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Posted to commits@fluss.apache.org by gi...@apache.org on 2026/08/18 11:24:53 UTC
(fluss-website) branch asf-site updated: Deploy website - based on ec517feef026c85bfbe16ecaec5cbbd3e0e5d3f8
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new 2aa7035173 Deploy website - based on ec517feef026c85bfbe16ecaec5cbbd3e0e5d3f8
2aa7035173 is described below
commit 2aa7035173f9f994ba00b01a2ac23eda8bd19d60
Author: gh-actions <ac...@github.com>
AuthorDate: Tue Aug 18 11:24:45 2026 +0000
Deploy website - based on ec517feef026c85bfbe16ecaec5cbbd3e0e5d3f8
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@@ -1 +1 @@
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similarity index 63%
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@@ -1 +1 @@
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similarity index 100%
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diff --git a/blog/2025/12/02/fluss-x-iceberg-why-your-lakehouse-is-not-streamhouse-yet/index.html b/blog/2025/12/02/fluss-x-iceberg-why-your-lakehouse-is-not-streamhouse-yet/index.html
index c0575cc385..f619dee445 100644
--- a/blog/2025/12/02/fluss-x-iceberg-why-your-lakehouse-is-not-streamhouse-yet/index.html
+++ b/blog/2025/12/02/fluss-x-iceberg-why-your-lakehouse-is-not-streamhouse-yet/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
-<script src="/assets/js/runtime~main.2bc92054.js" defer="defer"></script>
-<script src="/assets/js/main.665e7276.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
+<script src="/assets/js/main.f3cd8977.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>
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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>
@@ -125,7 +125,7 @@ Think of your data as having two thermal zones:</p>
<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="solution-2-primary-key-tables-with-native-upsert-support">Solution 2: Primary Key Tables with Native Upsert Support<a href="#solution-2-primary-key-tables-with-native-upsert-support" class="hash-link" aria-label="Direct link to Solution 2: Primary Key Tables with Native Upsert Support" title="Direct link to Solution 2: Primary Key Tables with Native Upsert Support" translate="no"></a></h3>
<p><strong>Addresses:</strong> Gap 3 (Primary Key Not Enforced) & Gap 4 (Update Write Amplification)</p>
<p>Fluss provides first-class primary key semantics using an LSM tree architecture:</p>
-<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">┌─────────────────────────────────────────────────┐</span><br></div><div class="token-line" [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">┌─────────────────────────────────────────────────┐</span><br></div><div class="token-line" [...]
<p><strong>How it works:</strong></p>
<p>When you write to a primary key table, Fluss:</p>
<ol>
diff --git a/blog/apache-fluss-graduates-to-top-level-project/index.html b/blog/apache-fluss-graduates-to-top-level-project/index.html
index 0623a99452..93f4abcd46 100644
--- a/blog/apache-fluss-graduates-to-top-level-project/index.html
+++ b/blog/apache-fluss-graduates-to-top-level-project/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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-<script src="/assets/js/main.665e7276.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
+<script src="/assets/js/main.f3cd8977.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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<p>As <a href="https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-new-top-level-projects-5" target="_blank" rel="noopener noreferrer" class="">officially announced by the Apache Software Foundation</a>, we are thrilled to share that <strong>Apache Fluss</strong> has graduated from the Apache Incubator to become a <strong>Top Level Project (TLP)</strong>.</p>
<p>The project's <a href="https://lists.apache.org/thread/kltvfrklyoqm9dj6dgwdzf82sm097427" target="_blank" rel="noopener noreferrer" class="">graduation proposal</a> received unanimous approval from the Apache Incubator Project Management Committee (IPMC) and was subsequently approved by the ASF Board of Directors. This milestone not only marks a new stage in Fluss's journey, but also further advances the convergence of streaming storage, the real-time Lakehouse, and AI data i [...]
<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="how-we-got-here">How We Got Here<a href="#how-we-got-here" class="hash-link" aria-label="Direct link to How We Got Here" title="Direct link to How We Got Here" translate="no"></a></h2>
@@ -77,7 +77,7 @@
<li class="">GitHub: <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">https://github.com/apache/fluss</a> (⭐ stars and contributions are welcome!)</li>
<li class="">Contribution guide: <a href="https://fluss.apache.org/community/how-to-contribute/overview/" target="_blank" rel="noopener noreferrer" class="">https://fluss.apache.org/community/how-to-contribute/overview/</a></li>
</ul>
-<p>A new journey has begun. Let this open river of data keep flowing. 🌊</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"><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/apache-fluss/">Apache Fluss</a></li><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/lakestream/">lakestream</a></ [...]
