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aria-label="跳到主要内容"><a class="skipToContent_fXgn" href="#__docusaurus_skipToContent_fallback">跳到主要内容</a></div><div class="main-wrapper"><div class="mb-[4.875rem] container"><div class="lg:row lg:flex"><main class="col col--9 col--offset-1" itemscope="" itemtype="http://schema.org/Blog"><article class="blog-article-content" itemprop="blogPost" itemscope="" itemtype="http://schema.org/BlogPosting"><header><div class="text-center mb-4"><a class="text-[#8592A6] cursor-pointer hover:no-underline" href="/zh-CN/blog">Blog</a><span class="px-2 text-[#8592A6]">/</span><span><span class="s-tags"><span class="s-tag">Best Practice</span></span></span></div><h1 class="blog-post-title text-[2rem] leading-normal lg:text-[2.5rem] text-center" itemprop="headline">Database dissection: how fast data queries are implemented</h1><div class="blog-info text-center flex justify-center text-sm text-black"><span class="authors"><span class="s-author text-black">Rong Hou</span></span><time datetime="2023-07-16T00:00:00.000Z" itemprop="datePublished" class="text-black ml-4">2023年7月16日</time></div></header><div id="__blog-post-container" class="markdown" itemprop="articleBody"><p>In data analytics, fast query performance is more of a result than a guarantee. What's more important than the result itself is the architectural design and mechanism that enables quick performance. This is exactly what this post is about. I will put you into context with a typical use case of Apache Doris, an open-source MPP-based analytic database.</p><p>The user in this case is an all-category Q&A website. As a billion-dollar listed company, they have their own data management platform. What Doris does is to support the data filtering, packaging, analyzing, and monitoring workloads of that platform. Based on their huge data size, the user demands quick data loading and quick response to queries. </p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="how-to-enable-quick-queries-on-huge-dataset">How to Enable Quick Queries on Huge Dataset<a href="#how-to-enable-quick-queries-on-huge-dataset" class="hash-link" aria-label="How to Enable Quick Queries on Huge Dataset的直接链接" title="How to Enable Quick Queries on Huge Dataset的直接链接"></a></h2><ul><li><strong>Scenario</strong>: user segmentation for the website</li><li><strong>Data size</strong>: 100 billion data objects, 2.4 million tags</li><li><strong>Requirements</strong>: query response time < 1 second; result packaging < 10 seconds</li></ul><p>For these goals, the engineers have made three critical changes in their data processing pipeline.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="1distribute-the-data">1.Distribute the data<a href="#1distribute-the-data" class="hash-link" aria-label="1.Distribute the data的直接链接" title="1.Distribute the data的直接链接"></a></h3><p>User segmentation is when analysts pick out a group of website users that share certain characteristics (tags). In the database system, this process is implemented by a bunch of set operations (union, intersection, and difference). </p><p><strong>Narration from the engineers:</strong></p><p>We realize that instead of executing set operations on one big dataset, we can divide our dataset into smaller ones, execute set operations on each of them, and then merge all the results. In this way, each small dataset is computed by one thread/queue. Then we have a queue to do the final merging. It's simple distributed computing thinking.</p><p><img loading="lazy" alt="distributed-computing-in-database" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_1-7c5ee52877c98c9502ba57d03becdd9b.png" width="1280" height="651" class="img_ev3q"></p><p>Example:</p><ol><li>Every 1 million users are put into one group with a <code>group_id</code>.</li><li>All user tags in that same group will relate to the corresponding <code>group_id</code>.</li><li>Calculate the union/intersection/difference within each group. (Enable multi-thread mode to increase computation efficiency.)</li><li>Merge the results from the groups.</li></ol><p>The problem here is, since user tags are randomly distributed across various machines, the computation entails multi-time shuffling, which brings huge network overhead. That leads to the second change.