introduction-to-youzan">Introduction to Youzan</h3>
+<p>China Youzan Co., Ltd (stock code 08083.HK). is an enterprise mainly engaged in retail technology services.<br />
+At present, it owns several tools and solutions to provide SaaS software products and talent services to help merchants operate mobile social e-commerce and new retail channels in an all-round way. <br />
+Currently Youzan has hundreds of millions of consumers and 6 million existing merchants.</p>
+
+<h3 id="history-of-kylin-in-youzan">History of Kylin in Youzan</h3>
+<p><img src="/images/blog/youzan/1 history_of_youzan_OLAP.png" alt="" /></p>
+
+<p>First of all, I would like to share why Youzan chose to upgrade to Kylin 4. Here, let me briefly reviewed the history of Youzan OLAP infra.</p>
+
+<p>In the early days of Youzan, in order to iterate develop process quickly, we chose the method of pre-computation + MySQL; in 2018, Druid was introduced because of query flexibility and development efficiency, but there were problems such as low pre-aggregation, not supporting precisely count distinct measure. In this situation, Youzan introduced Apache Kylin and ClickHouse. Kylin supports high aggregation, precisely count distinct measure and the lowest RT, while ClickHouse is quite flexible in usage(ad hoc query).</p>
+
+<p>From the introduction of Kylin in 2018 to now, Youzan has used Kylin for more than three years. With the continuous enrichment of business scenarios and the continuous accumulation of data volume, Youzan currently has 6 million existing merchants, GMV in 2020 is 107.3 billion, and the daily build data volume is 10 billion +. At present, Kylin has basically covered all the business scenarios of Youzan.</p>
+
+<h3 id="the-challenges-of-kylin-3">The challenges of Kylin 3</h3>
+<p>With Youzanâs rapid development and in-depth use of Kylin, we also encountered some challenges:</p>
+
+<ul>
+ <li>First of all, the build performance of Kylin on HBase cannot meet the favorable expectations, and the build performance will affect the userâs failure recovery time and stability experience;</li>
+ <li>Secondly, with the access of more large merchants (tens of millions of members in a single store, with hundreds of thousands of goods for each store), it also brings great challenges to our OLAP system. Kylin on HBase is limited by the single-point query of Query Server, and cannot support these complex scenarios well;</li>
+ <li>Finally, because HBase is not a cloud-native system, it is difficult to achieve flexible scale up and scale down. With the continuous growth of data volume, this system has peaks and valleys for businesses, which results in the average resource utilization rate is not high enough.</li>
+</ul>
+
+<p>Faced with these challenges, Youzan chose to move closer and upgrade to the more cloud-native Apache Kylin 4.</p>
+
+<h2 id="introduction-to-kylin-4">02 Introduction to Kylin 4</h2>
+<p>First of all, letâs introduce the main advantages of Kylin 4. Apache Kylin 4 completely depends on Spark for cubing job and query. It can make full use of Sparkâs parallelization, quantization(åéå), and global dynamic code generation technologies to improve the efficiency of large queries.<br />
+Here is a brief introduction to the principle of Kylin 4, that is storage engine, build engine and query engine.</p>
+
+<h3 id="storage-engine">Storage engine</h3>
+<p><img src="/images/blog/youzan/2 kylin4_storage.png" alt="" /></p>
+
+<p>First of all, letâs take a look at the new storage engine, comparison between Kylin on HBase and Kylin on Parquet. The cuboid data of Kylin on HBase is stored in the table of HBase. Single Segment corresponds to one HBase table. Aggregation is pushed down to HBase coprocessor.</p>
+
+<p>But as we know, HBase is not a real Columnar Storage and its throughput is not enough for OLAP System. Kylin 4 replaces HBase with Parquet, all the data is stored in files. Each segment will have a corresponding HDFS directory. All queries and cubing jobs read and write files without HBase . Although there will be a certain loss of performance for simple queries, the improvement brought about by complex queries is more considerable and worthwhile.</p>
+
+<h3 id="build-engine">Build engine</h3>
+<p><img src="/images/blog/youzan/3 kylin4_build_engine.png" alt="" /></p>
+
+<p>The second is the new build engine. Based on our test, the build speed of Kylin on Parquet has been optimized from 82 minutes to 15 minutes. There are several reasons:</p>
+
+<ul>
+ <li>Kylin 4 removes the encoding of the dimension, eliminating a building step of encoding;</li>
+ <li>Removed the HBase File generation step;</li>
+ <li>Kylin on Parquet changes the granularity of cubing to cuboid level, which is conducive to further improving parallelism of cubing job.</li>