+<p>A new journey has begun. Let this open river of data keep flowing. 🌊</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"><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/apache-fluss/">Apache Fluss</a></li><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/lakestream/">lakestream</a></ [...]
<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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diff --git a/blog/archive/index.html b/blog/archive/index.html
index a2b64b4724..6d32c4d6e8 100644
--- a/blog/archive/index.html
+++ b/blog/archive/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
-<script src="/assets/js/runtime~main.2bc92054.js" defer="defer"></script>
-<script src="/assets/js/main.665e7276.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
+<script src="/assets/js/main.f3cd8977.js" defer="defer"></script>
</head>
<body>
<svg style="display: none;"><defs>
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</defs></svg>
-<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
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diff --git a/blog/atom.xml b/blog/atom.xml
index 69b27291d8..11e8ec51e7 100644
--- a/blog/atom.xml
+++ b/blog/atom.xml
@@ -2,11 +2,79 @@
<feed xmlns="http://www.w3.org/2005/Atom">
<id>https://fluss.apache.org/blog/</id>
<title>Apache Fluss™ Blog</title>
- <updated>2026-08-06T00:00:00.000Z</updated>
+ <updated>2026-08-18T00:00:00.000Z</updated>
<generator>https://github.com/jpmonette/feed</generator>
<link rel="alternate" href="https://fluss.apache.org/blog/"/>
<subtitle>Apache Fluss™ Blog</subtitle>
<icon>https://fluss.apache.org/img/logo/fluss_favicon.svg</icon>
+ <entry>
+ <title type="html"><![CDATA[From Kafka to Fluss: How Rednote Migrated a Core Real-Time Indexing Pipeline]]></title>
+ <id>https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/</id>
+ <link href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/"/>
+ <updated>2026-08-18T00:00:00.000Z</updated>
+ <summary type="html"><![CDATA[A production case study in columnar streaming, cold-data isolation, and lakehouse integration.]]></summary>
+ <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" alt="From Kafka to Fluss: Xiaohongshu&#39;s production-grade migration of its core indexing pipeline" src="https://fluss.apache.org/assets/images/banner-d978c02a35df34891b55b615f9281366.png" width="1202" height="508" class="img_ev3q"></p>
+<p><em>A production case study in columnar streaming, cold-data isolation, and lakehouse integration</em>
+<em>Presented at Flink Forward Asia 2026.</em></p>
+<p><a href="http://rednote.com/" target="_blank" rel="noopener noreferrer" class="">Rednote</a> (Xiaohongshu) is a lifestyle community platform centered on content discovery and sharing. During the FIFA World Cup, it becomes a hub for live coverage, pre-match analysis, trending posts, and fan discussions.</p>
+<p>At that scale, hundreds of millions of users can hit live streaming, search, and feeds at once. Content must refresh in moments while advertising, search, and recommendation services stay stable—powered by a real-time indexing pipeline that continuously ingests data and updates indexes.</p>
+<p>That pipeline is critical to Rednote's content distribution and user experience, but its Kafka-based wide-table architecture was reaching cost and stability limits. To scale without compromising real-time performance, Rednote introduced <strong>Fluss</strong> into its core production path and began progressively migrating index data from Kafka to Fluss.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="why-the-indexing-pipeline-needed-to-change">Why the indexing pipeline needed to change<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#why-the-indexing-pipeline-needed-to-change" class="hash-link" aria-label="Direct link to Why the indexing pipeline needed to change" title="Direct link to Why the indexing pipeline needed to change" translate="no"></a></h2>
+<p>Rednote's search, recommendation, and advertising systems all depend on real-time indexes. Upstream processing platforms continuously produce wide tables, while many downstream online services consume different subsets of those tables. One service may need content features, another may care about user and interaction fields, and a third may build a specialized index for search or ads. The consumers are numerous, their column requirements differ, and their fan-out ratios vary dramatica [...]
