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Posted to commits@fluss.apache.org by gi...@apache.org on 2026/04/15 17:35:44 UTC
(fluss-website) branch asf-site updated: Deploy website - based on 7946902152a77bb179a35921953b09e7c1ec9c39
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new 93a257466 Deploy website - based on 7946902152a77bb179a35921953b09e7c1ec9c39
93a257466 is described below
commit 93a257466cbfea2b765d686e404a6a80eada2c18
Author: gh-actions <ac...@github.com>
AuthorDate: Wed Apr 15 17:35:38 2026 +0000
Deploy website - based on 7946902152a77bb179a35921953b09e7c1ec9c39
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<p>Apache Fluss represents a new architectural approach: the <strong>Streamhouse</strong> for real-time lakehouses. Instead of stitching together separate streaming and batch systems, the Streamhouse unifies them under a single architecture. In this model, Apache Iceberg continues to serve exactly the role it was designed for: a highly efficient, scalable cold storage layer for analytics, while Fluss fills the missing piece: a hot streaming storage layer with sub-second latency, columnar [...]
<p>After working on Fluss–Iceberg lakehouse integration and deploying this architecture at a massive scale, including Alibaba's 3 PB production deployment processing 40 GB/s, we're ready to share the architectural lessons learned. Specifically, why existing systems fall short, how Fluss and Iceberg naturally complement each other, and what this means for finally building true real-time lakehouses.</p>
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- <updated>2026-04-09T00:00:00.000Z</updated>
+ <updated>2026-04-16T00:00:00.000Z</updated>
<generator>https://github.com/jpmonette/feed</generator>
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<subtitle>Apache Fluss™ (Incubating) Blog</subtitle>
<icon>https://fluss.apache.org/img/logo/fluss_favicon.svg</icon>
+ <entry>
+ <title type="html"><![CDATA[Real-Time Unique User Counting at Scale with Apache Fluss]]></title>
+ <id>https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/</id>
+ <link href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/"/>
+ <updated>2026-04-16T00:00:00.000Z</updated>
+ <summary type="html"><![CDATA[UV (Unique Visitors) measures the count of distinct users who visited a page or triggered an event within a given time window — unlike PV (Page Views), which counts every request regardless of who made it. For any product or platform, accurate real-time UV statistics across dimensions like channel, city, date, and hour are a core analytical requirement.]]></summary>
+ <content type="html"><![CDATA[<p><strong>UV (Unique Visitors)</strong> measures the count of distinct users who visited a page or triggered an event within a given time window — unlike <strong>PV (Page Views)</strong>, which counts every request regardless of who made it. For any product or platform, accurate real-time UV statistics across dimensions like channel, city, date, and hour are a core analytical requirement.
+The full combination of four dimensions means <strong>16 grouping methods</strong>; when the dimension count increases to seven, the number of possible groupings reaches <strong>128</strong>.</p>
+<p>How can multi-dimensional deduplication be both accurate and flexible while maintaining real-time performance? Behind this challenge lie two very different computing paradigms: direct deduplication of raw data, or set operations based on bitmaps.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="problems-with-traditional-deduplication-schemes">Problems with Traditional Deduplication Schemes<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#problems-with-traditional-deduplication-schemes" class="hash-link" aria-label="Direct link to Problems with Traditional Deduplication Schemes" title="Direct link to Problems with Traditional Deduplication Schemes" translate="no"></a></h2>
+<p>Direct deduplication of raw data (e.g. <code>COUNT(DISTINCT user_id)</code>) is the most intuitive and accurate deduplication method: maintain a <strong>hash set</strong> for each dimension combination and record all user IDs that have appeared under the group.</p>
+<p>This approach works well with smaller data volumes and fewer dimensions, but as business scale grows, three core limitations emerge.</p>
+<p><strong>Performance increases linearly with the amount of data.</strong> Each dimension combination needs to independently store all user identities that have occurred. When daily page view logs reach billions and distinct users number in the tens or hundreds of millions, each group's hash set demands significant memory and CPU for insertion and lookup. Total compute cost scales with the product of dimension combinations and data volume, which is especially noticeable during peak peri [...]
+<p><strong>Multidimensional extensibility is limited.</strong> If an analyst needs UV by <strong>Channel x City</strong> and <strong>Channel x Date</strong>, the traditional scheme must calculate two separate deduplication results. The existing <strong>Channel x City</strong> result cannot be rolled up to get the <strong>Channel</strong>-level UV, because user sets across cities may overlap and simple summation leads to double counting. With each additional dimension, the computational t [...]
+<p><strong>Insufficient query flexibility.</strong> Pre-aggregation schemes must fix dimension combinations in advance. When a business team needs a new dimension on the fly (such as adding <strong>device type</strong>), adjusting the entire data processing pipeline is often required instead of simply modifying a query.</p>
+<p>These three limitations share a root cause: storing and computing on raw user identities at every dimension combination does not scale.</p>
+<p>Put it another way: what if instead of storing the raw ID of each user, we represented the entire user set as a compact structure — one bit per user?</p>
+<p>In the bitmap scheme, deduplication count equals counting the number of 1s in the bitmap (<code>popcount</code>), merging two user sets equals a bitwise <code>OR</code>, and cross-dimensional analysis equals a bitwise <code>AND</code>. All operations are CPU-native bit operations, blisteringly fast.</p>
+<p>More importantly, bitmaps naturally support roll-up: the bitmap of <code>Channel = app, City = Shanghai</code> combined with <code>OR</code> with the bitmap of <code>Channel = app, City = Beijing</code> yields the user set for <code>Channel = app</code> — no need to re-scan the original data.</p>
+<p>This is a fundamental difference between the two computing paradigms: <strong>direct deduplication</strong> requires independent calculations for each dimension combination, while the intermediate results of <strong>bitmap deduplication</strong> are naturally composable.</p>
+<p>But the raw bitmap has a problem: if the user ID space is sparse (such as <code>UUID</code> or hash values), the bitmap will be very large and waste memory. A compressed bitmap format that works efficiently on sparse data is needed. This is where <strong>RoaringBitmap</strong> comes in.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure1-7c9e1064160a84decb149ba63e55b50b.png" width="1356" height="768" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="how-roaringbitmap-works">How RoaringBitmap Works<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#how-roaringbitmap-works" class="hash-link" aria-label="Direct link to How RoaringBitmap Works" title="Direct link to How RoaringBitmap Works" translate="no"></a></h2>
+<p>The core idea of a bitmap is simple: use an array of bits to represent a collection of integers. If the user ID is <code>k</code>, set the <code>k</code>-th position to 1. The deduplication count is the number of 1s, and set merging is a bitwise <code>OR</code>. These operations map directly to CPU instructions, which is extremely efficient.</p>
+<p>However, the size of a raw bitmap depends on the maximum ID value. If the ID space is a <strong>32-bit integer</strong> (about 2.1 billion), a full bitmap takes <strong>256 MB</strong>. When the ID distribution is sparse, most bits are 0, and almost all memory is wasted.</p>
+<p><strong>RoaringBitmap</strong> solves this with a layered, adaptive compression strategy. It divides the 32-bit integer space into <strong>65,536 partitions</strong> by the upper 16 bits, and each partition holds up to 65,536 values (the lower 16 bits), called a <strong>Container</strong>. Each container automatically selects the optimal storage format based on its data density:</p>
+<ul>
+<li class=""><strong>ArrayContainer:</strong> When the number of elements is less than 4,096, values are stored in a sorted array of 16-bit integers. For sparse data, this saves tens of times more space than a raw bitmap.</li>
+<li class=""><strong>BitmapContainer:</strong> When the number of elements reaches 4,096 or more, the container switches to a raw 8 KB bitmap. For dense data, bitmaps are more compact than arrays.</li>
+<li class=""><strong>RunContainer:</strong> When data presents continuous interval characteristics (such as IDs 100–500), values are stored using run-length encoding, recording only the start and end of each run.</li>
+</ul>
+<p>This adaptive strategy allows RoaringBitmap to remain efficient across a variety of data distributions: as compact as an array when sparse, as fast as a bitmap when dense, and as economical as run-length encoding when continuous. In practice, its memory footprint is usually only one-tenth to one-hundredth of the original hash set.</p>
+<p>For deduplication scenarios, RoaringBitmap set operations map directly to analytical requirements: <code>OR</code> merges user sets, <code>AND</code> performs cross-dimensional analysis, <code>ANDNOT</code> performs exclusion analysis, and cardinality returns the deduplication count. All operations are accelerated by <strong>SIMD instructions</strong> and can be completed in milliseconds even on hundreds of millions of records.</p>
+<p>One prerequisite: the input to RoaringBitmap must be an integer, and the denser the ID space, the better the compression and efficiency. If the original user ID is a string (such as a <code>UUID</code> or phone number), a dictionary table is needed to map sparse IDs to dense integer IDs.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure2-88efa22dc33e081300bf9ecd388fd53d.png" width="1356" height="748" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="how-apache-fluss-enables-roaringbitmap-deduplication">How Apache Fluss Enables RoaringBitmap Deduplication<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#how-apache-fluss-enables-roaringbitmap-deduplication" class="hash-link" aria-label="Direct link to How Apache Fluss Enables RoaringBitmap Deduplication" title="Direct link to How Apache Fluss Enables RoaringBitmap Deduplication" translate="no"></a></h2>
+<p>A RoaringBitmap-based deduplication scheme requires three things: <strong>a dense integer ID space</strong>, <strong>bitmap merging at the storage layer</strong>, and <strong>native bitmap aggregation functions</strong>. Apache Fluss provides all three as built-in capabilities.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="auto-increment-columns-zero-code-dictionary-tables">Auto-Increment Columns: Zero-Code Dictionary Tables<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#auto-increment-columns-zero-code-dictionary-tables" class="hash-link" aria-label="Direct link to Auto-Increment Columns: Zero-Code Dictionary Tables" title="Direct link to Auto-Increment Columns: Zero-Code Dictionary Tables" translate="no"></a></h3>
+<p>The auto-increment column feature of Fluss lets you declare a field directly in the table definition. When a new user is written for the first time, Fluss automatically assigns an incrementing integer ID. No external ID service or historical data migration is required.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<p>A single <code>INSERT</code> statement is all that is needed to map sparse identities to dense integers. <code>int32</code> covers about 2.1 billion users, which is sufficient for most business scenarios. The generated ID is unique and monotonically increasing, which is naturally suited to RoaringBitmap's compression strategy.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="aggregate-merge-engine-merge-on-write">Aggregate Merge Engine: Merge on Write<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#aggregate-merge-engine-merge-on-write" class="hash-link" aria-label="Direct link to Aggregate Merge Engine: Merge on Write" title="Direct link to Aggregate Merge Engine: Merge on Write" translate="no"></a></h3>
+<p>The <strong>Aggregation Merge Engine</strong> of Fluss allows you to define aggregation rules for each column. When rows with the same primary key arrive, Fluss automatically merges them on the server side instead of overwriting or appending.</p>
+<p>This means the compute layer only needs to send raw events and does not need to maintain any aggregated intermediate state. With <strong>exactly-once semantics</strong> based on Flink Checkpoint, the write path is compact and reliable.</p>
+<p>For append-only aggregation operations such as bitmap union (UV deduplication), merging is naturally <strong>idempotent</strong> during writing. Adding the same <code>uid</code> to a bitmap multiple times does not change the result. In such scenarios, Flink jobs are only responsible for forwarding — no complex <code>UDAFs</code> or retraction messages are needed. For scenarios involving data retraction (such as order cancellation), rollback support for the aggregation merge engine is [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="built-in-rbm32--rbm64-aggregate-functions">Built-in <code>rbm32</code> / <code>rbm64</code> Aggregate Functions<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#built-in-rbm32--rbm64-aggregate-functions" class="hash-link" aria-label="Direct link to built-in-rbm32--rbm64-aggregate-functions" title="Direct link to built-in-rbm32--rbm64-aggregate-functions" translate="no"></a></h3>
+<p>Fluss supports RoaringBitmap as a first-class aggregation type. <code>rbm32</code> (32-bit) and <code>rbm64</code> (64-bit) perform native bitmap union on write.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<blockquote>
+<p>Note: the quickstart section extends this table with <code>'table.datalake.enabled' = 'true'</code> and <code>'table.datalake.freshness' = '30s'</code> to enable lake tiering for batch queries. The core deduplication behaviour is identical.</p>
+</blockquote>
+<p>Each event carries a single-element bitmap, which Fluss merges into the existing bitmap of the corresponding primary key on the server side. The deduplication logic is entirely handled by the storage layer without requiring any custom merge code.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="putting-it-together-the-full-data-flow">Putting It Together: The Full Data Flow<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#putting-it-together-the-full-data-flow" class="hash-link" aria-label="Direct link to Putting It Together: The Full Data Flow" title="Direct link to Putting It Together: The Full Data Flow" translate="no"></a></h3>
+<p>These three capabilities are interlinked: the auto-increment column provides a <strong>dense ID</strong> (the prerequisite for bitmap deduplication), the aggregation engine merges on write (deduplication is completed in the storage layer), and <code>rbm32</code> makes bitmap merging a native storage operation (no custom logic needed).</p>
+<p>The data flow is straightforward: when an event arrives, Flink obtains the dense <code>uid</code> by querying the dictionary table via a lookup join, calls <code>RB_BUILD(uid)</code> to build a single-element bitmap, and writes it to the aggregation table. Fluss automatically executes bitmap union on the server side. At query time, <code>RB_CARDINALITY()</code> extracts the UV count directly.</p>
+<p>The responsibilities are clearly separated: <strong>Flink</strong> handles mapping, joining, and forwarding without holding long-lived state; <strong>Fluss</strong> holds the persistent merged result; queries read the merged bitmap and extract the count. Deduplication happens at write time — the query side requires no aggregation at all.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="streaming-lakehouse-unification">Streaming Lakehouse Unification<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#streaming-lakehouse-unification" class="hash-link" aria-label="Direct link to Streaming Lakehouse Unification" title="Direct link to Streaming Lakehouse Unification" translate="no"></a></h3>
+<p>When data volumes grow to require analysis across longer time windows, this architecture can be seamlessly extended through Fluss's <strong>Lake-Stream integration</strong> capabilities. In a daily partitioning scenario, each day's UV data is stored as a bitmap partition. Historical partitions (e.g., older than 30 days) can be offloaded to <strong>Data Lake</strong> storage (Paimon or Iceberg), while recent partitions remain in Fluss for real-time updates.</p>
+<p>To count UV for an entire year, you only need to <code>OR</code> the 365 daily bitmap partitions and compute the cardinality. Regardless of whether data is in Fluss or the data lake, the bitmap union semantics are identical. This architecture balances storage cost and query flexibility, enabling accurate deduplication analysis across any time span.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure3-44f89c0af7bdf4da5feb4fecea914242.png" width="1350" height="768" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="end-to-end-quickstart">End-to-End Quickstart<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#end-to-end-quickstart" class="hash-link" aria-label="Direct link to End-to-End Quickstart" title="Direct link to End-to-End Quickstart" translate="no"></a></h2>
+<p>This section walks through the complete workflow with a minimal working example you can run locally.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="environment-preparation">Environment Preparation<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#environment-preparation" class="hash-link" aria-label="Direct link to Environment Preparation" title="Direct link to Environment Preparation" translate="no"></a></h3>
+<p>Requirements: <strong>Docker</strong> and <strong>Docker Compose</strong>.</p>
+<p><strong>1. Create a working directory and download dependencies</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">mkdir</span><span class="token plain"> fluss-rbm-quickstart </span> [...]
+<p>The UDF JAR is a self-contained fat JAR. Placing it in Flink's <code>lib/</code> directory makes all functions available without additional dependencies. The source code is available in the <a href="https://github.com/flink-extended/flink-roaringbitmap" target="_blank" rel="noopener noreferrer" class="">flink-roaringbitmap</a> repository. Paimon-related JARs enable the <strong>Lakehouse Tiering</strong> feature of Fluss, where data automatically tiers from Fluss into Paimon, supportin [...]
+<p><strong>2. Create <code>docker-compose.yml</code></strong></p>
+<div class="language-yaml codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">docker-compose.yml</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-yaml codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token key atrule" style="color:#3f6ec6">services [...]
+<p><strong>3. Start the environment</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose up </span><span cla [...]
+<p><strong>4. Start the Lakehouse Tiering Service</strong></p>
+<p>The tiering service continuously transfers data from Fluss to Paimon to support batch queries. Start it in the background before writing data:</p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose </span><span class= [...]
+<p><strong>5. Open the SQL Client</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose </span><span class= [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="create-a-catalog-and-register-the-udfs">Create a Catalog and Register the UDFs<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#create-a-catalog-and-register-the-udfs" class="hash-link" aria-label="Direct link to Create a Catalog and Register the UDFs" title="Direct link to Create a Catalog and Register the UDFs" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<p>Register the RoaringBitmap UDFs. The JAR contains three functions: <code>RB_BUILD</code> wraps a single integer as a bitmap; <code>RB_CARDINALITY</code> extracts the deduplication count from a bitmap; <code>RB_OR_AGG</code> executes bitmap <code>OR</code> aggregation for roll-up queries. The deduplication merge at write time is performed by Fluss's <code>rbm32</code> aggregation — these UDFs handle bitmap construction on the Flink side and analysis on the query side.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<blockquote>
+<p><strong>Note:</strong> These functions are currently provided by the external <a href="https://github.com/flink-extended/flink-roaringbitmap" target="_blank" rel="noopener noreferrer" class="">flink-roaringbitmap</a> library. Once <a href="https://cwiki.apache.org/confluence/display/FLUSS/FIP-37%3A+Native+RoaringBitmap+Integration+for+Apache+Fluss" target="_blank" rel="noopener noreferrer" class="">FIP-37: Native RoaringBitmap Integration for Apache Fluss</a> is completed, they will b [...]
+</blockquote>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="create-dictionary-and-aggregation-tables">Create Dictionary and Aggregation Tables<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#create-dictionary-and-aggregation-tables" class="hash-link" aria-label="Direct link to Create Dictionary and Aggregation Tables" title="Direct link to Create Dictionary and Aggregation Tables" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token comment" style="color:#8e908c;font-style:italic">-- D [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="write-sample-data">Write Sample Data<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#write-sample-data" class="hash-link" aria-label="Direct link to Write Sample Data" title="Direct link to Write Sample Data" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token comment" style="color:#8e908c;font-style:italic">-- S [...]