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="2pre-bind-a-data-group-to-a-machine">2.Pre-bind a data group to a machine<a href="#2pre-bind-a-data-group-to-a-machine" class="hash-link" aria-label="2.Pre-bind a data group to a machine的直接链接" title="2.Pre-bind a data group to a machine的直接链接"></a></h3><p>This is enabled by the Colocate mechanism of Apache Doris. The idea of Colocate is to place data chunks that are often accessed together onto the same node, so as to reduce cross-node data transfer and thus, get lower latency.</p><p><img loading="lazy" alt="colocate-mechanism" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_2-6f75c0c47ef7106018774d6a70bf0e99.png" width="1280" height="331" class="img_ev3q"></p><p>The implementation is simple: Bind one group key to one machine. Then naturally, data corresponding to that group key will be pre-bound to that machine. </p><p>The following is the query plan before we adopted Collocate: It is complicated, with a lot of data shuffling.</p><p><img loading="lazy" alt="complicated-data-shuffling" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_3-a6af7fe391aa9eaa717e558112e38d18.png" width="720" height="765" class="img_ev3q"></p><p>This is the query plan after. It is much simpler, which is why queries are much faster and less costly.</p><p><img loading="lazy" alt="simpler-query-plan-after-colocation-join" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_4-ad4a6e9be6d812a88220544a77ce1c73.png" width="1280" height="616" class="img_ev3q"></p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="3merge-the-operators">3.Merge the operators<a href="#3merge-the-operators" class="hash-link" aria-label="3.Merge the operators的直接链接" title="3.Merge the operators的直接链接"></a></h3><p>In data queries, the engineers realized that they often use a couple of functions in combination, so they decided to develop compound functions to further improve execution efficiency. They came to the Doris <a href="https://t.co/XD4uUSROft" target="_blank" rel="noopener noreferrer">community</a> and talked about their thoughts. The Doris developers provided support for them and soon the compound functions are ready for use on Doris. These are a few examples:</p><div class="codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#F8F8F2;--prism-background-color:#282A36"><div class="codeBlockContent_biex"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar"><code class="codeBlockLines_e6Vv"><span class="token-line" style="color:#F8F8F2"><span class="token plain">bitmap_and_count == bitmap_count(bitmap_and(bitmap1, bitmap2))</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">bitmap_and_not_count == bitmap_count(bitmap_not(bitmap1, bitmap_and(bitmap1, bitmap2))</span><br></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">orthogonal_bitmap_union_count==bitmap_and(bitmap1,bitmap_and(bitmap2,bitmap3)</span><br></span></code></pre><div class="buttonGroup__atx"><button type="button" aria-label="复制代码到剪贴板" title="复制" class="clean-btn"><span class="copyButtonIcons_eSgA" aria-hidden="true"><svg viewBox="0 0 24 24" class="copyButtonIcon_y97N"><path fill="currentColor" d="M19,21H8V7H19M19,5H8A2,2 0 0,0 6,7V21A2,2 0 0,0 8,23H19A2,2 0 0,0 21,21V7A2,2 0 0,0 19,5M16,1H4A2,2 0 0,0 2,3V17H4V3H16V1Z"></path></svg><svg viewBox="0 0 24 24" class="copyButtonSuccessIcon_LjdS"><path fill="currentColor" d="M21,7L9,19L3.5,13.5L4.91,12.09L9,16.17L19.59,5.59L21,7Z"></path></svg></span></button></div></div></div><p>Query execution with one compound function is much faster than that with a chain of simple functions, as you can tell from the lengths of the flow charts:</p><p><img loading="lazy" alt="operator-merging" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_5-8ad26e082d2a60188e8928ab82192330.png" width="1280" height="396" class="img_ev3q"></p><ul><li><strong>Multiple Simple functions</strong>: This involves three function executions and two intermediate storage. It's a long and slow process.</li><li><strong>One compound function</strong>: Simple in and out.