+<p>Kafka remains a mature and dependable messaging platform. But in this particular workload, row-oriented writes and full-row consumption amplified three structural problems.</p>
+<p><img decoding="async" loading="lazy" alt="Kafka-based wide-table distribution before the migration to Fluss" src="https://fluss.apache.org/assets/images/figure1-8f967d87077622a58029eae759abe52a.webp" width="2048" height="836" class="img_ev3q"></p>
+<p><em>Figure 1. Before the migration: Kafka wide-table distribution created read amplification, slow cold reads, and resource contention.</em></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="1-read-amplification-kept-growing">1. Read amplification kept growing<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#1-read-amplification-kept-growing" class="hash-link" aria-label="Direct link to 1. Read amplification kept growing" title="Direct link to 1. Read amplification kept growing" translate="no"></a></h3>
+<p>The same wide table was consumed repeatedly by multiple online services, even though each service needed only a fraction of the available columns. In a row-oriented model, a consumer interested in a handful of fields still had to read and process the entire record.</p>
+<p>As fan-out increased from 1x to 10x and 100x—and reached roughly 1,000x in some localized workloads—large volumes of irrelevant data were repeatedly transferred across the network. At Rednote's scale, that unnecessary I/O translated directly into bandwidth and compute costs.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="2-cold-data-replay-took-the-long-way-around">2. Cold-data replay took the long way around<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#2-cold-data-replay-took-the-long-way-around" class="hash-link" aria-label="Direct link to 2. Cold-data replay took the long way around" title="Direct link to 2. Cold-data replay took the long way around" translate="no"></a></h3>
+<p>Index building does not consume only the latest events. Batch and incremental jobs frequently need to scan historical data as well. In the previous architecture, cold data lived in object storage. A replay first had to pull that data back through a Kafka broker, which then forwarded it to the client.</p>
+<p>That extra hop lengthened the path and forced brokers designed for real-time delivery to carry substantial historical scan traffic.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="3-historical-scans-competed-with-online-traffic">3. Historical scans competed with online traffic<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#3-historical-scans-competed-with-online-traffic" class="hash-link" aria-label="Direct link to 3. Historical scans competed with online traffic" title="Direct link to 3. Historical scans competed with online traffic" translate="no"></a></h3>
+<p>Batch and incremental index builds often scan historical data with high concurrency. Those reads consumed broker network bandwidth and polluted the page cache, creating direct contention with latency-sensitive online consumers.</p>
+<p>For a core indexing pipeline, online stability comes first. If a historical rebuild can degrade real-time delivery, the architecture needs a cleaner boundary between hot-path consumption and cold-data access.</p>
+<p>The real issue was not simply a need to replace one messaging system with another. The organization and access pattern of the data no longer matched the way the data was being consumed. Rednote needed a platform that preserved streaming semantics while adding columnar reads, hot-and-cold tiering, and native integration with lake storage.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="what-changed-with-fluss">What changed with Fluss<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#what-changed-with-fluss" class="hash-link" aria-label="Direct link to What changed with Fluss" title="Direct link to What changed with Fluss" translate="no"></a></h2>
+<p>The migration changed how data was written, consumed, and replayed.</p>
+<p><img decoding="async" loading="lazy" alt="Fluss-based real-time indexing architecture and migration benefits" src="https://fluss.apache.org/assets/images/figure2-f63b69dfd5c6426aa5ee56376e14eea3.webp" width="2048" height="836" class="img_ev3q"></p>
+<p><em>Figure 2. After the migration: columnar consumption, direct object-store reads, and Paimon integration reshape the real-time indexing path.</em></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="online-services-moved-from-full-row-transfers-to-columnar-reads">Online services moved from full-row transfers to columnar reads<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#online-services-moved-from-full-row-transfers-to-columnar-reads" class="hash-link" aria-label="Direct link to Online services moved from full-row transfers to columnar reads" title="Direct link to Online services moved from full- [...]
+<p>Upstream systems write to Fluss through Apache Arrow. Downstream services can then read only the columns they actually need instead of fetching the full row every time.</p>
+<p>Consumption cost is therefore tied more closely to the data a service uses, rather than to the total width of the source table. In a pipeline with wide records, many consumers, and high fan-out, the value of column projection compounds as the workload grows.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="historical-replay-bypassed-the-broker">Historical replay bypassed the broker<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#historical-replay-bypassed-the-broker" class="hash-link" aria-label="Direct link to Historical replay bypassed the broker" title="Direct link to Historical replay bypassed the broker" translate="no"></a></h3>
+<p>Cold-data replay no longer has to flow entirely through the broker path. Index builders can read Fluss log files directly from object storage, separating large historical scans from the online messaging layer.</p>
+<p>This reduces pressure on broker NICs and cache resources. Real-time consumers stay on a low-latency path, while historical builds use a route better suited to parallel scans. The result is stronger isolation between two workloads with very different performance profiles.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="fluss-and-paimon-created-a-shared-lakestream-foundation">Fluss and Paimon created a shared Lakestream foundation<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#fluss-and-paimon-created-a-shared-lakestream-foundation" class="hash-link" aria-label="Direct link to Fluss and Paimon created a shared Lakestream foundation" title="Direct link to Fluss and Paimon created a shared Lakestream foundation" transla [...]