+<p>Each event is written as one row carrying a single-element bitmap. Fluss merges it server-side via <code>rbm32</code> union into the existing bitmap of the corresponding primary key. Flink jobs are only responsible for mapping and forwarding — they hold no aggregation state, require no <code>GROUP BY</code>, and do not process retraction messages.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure4-b5f8ffeafbf32ec7c4f1090336f94b81.png" width="1344" height="746" class="img_ev3q"></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="query-deduplication-results">Query Deduplication Results<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#query-deduplication-results" class="hash-link" aria-label="Direct link to Query Deduplication Results" title="Direct link to Query Deduplication Results" translate="no"></a></h3>
+<p>After writing the data, wait approximately 60 seconds for the data to tier into Paimon. For PK tables, the tiering service uses the <strong>KV snapshot</strong> as a synchronization checkpoint — it reads the snapshot state first, then replays subsequent <strong>CDC events</strong> from that point. The wait covers one KV snapshot period (30 seconds) plus one tiering checkpoint period. Switch to <strong>batch mode</strong> to query the tiered data. Batch mode supports <code>ORDER BY</co [...]
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SET</span><span class= [...]
+<p>Result:</p>
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----------+----------+----+----+----+</span><br></div><div class="token-line [...]
+<p>Note the <code>(app, Shanghai, 20260301, 10)</code> row: <code>user_1</code> appeared twice and <code>user_2</code> appeared once, yielding <code>uv = 2</code>. The three single-element bitmaps were merged by Fluss into a deduplicated set containing two distinct users. <code>pv = 3</code> is the raw event count from the sum aggregation. The query side requires no aggregation — results are ready at write time.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="multi-dimensional-roll-up-queries">Multi-Dimensional Roll-Up Queries<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#multi-dimensional-roll-up-queries" class="hash-link" aria-label="Direct link to Multi-Dimensional Roll-Up Queries" title="Direct link to Multi-Dimensional Roll-Up Queries" translate="no"></a></h3>
+<p>The preceding query reads the finest-granularity pre-aggregation results (<code>channel × city × ymd × hh</code>). The real power of bitmaps is supporting flexible roll-up: performing <code>OR</code> operations across fine-grained bitmaps yields coarse-grained deduplication counts without returning to the original data.</p>
+<p>Roll up by channel, overall UV across all cities, dates, and hours:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----+----+</span><br></div><div class="token-line" style="color:#36464e"><sp [...]
+<p><code>user_1</code> visited the <code>app</code> channel from both Shanghai and Beijing at multiple times. <code>RB_OR_AGG</code> combines all fine-grained bitmaps under the same channel into a single deduplicated set — each user is counted only once. The <code>app</code> channel has four distinct users (1, 2, 3, 5), not a simple sum of per-group UV counts.</p>
+<p>Roll up by date, daily UV across all channels and cities:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+----------+----+----+</span><br></div><div class="token-line" style="color:#36464e"><span c [...]
+<p>Active users are users 1–4 on March 1 and users 1, 2, 4, and 5 on March 2. Simply summing per-group UV counts would result in double counting, but the bitmap <code>OR</code> operation gives accurate results.</p>
+<p>Roll up by channel x date, a common dashboard view:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----------+----+----+</span><br></div><div class="token-line" style="color:# [...]
+<p>Global UV, total distinct users across all dimensions:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+----------+----------+</span><br></div><div class="token-line" style="color:#36464e"><span [...]
+<p>Five distinct users generated a total of 12 page views. One query, one full bitmap <code>OR</code> — no need to re-scan the original events.</p>
+<p>All roll-up queries follow the same pattern: <code>GROUP BY</code> the target dimensions, apply <code>RB_OR_AGG</code> on the bitmap column, and use <code>RB_CARDINALITY</code> to extract the count. The finest-granularity pre-aggregated bitmap is the building block for all coarser-grained analysis.</p>
+<blockquote>
+<p>In a production environment, replace the bounded <code>page_views</code> view with a streaming data source and add the <code>/*+ OPTIONS('lookup.insert-if-not-exists' = 'true') */</code> hint to automatically register new users and enable a real-time bitmap write pipeline. The storage-side schema remains identical.</p>
+</blockquote>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="clean-up-the-environment">Clean Up the Environment<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#clean-up-the-environment" class="hash-link" aria-label="Direct link to Clean Up the Environment" title="Direct link to Clean Up the Environment" translate="no"></a></h3>
+<p>After exiting the SQL Client, run the following to stop and remove all containers:</p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose down </span><span c [...]
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="beyond-uv-other-use-cases">Beyond UV: Other Use Cases<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#beyond-uv-other-use-cases" class="hash-link" aria-label="Direct link to Beyond UV: Other Use Cases" title="Direct link to Beyond UV: Other Use Cases" translate="no"></a></h2>
+<p>This pattern is not limited to UV counting. It extends naturally to a variety of analytical scenarios. The core structure is consistent: choose the appropriate primary key granularity, and bitmap merging is handled by the storage layer at write time.</p>
+<p><strong>Real-time multi-dimensional UV analysis.</strong> The main scenario in this post. Accurate UV statistics based on any combination of dimensions such as channel, city, date, and hour, with flexible dimension roll-up via bitmap union. Applicable to traffic dashboards, advertising effectiveness analysis, and activity monitoring.</p>
+<p><strong>User retention analysis.</strong> Store active user bitmaps at the granularity of <code>user × date</code>. The retained user set is obtained by <code>AND</code>-ing bitmaps from different dates. For example, the cardinality of <code>Day1_bitmap AND Day7_bitmap</code> gives the 7-day retention count.</p>
+<p><strong>Funnel analysis.</strong> Store an aggregation table for each funnel step, with bitmaps recording the set of users who reached that step. Conversion rates are computed by <code>AND</code>-ing bitmaps of adjacent steps. Since bitmaps support set difference (<code>ANDNOT</code>), you can further analyze the characteristics of users who dropped off at each step.</p>
+<p><strong>Audience segmentation and user profiling.</strong> Build user attribute labels (such as gender, province, and preference category) as bitmaps. Multi-label combination queries are converted into bitmap <code>AND</code>/<code>OR</code>/<code>ANDNOT</code> operations, enabling selection of tens of millions of users in milliseconds. This is a high-frequency scenario in advertising <strong>DMP</strong> and recommendation systems.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure5-86a3bd1f0dede51a77608a746ad9ff7b.png" width="1344" height="760" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="getting-started">Getting Started<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#getting-started" class="hash-link" aria-label="Direct link to Getting Started" title="Direct link to Getting Started" translate="no"></a></h2>
+<p>If you are evaluating real-time multi-dimensional exact deduplication schemes, Fluss's RoaringBitmap capability is a worthwhile starting point. The architecture follows a clear three-step pattern:</p>
+<ol>
+<li class=""><strong>Map</strong> sparse user identities to dense integers using an auto-increment <code>uid_dictionary</code> table.</li>
+<li class=""><strong>Deduplicate at write time</strong> using an <code>rbm32</code> aggregation table — no stateful Flink jobs, no complex UDAFs.</li>
+<li class=""><strong>Query directly</strong> using <code>RB_CARDINALITY()</code> for point reads or <code>RB_OR_AGG()</code> + <code>RB_CARDINALITY()</code> for any roll-up combination.</li>
+</ol>
+<p>The result is a system where deduplication is handled entirely by the storage layer, and the query side reads pre-computed results with no additional aggregation overhead.</p>
+<p><code>rbm32</code> / <code>rbm64</code> aggregation and the auto-increment dictionary scheme are available from <strong>Apache Fluss 0.9</strong>. See the <a href="https://fluss.apache.org/" target="_blank" rel="noopener noreferrer" class="">Fluss documentation</a> for the full configuration reference.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="join-the-community">Join the Community<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#join-the-community" class="hash-link" aria-label="Direct link to Join the Community" title="Direct link to Join the Community" translate="no"></a></h2>
+<p>Apache Fluss is an open-source project under active development within the Apache Software Foundation.</p>
+<ul>
+<li class="">GitHub: <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">github.com/apache/fluss</a></li>
+<li class="">Documentation: <a href="https://fluss.apache.org/" target="_blank" rel="noopener noreferrer" class="">fluss.apache.org</a></li>
+</ul>]]></content>
+ <author>
+ <name>Yang Wang</name>
+ <uri>https://github.com/platinumhamburg</uri>
+ </author>
+ </entry>
<entry>
<title type="html"><![CDATA[Why Apache Fluss Chose Rust for Its Multi-Language SDK]]></title>
<id>https://fluss.apache.org/blog/why-fluss-chose-rust-for-multi-language-sdk/</id>
@@ -1266,7 +1412,7 @@ and then ship the results into a <strong>separate serving</strong> system purely
<p><strong>This introduces an important trade-off:</strong> every additional system means duplicate storage, increased latency, and extra operational overhead. Data has to be landed, indexed, and kept in sync, even though the stream processor has already computed the end state.</p>
<p>With Fluss Primary Key Tables, you don’t need that extra serving layer. Your stateful computations in Flink can materialize directly into PK Tables, which are durable, consistent, and queryable in real time. This lets you power dashboards or APIs straight from the tables, cutting out redundant clusters and reducing end-to-end latency.</p>
<p><strong>In short:</strong> compute once in Flink, persist in Fluss, and serve directly, without duplicating storage or managing another system.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="-closing-thoughts">🚀 Closing Thoughts<a href="https://fluss.apache.org/blog/pk-key-tables-log-cache-streaming/#-closing-thoughts" class="hash-link" aria-label="Direct link to �🚀 Closing Thoughts" title="Direct link to 🚀 Closing Thoughts" translate="no"></a></h3>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="-closing-thoughts">🚀 Closing Thoughts<a href="https://fluss.apache.org/blog/pk-key-tables-log-cache-streaming/#-closing-thoughts" class="hash-link" aria-label="Direct link to 🚀 Closing Thoughts" title="Direct link to 🚀 Closing Thoughts" translate="no"></a></h3>
<p>The above are some examples use cases, we have seen recently and I'm only eager to see what users will build with Fluss - realtime features stores is another PK table use case currently under investigation.</p>
<p>Fluss Primary Key Tables are one of the most compelling features of the platform. They embody the <strong>stream-table duality</strong> in practice: every write is a log entry and a KV update; every recovery is a snapshot plus replay; every cache read is guaranteed to be consistent with the log.</p>
<p>The complexity of coordinating a log and a cache disappears. With Fluss, you don’t have to choose between speed and safety; you get both.</p>
@@ -2708,77 +2854,4 @@ The future planning for Fluss involves three key aspects, each corresponding to
<uri>https://github.com/wuchong</uri>
</author>
</entry>
- <entry>
- <title type="html"><![CDATA[Why Fluss? Top 4 Challenges of Using Kafka for Real-Time Analytics]]></title>
- <id>https://fluss.apache.org/blog/why-fluss/</id>
- <link href="https://fluss.apache.org/blog/why-fluss/"/>
- <updated>2024-12-11T00:00:00.000Z</updated>
- <summary type="html"><![CDATA[The industry is undergoing a clear and significant shift as big data computing transitions from offline to real-time processing.]]></summary>
- <content type="html"><![CDATA[<p>The industry is undergoing a clear and significant shift as big data computing transitions from offline to real-time processing.
-This transition is revolutionizing various sectors, including the E-commerce, automotive networking, finance, and beyond,
-where real-time data applications are becoming integral to operations. This evolution enables organizations to unlock greater
-value by leveraging real-time insights to drive business impact and enhance decision-making.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img1-a29d423ce0b5f952b79ad2babb903cf5.jpg" width="3656" height="1240" class="img_ev3q">
-The evolution of big data technology is becoming increasingly impactful, reshaping the computing architecture landscape.
-The traditional Hive-based data warehouse has given way to modern architectures, starting with Lakehouse models and progressing
-to the Paimon streaming Lakehouse, which has gained significant traction in markets like China and more recently started expanding to EU/US markets.
-The core driver behind these architectural innovations is the need to improve data processing timeliness. The data freshness is improved from traditional
-T+1 (next-day readiness) to T+1 hours, and now T+1 minutes. However, Lakehouse architectures, being file-system-based,
-inherently face minute-level latency as their practical upper limit.</p>
-<p>Yet, many critical use cases, such as search and recommendation systems, advertisement attribution, and anomaly detection, demand second-level latency.
-While big data technologies have advanced significantly, there remains a notable gap: a user-friendly, second-level storage solution tailored for big data analytics.</p>
-<p>In most real-time data scenarios, Apache Kafka has emerged as the go-to second-level storage solution. Its integration with Apache Flink represents the dominant architecture for building real-time data warehouses. However, while widely adopted, this combination presents significant challenges for achieving true real-time analytics at scale. The limitations of using Kafka for big data analytics highlight the need for a more robust solution to meet the demands of modern real-time use ca [...]
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="kafka-falls-short-in-real-time-analytics">Kafka Falls Short in Real-Time Analytics<a href="https://fluss.apache.org/blog/why-fluss/#kafka-falls-short-in-real-time-analytics" class="hash-link" aria-label="Direct link to Kafka Falls Short in Real-Time Analytics" title="Direct link to Kafka Falls Short in Real-Time Analytics" translate="no"></a></h2>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="no-support-for-updates">No Support for Updates<a href="https://fluss.apache.org/blog/why-fluss/#no-support-for-updates" class="hash-link" aria-label="Direct link to No Support for Updates" title="Direct link to No Support for Updates" translate="no"></a></h3>
-<p>The first significant challenge with Apache Kafka is its lack of support for updates, a critical feature for data warehouses. In data warehousing, updates are often essential to correct or amend data. However, Kafka's inability to handle updates results in duplicate records for the same primary key. When consumed by a computing engine, this duplication necessitates costly deduplication processes to ensure accurate results.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img2-d2d1a314768a1be3da0308e20851d3f3.jpg" width="3656" height="1172" class="img_ev3q"></p>
-<p>In Apache Flink, for instance, handling this issue requires materializing all upstream data in state, which is resource-intensive. Every time data is consumed from Kafka, the deduplication process incurs a substantial overhead, significantly increasing computational and storage costs. This limitation not only impacts performance but also hampers the reusability of Kafka-stored data for downstream business processes.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="lack-of-querying-capabilities">Lack of Querying Capabilities<a href="https://fluss.apache.org/blog/why-fluss/#lack-of-querying-capabilities" class="hash-link" aria-label="Direct link to Lack of Querying Capabilities" title="Direct link to Lack of Querying Capabilities" translate="no"></a></h3>
-<p>The second major limitation of Apache Kafka is hard to debug, primarily due to its lack of native querying capabilities. In data warehousing, querying is a fundamental feature that facilitates troubleshooting and ad-hoc analysis to understand data trends. Unfortunately, Kafka operates much like a black box, making it challenging to perform these critical tasks without additional tools or layers.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img3-32732f50a5420dad7d1dd6c5b3d17d6f.jpg" width="3696" height="1214" class="img_ev3q"></p>
-<p>To address this limitation, the industry has adopted two primary approaches, each with its own trade-offs:</p>
-<ul>
-<li class=""><strong>Synchronizing Kafka Data to an OLAP System:</strong> This allows for querying data using the OLAP system’s capabilities. However, this approach introduces additional components into the architecture, increasing both complexity and cost. Moreover, it risks data inconsistencies due to synchronization delays.</li>
-<li class=""><strong>Querying Kafka Directly Using Trino:</strong> While Trino can query Kafka, it relies solely on full scans, which are inefficient for large-scale operations. For instance, a simple query on just 1GB of Kafka data can take up to one minute, rendering it impractical for large datasets or real-time requirements.</li>
-</ul>
-<p>These limitations make Kafka unsuitable for efficient and scalable data exploration in modern data warehousing workflows.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="difficulty-with-data-backfilling">Difficulty with Data Backfilling<a href="https://fluss.apache.org/blog/why-fluss/#difficulty-with-data-backfilling" class="hash-link" aria-label="Direct link to Difficulty with Data Backfilling" title="Direct link to Difficulty with Data Backfilling" translate="no"></a></h3>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img4-5a57c638bb5dd478a86a9fcd1683734f.jpg" width="3526" height="1210" class="img_ev3q">
-The third significant issue with Apache Kafka is processing historical data, also known as data backfilling, a common requirement in data warehousing.
-For instance, in logistics, it usually needs to process and analyze historical data from several months ago. However, Kafka only retain data for a few days due to the high cost of storage.
-Even though Kafka community has introduced <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage" target="_blank" rel="noopener noreferrer" class="">Tiered Storage</a> to address the long-term data issue, it still has limitations.
-Reading historical data still require all the data to pass through Kafka brokers, which can lead to brokers unstable and <a href="https://www.warpstream.com/blog/tiered-storage-wont-fix-kafka#increased-complexity-and-operational-burden" target="_blank" rel="noopener noreferrer" class="">disrupt live traffics</a>.</p>
-<p>These limitations highlight Kafka’s inefficiency as a solution for data backfilling in large-scale and long-term analytical use cases, further underscoring the need for more robust alternatives in real-time data warehousing.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="excessive-network-costs">Excessive Network Costs<a href="https://fluss.apache.org/blog/why-fluss/#excessive-network-costs" class="hash-link" aria-label="Direct link to Excessive Network Costs" title="Direct link to Excessive Network Costs" translate="no"></a></h3>
-<p>The final major challenge with Apache Kafka lies in its high network costs, which account for an estimated 88% of Kafka's overall operational expenses. In data warehousing, the "one write, multiple read" pattern is common, with each consumer often requiring only a subset of the data. However, Kafka's design mandates that consumers read the entire dataset, regardless of how much is actually needed.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img5-e185731d5e249a6c80b962b77b5a16ad.jpg" width="3636" height="1206" class="img_ev3q"></p>
-<p>For instance, among the tens of thousands of Flink SQL jobs at Alibaba, only 49% of the upstream columns are utilized per job on average. Despite this, consumers must read all the columns and pay 100% networking cost, which is highly inefficient and wasteful.</p>
-<p>In summary, using Kafka for real-time analytics presents several critical issues: <strong>(1) lack of support for updates</strong>, <strong>(2) absence of querying capabilities</strong>, <strong>(3) difficulty with data backfilling</strong>, and <strong>(4) excessive network costs</strong>. These limitations make the combination of Flink and Kafka less than ideal for real-time data warehousing.