</li></ul><h2 class="anchor anchorWithStickyNavbar_LWe7" id="how-to-quickly-ingest-large-amounts-of-data">How to Quickly Ingest Large Amounts of Data<a href="#how-to-quickly-ingest-large-amounts-of-data" class="hash-link" aria-label="How to Quickly Ingest Large Amounts of Data的直接链接" title="How to Quickly Ingest Large Amounts of Data的直接链接"></a></h2><p>This is about putting the right workload on the right component. Apache Doris supports a variety of data loading methods. After trials and errors, the user settled on Spark Load and thus decreased their data loading time by 90%. </p><p><strong>Narration from the engineers:</strong></p><p>In offline data ingestion, we used to perform most computation in Apache Hive, write the data files to HDFS, and pull data regularly from HDFS to Apache Doris. However, after Doris obtains parquet files from HDFS, it performs a series of operations on them before it can turn them into segment files: decompressing, bucketing, sorting, aggregating, and compressing. These workloads will be borne by Doris backends, which have to undertake a few bitmap operations at the same time. So there is a huge pressure on the CPU. </p><p><img loading="lazy" alt="Broker-Load" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_6-10aa0935e2acd8774b0cb1f70d7013e8.png" width="1280" height="629" class="img_ev3q"></p><p>So we decided on the Spark Load method. It allows us to split the ingestion process into two parts: computation and storage, so we can move all the bucketing, sorting, aggregating, and compressing to Spark clusters. Then Spark writes the output to HDFS, from which Doris pulls data and flushes it to the local disks.</p><p><img loading="lazy" alt="Spark-Load" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_7-5eacf11ecef47a4bdebd2b820d1f2bd6.png" width="1280" height="372" class="img_ev3q"></p><p>When ingesting 1.2 TB data (that's 110 billion rows), the Spark Load method only took 55 minutes. </p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="a-vectorized-execution-engine">A Vectorized Execution Engine<a href="#a-vectorized-execution-engine" class="hash-link" aria-label="A Vectorized Execution Engine的直接链接" title="A Vectorized Execution Engine的直接链接"></a></h2><p>In addition to the above changes, a large part of the performance of a database relies on its execution engine. In the case of Apache Doris, it has fully vectorized its storage and computation layers since version 1.1. The longtime user also witnessed this revolution, so we invited them to test how the vectorized engine worked.</p><p>They compared query response time before and after the vectorization in seven of its frequent scenarios:</p><ul><li>Scenario 1: Simple user segmentation (hundreds of filtering conditions), data packaging of a multi-million user group.</li><li>Scenario 2: Complicated user segmentation (thousands of filtering conditions), data packaging of a tens-of-million user group.</li><li>Scenario 3: Multi-dimensional filtering (6 dimensions), single-table query, <strong>single-date flat table</strong>, data aggregation, 180 million rows per day.</li><li>Scenario 4: Multi-dimensional filtering (6 dimensions), single-table query, <strong>multi-date flat table</strong>, data aggregation, 180 million rows per day.</li><li>Scenario 5: <strong>Single-table query</strong>, COUNT, 180 million rows per day.</li><li>Scenario 6: <strong>Multi-table query</strong>, (Table A: 180 million rows, SUM, COUNT; Table B: 1.5 million rows, bitmap aggregation), aggregate Table A and Table B, join them with Table C, and then join the sub-tables, six joins in total.</li><li>Scenario 7: Single-table query, 500 million rows of itemized data</li></ul><p>The results are as below:</p><p><img loading="lazy" alt="performance-after-vectorization" src="https://cdnd.selectdb.com/zh-CN/assets/images/Zhihu_8-db8b7d375c494f0e806a2286ea9144b0.png" width="1280" height="591" class="img_ev3q"></p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="conclusion">Conclusion<a href="#conclusion" class="hash-link" aria-label="Conclusion的直接链接" title="Conclusion的直接链接"></a></h2><p>In short, what contributed to the fast data loading and data queries in this case?