+<p>Rednote also uses Fluss's Lakestream to write the same real-time data into Apache Paimon. During migration, the Paimon tables support data exploration and dual-run validation, helping the team compare the new and existing pipelines for correctness. In production, the lake copy can also serve historical replay, offline analytics, and batch index construction.</p>
+<p>Instead of maintaining separate and disconnected real-time and historical datasets, the architecture moves toward a common data foundation that can serve both.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-gains-appeared-across-the-entire-pipeline">The gains appeared across the entire pipeline<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#the-gains-appeared-across-the-entire-pipeline" class="hash-link" aria-label="Direct link to The gains appeared across the entire pipeline" title="Direct link to The gains appeared across the entire pipeline" translate="no"></a></h2>
+<p>Because the migration changed data layout, access paths, and workload isolation at the same time, the benefits were not confined to a single benchmark.</p>
+<p><strong>On the write path, CPU usage fell by about 30%, while write traffic fell by roughly 50%.</strong> Arrow-based writes and a layout better suited to the indexing workload reduced resource consumption and created more stability headroom for traffic peaks.</p>
+<p><strong>For index construction, batch and incremental build times fell by roughly 50% to 80%.</strong> Builders could pull data in parallel more efficiently, allowing changes in the business to propagate faster into search, recommendation, and advertising indexes.</p>
+<p><strong>Across online workloads, bandwidth savings ranged from about 30% to 90%, while peak throughput increased by approximately 3x.</strong> Columnar reads eliminated large amounts of unnecessary data transfer, particularly for wide tables with many downstream consumers.</p>
+<p>The significance lies in the combination. The core pipeline gained lower resource overhead, faster index builds, and greater peak capacity at the same time. Together, those improvements create the operating margin a production system needs in order to keep scaling.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-hardest-part-was-proving-production-readiness">The hardest part was proving production readiness<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#the-hardest-part-was-proving-production-readiness" class="hash-link" aria-label="Direct link to The hardest part was proving production readiness" title="Direct link to The hardest part was proving production readiness" translate="no"></a></h2>
+<p>Clear performance gains did not make the migration simple. For infrastructure at Rednote's scale, a new system must answer more than whether its features work. It must demonstrate predictable performance, long-running stability, data correctness, and operational maturity.</p>
+<p>The team therefore treated Fluss as production infrastructure from the outset, not as an experimental swap. It ran comprehensive load tests against the critical Kafka baselines, followed by extended dual-run validation to confirm data correctness. Only after the new path had passed those checks did the team progressively connect core workloads and expose it to real production traffic.</p>
+<p>Platform capabilities had to mature alongside the data path: table management, multi-tenancy, monitoring and alerting, data lineage, cluster operations, multi-cloud control, cross-region backup, and lake-ingestion job management. These capabilities are less visible than a throughput chart, but they determine whether a system can move from a successful test to years of reliable operation.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="from-one-critical-pipeline-to-a-shared-real-time-data-platform">From one critical pipeline to a shared real-time data platform<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#from-one-critical-pipeline-to-a-shared-real-time-data-platform" class="hash-link" aria-label="Direct link to From one critical pipeline to a shared real-time data platform" title="Direct link to From one critical pipeline to a shar [...]