-But why Kafka lacks these abilities? Is it possible to add the abilities to Kafka?</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="kafka-is-not-designed-for-analytics">Kafka is not designed for Analytics<a href="https://fluss.apache.org/blog/why-fluss/#kafka-is-not-designed-for-analytics" class="hash-link" aria-label="Direct link to Kafka is not designed for Analytics" title="Direct link to Kafka is not designed for Analytics" translate="no"></a></h2>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img6-4741286b7e5d1410d5a8d7036e3cad8d.jpg" width="3620" height="1148" class="img_ev3q">
-The root cause of these challenges lies in the fundamental design philosophy of Kafka: <strong>Kafka is designed for streaming events, NOT for streaming analytics</strong>. Each system has its own focus and strengths, and Kafka's design is optimized for message queue scenarios rather than analytical workloads.</p>
-<p>In Kafka, data is typically stored in a row-oriented format, such as CSV, JSON, or Avro.
-While this is highly efficient for use cases involving message streaming, it becomes a bottleneck for analytical scenarios.
-Data analysis requires handling large volumes of data, and therefore heavily relies on data skipping capabilities from storage, such as column pruning and predicate pushdown.
-For this reason, columnar storage is far better suited for analytical workloads, whereas Kafka's row-based storage is not designed to meet these demands effectively.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="introducing-fluss">Introducing Fluss<a href="https://fluss.apache.org/blog/why-fluss/#introducing-fluss" class="hash-link" aria-label="Direct link to Introducing Fluss" title="Direct link to Introducing Fluss" translate="no"></a></h2>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img7-06886bca9797751895c82d707cb04b2d.jpg" width="3644" height="1210" class="img_ev3q">
-When visualizing the data ecosystem as a four-quadrant matrix, an intriguing pattern emerges.
-The left side of the matrix represents operational systems, while the right side represents analytical systems.
-The top half is dedicated to stream storage, and the bottom half to table storage.
-In operational systems, both databases and stream storage predominantly use row-based formats, as row storage is more efficient for transactional workloads. Conversely, in analytical systems like Apache Iceberg and Snowflake, columnar storage is preferred due to its superior performance in analytical scenarios.</p>
-<p>Interestingly, the upper-right quadrant of the matrix remains empty, indicating a significant gap in the market: a streaming storage for analytical scenarios. Unsurprisingly, such a storage would likely adopt a columnar format to effectively address the needs of real-time analytics.</p>
-<p>To fill the gap and resolve problems in Flink and streaming analytics, we embarked on a journey two years ago to create a streaming storage. We named the project "<strong>FL</strong>ink <strong>U</strong>nified <strong>S</strong>treaming <strong>S</strong>torage", and from its initials, we derived the name <strong>Fluss</strong>.
-<img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img8-87cfb5887e9a49febaacd805de5216fe.jpg" width="3700" height="946" class="img_ev3q">
-Interestingly, Flink is derived from the German word for "<strong>agile</strong>", and Fluss, which translates to "<strong>river</strong>" in German, resonates deeply with the project’s vision.
-It symbolizes the streaming data is continuously flowing, distributing and converging into data lakes, just like a river.
-The launch of Fluss coincides with Flink's 10th anniversary, making it a fitting tribute to the project’s heritage and its enduring contribution to the streaming data landscape.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img9-fb689d41f612ef840508c4c20399822c.jpg" width="3686" height="1242" class="img_ev3q"></p>
-<p>Learn more details about Fluss in the next <a class="" href="https://fluss.apache.org/blog/fluss-intro/">blog post</a>, where we will delve deeper into Fluss's architecture, design principles, key features, and explore how it addresses the challenges of using Kafka for real-time analytics.</p>]]></content>
- <author>
- <name>Jark Wu</name>
- <uri>https://github.com/wuchong</uri>
- </author>
- </entry>
</feed>
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diff --git a/blog/fluss-for-ai/index.html b/blog/fluss-for-ai/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p><img decoding="async" loading="lazy" src="/assets/images/banner-a3fa3c1c723340acae7046f86be1de82.png" width="1024" height="1024" class="img_ev3q">
<strong>Apache Fluss (Incubating) started as streaming storage for real-time analytics</strong>, built to work closely with stream processors like Apache Flink.
Its focus has always been on freshness, efficient analytical access, and continuous data, making fast-changing streams directly usable without forcing them
diff --git a/blog/fluss-intro/index.html b/blog/fluss-intro/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
Today, we are excited to introduce Fluss, a cutting-edge streaming storage system designed to power real-time analytics.
We are going to explore Fluss's architecture, design principles, key features, and how it addresses the challenges of using Kafka for real-time analytics.</p>
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="key-features-of-fluss">Key Features of Fluss<a href="#key-features-of-fluss" class="hash-link" aria-label="Direct link to Key Features of Fluss" title="Direct link to Key Features of Fluss" translate="no"></a></h2>
diff --git a/blog/fluss-java-client/index.html b/blog/fluss-java-client/index.html
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<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction" translate="no"></a></h2>
<p>Apache Fluss is a streaming data storage system built for real-time analytics, serving as a low-latency data layer in modern data Lakehouses.
It supports sub-second streaming reads and writes, storing data in a columnar format for efficiency, and offers two flexible table types: <strong>append-only Log Tables</strong> and <strong>updatable Primary Key Tables</strong>.
diff --git a/blog/fluss-joins-asf/index.html b/blog/fluss-joins-asf/index.html
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neutral, and standardized. Moving forward, Fluss will leverage the ASF ecosystem to accelerate the building of a global developer community, continuously driving innovation and adoption of next-generation real-time data infrastructure.</p>
<p><img decoding="async" loading="lazy" alt="ASF" src="/assets/images/asf-0621eab6f6aadbdfebfc5e24a36667c1.png" width="1231" height="631" class="img_ev3q"></p>
<p>The Fluss community has recently completed all donation procedures and successfully transferred the project to the Apache Software Foundation.
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Fluss is a <strong>streaming storage system</strong> designed to power real-time analytics. It aspires to change how organizations approach real-time data by acting as the <strong>real-time data layer</strong> for the Lakehouse.
Its cutting-edge design enables businesses to achieve <strong>sub-second latency</strong>, <strong>high throughput</strong>, and <strong>cost efficiency</strong> for data analytics, making it the ideal solution for modern data-driven applications.</p>
<p>We have historically invested a lot of effort into advancing the data streaming ecosystem, being major contributors to <a href="https://flink.apache.org/" target="_blank" rel="noopener noreferrer" class="">Apache Flink®</a>, <a href="https://www.ververica.com/blog/ververica-donates-flink-cdc-empowering-real-time-data-integration-for-the-community" target="_blank" rel="noopener noreferrer" class="">Apache Flink CDC</a>, and <a href="https://paimon.apache.org/" target="_blank" rel="noop [...]
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p>Fraud detection is a mission-critical capability for businesses operating in financial services, e-commerce, and digital payments. Detecting suspicious transactions in real time can prevent significant losses and protect customers. This blog demonstrates how to build a streamhouse that processes bank transactions in real time, detects fraud, and serves data seamlessly across hot (sub‑second latency) and cold (minutes‑latency) layers.
Real-time detection and historical analytics are combined, enabling businesses to act quickly while maintaining a complete audit trail.</p>
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="streamhouse-architecture">Streamhouse Architecture<a href="#streamhouse-architecture" class="hash-link" aria-label="Direct link to Streamhouse Architecture" title="Direct link to Streamhouse Architecture" translate="no"></a></h2>
diff --git a/blog/fluss_rust_client_release/index.html b/blog/fluss_rust_client_release/index.html
index 7d18439d0..92e4c15c5 100644
--- a/blog/fluss_rust_client_release/index.html
+++ b/blog/fluss_rust_client_release/index.html
@@ -15,14 +15,14 @@
<link rel="icon" href="/img/logo.svg">
<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
-<script src="/assets/js/runtime~main.21c219fa.js" defer="defer"></script>
-<script src="/assets/js/main.f942df26.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
+<script src="/assets/js/main.16a8eb46.js" defer="defer"></script>
</head>
<body>
<svg style="display: none;"><defs>
<symbol id="theme-svg-external-link" viewBox="0 0 24 24"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"/></symbol>
</defs></svg>
-<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p>We are excited to announce the release of <a href="https://github.com/apache/fluss-rust" target="_blank" rel="noopener noreferrer" class="">fluss-rust clients</a> 0.1.0, the first official release of the <a href="https://clients.fluss.apache.org/user-guide/rust/installation" target="_blank" rel="noopener noreferrer" class="">Rust</a>, <a href="https://clients.fluss.apache.org/user-guide/python/installation" target="_blank" rel="noopener noreferrer" class="">Python</a>, and <a href="ht [...]
<p>Under the hood, all three clients share a single Rust core that handles protocol negotiation, batching, retries, and <a href="https://arrow.apache.org/" target="_blank" rel="noopener noreferrer" class="">Apache Arrow</a>-based data exchange, with thin language-specific bindings on top. This was a deliberate community decision to deliver native performance and feature parity across every language from day one.</p>
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="highlights">Highlights<a href="#highlights" class="hash-link" aria-label="Direct link to Highlights" title="Direct link to Highlights" translate="no"></a></h2>
diff --git a/blog/hands-on-fluss-lakehouse/index.html b/blog/hands-on-fluss-lakehouse/index.html
index 8a8c7e150..1a30af205 100644
--- a/blog/hands-on-fluss-lakehouse/index.html
+++ b/blog/hands-on-fluss-lakehouse/index.html
@@ -15,14 +15,14 @@
<link rel="icon" href="/img/logo.svg">
<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
-<script src="/assets/js/runtime~main.21c219fa.js" defer="defer"></script>
-<script src="/assets/js/main.f942df26.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
+<script src="/assets/js/main.16a8eb46.js" defer="defer"></script>
</head>
<body>
<svg style="display: none;"><defs>
<symbol id="theme-svg-external-link" viewBox="0 0 24 24"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"/></symbol>
</defs></svg>
-<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p><img decoding="async" loading="lazy" src="/assets/images/streamhouse-7b7f1260fc996ca89b50dbb9454584ab.png" width="3057" height="1237" class="img_ev3q"></p>
<p>In this hands-on tutorial, we'll walk you through setting up a local Fluss lakehouse environment, running some practical data operations, and getting first-hand experience with the complete Fluss lakehouse architecture. By the end, you'll have a working environment for experimenting with Fluss's powerful data processing capabilities.</p>
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="integrate-with-paimon-minio-lakehouse">Integrate with Paimon Minio Lakehouse<a href="#integrate-with-paimon-minio-lakehouse" class="hash-link" aria-label="Direct link to Integrate with Paimon Minio Lakehouse" title="Direct link to Integrate with Paimon Minio Lakehouse" translate="no"></a></h2>
@@ -159,7 +159,7 @@
<p>Open the Minio WebUI, and you'll see the Paimon files in your bucket:</p>
<p><img decoding="async" loading="lazy" src="/assets/images/fluss-bucket-data-47a0df43a938f44f5671f184588fd5ef.png" width="1508" height="530" class="img_ev3q"></p>
<p>You can also check the Parquet files and manifest in your local filesystem under <code>/tmp/minio-data</code>:</p>
-<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">/tmp/minio-data ❯ tree .</span><br></div><div class="token-line" style="color:#36464e"><span [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">/tmp/minio-data ❯ tree .</span><br></div><div class="token-line" style="color:#36464e"><span [...]
</li>
<li class="">
<p>View Snapshots</p>
diff --git a/blog/index.html b/blog/index.html
index fadcd03ac..96600f8eb 100644
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+++ b/blog/index.html
@@ -3,7 +3,7 @@
<head>
<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.0">
-<title data-rh="true">Blog | Apache Fluss™ (Incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" name="twitter:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" property="og:url" content="https://fluss.a [...]
+<title data-rh="true">Blog | Apache Fluss™ (Incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" name="twitter:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" property="og:url" content="https://fluss.a [...]
<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache Fluss™ (Incubating) Atom Feed">
@@ -15,14 +15,23 @@
<link rel="icon" href="/img/logo.svg">
<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
-<script src="/assets/js/runtime~main.21c219fa.js" defer="defer"></script>
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+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
+The full combination of four dimensions means <strong>16 grouping methods</strong>; when the dimension count increases to seven, the number of possible groupings reaches <strong>128</strong>.</p>
+<p>How can multi-dimensional deduplication be both accurate and flexible while maintaining real-time performance? Behind this challenge lie two very different computing paradigms: direct deduplication of raw data, or set operations based on bitmaps.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="problems-with-traditional-deduplication-schemes">Problems with Traditional Deduplication Schemes<a href="#problems-with-traditional-deduplication-schemes" class="hash-link" aria-label="Direct link to Problems with Traditional Deduplication Schemes" title="Direct link to Problems with Traditional Deduplication Schemes" translate="no"></a></h2>
+<p>Direct deduplication of raw data (e.g. <code>COUNT(DISTINCT user_id)</code>) is the most intuitive and accurate deduplication method: maintain a <strong>hash set</strong> for each dimension combination and record all user IDs that have appeared under the group.</p>
+<p>This approach works well with smaller data volumes and fewer dimensions, but as business scale grows, three core limitations emerge.</p>
+<p><strong>Performance increases linearly with the amount of data.</strong> Each dimension combination needs to independently store all user identities that have occurred. When daily page view logs reach billions and distinct users number in the tens or hundreds of millions, each group's hash set demands significant memory and CPU for insertion and lookup. Total compute cost scales with the product of dimension combinations and data volume, which is especially noticeable during peak [...]
+<p><strong>Multidimensional extensibility is limited.</strong> If an analyst needs UV by <strong>Channel x City</strong> and <strong>Channel x Date</strong>, the traditional scheme must calculate two separate deduplication results. The existing <strong>Channel x City</strong> result cannot be rolled up to get the <strong>Channel</strong>-level UV, because user sets across cities may overlap and simple summation leads to double counting. With each additional dimension, the computational t [...]
+<p><strong>Insufficient query flexibility.</strong> Pre-aggregation schemes must fix dimension combinations in advance. When a business team needs a new dimension on the fly (such as adding <strong>device type</strong>), adjusting the entire data processing pipeline is often required instead of simply modifying a query.</p>
+<p>These three limitations share a root cause: storing and computing on raw user identities at every dimension combination does not scale.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Real-Time Unique User Counting at Scale with Apache Fluss" href="/blog/roaringbitmap-uv-deduplication/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="title_f1Hy"><a href="/blog/why-fluss-chose-r [...]
<p>If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.</p>
<p>Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The <a class="" href="/blog/fluss-java-client/">Java client</a> works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.</p>
<p>We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."</p>
@@ -91,9 +100,7 @@ Fluss solves the problems of redundant data transfer, difficulties in data profi
By combining columnar storage with real-time update capabilities, Fluss supports column pruning, key-value point lookups, Delta Join, and seamless lake–stream integration, thereby <strong>cutting I/O and compute overhead</strong> while enhancing job stability and profiling efficiency.</p>
<p>Already deployed on Taobao’s A/B-testing platform for critical services such as search and recommendation, the system proved its resilience during the 618 Grand Promotion:
<strong>it handled tens of millions of requests with sub-second latency</strong>, lowered resource usage by <strong>30%</strong>, and removed more than 100 TB from state storage.
-Looking ahead, the team will continue to extend Fluss within a <strong>Lakehouse architecture</strong> and broaden its use across <strong>AI-driven</strong> workloads.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about How Taobao uses Apache Fluss (Incubating) for Real-Time Processing in Search and RecSys" href="/blog/taobao-practice/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class=" [...]
-<p><img decoding="async" loading="lazy" src="/assets/images/streamhouse-7b7f1260fc996ca89b50dbb9454584ab.png" width="3057" height="1237" class="img_ev3q"></p>
-<p>In this hands-on tutorial, we'll walk you through setting up a local Fluss lakehouse environment, running some practical data operations, and getting first-hand experience with the complete Fluss lakehouse architecture. By the end, you'll have a working environment for experimenting with Fluss's powerful data processing capabilities.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about From Stream to Lake: Hands-On wi [...]
+Looking ahead, the team will continue to extend Fluss within a <strong>Lakehouse architecture</strong> and broaden its use across <strong>AI-driven</strong> workloads.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about How Taobao uses Apache Fluss (Incubating) for Real-Time Processing in Search and RecSys" href="/blog/taobao-practice/"><b>Read more</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page [...]
<p>Copyright © 2026 The Apache Software Foundation, Licensed under the Apache License, Version 2.0.</p>
<p>Apache, the names of Apache projects, and the feather logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p></div></div></div></footer></div>
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diff --git a/blog/page/2/index.html b/blog/page/2/index.html
index e6f42233b..83e9f450d 100644
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<head>
<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.0">
-<title data-rh="true">Blog | Apache Fluss™ (Incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" name="twitter:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" property="og:url" content="https://fluss.a [...]
+<title data-rh="true">Blog | Apache Fluss™ (Incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" name="twitter:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" property="og:url" content="https://fluss.a [...]
<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache Fluss™ (Incubating) Atom Feed">
@@ -15,14 +15,16 @@
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<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
-<script src="/assets/js/runtime~main.21c219fa.js" defer="defer"></script>
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+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
+<script src="/assets/js/main.16a8eb46.js" defer="defer"></script>
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</defs></svg>
-<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
+<p><img decoding="async" loading="lazy" src="/assets/images/streamhouse-7b7f1260fc996ca89b50dbb9454584ab.png" width="3057" height="1237" class="img_ev3q"></p>
+<p>In this hands-on tutorial, we'll walk you through setting up a local Fluss lakehouse environment, running some practical data operations, and getting first-hand experience with the complete Fluss lakehouse architecture. By the end, you'll have a working environment for experimenting with Fluss's powerful data processing capabilities.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about From Stream to Lake: Hands-On wi [...]
neutral, and standardized. Moving forward, Fluss will leverage the ASF ecosystem to accelerate the building of a global developer community, continuously driving innovation and adoption of next-generation real-time data infrastructure.</p>
<p><img decoding="async" loading="lazy" alt="ASF" src="/assets/images/asf-0621eab6f6aadbdfebfc5e24a36667c1.png" width="1231" height="631" class="img_ev3q"></p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Fluss Joins the Apache Incubator" href="/blog/fluss-joins-asf/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="title_f1Hy"><a href="/blog/fluss-java-client/">Apache Fluss Java Cl [...]