+ <li>Enhanced implementation for global dictionary. In the new algorithm, dictionary and source data are hashed into the same buckets, making it possible for loading only piece of dictionary bucket to encode source data.</li>
+</ul>
+
+<p>As you can see on the right, after upgradation to Kylin 4, cubing job changes from ten steps to two steps, the performance improvement of the construction is very obvious.</p>
+
+<h3 id="query-engine">Query engine</h3>
+<p><img src="/images/blog/youzan/4 kylin4_query.png" alt="" /></p>
+
+<p>Next is the new query engine of Kylin 4. As you can see, the calculation of Kylin on HBase is completely dependent on the coprocessor of HBase and query server process. When the data is read from HBase into query server to do aggregation, sorting, etc, the bottleneck will be restricted by the single point of query server. But Kylin 4 is converted to a fully distributed query mechanism based on Spark, whatâs more, it âs able to do configuration tuning automatically in spark query step !</p>
+
+<h2 id="how-to-optimize-performance-of-kylin-4">03 How to optimize performance of Kylin 4</h2>
+<p>Next, Iâd like to share some performance optimizations made by Youzan in Kylin 4.</p>
+
+<h3 id="optimization-of-query-engine">Optimization of query engine</h3>
+<p>#### 1.Cache Calcite physical plan<br />
+<img src="/images/blog/youzan/5 cache_calcite_plan.png" alt="" /></p>
+
+<p>In Kylin4, SQL will be analyzed, optimized and do code generation in calcite. This step takes up about 150ms for some queries. We have supported PreparedStatementCache in Kylin4 to cache calcite plan, so that the structured SQL donât have to do the same step again. With this optimization it saved about 150ms of time cost.</p>
+
+<h4 id="tunning-spark-configuration">2.Tunning spark configuration</h4>
+<p><img src="/images/blog/youzan/6 tuning_spark_configuration.png" alt="" /></p>
+
+<p>Kylin4 uses spark as query engine. As spark is a distributed engine designed for massive data processing, itâs inevitable to loose some performance for small queries. We have tried to do some tuning to catch up with the latency in Kylin on HBase for small queries.</p>
+
+<p>Our first optimization is to make more calculations finish in memory. The key is to avoid data spill during aggregation, shuffle and sort. Tuning the following configuration is helpful.</p>
+
+<ul>
+ <li>1.set <code class="highlighter-rouge">spark.sql.objectHashAggregate.sortBased.fallbackThreshold</code> to larger value to avoid HashAggregate fall back to Sort Based Aggregate, which really kills performance when happens.</li>
+ <li>2.set <code class="highlighter-rouge">spark.shuffle.spill.initialMemoryThreshold</code> to a large value to avoid to many spills during shuffle.</li>
+</ul>
+
+<p>Secondly, we route small queries to Query Server which run spark in local mode. Because the overhead of task schedule, shuffle read and variable broadcast is enlarged for small queries on YARN/Standalone mode.</p>
+
+<p>Thirdly, we use RAM disk to enhance shuffle performance. Mount RAM disk as TMPFS and set spark.local.dir to directory using RAM disk.</p>
+
+<p>Lastly, we disabled sparkâs whole stage code generation for small queries, for sparkâs whole stage code generation will cost about 100ms~200ms, whereas itâs not beneficial to small queries which is a simple project.</p>
+
+<h4 id="parquet-optimization">3.Parquet optimization</h4>
+<p><img src="/images/blog/youzan/7 parquet_optimization.png" alt="" /></p>
+
+<p>Optimizing parquet is also important for queries.</p>
+
+<p>The first principal is that weâd better always include shard by column in our filter condition, for parquet files are shard by shard-by-column, filter using shard by column reduces the data files to read.</p>
+
+<p>Then look into parquet files, data within files are sorted by rowkey columns, that is to say, prefix match in query is as important as Kylin on HBase. When a query condition satisfies prefix match, it can filter row groups with columnâs max/min index. Furthermore, we can reduce row group size to make finer index granularity, but be aware that the compression rate will be lower if we set row group size smaller.</p>
+
+<h4 id="dynamic-elimination-of-partitioning-dimensions">4.Dynamic elimination of partitioning dimensions</h4>
+<p>Kylin4 have a new ability that the older version is not capable of, which is able to reduce dozens of times of data reading and computing for some big queries. Itâs offen the case that partition column is used to filter data but not used as group dimension. For those cases Kylin would always choose cuboid with partition column, but now it is able to use different cuboid in that query to reduce IO read and computing.</p>
+