</p><ul><li>The Colocate mechanism that's designed for distributed computing</li><li>Collaboration between database users and <a href="https://join.slack.com/t/apachedoriscommunity/shared_invite/zt-2kl08hzc0-SPJe4VWmL_qzrFd2u2XYQA" target="_blank" rel="noopener noreferrer">developers</a> that enables the operator merging</li><li>Support for a wide range of data loading methods to choose from</li><li>A vectorized engine that brings overall performance increase</li></ul><p>It takes efforts from both the database developers and users to make fast performance possible. The user's experience and knowledge of their own status quo will allow them to figure out the quickest path, while a good database design will help pave the way and make users' life easier.</p></div></article><div class="pl-4 mt-20 text-[#1D1D1D]"><div class="text-[2rem] leading-[3.25rem]">Recent posts</div><div class="mt-4 flex flex-col"><a href="https://doris.apache.org/blog/evolution-of-the-apache-doris-execution-engine" target="_blank" rel="noopener noreferrer" class="text-lg leading-10 hover:no-underline hover:text-[#444FD9]">Steps to industry-leading query speed: evolution of the Apache Doris execution engine</a><a href="https://doris.apache.org/blog/job-scheduler-for-task-automation" target="_blank" rel="noopener noreferrer" class="text-lg leading-10 hover:no-underline hover:text-[#444FD9]">Another lifesaver for data engineers: Apache Doris Job Scheduler for task automation</a><a href="https://doris.apache.org/blog/release-note-2.0.11" target="_blank" rel="noopener noreferrer" class="text-lg leading-10 hover:no-underline hover:text-[#444FD9]">Apache Doris version 2.0.11 has been released</a><a href="https://doris.apache.org/blog/apache-doris-for-log-and-time-series-data-analysis-in-netease" target="_blank" rel="noopener noreferrer" class="text-lg leading-10 hover:no-underline hover:text-[#444FD9]">Apache Doris for log and time series data analysis in NetEase, why not Elasticsearch and InfluxDB?</a></div><a class="flex group text-primary items-center text-base cursor-pointer hover:no-underline mt-4" href="/zh-CN/blog"><span class="mr-2">View all blogs</span><span class="transition-slide"><svg xmlns="http://www.w3.org/2000/svg" class="transition-slide" width="1em" height="1em" viewBox="0 0 16 14" fill="none"><path d="M9.37549 12.3542L14.8755 6.85419L9.37549 1.35419" stroke="currentColor" stroke-width="1.65" stroke-linecap="round" stroke-linejoin="round"></path><path d="M1.12549 6.85419L14.8755 6.85419" stroke="currentColor" stroke-width="1.65" 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16.896-38.4 38.4-38.4h318.976z m-153.088 191.488c20.992 0 38.4 16.896 38.4 38.4 0 20.992-16.896 38.4-38.4 38.4H284.16c-20.992 0-38.4-16.896-38.4-38.4 0-20.992 16.896-38.4 38.4-38.4h165.888z m0 0" p-id="8501" fill="currentColor"></path></svg></a><span class="ml-4">本页导航</span><ul class="table-of-contents table-of-contents__left-border"><li><a href="#how-to-enable-quick-queries-on-huge-dataset" class="table-of-contents__link toc-highlight">How to Enable Quick Queries on Huge Dataset</a><ul><li><a href="#1distribute-the-data" class="table-of-contents__link toc-highlight">1.Distribute the data</a></li><li><a href="#2pre-bind-a-data-group-to-a-machine" class="table-of-contents__link toc-highlight">2.Pre-bind a data group to a machine</a></li><li><a href="#3merge-the-operators" class="table-of-contents__link toc-highlight">3.Merge the operators</a></li></ul></li><li><a href="#how-to-quickly-ingest-large-amounts-of-data" class="table-of-contents__link toc-highlight">How to Quickly Ingest Large Amounts of Data</a></li><li><a href="#a-vectorized-execution-engine" class="table-of-contents__link toc-highlight">A Vectorized Execution Engine</a></li><li><a href="#conclusion" class="table-of-contents__link toc-highlight">Conclusion</a></li></ul></div></div></div></div></div></div><div class="footer pt-16 pb-10"><div class="container"><div class="footer-box"><div class="left"><img src="/zh-CN/images/asf_logo_apache.svg" alt="" class="themedImage_ToTc themedImage--light_HNdA footer__logo"><img src="/zh-CN/images/asf_logo_apache.svg" alt="" class="themedImage_ToTc themedImage--dark_i4oU footer__logo"><div class="row footer__links"><div class="col footer__col"><div class="footer__title">ASF</div><ul class="footer__items clean-list"><li class="footer__item"><a href="https://www.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">Foundation<svg width="13.5" height="13.5" aria-hidden="true" viewBox="0 0 24 24" class="iconExternalLink_nPIU"><path 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