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction" translate="no"></a></h2>
@@ -59,10 +61,7 @@ with more than 200 code commits completed. Our heartfelt thanks go out to every
<li class=""><strong>Streaming & Lakehouse Unification:</strong> Seamlessly integrates streaming and lakehouse storage for unified data processing.</li>
</ul></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Towards A Unified Streaming & Lakehouse Architecture" href="/blog/unified-streaming-lakehouse/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="title_f1Hy"><a href="/blog/fluss-intro/">Introducing Fluss: Streaming Storage for Real-Time Analytics</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2024-12-12T00:0 [...]
Today, we are excited to introduce Fluss, a cutting-edge streaming storage system designed to power real-time analytics.
-We are going to explore Fluss's architecture, design principles, key features, and how it addresses the challenges of using Kafka for real-time analytics.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Introducing Fluss: Streaming Storage for Real-Time Analytics" href="/blog/fluss-intro/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="title_f1Hy"><a href="/blog/why-fluss/"> [...]
-This transition is revolutionizing various sectors, including the E-commerce, automotive networking, finance, and beyond,
-where real-time data applications are becoming integral to operations. This evolution enables organizations to unlock greater
-value by leveraging real-time insights to drive business impact and enhance decision-making.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Why Fluss? Top 4 Challenges of Using Kafka for Real-Time Analytics" href="/blog/why-fluss/"><b>Read more</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"><a class="pagination-nav__link pagination-nav__link--prev" href="/blog/"><div class="pagi [...]
+We are going to explore Fluss's architecture, design principles, key features, and how it addresses the challenges of using Kafka for real-time analytics.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Introducing Fluss: Streaming Storage for Real-Time Analytics" href="/blog/fluss-intro/"><b>Read more</b></a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"><a class="pagination-nav__ [...]
<p>Copyright © 2026 The Apache Software Foundation, Licensed under the Apache License, Version 2.0.</p>
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diff --git a/blog/page/3/index.html b/blog/page/3/index.html
index 1b17e0ab4..d6f8fe326 100644
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@@ -3,7 +3,7 @@
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<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.0">
-<title data-rh="true">Blog | Apache Fluss™ (Incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" name="twitter:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" property="og:url" content="https://fluss.a [...]
+<title data-rh="true">Blog | Apache Fluss™ (Incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" name="twitter:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" property="og:url" content="https://fluss.a [...]
<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache Fluss™ (Incubating) Atom Feed">
@@ -15,14 +15,17 @@
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<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
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<symbol id="theme-svg-external-link" viewBox="0 0 24 24"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"/></symbol>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
+This transition is revolutionizing various sectors, including the E-commerce, automotive networking, finance, and beyond,
+where real-time data applications are becoming integral to operations. This evolution enables organizations to unlock greater
+value by leveraging real-time insights to drive business impact and enhance decision-making.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Why Fluss? Top 4 Challenges of Using Kafka for Real-Time Analytics" href="/blog/why-fluss/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="title_f1Hy"><a href="/blog/fluss-open-source/">Fluss is Now Open Source</a></h2><div class="container_ [...]
Fluss is a <strong>streaming storage system</strong> designed to power real-time analytics. It aspires to change how organizations approach real-time data by acting as the <strong>real-time data layer</strong> for the Lakehouse.
Its cutting-edge design enables businesses to achieve <strong>sub-second latency</strong>, <strong>high throughput</strong>, and <strong>cost efficiency</strong> for data analytics, making it the ideal solution for modern data-driven applications.</p>
<p>We have historically invested a lot of effort into advancing the data streaming ecosystem, being major contributors to <a href="https://flink.apache.org/" target="_blank" rel="noopener noreferrer" class="">Apache Flink®</a>, <a href="https://www.ververica.com/blog/ververica-donates-flink-cdc-empowering-real-time-data-integration-for-the-community" target="_blank" rel="noopener noreferrer" class="">Apache Flink CDC</a>, and <a href="https://paimon.apache.org/" target="_blank" rel="noop [...]
diff --git a/blog/partial-updates/index.html b/blog/partial-updates/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p>Traditional streaming data pipelines often need to join many tables or streams on a primary key to create a wide view.
For example, imagine you’re building a real-time recommendation engine for an e-commerce platform.
To serve highly personalized recommendations, your system needs a complete 360° view of each user, including:
diff --git a/blog/pk-key-tables-log-cache-streaming/index.html b/blog/pk-key-tables-log-cache-streaming/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
Common architectures include combinations such as Kafka with Redis, or Debezium feeding changes into a key-value store.
While these patterns underpin a significant portion of production infrastructure, they also introduce <strong>complexity</strong>, <strong>fragility</strong>, and <strong>operational overhead</strong>.</p>
<p>Apache Fluss (Incubating) addresses this challenge with an elegant solution: <strong>Primary Key Tables (PK Tables)</strong>.
@@ -113,7 +113,7 @@ and then ship the results into a <strong>separate serving</strong> system purely
<p><strong>This introduces an important trade-off:</strong> every additional system means duplicate storage, increased latency, and extra operational overhead. Data has to be landed, indexed, and kept in sync, even though the stream processor has already computed the end state.</p>
<p>With Fluss Primary Key Tables, you don’t need that extra serving layer. Your stateful computations in Flink can materialize directly into PK Tables, which are durable, consistent, and queryable in real time. This lets you power dashboards or APIs straight from the tables, cutting out redundant clusters and reducing end-to-end latency.</p>
<p><strong>In short:</strong> compute once in Flink, persist in Fluss, and serve directly, without duplicating storage or managing another system.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="-closing-thoughts">🚀 Closing Thoughts<a href="#-closing-thoughts" class="hash-link" aria-label="Direct link to 🚀 Closing Thoughts" title="Direct link to 🚀 Closing Thoughts" translate="no"></a></h3>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="-closing-thoughts">🚀 Closing Thoughts<a href="#-closing-thoughts" class="hash-link" aria-label="Direct link to 🚀 Closing Thoughts" title="Direct link to 🚀 Closing Thoughts" translate="no"></a></h3>
<p>The above are some examples use cases, we have seen recently and I'm only eager to see what users will build with Fluss - realtime features stores is another PK table use case currently under investigation.</p>
<p>Fluss Primary Key Tables are one of the most compelling features of the platform. They embody the <strong>stream-table duality</strong> in practice: every write is a log entry and a KV update; every recovery is a snapshot plus replay; every cache read is guaranteed to be consistent with the log.</p>
<p>The complexity of coordinating a log and a cache disappears. With Fluss, you don’t have to choose between speed and safety; you get both.</p>
diff --git a/blog/releases/0.6/index.html b/blog/releases/0.6/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
three months of intensive development, bringing together the expertise and efforts of 45 contributors worldwide,
with more than 200 code commits completed. Our heartfelt thanks go out to every contributor for their invaluable support!</p>
<p><img decoding="async" loading="lazy" alt="Release Announcement" src="/assets/images/announce-9ab924e071ec69fe7af8765954f9e966.png" width="1224" height="440" class="img_ev3q"></p>
diff --git a/blog/releases/0.7/index.html b/blog/releases/0.7/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p>🌊 We are excited to announce the official release of <strong>Fluss 0.7</strong>!</p>
<p>This version has undergone extensive improvements in <strong>stability</strong>, <strong>architecture</strong>, <strong>performance optimization</strong>, and <strong>security</strong>, further enhancing its readiness for <strong>production environments</strong>. Over the past three months, we have completed more than <strong>250 commits</strong>, making this release a significant milestone toward becoming a mature, production-grade streaming storage platform.</p>
<p><img decoding="async" loading="lazy" alt="Improvements Diagram" src="/assets/images/overview-255047ec7648f12c63660b40d1147c86.png" width="1248" height="778" class="img_ev3q"></p>
diff --git a/blog/releases/0.8/index.html b/blog/releases/0.8/index.html
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<symbol id="theme-svg-external-link" viewBox="0 0 24 24"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"/></symbol>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p>🌊 We are excited to announce the official release of <strong>Apache Fluss (Incubating) 0.8</strong>!</p>
<p>This is our first release under the incubator of the Apache Software Foundation, marking a significant milestone in our journey to provide a robust streaming storage platform for real-time analytics.</p>
<p>Over the past four months, the community has made tremendous progress, delivering nearly 400 commits that push the boundaries of the Streaming Lakehouse ecosystem. This release includes multiple stability optimizations and introduces deeper integrations, performance breakthroughs, and next-generation stream processing capabilities. Highlights:</p>
diff --git a/blog/releases/0.9/index.html b/blog/releases/0.9/index.html
index 75e820782..5a786edd5 100644
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p>🌊 We are excited to announce the official release of <strong>Apache Fluss (Incubating) 0.9</strong>!</p>
<p>This release marks a major milestone for the project. Fluss 0.9 significantly expands Fluss’s capabilities as a <strong>streaming storage system for real-time analytics, AI, and state-heavy streaming workloads</strong>, with a strong focus on:</p>
<ul>
diff --git a/blog/roaringbitmap-uv-deduplication/index.html b/blog/roaringbitmap-uv-deduplication/index.html
new file mode 100644
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+++ b/blog/roaringbitmap-uv-deduplication/index.html
@@ -0,0 +1,163 @@
+<!doctype html>
+<html lang="en" dir="ltr" class="blog-wrapper blog-post-page plugin-blog plugin-id-default" data-has-hydrated="false">
+<head>
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+<title data-rh="true">Real-Time Unique User Counting at Scale with Apache Fluss | Apache Fluss™ (Incubating)</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data-rh="true" name="twitter:image" content="https://fluss.apache.org/img/logo/png/colored_logo.png"><meta data [...]
+<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache Fluss™ (Incubating) Atom Feed">
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+The full combination of four dimensions means <strong>16 grouping methods</strong>; when the dimension count increases to seven, the number of possible groupings reaches <strong>128</strong>.</p>
+<p>How can multi-dimensional deduplication be both accurate and flexible while maintaining real-time performance? Behind this challenge lie two very different computing paradigms: direct deduplication of raw data, or set operations based on bitmaps.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="problems-with-traditional-deduplication-schemes">Problems with Traditional Deduplication Schemes<a href="#problems-with-traditional-deduplication-schemes" class="hash-link" aria-label="Direct link to Problems with Traditional Deduplication Schemes" title="Direct link to Problems with Traditional Deduplication Schemes" translate="no"></a></h2>
+<p>Direct deduplication of raw data (e.g. <code>COUNT(DISTINCT user_id)</code>) is the most intuitive and accurate deduplication method: maintain a <strong>hash set</strong> for each dimension combination and record all user IDs that have appeared under the group.</p>
+<p>This approach works well with smaller data volumes and fewer dimensions, but as business scale grows, three core limitations emerge.</p>
+<p><strong>Performance increases linearly with the amount of data.</strong> Each dimension combination needs to independently store all user identities that have occurred. When daily page view logs reach billions and distinct users number in the tens or hundreds of millions, each group's hash set demands significant memory and CPU for insertion and lookup. Total compute cost scales with the product of dimension combinations and data volume, which is especially noticeable during peak [...]
+<p><strong>Multidimensional extensibility is limited.</strong> If an analyst needs UV by <strong>Channel x City</strong> and <strong>Channel x Date</strong>, the traditional scheme must calculate two separate deduplication results. The existing <strong>Channel x City</strong> result cannot be rolled up to get the <strong>Channel</strong>-level UV, because user sets across cities may overlap and simple summation leads to double counting. With each additional dimension, the computational t [...]
+<p><strong>Insufficient query flexibility.</strong> Pre-aggregation schemes must fix dimension combinations in advance. When a business team needs a new dimension on the fly (such as adding <strong>device type</strong>), adjusting the entire data processing pipeline is often required instead of simply modifying a query.</p>
+<p>These three limitations share a root cause: storing and computing on raw user identities at every dimension combination does not scale.</p>
+<p>Put it another way: what if instead of storing the raw ID of each user, we represented the entire user set as a compact structure — one bit per user?</p>
+<p>In the bitmap scheme, deduplication count equals counting the number of 1s in the bitmap (<code>popcount</code>), merging two user sets equals a bitwise <code>OR</code>, and cross-dimensional analysis equals a bitwise <code>AND</code>. All operations are CPU-native bit operations, blisteringly fast.</p>
+<p>More importantly, bitmaps naturally support roll-up: the bitmap of <code>Channel = app, City = Shanghai</code> combined with <code>OR</code> with the bitmap of <code>Channel = app, City = Beijing</code> yields the user set for <code>Channel = app</code> — no need to re-scan the original data.</p>
+<p>This is a fundamental difference between the two computing paradigms: <strong>direct deduplication</strong> requires independent calculations for each dimension combination, while the intermediate results of <strong>bitmap deduplication</strong> are naturally composable.</p>
+<p>But the raw bitmap has a problem: if the user ID space is sparse (such as <code>UUID</code> or hash values), the bitmap will be very large and waste memory. A compressed bitmap format that works efficiently on sparse data is needed. This is where <strong>RoaringBitmap</strong> comes in.</p>
+<p><img decoding="async" loading="lazy" src="/assets/images/figure1-7c9e1064160a84decb149ba63e55b50b.png" width="1356" height="768" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="how-roaringbitmap-works">How RoaringBitmap Works<a href="#how-roaringbitmap-works" class="hash-link" aria-label="Direct link to How RoaringBitmap Works" title="Direct link to How RoaringBitmap Works" translate="no"></a></h2>
+<p>The core idea of a bitmap is simple: use an array of bits to represent a collection of integers. If the user ID is <code>k</code>, set the <code>k</code>-th position to 1. The deduplication count is the number of 1s, and set merging is a bitwise <code>OR</code>. These operations map directly to CPU instructions, which is extremely efficient.</p>
+<p>However, the size of a raw bitmap depends on the maximum ID value. If the ID space is a <strong>32-bit integer</strong> (about 2.1 billion), a full bitmap takes <strong>256 MB</strong>. When the ID distribution is sparse, most bits are 0, and almost all memory is wasted.</p>
+<p><strong>RoaringBitmap</strong> solves this with a layered, adaptive compression strategy. It divides the 32-bit integer space into <strong>65,536 partitions</strong> by the upper 16 bits, and each partition holds up to 65,536 values (the lower 16 bits), called a <strong>Container</strong>. Each container automatically selects the optimal storage format based on its data density:</p>
+<ul>
+<li class=""><strong>ArrayContainer:</strong> When the number of elements is less than 4,096, values are stored in a sorted array of 16-bit integers. For sparse data, this saves tens of times more space than a raw bitmap.</li>
+<li class=""><strong>BitmapContainer:</strong> When the number of elements reaches 4,096 or more, the container switches to a raw 8 KB bitmap. For dense data, bitmaps are more compact than arrays.</li>
+<li class=""><strong>RunContainer:</strong> When data presents continuous interval characteristics (such as IDs 100–500), values are stored using run-length encoding, recording only the start and end of each run.</li>
+</ul>
+<p>This adaptive strategy allows RoaringBitmap to remain efficient across a variety of data distributions: as compact as an array when sparse, as fast as a bitmap when dense, and as economical as run-length encoding when continuous. In practice, its memory footprint is usually only one-tenth to one-hundredth of the original hash set.</p>
+<p>For deduplication scenarios, RoaringBitmap set operations map directly to analytical requirements: <code>OR</code> merges user sets, <code>AND</code> performs cross-dimensional analysis, <code>ANDNOT</code> performs exclusion analysis, and cardinality returns the deduplication count. All operations are accelerated by <strong>SIMD instructions</strong> and can be completed in milliseconds even on hundreds of millions of records.</p>
+<p>One prerequisite: the input to RoaringBitmap must be an integer, and the denser the ID space, the better the compression and efficiency. If the original user ID is a string (such as a <code>UUID</code> or phone number), a dictionary table is needed to map sparse IDs to dense integer IDs.</p>
+<p><img decoding="async" loading="lazy" src="/assets/images/figure2-88efa22dc33e081300bf9ecd388fd53d.png" width="1356" height="748" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="how-apache-fluss-enables-roaringbitmap-deduplication">How Apache Fluss Enables RoaringBitmap Deduplication<a href="#how-apache-fluss-enables-roaringbitmap-deduplication" class="hash-link" aria-label="Direct link to How Apache Fluss Enables RoaringBitmap Deduplication" title="Direct link to How Apache Fluss Enables RoaringBitmap Deduplication" translate="no"></a></h2>
+<p>A RoaringBitmap-based deduplication scheme requires three things: <strong>a dense integer ID space</strong>, <strong>bitmap merging at the storage layer</strong>, and <strong>native bitmap aggregation functions</strong>. Apache Fluss provides all three as built-in capabilities.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="auto-increment-columns-zero-code-dictionary-tables">Auto-Increment Columns: Zero-Code Dictionary Tables<a href="#auto-increment-columns-zero-code-dictionary-tables" class="hash-link" aria-label="Direct link to Auto-Increment Columns: Zero-Code Dictionary Tables" title="Direct link to Auto-Increment Columns: Zero-Code Dictionary Tables" translate="no"></a></h3>
+<p>The auto-increment column feature of Fluss lets you declare a field directly in the table definition. When a new user is written for the first time, Fluss automatically assigns an incrementing integer ID. No external ID service or historical data migration is required.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<p>A single <code>INSERT</code> statement is all that is needed to map sparse identities to dense integers. <code>int32</code> covers about 2.1 billion users, which is sufficient for most business scenarios. The generated ID is unique and monotonically increasing, which is naturally suited to RoaringBitmap's compression strategy.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="aggregate-merge-engine-merge-on-write">Aggregate Merge Engine: Merge on Write<a href="#aggregate-merge-engine-merge-on-write" class="hash-link" aria-label="Direct link to Aggregate Merge Engine: Merge on Write" title="Direct link to Aggregate Merge Engine: Merge on Write" translate="no"></a></h3>
+<p>The <strong>Aggregation Merge Engine</strong> of Fluss allows you to define aggregation rules for each column. When rows with the same primary key arrive, Fluss automatically merges them on the server side instead of overwriting or appending.</p>
+<p>This means the compute layer only needs to send raw events and does not need to maintain any aggregated intermediate state. With <strong>exactly-once semantics</strong> based on Flink Checkpoint, the write path is compact and reliable.</p>
+<p>For append-only aggregation operations such as bitmap union (UV deduplication), merging is naturally <strong>idempotent</strong> during writing. Adding the same <code>uid</code> to a bitmap multiple times does not change the result. In such scenarios, Flink jobs are only responsible for forwarding — no complex <code>UDAFs</code> or retraction messages are needed. For scenarios involving data retraction (such as order cancellation), rollback support for the aggregation merge engine is [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="built-in-rbm32--rbm64-aggregate-functions">Built-in <code>rbm32</code> / <code>rbm64</code> Aggregate Functions<a href="#built-in-rbm32--rbm64-aggregate-functions" class="hash-link" aria-label="Direct link to built-in-rbm32--rbm64-aggregate-functions" title="Direct link to built-in-rbm32--rbm64-aggregate-functions" translate="no"></a></h3>
+<p>Fluss supports RoaringBitmap as a first-class aggregation type. <code>rbm32</code> (32-bit) and <code>rbm64</code> (64-bit) perform native bitmap union on write.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<blockquote>
+<p>Note: the quickstart section extends this table with <code>'table.datalake.enabled' = 'true'</code> and <code>'table.datalake.freshness' = '30s'</code> to enable lake tiering for batch queries. The core deduplication behaviour is identical.</p>
+</blockquote>
+<p>Each event carries a single-element bitmap, which Fluss merges into the existing bitmap of the corresponding primary key on the server side. The deduplication logic is entirely handled by the storage layer without requiring any custom merge code.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="putting-it-together-the-full-data-flow">Putting It Together: The Full Data Flow<a href="#putting-it-together-the-full-data-flow" class="hash-link" aria-label="Direct link to Putting It Together: The Full Data Flow" title="Direct link to Putting It Together: The Full Data Flow" translate="no"></a></h3>
+<p>These three capabilities are interlinked: the auto-increment column provides a <strong>dense ID</strong> (the prerequisite for bitmap deduplication), the aggregation engine merges on write (deduplication is completed in the storage layer), and <code>rbm32</code> makes bitmap merging a native storage operation (no custom logic needed).</p>
+<p>The data flow is straightforward: when an event arrives, Flink obtains the dense <code>uid</code> by querying the dictionary table via a lookup join, calls <code>RB_BUILD(uid)</code> to build a single-element bitmap, and writes it to the aggregation table. Fluss automatically executes bitmap union on the server side. At query time, <code>RB_CARDINALITY()</code> extracts the UV count directly.</p>
+<p>The responsibilities are clearly separated: <strong>Flink</strong> handles mapping, joining, and forwarding without holding long-lived state; <strong>Fluss</strong> holds the persistent merged result; queries read the merged bitmap and extract the count. Deduplication happens at write time — the query side requires no aggregation at all.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="streaming-lakehouse-unification">Streaming Lakehouse Unification<a href="#streaming-lakehouse-unification" class="hash-link" aria-label="Direct link to Streaming Lakehouse Unification" title="Direct link to Streaming Lakehouse Unification" translate="no"></a></h3>
+<p>When data volumes grow to require analysis across longer time windows, this architecture can be seamlessly extended through Fluss's <strong>Lake-Stream integration</strong> capabilities. In a daily partitioning scenario, each day's UV data is stored as a bitmap partition. Historical partitions (e.g., older than 30 days) can be offloaded to <strong>Data Lake</strong> storage (Paimon or Iceberg), while recent partitions remain in Fluss for real-time updates.</p>
+<p>To count UV for an entire year, you only need to <code>OR</code> the 365 daily bitmap partitions and compute the cardinality. Regardless of whether data is in Fluss or the data lake, the bitmap union semantics are identical. This architecture balances storage cost and query flexibility, enabling accurate deduplication analysis across any time span.</p>
+<p><img decoding="async" loading="lazy" src="/assets/images/figure3-44f89c0af7bdf4da5feb4fecea914242.png" width="1350" height="768" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="end-to-end-quickstart">End-to-End Quickstart<a href="#end-to-end-quickstart" class="hash-link" aria-label="Direct link to End-to-End Quickstart" title="Direct link to End-to-End Quickstart" translate="no"></a></h2>
+<p>This section walks through the complete workflow with a minimal working example you can run locally.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="environment-preparation">Environment Preparation<a href="#environment-preparation" class="hash-link" aria-label="Direct link to Environment Preparation" title="Direct link to Environment Preparation" translate="no"></a></h3>
+<p>Requirements: <strong>Docker</strong> and <strong>Docker Compose</strong>.</p>
+<p><strong>1. Create a working directory and download dependencies</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">mkdir</span><span class="token plain"> fluss-rbm-quickstart </span> [...]