+<p>The key of this optimization is to split a query into two parts, one of the part uses all segmentâs data so that partition column doesnât have to be included in cuboid, the other part that uses part of segments data will choose cuboid with partition dimension to do the data filter.</p>
+
+<p>We have tested that in some situations the response time reduced from 20s to 6s, 10s to 3s.</p>
+
+<p><img src="/images/blog/youzan/8 Dynamic_elimination_of_partitioning_dimensions.png" alt="" /></p>
+
+<h3 id="optimization-of-build-engine">Optimization of build engine</h3>
+<p>#### 1.cache parent dataset<br />
+<img src="/images/blog/youzan/9 cache_parent_dataset.png" alt="" /></p>
+
+<p>Kylin build cube layer by layer. For a parent layer with multi cuboids to build, we can choose to cache parent dataset by setting kylin.engine.spark.parent-dataset.max.persist.count to a number greater than 0. But notice that if you set this value too small, it will affect the parallelism of build job, as the build granularity is at cuboid level.</p>
+
+<h2 id="practice-of-kylin-4-in-youzan">04 Practice of Kylin 4 in Youzan</h2>
+<p>After introducing Youzanâs experience of performance optimization, letâs share the optimization effect. That is, Kylin 4âs practice in Youzan includes the upgrade process and the performance of online system.</p>
+
+<h3 id="upgrade-metadata-to-adapt-to-kylin-4">Upgrade metadata to adapt to Kylin 4</h3>
+<p>First of all, for metadata for Kylin 3 which stored on HBase, we have developed a tool for seamless upgrading of metadata. First of all, our metadata in Kylin on HBase is stored in HBase. We export the metadata in HBase into local files, and then use tools to transform and write back the new metadata into MySQL. We also updated the operation documents and general principles in the official wiki of Apache Kylin. For more details, you can refer to: <a href="https://wiki.apache.org/confluence/display/KYLIN/How+to+migrate+metadata+to+Kylin+4">How to migrate metadata to Kylin 4</a>.</p>
+
+<p>Letâs give a general introduction to some compatibility in the whole process. The project metadata, tables metadata, permission-related metadata, and model metadata do not need be modified. What needs to be modified is the cube metadata, including the type of storage and query used by Cube. After updating these two fields, you need to recalculate the Cube signature. The function of this signature is designed internally by Kylin to avoid some problems caused by Cube after Cube is determined.</p>
+
+<h3 id="performance-of-kylin-4-on-youzan-online-system">Performance of Kylin 4 on Youzan online system</h3>
+<p><img src="/images/blog/youzan/10 commodity_insight.png" alt="" /></p>
+
+<p>After the migration of metadata to Kylin4, letâs share the qualitative changes and substantial performance improvements brought about by some of the promising scenarios. First of all, in a scenario like Commodity Insight, there is a large store with several hundred thousand of commodities. We have to analyze its transactions and traffic, etc. There are more than a dozen precise precisely count distinct measures in single cube. Precisely count distinct measure is actually very inefficient if it is not optimized through pre-calculation and Bitmap. Kylin currently uses Bitmap to support precisely count distinct measure. In a scene that requires complex queries to sort hundreds of thousands of commodities in various UV(precisely count distinct measure), the RT of Kylin 2 is 27 seconds, while the RT of Kylin 4 is reduced from 27 seconds to less than 2 seconds.</p>
+
+<p>What I find most appealing to me about Kylin 4 is that itâs like a manual transmission car, you can control its query concurrency at your will, whereas you canât change query concurrency in Kylin on HBase freely, because its concurrency is completely tied to the number of regions.</p>
+
+<h3 id="plan-for-kylin-4-in-youzan">Plan for Kylin 4 in Youzan</h3>
+<p>We have made full test, fixed several bugs and improved apache KYLIN4 for several months. Now we are migrating cubes from older version to newer version. For the cubes already migrated to KYLIN4, its small queriesâ performance meet our expectations, its complex query and build performance did bring us a big surprise. We are planning to migrate all cubes from older version to Kylin4.</p>
+</description>
+ <pubDate>Thu, 17 Jun 2021 08:00:00 -0700</pubDate>
+ <link>http://kylin.apache.org/blog/2021/06/17/Why-did-Youzan-choose-Kylin4/</link>
+ <guid isPermaLink="true">http://kylin.apache.org/blog/2021/06/17/Why-did-Youzan-choose-Kylin4/</guid>
+
+
+ <category>blog</category>
+
+ </item>
+
+ <item>
<title>ä½ ç¦»å¯è§åé
·ç«å¤§å±åªå·®ä¸å¥ Kylin + Davinci</title>
<description><p>Kylin æä¾ä¸ BI å·¥å
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@@ -1647,61 +1743,5 @@ Security: (depend on your security setti
</item>
- <item>