+<p>The UDF JAR is a self-contained fat JAR. Placing it in Flink's <code>lib/</code> directory makes all functions available without additional dependencies. The source code is available in the <a href="https://github.com/flink-extended/flink-roaringbitmap" target="_blank" rel="noopener noreferrer" class="">flink-roaringbitmap</a> repository. Paimon-related JARs enable the <strong>Lakehouse Tiering</strong> feature of Fluss, where data automatically tiers from Fluss into Paimon, supp [...]
+<p><strong>2. Create <code>docker-compose.yml</code></strong></p>
+<div class="language-yaml codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">docker-compose.yml</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-yaml codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token key atrule" style="color:#3f6ec6">services [...]
+<p><strong>3. Start the environment</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose up </span><span cla [...]
+<p><strong>4. Start the Lakehouse Tiering Service</strong></p>
+<p>The tiering service continuously transfers data from Fluss to Paimon to support batch queries. Start it in the background before writing data:</p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose </span><span class= [...]
+<p><strong>5. Open the SQL Client</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose </span><span class= [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="create-a-catalog-and-register-the-udfs">Create a Catalog and Register the UDFs<a href="#create-a-catalog-and-register-the-udfs" class="hash-link" aria-label="Direct link to Create a Catalog and Register the UDFs" title="Direct link to Create a Catalog and Register the UDFs" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<p>Register the RoaringBitmap UDFs. The JAR contains three functions: <code>RB_BUILD</code> wraps a single integer as a bitmap; <code>RB_CARDINALITY</code> extracts the deduplication count from a bitmap; <code>RB_OR_AGG</code> executes bitmap <code>OR</code> aggregation for roll-up queries. The deduplication merge at write time is performed by Fluss's <code>rbm32</code> aggregation — these UDFs handle bitmap construction on the Flink side and analysis on the query side.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<blockquote>
+<p><strong>Note:</strong> These functions are currently provided by the external <a href="https://github.com/flink-extended/flink-roaringbitmap" target="_blank" rel="noopener noreferrer" class="">flink-roaringbitmap</a> library. Once <a href="https://cwiki.apache.org/confluence/display/FLUSS/FIP-37%3A+Native+RoaringBitmap+Integration+for+Apache+Fluss" target="_blank" rel="noopener noreferrer" class="">FIP-37: Native RoaringBitmap Integration for Apache Fluss</a> is completed, they will b [...]
+</blockquote>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="create-dictionary-and-aggregation-tables">Create Dictionary and Aggregation Tables<a href="#create-dictionary-and-aggregation-tables" class="hash-link" aria-label="Direct link to Create Dictionary and Aggregation Tables" title="Direct link to Create Dictionary and Aggregation Tables" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token comment" style="color:#8e908c;font-style:italic">-- D [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="write-sample-data">Write Sample Data<a href="#write-sample-data" class="hash-link" aria-label="Direct link to Write Sample Data" title="Direct link to Write Sample Data" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token comment" style="color:#8e908c;font-style:italic">-- S [...]
+<p>Each event is written as one row carrying a single-element bitmap. Fluss merges it server-side via <code>rbm32</code> union into the existing bitmap of the corresponding primary key. Flink jobs are only responsible for mapping and forwarding — they hold no aggregation state, require no <code>GROUP BY</code>, and do not process retraction messages.</p>
+<p><img decoding="async" loading="lazy" src="/assets/images/figure4-b5f8ffeafbf32ec7c4f1090336f94b81.png" width="1344" height="746" class="img_ev3q"></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="query-deduplication-results">Query Deduplication Results<a href="#query-deduplication-results" class="hash-link" aria-label="Direct link to Query Deduplication Results" title="Direct link to Query Deduplication Results" translate="no"></a></h3>
+<p>After writing the data, wait approximately 60 seconds for the data to tier into Paimon. For PK tables, the tiering service uses the <strong>KV snapshot</strong> as a synchronization checkpoint — it reads the snapshot state first, then replays subsequent <strong>CDC events</strong> from that point. The wait covers one KV snapshot period (30 seconds) plus one tiering checkpoint period. Switch to <strong>batch mode</strong> to query the tiered data. Batch mode supports <code>ORDER BY</co [...]
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SET</span><span class= [...]
+<p>Result:</p>
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----------+----------+----+----+----+</span><br></div><div class="token-line [...]
+<p>Note the <code>(app, Shanghai, 20260301, 10)</code> row: <code>user_1</code> appeared twice and <code>user_2</code> appeared once, yielding <code>uv = 2</code>. The three single-element bitmaps were merged by Fluss into a deduplicated set containing two distinct users. <code>pv = 3</code> is the raw event count from the sum aggregation. The query side requires no aggregation — results are ready at write time.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="multi-dimensional-roll-up-queries">Multi-Dimensional Roll-Up Queries<a href="#multi-dimensional-roll-up-queries" class="hash-link" aria-label="Direct link to Multi-Dimensional Roll-Up Queries" title="Direct link to Multi-Dimensional Roll-Up Queries" translate="no"></a></h3>
+<p>The preceding query reads the finest-granularity pre-aggregation results (<code>channel × city × ymd × hh</code>). The real power of bitmaps is supporting flexible roll-up: performing <code>OR</code> operations across fine-grained bitmaps yields coarse-grained deduplication counts without returning to the original data.</p>
+<p>Roll up by channel, overall UV across all cities, dates, and hours:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----+----+</span><br></div><div class="token-line" style="color:#36464e"><sp [...]
+<p><code>user_1</code> visited the <code>app</code> channel from both Shanghai and Beijing at multiple times. <code>RB_OR_AGG</code> combines all fine-grained bitmaps under the same channel into a single deduplicated set — each user is counted only once. The <code>app</code> channel has four distinct users (1, 2, 3, 5), not a simple sum of per-group UV counts.</p>
+<p>Roll up by date, daily UV across all channels and cities:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+----------+----+----+</span><br></div><div class="token-line" style="color:#36464e"><span c [...]
+<p>Active users are users 1–4 on March 1 and users 1, 2, 4, and 5 on March 2. Simply summing per-group UV counts would result in double counting, but the bitmap <code>OR</code> operation gives accurate results.</p>
+<p>Roll up by channel x date, a common dashboard view:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----------+----+----+</span><br></div><div class="token-line" style="color:# [...]
+<p>Global UV, total distinct users across all dimensions:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+----------+----------+</span><br></div><div class="token-line" style="color:#36464e"><span [...]
+<p>Five distinct users generated a total of 12 page views. One query, one full bitmap <code>OR</code> — no need to re-scan the original events.</p>
+<p>All roll-up queries follow the same pattern: <code>GROUP BY</code> the target dimensions, apply <code>RB_OR_AGG</code> on the bitmap column, and use <code>RB_CARDINALITY</code> to extract the count. The finest-granularity pre-aggregated bitmap is the building block for all coarser-grained analysis.</p>
+<blockquote>
+<p>In a production environment, replace the bounded <code>page_views</code> view with a streaming data source and add the <code>/*+ OPTIONS('lookup.insert-if-not-exists' = 'true') */</code> hint to automatically register new users and enable a real-time bitmap write pipeline. The storage-side schema remains identical.</p>
+</blockquote>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="clean-up-the-environment">Clean Up the Environment<a href="#clean-up-the-environment" class="hash-link" aria-label="Direct link to Clean Up the Environment" title="Direct link to Clean Up the Environment" translate="no"></a></h3>
+<p>After exiting the SQL Client, run the following to stop and remove all containers:</p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose down </span><span c [...]
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="beyond-uv-other-use-cases">Beyond UV: Other Use Cases<a href="#beyond-uv-other-use-cases" class="hash-link" aria-label="Direct link to Beyond UV: Other Use Cases" title="Direct link to Beyond UV: Other Use Cases" translate="no"></a></h2>
+<p>This pattern is not limited to UV counting. It extends naturally to a variety of analytical scenarios. The core structure is consistent: choose the appropriate primary key granularity, and bitmap merging is handled by the storage layer at write time.</p>
+<p><strong>Real-time multi-dimensional UV analysis.</strong> The main scenario in this post. Accurate UV statistics based on any combination of dimensions such as channel, city, date, and hour, with flexible dimension roll-up via bitmap union. Applicable to traffic dashboards, advertising effectiveness analysis, and activity monitoring.</p>
+<p><strong>User retention analysis.</strong> Store active user bitmaps at the granularity of <code>user × date</code>. The retained user set is obtained by <code>AND</code>-ing bitmaps from different dates. For example, the cardinality of <code>Day1_bitmap AND Day7_bitmap</code> gives the 7-day retention count.</p>
+<p><strong>Funnel analysis.</strong> Store an aggregation table for each funnel step, with bitmaps recording the set of users who reached that step. Conversion rates are computed by <code>AND</code>-ing bitmaps of adjacent steps. Since bitmaps support set difference (<code>ANDNOT</code>), you can further analyze the characteristics of users who dropped off at each step.</p>
+<p><strong>Audience segmentation and user profiling.</strong> Build user attribute labels (such as gender, province, and preference category) as bitmaps. Multi-label combination queries are converted into bitmap <code>AND</code>/<code>OR</code>/<code>ANDNOT</code> operations, enabling selection of tens of millions of users in milliseconds. This is a high-frequency scenario in advertising <strong>DMP</strong> and recommendation systems.</p>
+<p><img decoding="async" loading="lazy" src="/assets/images/figure5-86a3bd1f0dede51a77608a746ad9ff7b.png" width="1344" height="760" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="getting-started">Getting Started<a href="#getting-started" class="hash-link" aria-label="Direct link to Getting Started" title="Direct link to Getting Started" translate="no"></a></h2>
+<p>If you are evaluating real-time multi-dimensional exact deduplication schemes, Fluss's RoaringBitmap capability is a worthwhile starting point. The architecture follows a clear three-step pattern:</p>
+<ol>
+<li class=""><strong>Map</strong> sparse user identities to dense integers using an auto-increment <code>uid_dictionary</code> table.</li>
+<li class=""><strong>Deduplicate at write time</strong> using an <code>rbm32</code> aggregation table — no stateful Flink jobs, no complex UDAFs.</li>
+<li class=""><strong>Query directly</strong> using <code>RB_CARDINALITY()</code> for point reads or <code>RB_OR_AGG()</code> + <code>RB_CARDINALITY()</code> for any roll-up combination.</li>
+</ol>
+<p>The result is a system where deduplication is handled entirely by the storage layer, and the query side reads pre-computed results with no additional aggregation overhead.</p>
+<p><code>rbm32</code> / <code>rbm64</code> aggregation and the auto-increment dictionary scheme are available from <strong>Apache Fluss 0.9</strong>. See the <a href="https://fluss.apache.org" target="_blank" rel="noopener noreferrer" class="">Fluss documentation</a> for the full configuration reference.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="join-the-community">Join the Community<a href="#join-the-community" class="hash-link" aria-label="Direct link to Join the Community" title="Direct link to Join the Community" translate="no"></a></h2>
+<p>Apache Fluss is an open-source project under active development within the Apache Software Foundation.</p>
+<ul>
+<li class="">GitHub: <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">github.com/apache/fluss</a></li>
+<li class="">Documentation: <a href="https://fluss.apache.org" target="_blank" rel="noopener noreferrer" class="">fluss.apache.org</a></li>
+</ul></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Blog post page navigation"><a class="pagination-nav__link pagination-nav__link--next" href="/blog/why-fluss-chose-rust-for-multi-language-sdk/"><div class="pagination-nav__sublabel">Older post</div><div class="pagination-nav__label">Why Apache Fluss Chose Rust for Its Multi-Language SDK</div></a></nav></main><div class="col col--2"><div class="tableOfContents_bqdL thin-scrollbar"><ul class="table-of-contents ta [...]
+ <p>Copyright © 2026 The Apache Software Foundation, Licensed under the Apache License, Version 2.0.</p>
+ <p>Apache, the names of Apache projects, and the feather logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p></div></div></div></footer></div></div>
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+</html>
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<title>Apache Fluss™ (Incubating) Blog</title>
<link>https://fluss.apache.org/blog/</link>
<description>Apache Fluss™ (Incubating) Blog</description>
- <lastBuildDate>Thu, 09 Apr 2026 00:00:00 GMT</lastBuildDate>
+ <lastBuildDate>Thu, 16 Apr 2026 00:00:00 GMT</lastBuildDate>
<docs>https://validator.w3.org/feed/docs/rss2.html</docs>
<generator>https://github.com/jpmonette/feed</generator>
<language>en</language>
+ <item>
+ <title><![CDATA[Real-Time Unique User Counting at Scale with Apache Fluss]]></title>
+ <link>https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/</link>
+ <guid>https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/</guid>
+ <pubDate>Thu, 16 Apr 2026 00:00:00 GMT</pubDate>
+ <description><![CDATA[UV (Unique Visitors) measures the count of distinct users who visited a page or triggered an event within a given time window — unlike PV (Page Views), which counts every request regardless of who made it. For any product or platform, accurate real-time UV statistics across dimensions like channel, city, date, and hour are a core analytical requirement.]]></description>
+ <content:encoded><![CDATA[<p><strong>UV (Unique Visitors)</strong> measures the count of distinct users who visited a page or triggered an event within a given time window — unlike <strong>PV (Page Views)</strong>, which counts every request regardless of who made it. For any product or platform, accurate real-time UV statistics across dimensions like channel, city, date, and hour are a core analytical requirement.
+The full combination of four dimensions means <strong>16 grouping methods</strong>; when the dimension count increases to seven, the number of possible groupings reaches <strong>128</strong>.</p>
+<p>How can multi-dimensional deduplication be both accurate and flexible while maintaining real-time performance? Behind this challenge lie two very different computing paradigms: direct deduplication of raw data, or set operations based on bitmaps.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="problems-with-traditional-deduplication-schemes">Problems with Traditional Deduplication Schemes<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#problems-with-traditional-deduplication-schemes" class="hash-link" aria-label="Direct link to Problems with Traditional Deduplication Schemes" title="Direct link to Problems with Traditional Deduplication Schemes" translate="no"></a></h2>
+<p>Direct deduplication of raw data (e.g. <code>COUNT(DISTINCT user_id)</code>) is the most intuitive and accurate deduplication method: maintain a <strong>hash set</strong> for each dimension combination and record all user IDs that have appeared under the group.</p>
+<p>This approach works well with smaller data volumes and fewer dimensions, but as business scale grows, three core limitations emerge.</p>
+<p><strong>Performance increases linearly with the amount of data.</strong> Each dimension combination needs to independently store all user identities that have occurred. When daily page view logs reach billions and distinct users number in the tens or hundreds of millions, each group's hash set demands significant memory and CPU for insertion and lookup. Total compute cost scales with the product of dimension combinations and data volume, which is especially noticeable during peak peri [...]