- <title>Apache Kylin v3.0.0-alpha Release Announcement</title>
- <description><p>The Apache Kylin community is pleased to announce the release of Apache Kylin v3.0.0-alpha.</p>
-
-<p>Apache Kylin is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Big Data supporting extremely large datasets.</p>
-
-<p>This is the first release of the new generation v3.x, the main feature introduced is the Real-time OLAP. All of the changes can be found in the <a href="/docs/release_notes.html">release notes</a>. Here we just highlight the main features.</p>
-
-<h1 id="important-features">Important features</h1>
-
-<h3 id="kylin-3654---real-time-olap">KYLIN-3654 - Real-time OLAP</h3>
-<p>With the newly introduced Kylin real-time receiver and coordinator components, Kylin can implement a millisecond-level data preparation delay for streaming data from sources like Apache Kafka. This means since v3.0 on, Kylin can support sub-second level OLAP over historical batch data, near real-time streaming as well as real-time streaming. The user can use one OLAP platform to serve different scenarios. This solution has been deployed and verified in early adopters like eBay since 2018. For how to enable it, please refer to <a href="/docs30/tutorial/realtime_olap.html">this tutorial</a>.</p>
-
-<h3 id="kylin-3795---submit-spark-jobs-via-apache-livy">KYLIN-3795 - Submit Spark jobs via Apache Livy</h3>
-<p>This feature allows the administrator to configure Kylin to integrate with Apache Livy (incubating) for Spark job submissions. The Spark job is submitted to the Livy Server through Livyâs REST API, instead of starting the Spark Driver process in local, which facilitates the management and monitoring of the Spark resources, and also releases the pressure of the nodes where the Kylin job server is running.</p>
-
-<h3 id="kylin-3820---a-curator-based-job-scheduler">KYLIN-3820 - A curator-based job scheduler</h3>
-<p>A new job scheduler is added to automatically discover the Kylin nodes and do an automatic leader selection among them (only the leader will submit jobs). With this feature, you can easily deploy and scale out Kylin nodes without manually update the node address in <code class="highlighter-rouge">kylin.properties</code> and restart Kylin to take effective.</p>
-
-<h1 id="other-enhancements">Other enhancements</h1>
-
-<h3 id="kylin-3716---fastthreadlocal-replaces-threadlocal">KYLIN-3716 - FastThreadLocal replaces ThreadLocal</h3>
-<p>Using FastThreadLocal instead of ThreadLocal can improve Kylinâs overall performance to some extent.</p>
-
-<h3 id="kylin-3867---enable-jdbc-to-use-key-store--trust-store-for-https-connection">KYLIN-3867 - Enable JDBC to use key store &amp; trust store for https connection</h3>
-<p>By using HTTPS, the authentication information used by JDBC is protected, making Kylin more secure.</p>
-
-<h3 id="kylin-3905---enable-shrunken-dictionary-default">KYLIN-3905 - Enable shrunken dictionary default</h3>
-<p>By default, the shrunken dictionary is enabled, and the precise counting scene for high cardinal dimensions can significantly reduce the build time.</p>
-
-<h3 id="kylin-3839---storage-clean-up-after-the-refreshing-and-deleting-a-segment">KYLIN-3839 - Storage clean up after the refreshing and deleting a segment</h3>
-<p>Clear unnecessary data files in a timely manner</p>
-
-<p><strong>Download</strong></p>
-
-<p>To download Apache Kylin v3.0.0-alpha source code or binary package, visit the <a href="http://kylin.apache.org/download">download</a> page.</p>
-
-<p><strong>Upgrade</strong></p>
-
-<p>Follow the <a href="/docs/howto/howto_upgrade.html">upgrade guide</a>.</p>
-
-<p><strong>Feedback</strong></p>
-
-<p>If you face issue or question, please send mail to Apache Kylin dev or user mailing list:Â dev@kylin.apache.org , user@kylin.apache.org; Before sending, please make sure you have subscribed the mailing list by dropping an email to dev-subscribe@kylin.apache.org or user-subscribe@kylin.apache.org.</p>
-
-<p><em>Great thanks to everyone who contributed!</em></p>
-</description>
- <pubDate>Fri, 19 Apr 2019 13:00:00 -0700</pubDate>
- <link>http://kylin.apache.org/blog/2019/04/19/release-v3.0.0-alpha/</link>
- <guid isPermaLink="true">http://kylin.apache.org/blog/2019/04/19/release-v3.0.0-alpha/</guid>
-
-
- <category>blog</category>
-
- </item>
-
</channel>
</rss>
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--- kylin/site/website.iml (added)
+++ kylin/site/website.iml Tue Jul 6 07:50:56 2021
@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="WEB_MODULE" version="4">
+ <component name="NewModuleRootManager" inherit-compiler-output="true">
+ <exclude-output />
+ <content url="file://$MODULE_DIR$" />
+ <orderEntry type="inheritedJdk" />
+ <orderEntry type="sourceFolder" forTests="false" />
+ </component>
+</module>
\ No newline at end of file