+<p><strong>Multidimensional extensibility is limited.</strong> If an analyst needs UV by <strong>Channel x City</strong> and <strong>Channel x Date</strong>, the traditional scheme must calculate two separate deduplication results. The existing <strong>Channel x City</strong> result cannot be rolled up to get the <strong>Channel</strong>-level UV, because user sets across cities may overlap and simple summation leads to double counting. With each additional dimension, the computational t [...]
+<p><strong>Insufficient query flexibility.</strong> Pre-aggregation schemes must fix dimension combinations in advance. When a business team needs a new dimension on the fly (such as adding <strong>device type</strong>), adjusting the entire data processing pipeline is often required instead of simply modifying a query.</p>
+<p>These three limitations share a root cause: storing and computing on raw user identities at every dimension combination does not scale.</p>
+<p>Put it another way: what if instead of storing the raw ID of each user, we represented the entire user set as a compact structure — one bit per user?</p>
+<p>In the bitmap scheme, deduplication count equals counting the number of 1s in the bitmap (<code>popcount</code>), merging two user sets equals a bitwise <code>OR</code>, and cross-dimensional analysis equals a bitwise <code>AND</code>. All operations are CPU-native bit operations, blisteringly fast.</p>
+<p>More importantly, bitmaps naturally support roll-up: the bitmap of <code>Channel = app, City = Shanghai</code> combined with <code>OR</code> with the bitmap of <code>Channel = app, City = Beijing</code> yields the user set for <code>Channel = app</code> — no need to re-scan the original data.</p>
+<p>This is a fundamental difference between the two computing paradigms: <strong>direct deduplication</strong> requires independent calculations for each dimension combination, while the intermediate results of <strong>bitmap deduplication</strong> are naturally composable.</p>
+<p>But the raw bitmap has a problem: if the user ID space is sparse (such as <code>UUID</code> or hash values), the bitmap will be very large and waste memory. A compressed bitmap format that works efficiently on sparse data is needed. This is where <strong>RoaringBitmap</strong> comes in.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure1-7c9e1064160a84decb149ba63e55b50b.png" width="1356" height="768" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="how-roaringbitmap-works">How RoaringBitmap Works<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#how-roaringbitmap-works" class="hash-link" aria-label="Direct link to How RoaringBitmap Works" title="Direct link to How RoaringBitmap Works" translate="no"></a></h2>
+<p>The core idea of a bitmap is simple: use an array of bits to represent a collection of integers. If the user ID is <code>k</code>, set the <code>k</code>-th position to 1. The deduplication count is the number of 1s, and set merging is a bitwise <code>OR</code>. These operations map directly to CPU instructions, which is extremely efficient.</p>
+<p>However, the size of a raw bitmap depends on the maximum ID value. If the ID space is a <strong>32-bit integer</strong> (about 2.1 billion), a full bitmap takes <strong>256 MB</strong>. When the ID distribution is sparse, most bits are 0, and almost all memory is wasted.</p>
+<p><strong>RoaringBitmap</strong> solves this with a layered, adaptive compression strategy. It divides the 32-bit integer space into <strong>65,536 partitions</strong> by the upper 16 bits, and each partition holds up to 65,536 values (the lower 16 bits), called a <strong>Container</strong>. Each container automatically selects the optimal storage format based on its data density:</p>
+<ul>
+<li class=""><strong>ArrayContainer:</strong> When the number of elements is less than 4,096, values are stored in a sorted array of 16-bit integers. For sparse data, this saves tens of times more space than a raw bitmap.</li>
+<li class=""><strong>BitmapContainer:</strong> When the number of elements reaches 4,096 or more, the container switches to a raw 8 KB bitmap. For dense data, bitmaps are more compact than arrays.</li>
+<li class=""><strong>RunContainer:</strong> When data presents continuous interval characteristics (such as IDs 100–500), values are stored using run-length encoding, recording only the start and end of each run.</li>
+</ul>
+<p>This adaptive strategy allows RoaringBitmap to remain efficient across a variety of data distributions: as compact as an array when sparse, as fast as a bitmap when dense, and as economical as run-length encoding when continuous. In practice, its memory footprint is usually only one-tenth to one-hundredth of the original hash set.</p>
+<p>For deduplication scenarios, RoaringBitmap set operations map directly to analytical requirements: <code>OR</code> merges user sets, <code>AND</code> performs cross-dimensional analysis, <code>ANDNOT</code> performs exclusion analysis, and cardinality returns the deduplication count. All operations are accelerated by <strong>SIMD instructions</strong> and can be completed in milliseconds even on hundreds of millions of records.</p>
+<p>One prerequisite: the input to RoaringBitmap must be an integer, and the denser the ID space, the better the compression and efficiency. If the original user ID is a string (such as a <code>UUID</code> or phone number), a dictionary table is needed to map sparse IDs to dense integer IDs.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure2-88efa22dc33e081300bf9ecd388fd53d.png" width="1356" height="748" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="how-apache-fluss-enables-roaringbitmap-deduplication">How Apache Fluss Enables RoaringBitmap Deduplication<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#how-apache-fluss-enables-roaringbitmap-deduplication" class="hash-link" aria-label="Direct link to How Apache Fluss Enables RoaringBitmap Deduplication" title="Direct link to How Apache Fluss Enables RoaringBitmap Deduplication" translate="no"></a></h2>
+<p>A RoaringBitmap-based deduplication scheme requires three things: <strong>a dense integer ID space</strong>, <strong>bitmap merging at the storage layer</strong>, and <strong>native bitmap aggregation functions</strong>. Apache Fluss provides all three as built-in capabilities.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="auto-increment-columns-zero-code-dictionary-tables">Auto-Increment Columns: Zero-Code Dictionary Tables<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#auto-increment-columns-zero-code-dictionary-tables" class="hash-link" aria-label="Direct link to Auto-Increment Columns: Zero-Code Dictionary Tables" title="Direct link to Auto-Increment Columns: Zero-Code Dictionary Tables" translate="no"></a></h3>
+<p>The auto-increment column feature of Fluss lets you declare a field directly in the table definition. When a new user is written for the first time, Fluss automatically assigns an incrementing integer ID. No external ID service or historical data migration is required.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<p>A single <code>INSERT</code> statement is all that is needed to map sparse identities to dense integers. <code>int32</code> covers about 2.1 billion users, which is sufficient for most business scenarios. The generated ID is unique and monotonically increasing, which is naturally suited to RoaringBitmap's compression strategy.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="aggregate-merge-engine-merge-on-write">Aggregate Merge Engine: Merge on Write<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#aggregate-merge-engine-merge-on-write" class="hash-link" aria-label="Direct link to Aggregate Merge Engine: Merge on Write" title="Direct link to Aggregate Merge Engine: Merge on Write" translate="no"></a></h3>
+<p>The <strong>Aggregation Merge Engine</strong> of Fluss allows you to define aggregation rules for each column. When rows with the same primary key arrive, Fluss automatically merges them on the server side instead of overwriting or appending.</p>
+<p>This means the compute layer only needs to send raw events and does not need to maintain any aggregated intermediate state. With <strong>exactly-once semantics</strong> based on Flink Checkpoint, the write path is compact and reliable.</p>
+<p>For append-only aggregation operations such as bitmap union (UV deduplication), merging is naturally <strong>idempotent</strong> during writing. Adding the same <code>uid</code> to a bitmap multiple times does not change the result. In such scenarios, Flink jobs are only responsible for forwarding — no complex <code>UDAFs</code> or retraction messages are needed. For scenarios involving data retraction (such as order cancellation), rollback support for the aggregation merge engine is [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="built-in-rbm32--rbm64-aggregate-functions">Built-in <code>rbm32</code> / <code>rbm64</code> Aggregate Functions<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#built-in-rbm32--rbm64-aggregate-functions" class="hash-link" aria-label="Direct link to built-in-rbm32--rbm64-aggregate-functions" title="Direct link to built-in-rbm32--rbm64-aggregate-functions" translate="no"></a></h3>
+<p>Fluss supports RoaringBitmap as a first-class aggregation type. <code>rbm32</code> (32-bit) and <code>rbm64</code> (64-bit) perform native bitmap union on write.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<blockquote>
+<p>Note: the quickstart section extends this table with <code>'table.datalake.enabled' = 'true'</code> and <code>'table.datalake.freshness' = '30s'</code> to enable lake tiering for batch queries. The core deduplication behaviour is identical.</p>
+</blockquote>
+<p>Each event carries a single-element bitmap, which Fluss merges into the existing bitmap of the corresponding primary key on the server side. The deduplication logic is entirely handled by the storage layer without requiring any custom merge code.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="putting-it-together-the-full-data-flow">Putting It Together: The Full Data Flow<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#putting-it-together-the-full-data-flow" class="hash-link" aria-label="Direct link to Putting It Together: The Full Data Flow" title="Direct link to Putting It Together: The Full Data Flow" translate="no"></a></h3>
+<p>These three capabilities are interlinked: the auto-increment column provides a <strong>dense ID</strong> (the prerequisite for bitmap deduplication), the aggregation engine merges on write (deduplication is completed in the storage layer), and <code>rbm32</code> makes bitmap merging a native storage operation (no custom logic needed).</p>
+<p>The data flow is straightforward: when an event arrives, Flink obtains the dense <code>uid</code> by querying the dictionary table via a lookup join, calls <code>RB_BUILD(uid)</code> to build a single-element bitmap, and writes it to the aggregation table. Fluss automatically executes bitmap union on the server side. At query time, <code>RB_CARDINALITY()</code> extracts the UV count directly.</p>
+<p>The responsibilities are clearly separated: <strong>Flink</strong> handles mapping, joining, and forwarding without holding long-lived state; <strong>Fluss</strong> holds the persistent merged result; queries read the merged bitmap and extract the count. Deduplication happens at write time — the query side requires no aggregation at all.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="streaming-lakehouse-unification">Streaming Lakehouse Unification<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#streaming-lakehouse-unification" class="hash-link" aria-label="Direct link to Streaming Lakehouse Unification" title="Direct link to Streaming Lakehouse Unification" translate="no"></a></h3>
+<p>When data volumes grow to require analysis across longer time windows, this architecture can be seamlessly extended through Fluss's <strong>Lake-Stream integration</strong> capabilities. In a daily partitioning scenario, each day's UV data is stored as a bitmap partition. Historical partitions (e.g., older than 30 days) can be offloaded to <strong>Data Lake</strong> storage (Paimon or Iceberg), while recent partitions remain in Fluss for real-time updates.</p>
+<p>To count UV for an entire year, you only need to <code>OR</code> the 365 daily bitmap partitions and compute the cardinality. Regardless of whether data is in Fluss or the data lake, the bitmap union semantics are identical. This architecture balances storage cost and query flexibility, enabling accurate deduplication analysis across any time span.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure3-44f89c0af7bdf4da5feb4fecea914242.png" width="1350" height="768" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="end-to-end-quickstart">End-to-End Quickstart<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#end-to-end-quickstart" class="hash-link" aria-label="Direct link to End-to-End Quickstart" title="Direct link to End-to-End Quickstart" translate="no"></a></h2>
+<p>This section walks through the complete workflow with a minimal working example you can run locally.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="environment-preparation">Environment Preparation<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#environment-preparation" class="hash-link" aria-label="Direct link to Environment Preparation" title="Direct link to Environment Preparation" translate="no"></a></h3>
+<p>Requirements: <strong>Docker</strong> and <strong>Docker Compose</strong>.</p>
+<p><strong>1. Create a working directory and download dependencies</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">mkdir</span><span class="token plain"> fluss-rbm-quickstart </span> [...]
+<p>The UDF JAR is a self-contained fat JAR. Placing it in Flink's <code>lib/</code> directory makes all functions available without additional dependencies. The source code is available in the <a href="https://github.com/flink-extended/flink-roaringbitmap" target="_blank" rel="noopener noreferrer" class="">flink-roaringbitmap</a> repository. Paimon-related JARs enable the <strong>Lakehouse Tiering</strong> feature of Fluss, where data automatically tiers from Fluss into Paimon, supportin [...]
+<p><strong>2. Create <code>docker-compose.yml</code></strong></p>
+<div class="language-yaml codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">docker-compose.yml</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-yaml codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token key atrule" style="color:#3f6ec6">services [...]
+<p><strong>3. Start the environment</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose up </span><span cla [...]
+<p><strong>4. Start the Lakehouse Tiering Service</strong></p>
+<p>The tiering service continuously transfers data from Fluss to Paimon to support batch queries. Start it in the background before writing data:</p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose </span><span class= [...]
+<p><strong>5. Open the SQL Client</strong></p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose </span><span class= [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="create-a-catalog-and-register-the-udfs">Create a Catalog and Register the UDFs<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#create-a-catalog-and-register-the-udfs" class="hash-link" aria-label="Direct link to Create a Catalog and Register the UDFs" title="Direct link to Create a Catalog and Register the UDFs" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<p>Register the RoaringBitmap UDFs. The JAR contains three functions: <code>RB_BUILD</code> wraps a single integer as a bitmap; <code>RB_CARDINALITY</code> extracts the deduplication count from a bitmap; <code>RB_OR_AGG</code> executes bitmap <code>OR</code> aggregation for roll-up queries. The deduplication merge at write time is performed by Fluss's <code>rbm32</code> aggregation — these UDFs handle bitmap construction on the Flink side and analysis on the query side.</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">CREATE</span><span cla [...]
+<blockquote>
+<p><strong>Note:</strong> These functions are currently provided by the external <a href="https://github.com/flink-extended/flink-roaringbitmap" target="_blank" rel="noopener noreferrer" class="">flink-roaringbitmap</a> library. Once <a href="https://cwiki.apache.org/confluence/display/FLUSS/FIP-37%3A+Native+RoaringBitmap+Integration+for+Apache+Fluss" target="_blank" rel="noopener noreferrer" class="">FIP-37: Native RoaringBitmap Integration for Apache Fluss</a> is completed, they will b [...]
+</blockquote>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="create-dictionary-and-aggregation-tables">Create Dictionary and Aggregation Tables<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#create-dictionary-and-aggregation-tables" class="hash-link" aria-label="Direct link to Create Dictionary and Aggregation Tables" title="Direct link to Create Dictionary and Aggregation Tables" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token comment" style="color:#8e908c;font-style:italic">-- D [...]
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="write-sample-data">Write Sample Data<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#write-sample-data" class="hash-link" aria-label="Direct link to Write Sample Data" title="Direct link to Write Sample Data" translate="no"></a></h3>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token comment" style="color:#8e908c;font-style:italic">-- S [...]
+<p>Each event is written as one row carrying a single-element bitmap. Fluss merges it server-side via <code>rbm32</code> union into the existing bitmap of the corresponding primary key. Flink jobs are only responsible for mapping and forwarding — they hold no aggregation state, require no <code>GROUP BY</code>, and do not process retraction messages.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure4-b5f8ffeafbf32ec7c4f1090336f94b81.png" width="1344" height="746" class="img_ev3q"></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="query-deduplication-results">Query Deduplication Results<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#query-deduplication-results" class="hash-link" aria-label="Direct link to Query Deduplication Results" title="Direct link to Query Deduplication Results" translate="no"></a></h3>
+<p>After writing the data, wait approximately 60 seconds for the data to tier into Paimon. For PK tables, the tiering service uses the <strong>KV snapshot</strong> as a synchronization checkpoint — it reads the snapshot state first, then replays subsequent <strong>CDC events</strong> from that point. The wait covers one KV snapshot period (30 seconds) plus one tiering checkpoint period. Switch to <strong>batch mode</strong> to query the tiered data. Batch mode supports <code>ORDER BY</co [...]
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SET</span><span class= [...]
+<p>Result:</p>
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----------+----------+----+----+----+</span><br></div><div class="token-line [...]
+<p>Note the <code>(app, Shanghai, 20260301, 10)</code> row: <code>user_1</code> appeared twice and <code>user_2</code> appeared once, yielding <code>uv = 2</code>. The three single-element bitmaps were merged by Fluss into a deduplicated set containing two distinct users. <code>pv = 3</code> is the raw event count from the sum aggregation. The query side requires no aggregation — results are ready at write time.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="multi-dimensional-roll-up-queries">Multi-Dimensional Roll-Up Queries<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#multi-dimensional-roll-up-queries" class="hash-link" aria-label="Direct link to Multi-Dimensional Roll-Up Queries" title="Direct link to Multi-Dimensional Roll-Up Queries" translate="no"></a></h3>
+<p>The preceding query reads the finest-granularity pre-aggregation results (<code>channel × city × ymd × hh</code>). The real power of bitmaps is supporting flexible roll-up: performing <code>OR</code> operations across fine-grained bitmaps yields coarse-grained deduplication counts without returning to the original data.</p>
+<p>Roll up by channel, overall UV across all cities, dates, and hours:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----+----+</span><br></div><div class="token-line" style="color:#36464e"><sp [...]
+<p><code>user_1</code> visited the <code>app</code> channel from both Shanghai and Beijing at multiple times. <code>RB_OR_AGG</code> combines all fine-grained bitmaps under the same channel into a single deduplicated set — each user is counted only once. The <code>app</code> channel has four distinct users (1, 2, 3, 5), not a simple sum of per-group UV counts.</p>
+<p>Roll up by date, daily UV across all channels and cities:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+----------+----+----+</span><br></div><div class="token-line" style="color:#36464e"><span c [...]
+<p>Active users are users 1–4 on March 1 and users 1, 2, 4, and 5 on March 2. Simply summing per-group UV counts would result in double counting, but the bitmap <code>OR</code> operation gives accurate results.</p>
+<p>Roll up by channel x date, a common dashboard view:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+--------------+----------+----+----+</span><br></div><div class="token-line" style="color:# [...]
+<p>Global UV, total distinct users across all dimensions:</p>
+<div class="language-sql codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockTitle_OeMC">Flink SQL</div><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-sql codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token keyword" style="color:#3f6ec6">SELECT</span><span cla [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token plain">+----------+----------+</span><br></div><div class="token-line" style="color:#36464e"><span [...]
+<p>Five distinct users generated a total of 12 page views. One query, one full bitmap <code>OR</code> — no need to re-scan the original events.</p>
+<p>All roll-up queries follow the same pattern: <code>GROUP BY</code> the target dimensions, apply <code>RB_OR_AGG</code> on the bitmap column, and use <code>RB_CARDINALITY</code> to extract the count. The finest-granularity pre-aggregated bitmap is the building block for all coarser-grained analysis.</p>
+<blockquote>
+<p>In a production environment, replace the bounded <code>page_views</code> view with a streaming data source and add the <code>/*+ OPTIONS('lookup.insert-if-not-exists' = 'true') */</code> hint to automatically register new users and enable a real-time bitmap write pipeline. The storage-side schema remains identical.</p>
+</blockquote>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="clean-up-the-environment">Clean Up the Environment<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#clean-up-the-environment" class="hash-link" aria-label="Direct link to Clean Up the Environment" title="Direct link to Clean Up the Environment" translate="no"></a></h3>
+<p>After exiting the SQL Client, run the following to stop and remove all containers:</p>
+<div class="language-bash codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#36464e;--prism-background-color:#f5f5f5"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-bash codeBlock_bY9V thin-scrollbar" style="color:#36464e;background-color:#f5f5f5"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#36464e"><span class="token function" style="color:#a846b9">docker</span><span class="token plain"> compose down </span><span c [...]
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="beyond-uv-other-use-cases">Beyond UV: Other Use Cases<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#beyond-uv-other-use-cases" class="hash-link" aria-label="Direct link to Beyond UV: Other Use Cases" title="Direct link to Beyond UV: Other Use Cases" translate="no"></a></h2>
+<p>This pattern is not limited to UV counting. It extends naturally to a variety of analytical scenarios. The core structure is consistent: choose the appropriate primary key granularity, and bitmap merging is handled by the storage layer at write time.</p>
+<p><strong>Real-time multi-dimensional UV analysis.</strong> The main scenario in this post. Accurate UV statistics based on any combination of dimensions such as channel, city, date, and hour, with flexible dimension roll-up via bitmap union. Applicable to traffic dashboards, advertising effectiveness analysis, and activity monitoring.</p>
+<p><strong>User retention analysis.</strong> Store active user bitmaps at the granularity of <code>user × date</code>. The retained user set is obtained by <code>AND</code>-ing bitmaps from different dates. For example, the cardinality of <code>Day1_bitmap AND Day7_bitmap</code> gives the 7-day retention count.</p>
+<p><strong>Funnel analysis.</strong> Store an aggregation table for each funnel step, with bitmaps recording the set of users who reached that step. Conversion rates are computed by <code>AND</code>-ing bitmaps of adjacent steps. Since bitmaps support set difference (<code>ANDNOT</code>), you can further analyze the characteristics of users who dropped off at each step.</p>
+<p><strong>Audience segmentation and user profiling.</strong> Build user attribute labels (such as gender, province, and preference category) as bitmaps. Multi-label combination queries are converted into bitmap <code>AND</code>/<code>OR</code>/<code>ANDNOT</code> operations, enabling selection of tens of millions of users in milliseconds. This is a high-frequency scenario in advertising <strong>DMP</strong> and recommendation systems.</p>
+<p><img decoding="async" loading="lazy" src="https://fluss.apache.org/assets/images/figure5-86a3bd1f0dede51a77608a746ad9ff7b.png" width="1344" height="760" class="img_ev3q"></p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="getting-started">Getting Started<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#getting-started" class="hash-link" aria-label="Direct link to Getting Started" title="Direct link to Getting Started" translate="no"></a></h2>
+<p>If you are evaluating real-time multi-dimensional exact deduplication schemes, Fluss's RoaringBitmap capability is a worthwhile starting point. The architecture follows a clear three-step pattern:</p>
+<ol>
+<li class=""><strong>Map</strong> sparse user identities to dense integers using an auto-increment <code>uid_dictionary</code> table.</li>
+<li class=""><strong>Deduplicate at write time</strong> using an <code>rbm32</code> aggregation table — no stateful Flink jobs, no complex UDAFs.</li>
+<li class=""><strong>Query directly</strong> using <code>RB_CARDINALITY()</code> for point reads or <code>RB_OR_AGG()</code> + <code>RB_CARDINALITY()</code> for any roll-up combination.</li>
+</ol>
+<p>The result is a system where deduplication is handled entirely by the storage layer, and the query side reads pre-computed results with no additional aggregation overhead.</p>
+<p><code>rbm32</code> / <code>rbm64</code> aggregation and the auto-increment dictionary scheme are available from <strong>Apache Fluss 0.9</strong>. See the <a href="https://fluss.apache.org/" target="_blank" rel="noopener noreferrer" class="">Fluss documentation</a> for the full configuration reference.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="join-the-community">Join the Community<a href="https://fluss.apache.org/blog/roaringbitmap-uv-deduplication/#join-the-community" class="hash-link" aria-label="Direct link to Join the Community" title="Direct link to Join the Community" translate="no"></a></h2>
+<p>Apache Fluss is an open-source project under active development within the Apache Software Foundation.</p>
+<ul>
+<li class="">GitHub: <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">github.com/apache/fluss</a></li>
+<li class="">Documentation: <a href="https://fluss.apache.org/" target="_blank" rel="noopener noreferrer" class="">fluss.apache.org</a></li>
+</ul>]]></content:encoded>
+ </item>
<item>
<title><![CDATA[Why Apache Fluss Chose Rust for Its Multi-Language SDK]]></title>
<link>https://fluss.apache.org/blog/why-fluss-chose-rust-for-multi-language-sdk/</link>
@@ -1211,7 +1353,7 @@ and then ship the results into a <strong>separate serving</strong> system purely
<p><strong>This introduces an important trade-off:</strong> every additional system means duplicate storage, increased latency, and extra operational overhead. Data has to be landed, indexed, and kept in sync, even though the stream processor has already computed the end state.</p>
<p>With Fluss Primary Key Tables, you don’t need that extra serving layer. Your stateful computations in Flink can materialize directly into PK Tables, which are durable, consistent, and queryable in real time. This lets you power dashboards or APIs straight from the tables, cutting out redundant clusters and reducing end-to-end latency.</p>
<p><strong>In short:</strong> compute once in Flink, persist in Fluss, and serve directly, without duplicating storage or managing another system.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="-closing-thoughts">🚀 Closing Thoughts<a href="https://fluss.apache.org/blog/pk-key-tables-log-cache-streaming/#-closing-thoughts" class="hash-link" aria-label="Direct link to �🚀 Closing Thoughts" title="Direct link to 🚀 Closing Thoughts" translate="no"></a></h3>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="-closing-thoughts">🚀 Closing Thoughts<a href="https://fluss.apache.org/blog/pk-key-tables-log-cache-streaming/#-closing-thoughts" class="hash-link" aria-label="Direct link to 🚀 Closing Thoughts" title="Direct link to 🚀 Closing Thoughts" translate="no"></a></h3>
<p>The above are some examples use cases, we have seen recently and I'm only eager to see what users will build with Fluss - realtime features stores is another PK table use case currently under investigation.</p>
<p>Fluss Primary Key Tables are one of the most compelling features of the platform. They embody the <strong>stream-table duality</strong> in practice: every write is a log entry and a KV update; every recovery is a snapshot plus replay; every cache read is guaranteed to be consistent with the log.</p>
<p>The complexity of coordinating a log and a cache disappears. With Fluss, you don’t have to choose between speed and safety; you get both.</p>
@@ -2603,74 +2745,5 @@ The future planning for Fluss involves three key aspects, each corresponding to
</ul>
<p>There are more exciting work happening in the community. You can check out <a class="" href="https://fluss.apache.org/roadmap/">Fluss Roadmap</a> for a comprehensive future planning.</p>]]></content:encoded>
</item>
- <item>
- <title><![CDATA[Why Fluss? Top 4 Challenges of Using Kafka for Real-Time Analytics]]></title>
- <link>https://fluss.apache.org/blog/why-fluss/</link>
- <guid>https://fluss.apache.org/blog/why-fluss/</guid>
- <pubDate>Wed, 11 Dec 2024 00:00:00 GMT</pubDate>
- <description><![CDATA[The industry is undergoing a clear and significant shift as big data computing transitions from offline to real-time processing.]]></description>
- <content:encoded><![CDATA[<p>The industry is undergoing a clear and significant shift as big data computing transitions from offline to real-time processing.
-This transition is revolutionizing various sectors, including the E-commerce, automotive networking, finance, and beyond,
-where real-time data applications are becoming integral to operations. This evolution enables organizations to unlock greater
-value by leveraging real-time insights to drive business impact and enhance decision-making.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img1-a29d423ce0b5f952b79ad2babb903cf5.jpg" width="3656" height="1240" class="img_ev3q">
-The evolution of big data technology is becoming increasingly impactful, reshaping the computing architecture landscape.
-The traditional Hive-based data warehouse has given way to modern architectures, starting with Lakehouse models and progressing
-to the Paimon streaming Lakehouse, which has gained significant traction in markets like China and more recently started expanding to EU/US markets.
-The core driver behind these architectural innovations is the need to improve data processing timeliness. The data freshness is improved from traditional
-T+1 (next-day readiness) to T+1 hours, and now T+1 minutes. However, Lakehouse architectures, being file-system-based,
-inherently face minute-level latency as their practical upper limit.</p>
-<p>Yet, many critical use cases, such as search and recommendation systems, advertisement attribution, and anomaly detection, demand second-level latency.
-While big data technologies have advanced significantly, there remains a notable gap: a user-friendly, second-level storage solution tailored for big data analytics.</p>
-<p>In most real-time data scenarios, Apache Kafka has emerged as the go-to second-level storage solution. Its integration with Apache Flink represents the dominant architecture for building real-time data warehouses. However, while widely adopted, this combination presents significant challenges for achieving true real-time analytics at scale. The limitations of using Kafka for big data analytics highlight the need for a more robust solution to meet the demands of modern real-time use ca [...]
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="kafka-falls-short-in-real-time-analytics">Kafka Falls Short in Real-Time Analytics<a href="https://fluss.apache.org/blog/why-fluss/#kafka-falls-short-in-real-time-analytics" class="hash-link" aria-label="Direct link to Kafka Falls Short in Real-Time Analytics" title="Direct link to Kafka Falls Short in Real-Time Analytics" translate="no"></a></h2>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="no-support-for-updates">No Support for Updates<a href="https://fluss.apache.org/blog/why-fluss/#no-support-for-updates" class="hash-link" aria-label="Direct link to No Support for Updates" title="Direct link to No Support for Updates" translate="no"></a></h3>
-<p>The first significant challenge with Apache Kafka is its lack of support for updates, a critical feature for data warehouses. In data warehousing, updates are often essential to correct or amend data. However, Kafka's inability to handle updates results in duplicate records for the same primary key. When consumed by a computing engine, this duplication necessitates costly deduplication processes to ensure accurate results.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img2-d2d1a314768a1be3da0308e20851d3f3.jpg" width="3656" height="1172" class="img_ev3q"></p>
-<p>In Apache Flink, for instance, handling this issue requires materializing all upstream data in state, which is resource-intensive. Every time data is consumed from Kafka, the deduplication process incurs a substantial overhead, significantly increasing computational and storage costs. This limitation not only impacts performance but also hampers the reusability of Kafka-stored data for downstream business processes.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="lack-of-querying-capabilities">Lack of Querying Capabilities<a href="https://fluss.apache.org/blog/why-fluss/#lack-of-querying-capabilities" class="hash-link" aria-label="Direct link to Lack of Querying Capabilities" title="Direct link to Lack of Querying Capabilities" translate="no"></a></h3>
-<p>The second major limitation of Apache Kafka is hard to debug, primarily due to its lack of native querying capabilities. In data warehousing, querying is a fundamental feature that facilitates troubleshooting and ad-hoc analysis to understand data trends. Unfortunately, Kafka operates much like a black box, making it challenging to perform these critical tasks without additional tools or layers.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img3-32732f50a5420dad7d1dd6c5b3d17d6f.jpg" width="3696" height="1214" class="img_ev3q"></p>
-<p>To address this limitation, the industry has adopted two primary approaches, each with its own trade-offs:</p>
-<ul>
-<li class=""><strong>Synchronizing Kafka Data to an OLAP System:</strong> This allows for querying data using the OLAP system’s capabilities. However, this approach introduces additional components into the architecture, increasing both complexity and cost. Moreover, it risks data inconsistencies due to synchronization delays.</li>
-<li class=""><strong>Querying Kafka Directly Using Trino:</strong> While Trino can query Kafka, it relies solely on full scans, which are inefficient for large-scale operations. For instance, a simple query on just 1GB of Kafka data can take up to one minute, rendering it impractical for large datasets or real-time requirements.</li>
-</ul>
-<p>These limitations make Kafka unsuitable for efficient and scalable data exploration in modern data warehousing workflows.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="difficulty-with-data-backfilling">Difficulty with Data Backfilling<a href="https://fluss.apache.org/blog/why-fluss/#difficulty-with-data-backfilling" class="hash-link" aria-label="Direct link to Difficulty with Data Backfilling" title="Direct link to Difficulty with Data Backfilling" translate="no"></a></h3>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img4-5a57c638bb5dd478a86a9fcd1683734f.jpg" width="3526" height="1210" class="img_ev3q">
-The third significant issue with Apache Kafka is processing historical data, also known as data backfilling, a common requirement in data warehousing.
-For instance, in logistics, it usually needs to process and analyze historical data from several months ago. However, Kafka only retain data for a few days due to the high cost of storage.
-Even though Kafka community has introduced <a href="https://cwiki.apache.org/confluence/display/KAFKA/KIP-405%3A+Kafka+Tiered+Storage" target="_blank" rel="noopener noreferrer" class="">Tiered Storage</a> to address the long-term data issue, it still has limitations.
-Reading historical data still require all the data to pass through Kafka brokers, which can lead to brokers unstable and <a href="https://www.warpstream.com/blog/tiered-storage-wont-fix-kafka#increased-complexity-and-operational-burden" target="_blank" rel="noopener noreferrer" class="">disrupt live traffics</a>.</p>
-<p>These limitations highlight Kafka’s inefficiency as a solution for data backfilling in large-scale and long-term analytical use cases, further underscoring the need for more robust alternatives in real-time data warehousing.</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="excessive-network-costs">Excessive Network Costs<a href="https://fluss.apache.org/blog/why-fluss/#excessive-network-costs" class="hash-link" aria-label="Direct link to Excessive Network Costs" title="Direct link to Excessive Network Costs" translate="no"></a></h3>
-<p>The final major challenge with Apache Kafka lies in its high network costs, which account for an estimated 88% of Kafka's overall operational expenses. In data warehousing, the "one write, multiple read" pattern is common, with each consumer often requiring only a subset of the data. However, Kafka's design mandates that consumers read the entire dataset, regardless of how much is actually needed.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img5-e185731d5e249a6c80b962b77b5a16ad.jpg" width="3636" height="1206" class="img_ev3q"></p>
-<p>For instance, among the tens of thousands of Flink SQL jobs at Alibaba, only 49% of the upstream columns are utilized per job on average. Despite this, consumers must read all the columns and pay 100% networking cost, which is highly inefficient and wasteful.</p>
-<p>In summary, using Kafka for real-time analytics presents several critical issues: <strong>(1) lack of support for updates</strong>, <strong>(2) absence of querying capabilities</strong>, <strong>(3) difficulty with data backfilling</strong>, and <strong>(4) excessive network costs</strong>. These limitations make the combination of Flink and Kafka less than ideal for real-time data warehousing.
-But why Kafka lacks these abilities? Is it possible to add the abilities to Kafka?</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="kafka-is-not-designed-for-analytics">Kafka is not designed for Analytics<a href="https://fluss.apache.org/blog/why-fluss/#kafka-is-not-designed-for-analytics" class="hash-link" aria-label="Direct link to Kafka is not designed for Analytics" title="Direct link to Kafka is not designed for Analytics" translate="no"></a></h2>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img6-4741286b7e5d1410d5a8d7036e3cad8d.jpg" width="3620" height="1148" class="img_ev3q">
-The root cause of these challenges lies in the fundamental design philosophy of Kafka: <strong>Kafka is designed for streaming events, NOT for streaming analytics</strong>. Each system has its own focus and strengths, and Kafka's design is optimized for message queue scenarios rather than analytical workloads.</p>
-<p>In Kafka, data is typically stored in a row-oriented format, such as CSV, JSON, or Avro.
-While this is highly efficient for use cases involving message streaming, it becomes a bottleneck for analytical scenarios.
-Data analysis requires handling large volumes of data, and therefore heavily relies on data skipping capabilities from storage, such as column pruning and predicate pushdown.
-For this reason, columnar storage is far better suited for analytical workloads, whereas Kafka's row-based storage is not designed to meet these demands effectively.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="introducing-fluss">Introducing Fluss<a href="https://fluss.apache.org/blog/why-fluss/#introducing-fluss" class="hash-link" aria-label="Direct link to Introducing Fluss" title="Direct link to Introducing Fluss" translate="no"></a></h2>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img7-06886bca9797751895c82d707cb04b2d.jpg" width="3644" height="1210" class="img_ev3q">
-When visualizing the data ecosystem as a four-quadrant matrix, an intriguing pattern emerges.
-The left side of the matrix represents operational systems, while the right side represents analytical systems.
-The top half is dedicated to stream storage, and the bottom half to table storage.
-In operational systems, both databases and stream storage predominantly use row-based formats, as row storage is more efficient for transactional workloads. Conversely, in analytical systems like Apache Iceberg and Snowflake, columnar storage is preferred due to its superior performance in analytical scenarios.</p>
-<p>Interestingly, the upper-right quadrant of the matrix remains empty, indicating a significant gap in the market: a streaming storage for analytical scenarios. Unsurprisingly, such a storage would likely adopt a columnar format to effectively address the needs of real-time analytics.</p>
-<p>To fill the gap and resolve problems in Flink and streaming analytics, we embarked on a journey two years ago to create a streaming storage. We named the project "<strong>FL</strong>ink <strong>U</strong>nified <strong>S</strong>treaming <strong>S</strong>torage", and from its initials, we derived the name <strong>Fluss</strong>.
-<img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img8-87cfb5887e9a49febaacd805de5216fe.jpg" width="3700" height="946" class="img_ev3q">
-Interestingly, Flink is derived from the German word for "<strong>agile</strong>", and Fluss, which translates to "<strong>river</strong>" in German, resonates deeply with the project’s vision.
-It symbolizes the streaming data is continuously flowing, distributing and converging into data lakes, just like a river.
-The launch of Fluss coincides with Flink's 10th anniversary, making it a fitting tribute to the project’s heritage and its enduring contribution to the streaming data landscape.</p>
-<p><img decoding="async" loading="lazy" alt="FF Announcement" src="https://fluss.apache.org/assets/images/img9-fb689d41f612ef840508c4c20399822c.jpg" width="3686" height="1242" class="img_ev3q"></p>
-<p>Learn more details about Fluss in the next <a class="" href="https://fluss.apache.org/blog/fluss-intro/">blog post</a>, where we will delve deeper into Fluss's architecture, design principles, key features, and explore how it addresses the challenges of using Kafka for real-time analytics.</p>]]></content:encoded>
- </item>
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diff --git a/blog/tags/apache-fluss/index.html b/blog/tags/apache-fluss/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
<p>Apache Fluss represents a new architectural approach: the <strong>Streamhouse</strong> for real-time lakehouses. Instead of stitching together separate streaming and batch systems, the Streamhouse unifies them under a single architecture. In this model, Apache Iceberg continues to serve exactly the role it was designed for: a highly efficient, scalable cold storage layer for analytics, while Fluss fills the missing piece: a hot streaming storage layer with sub-second latency, columnar [...]
<p>After working on Fluss–Iceberg lakehouse integration and deploying this architecture at a massive scale, including Alibaba's 3 PB production deployment processing 40 GB/s, we're ready to share the architectural lessons learned. Specifically, why existing systems fall short, how Fluss and Iceberg naturally complement each other, and what this means for finally building true real-time lakehouses.</p>
<p><img decoding="async" loading="lazy" alt="Banner" src="/assets/images/fluss-lakehouse-streaming_comp-62d4129ae29a5e1b5453892ae27cc212.png" width="768" height="336" class="img_ev3q"></p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/streaming-lakehouse/">Streaming Lakehouse</a></li><li class="tag_QGVx"><a rel="tag" class [...]
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<p>Apache Fluss represents a new architectural approach: the <strong>Streamhouse</strong> for real-time lakehouses. Instead of stitching together separate streaming and batch systems, the Streamhouse unifies them under a single architecture. In this model, Apache Iceberg continues to serve exactly the role it was designed for: a highly efficient, scalable cold storage layer for analytics, while Fluss fills the missing piece: a hot streaming storage layer with sub-second latency, columnar [...]
<p>After working on Fluss–Iceberg lakehouse integration and deploying this architecture at a massive scale, including Alibaba's 3 PB production deployment processing 40 GB/s, we're ready to share the architectural lessons learned. Specifically, why existing systems fall short, how Fluss and Iceberg naturally complement each other, and what this means for finally building true real-time lakehouses.</p>
<p><img decoding="async" loading="lazy" alt="Banner" src="/assets/images/fluss-lakehouse-streaming_comp-62d4129ae29a5e1b5453892ae27cc212.png" width="768" height="336" class="img_ev3q"></p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/streaming-lakehouse/">Streaming Lakehouse</a></li><li class="tag_QGVx"><a rel="tag" class [...]
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<p>If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.</p>
<p>Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The <a class="" href="/blog/fluss-java-client/">Java client</a> works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.</p>
<p>We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."</p>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
<p>If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.</p>
<p>Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The <a class="" href="/blog/fluss-java-client/">Java client</a> works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.</p>
<p>We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."</p>
diff --git a/blog/tags/fluss-rs/index.html b/blog/tags/fluss-rs/index.html
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<p>If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.</p>
<p>Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The <a class="" href="/blog/fluss-java-client/">Java client</a> works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.</p>
<p>We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."</p>
diff --git a/blog/tags/fluss-rust/index.html b/blog/tags/fluss-rust/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
<p>If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.</p>
<p>Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The <a class="" href="/blog/fluss-java-client/">Java client</a> works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.</p>
<p>We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."</p>
diff --git a/blog/tags/index.html b/blog/tags/index.html
index 13055ccc1..7295486db 100644
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<p>Copyright © 2026 The Apache Software Foundation, Licensed under the Apache License, Version 2.0.</p>
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diff --git a/blog/tags/pyfluss/index.html b/blog/tags/pyfluss/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
<p>If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.</p>
<p>Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The <a class="" href="/blog/fluss-java-client/">Java client</a> works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.</p>
<p>We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."</p>
diff --git a/blog/tags/real-time-analytics/index.html b/blog/tags/real-time-analytics/index.html
index cd625b39c..902834ecd 100644
--- a/blog/tags/real-time-analytics/index.html
+++ b/blog/tags/real-time-analytics/index.html
@@ -15,14 +15,14 @@
<link rel="icon" href="/img/logo.svg">
<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
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</head>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
<p>Apache Fluss represents a new architectural approach: the <strong>Streamhouse</strong> for real-time lakehouses. Instead of stitching together separate streaming and batch systems, the Streamhouse unifies them under a single architecture. In this model, Apache Iceberg continues to serve exactly the role it was designed for: a highly efficient, scalable cold storage layer for analytics, while Fluss fills the missing piece: a hot streaming storage layer with sub-second latency, columnar [...]
<p>After working on Fluss–Iceberg lakehouse integration and deploying this architecture at a massive scale, including Alibaba's 3 PB production deployment processing 40 GB/s, we're ready to share the architectural lessons learned. Specifically, why existing systems fall short, how Fluss and Iceberg naturally complement each other, and what this means for finally building true real-time lakehouses.</p>
<p><img decoding="async" loading="lazy" alt="Banner" src="/assets/images/fluss-lakehouse-streaming_comp-62d4129ae29a5e1b5453892ae27cc212.png" width="768" height="336" class="img_ev3q"></p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/streaming-lakehouse/">Streaming Lakehouse</a></li><li class="tag_QGVx"><a rel="tag" class [...]
diff --git a/blog/tags/releases/index.html b/blog/tags/releases/index.html
index e75a441c7..bed21a913 100644
--- a/blog/tags/releases/index.html
+++ b/blog/tags/releases/index.html
@@ -15,14 +15,14 @@
<link rel="icon" href="/img/logo.svg">
<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
-<script src="/assets/js/runtime~main.21c219fa.js" defer="defer"></script>
-<script src="/assets/js/main.f942df26.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
+<script src="/assets/js/main.16a8eb46.js" defer="defer"></script>
</head>
<body>
<svg style="display: none;"><defs>
<symbol id="theme-svg-external-link" viewBox="0 0 24 24"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"/></symbol>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
<p>We are excited to announce the release of <a href="https://github.com/apache/fluss-rust" target="_blank" rel="noopener noreferrer" class="">fluss-rust clients</a> 0.1.0, the first official release of the <a href="https://clients.fluss.apache.org/user-guide/rust/installation" target="_blank" rel="noopener noreferrer" class="">Rust</a>, <a href="https://clients.fluss.apache.org/user-guide/python/installation" target="_blank" rel="noopener noreferrer" class="">Python</a>, and <a href="ht [...]
<p>Under the hood, all three clients share a single Rust core that handles protocol negotiation, batching, retries, and <a href="https://arrow.apache.org/" target="_blank" rel="noopener noreferrer" class="">Apache Arrow</a>-based data exchange, with thin language-specific bindings on top. This was a deliberate community decision to deliver native performance and feature parity across every language from day one.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tag [...]
<p>🌊 We are excited to announce the official release of <strong>Apache Fluss (Incubating) 0.9</strong>!</p>
diff --git a/blog/tags/streaming-lakehouse/index.html b/blog/tags/streaming-lakehouse/index.html
index 89b7c4a2d..672471295 100644
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<link rel="manifest" href="/manifest.json">
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+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
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<body>
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<symbol id="theme-svg-external-link" viewBox="0 0 24 24"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"/></symbol>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div role="region" aria-label="Skip to main content"><a class="skipToContent_fXgn" href="#__d [...]
<p>Apache Fluss represents a new architectural approach: the <strong>Streamhouse</strong> for real-time lakehouses. Instead of stitching together separate streaming and batch systems, the Streamhouse unifies them under a single architecture. In this model, Apache Iceberg continues to serve exactly the role it was designed for: a highly efficient, scalable cold storage layer for analytics, while Fluss fills the missing piece: a hot streaming storage layer with sub-second latency, columnar [...]
<p>After working on Fluss–Iceberg lakehouse integration and deploying this architecture at a massive scale, including Alibaba's 3 PB production deployment processing 40 GB/s, we're ready to share the architectural lessons learned. Specifically, why existing systems fall short, how Fluss and Iceberg naturally complement each other, and what this means for finally building true real-time lakehouses.</p>
<p><img decoding="async" loading="lazy" alt="Banner" src="/assets/images/fluss-lakehouse-streaming_comp-62d4129ae29a5e1b5453892ae27cc212.png" width="768" height="336" class="img_ev3q"></p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/streaming-lakehouse/">Streaming Lakehouse</a></li><li class="tag_QGVx"><a rel="tag" class [...]
diff --git a/blog/taobao-practice/index.html b/blog/taobao-practice/index.html
index 53d11d657..b4bc25422 100644
--- a/blog/taobao-practice/index.html
+++ b/blog/taobao-practice/index.html
@@ -15,14 +15,14 @@
<link rel="icon" href="/img/logo.svg">
<link rel="manifest" href="/manifest.json">
<meta name="theme-color" content="#0071e3"><link rel="stylesheet" href="/assets/css/styles.ac3876d3.css">
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+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
+<script src="/assets/js/main.16a8eb46.js" defer="defer"></script>
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="introduction">Introduction<a href="#introduction" class="hash-link" aria-label="Direct link to Introduction" title="Direct link to Introduction" translate="no"></a></h3>
<p>The Data Development Team of Taobao has built a new <strong>generation of real-time data warehouse</strong> based on Apache Fluss.
Fluss solves the problems of redundant data transfer, difficulties in data profiling, and challenges in large scale stateful workload operations and maintenance.
diff --git a/blog/tiering-service/index.html b/blog/tiering-service/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p><img decoding="async" loading="lazy" src="/assets/images/background-8a1ebc79c438a3deabba060edf8eaa0b.png" width="2036" height="924" class="img_ev3q"></p>
<p>At the core of Fluss’s Lakehouse architecture sits the <strong>Tiering Service:</strong> a smart,
policy-driven data pipeline that seamlessly bridges your real-time Fluss cluster and your cost-efficient lakehouse storage. It continuously ingests fresh events from the fluss cluster, automatically migrating older or less-frequently accessed data into colder storage tiers without interrupting ongoing queries. By balancing hot, warm, and cold storage according to configurable rules, the Tiering Service ensures that recent data remains instantly queryable while historical records are arch [...]
diff --git a/blog/unified-streaming-lakehouse/index.html b/blog/unified-streaming-lakehouse/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<p>Fluss is designed to address the demands of real-time analytics with the following key capabilities:</p>
<ul>
<li class=""><strong>Real-Time Stream Reading and Writing:</strong> Supports millisecond-level end-to-end latency.</li>
diff --git a/blog/unveil-fluss-logo/index.html b/blog/unveil-fluss-logo/index.html
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+<script>document.documentElement.setAttribute("data-theme","light"),document.documentElement.setAttribute("data-theme-choice","light"),function(){try{const c=new URLSearchParams(window.location.search).entries();for(var[t,e]of c)if(t.startsWith("docusaurus-data-")){var a=t.replace("docusaurus-data-","data-");document.documentElement.setAttribute(a,e)}}catch(t){}}()</script><div id="__docusaurus"><div class="blog-post-page-no-sidebar"><div role="region" aria-label="Skip to main content">< [...]
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="introducing-the-little-otter">Introducing the Little Otter<a href="#introducing-the-little-otter" class="hash-link" aria-label="Direct link to Introducing the Little Otter" title="Direct link to Introducing the Little Otter" translate="no"></a></h2>
<p>Today is <a href="https://www.otter.org/world-otter-day" target="_blank" rel="noopener noreferrer" class="">World Otter Day</a>, and we are thrilled to introduce the little otter to the Fluss community! 🎉</p>
<p>Since open-sourced half a year ago, many community members and friends have asked us:
diff --git a/blog/why-fluss-chose-rust-for-multi-language-sdk/index.html b/blog/why-fluss-chose-rust-for-multi-language-sdk/index.html
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<p>If you maintain a data system that only speaks Java, you will eventually hear from someone who doesn't. A Python team building a feature store. A C++ service that needs sub-millisecond writes. An AI agent that wants to call your system through a tool binding. They all need the same capabilities (writes, reads, lookups) and none of them want to spin up a JVM to get them.</p>
<p>Apache Fluss, streaming storage for real-time analytics and AI, hit this exact inflection point. The <a class="" href="/blog/fluss-java-client/">Java client</a> works well for Flink-based compute, where the JVM is already the world you live in. But outside that world, asking consumers to run a JVM sidecar just to write a record or look up a key creates friction that compounds across every service, every pipeline, every agent in the stack.</p>
<p>We could have written a separate client for each language. Maintain five copies of the wire protocol, five implementations of the batching logic, five sets of retry semantics and idempotence tracking. That path scales linearly with languages and ends predictably: the Java client gets features first, the Python client gets them six months later with slightly different edge-case behavior, and the C++ client is perpetually "almost done."</p>
@@ -96,7 +96,7 @@
On the project side, the community is working toward moving fluss-rust into the main Apache Fluss repository. This would unify the release process, simplify cross-repo coordination, and signal the project's long-term commitment to the multi-language SDK as a first-class part of Fluss.</p>
<p>If any of this is interesting to you, we welcome contributions, bug reports, and feedback.</p>
<hr>
-<p>And before you go 😊 don't forget to give some ❤️ via ⭐ on GitHub: <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">Apache Fluss</a> and <a href="https://github.com/apache/fluss-rust" target="_blank" rel="noopener noreferrer" class="">Fluss Rust SDK</a></p></div><footer class="docusaurus-mt-lg"><div class="row margin-top--sm theme-blog-footer-edit-meta-row"><div class="col"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"> [...]
+<p>And before you go 😊 don't forget to give some ❤️ via ⭐ on GitHub: <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">Apache Fluss</a> and <a href="https://github.com/apache/fluss-rust" target="_blank" rel="noopener noreferrer" class="">Fluss Rust SDK</a></p></div><footer class="docusaurus-mt-lg"><div class="row margin-top--sm theme-blog-footer-edit-meta-row"><div class="col"><b>Tags:</b><ul class="tags_jXut padding--none margin-left--sm"> [...]
<p>Copyright © 2026 The Apache Software Foundation, Licensed under the Apache License, Version 2.0.</p>
<p>Apache, the names of Apache projects, and the feather logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p></div></div></div></footer></div></div>
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This transition is revolutionizing various sectors, including the E-commerce, automotive networking, finance, and beyond,
where real-time data applications are becoming integral to operations. This evolution enables organizations to unlock greater
value by leveraging real-time insights to drive business impact and enhance decision-making.</p>
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index 2777f8057..790196568 100644
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index 357fa043d..2425343ea 100644
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-<script src="/assets/js/main.f942df26.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.10b2c66d.js" defer="defer"></script>
+<script src="/assets/js/main.16a8eb46.js" defer="defer"></script>
</head>
<body>
<svg style="display: none;"><defs>
diff --git a/sitemap.xml b/sitemap.xml
index f13016598..215ff73c5 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="UTF-8"?><urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:news="http://www.google.com/schemas/sitemap-news/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml" xmlns:image="http://www.google.com/schemas/sitemap-image/1.1" xmlns:video="http://www.google.com/schemas/sitemap-video/1.1"><url><loc>https://fluss.apache.org/blog/</loc><changefreq>weekly</changefreq><priority>0.5</priority></url><url><loc>https://fluss.apache.org/blog/2025/12/02/fluss-x-i [...]
\ No newline at end of file
+<?xml version="1.0" encoding="UTF-8"?><urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:news="http://www.google.com/schemas/sitemap-news/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml" xmlns:image="http://www.google.com/schemas/sitemap-image/1.1" xmlns:video="http://www.google.com/schemas/sitemap-video/1.1"><url><loc>https://fluss.apache.org/blog/</loc><changefreq>weekly</changefreq><priority>0.5</priority></url><url><loc>https://fluss.apache.org/blog/2025/12/02/fluss-x-i [...]
\ No newline at end of file
diff --git a/sw.js b/sw.js
index 5c2c0b936..ba1981b6a 100644
--- a/sw.js
+++ b/sw.js
@@ -4568,7 +4568,7 @@ function getPossibleURLs(url) {
(async () => {
const params = parseSwParams();
// eslint-disable-next-line no-underscore-dangle
- const precacheManifest = [{"revision":"6e11d3a273ce639b6ce5c9eaa7629881","url":"manifest.json"},{"revision":"6362bef0754acb75fc21aab3cf417e86","url":"index.html"},{"revision":"e7181d968b65b1bac36dbf7d9b38a207","url":"404.html"},{"revision":"6b1d9719271ef37c629ae0bb8aa6a101","url":"search/index.html"},{"revision":"3f883136529e25a0a63f9a050d1ffd42","url":"roadmap/index.html"},{"revision":"8201e11a47780ff3bc2695e34a59e5f1","url":"learn/videos/index.html"},{"revision":"85fed8ee784817fb7d [...]
+ const precacheManifest = [{"revision":"6e11d3a273ce639b6ce5c9eaa7629881","url":"manifest.json"},{"revision":"49c1178781e5bdc59c478dafe5bb70a8","url":"index.html"},{"revision":"ec0764197414daf67a537a0cf46172a0","url":"404.html"},{"revision":"a8dfd2841eb3028a924834ce1b6f7413","url":"search/index.html"},{"revision":"52130c09b2b72acaf1f37e32f67fae54","url":"roadmap/index.html"},{"revision":"510ee0cb20ac4b3942f4153db18c56f0","url":"learn/videos/index.html"},{"revision":"8e086159b74f6cec24 [...]
const controller = new workbox_precaching__WEBPACK_IMPORTED_MODULE_0__.PrecacheController({
// Safer to turn this true?
fallbackToNetwork: true,