+<p>Fluss is now running in Rednote's core production indexing path. According to the figures shared in this blog post, a single table is already ingesting roughly 1 billion records and 10 TB per day, and the deployment has completed performance and stability validation.</p>
+<p>In the second half of 2026, Rednote plans to continue the progressive migration of existing Kafka-based index pipelines. The next phase is expected to span more than 100 tables and infrastructure measured in hundreds of thousands of CPU cores. The rollout will remain phased and controlled, allowing the old and new paths to hand off traffic gradually rather than through a high-risk cutover.</p>
+<p>For historical replay, the team plans to make greater use of Paimon's column projection and the unified streaming-and-lakehouse architecture to improve object-storage efficiency. For real-time serving, it will also explore Fluss primary-key tables for enrichment, point lookups, partial updates, and continuously updated state.</p>
+<p><img decoding="async" loading="lazy" alt="Rednote Fluss real-time data platform progress, architecture, and roadmap" src="https://fluss.apache.org/assets/images/figure3-d5b49dda5cb1c9f44f3df13b069a8aba.webp" width="2048" height="759" class="img_ev3q"></p>
+<p><em>Figure 3. Rednote's Fluss real-time data platform: current progress, platform capabilities, ecosystem integration, and future plans.</em></p>
+<p>That roadmap changes the role Fluss plays inside Rednote. It is no longer merely an alternative transport layer; it is evolving into a more unified real-time data service. Over time, the platform is expected to integrate more deeply with compute engines such as Flink, Spark, and StarRocks, and with lake technologies including Paimon and Lance, supporting workloads across the community, search and recommendation, advertising, security, commerce, and live streaming.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-takeaway-this-was-bigger-than-a-broker-swap">The takeaway: this was bigger than a broker swap<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#the-takeaway-this-was-bigger-than-a-broker-swap" class="hash-link" aria-label="Direct link to The takeaway: this was bigger than a broker swap" title="Direct link to The takeaway: this was bigger than a broker swap" translate="no"></a></h2>
+<p>Rednote's move from Kafka to Fluss was not a rejection of a mature technology, nor was it a component change made for a single headline benchmark. It addressed a connected set of scaling problems: wide tables being read in full by many services, historical replay consuming online resources, a disconnect between streaming and lake data, and a core pipeline that needed more throughput with lower cost and stronger isolation.</p>
+<p>Columnar stream storage, direct object-store reads, and lakehouse integration allowed those issues to be reorganized within one architecture. Just as importantly, Rednote used load testing, dual runs, phased migration, and platform engineering to show that Fluss could move beyond technical promise and carry the pressure of a core production indexing workload.</p>
+<p>For teams building real-time search, recommendation, advertising, or AI data infrastructure, the lesson is straightforward: when data volumes keep growing, scaling is not only about raising a throughput ceiling. It is also about changing how data is organized, how it is accessed, and how safely the platform can absorb the next order of magnitude.</p>]]></content>
+ <author>
+ <name>Han Liu</name>
+ </author>
+ <category label="Apache Fluss" term="Apache Fluss"/>
+ <category label="Real-time Analytics" term="Real-time Analytics"/>
+ <category label="Streaming Lakehouse" term="Streaming Lakehouse"/>
+ </entry>
<entry>
<title type="html"><![CDATA[Apache Fluss Graduates to a Top Level Project]]></title>
<id>https://fluss.apache.org/blog/apache-fluss-graduates-to-top-level-project/</id>
@@ -2939,132 +3007,4 @@ In key business scenarios such as log collection and analysis, search recommenda
<uri>https://github.com/wuchong</uri>
</author>
</entry>
- <entry>
- <title type="html"><![CDATA[Apache Fluss Java Client: A Deep Dive]]></title>
- <id>https://fluss.apache.org/blog/fluss-java-client/</id>
- <link href="https://fluss.apache.org/blog/fluss-java-client/"/>
- <updated>2025-07-07T00:00:00.000Z</updated>
- <summary type="html"><![CDATA[Banner]]></summary>
- <content type="html"><![CDATA[<p><img decoding="async" loading="lazy" alt="Banner" src="https://fluss.apache.org/assets/images/banner-9b4e9683efcde75c4961249525c8f269.png" width="1477" height="975" class="img_ev3q"></p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="introduction">Introduction<a href="https://fluss.apache.org/blog/fluss-java-client/#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>.
-In practice, this means Fluss can ingest high-throughput event streams <em>(using log tables)</em> while also maintaining <em>up-to-date</em> reference data or state <em>(using primary key tables)</em>, a combination ideal for
-scenarios like IoT, where you might stream sensor readings and look up information for those sensors in real-time, without
-the need for external K/V stores.</p>
-<p>In this tutorial, we'll introduce the <strong>Fluss Java Client</strong> by walking through a simple home IoT system example.
-We will use <code>Fluss's Admin client</code> to create a primary key table for sensor information and a log table for sensor readings, then use the client
-to write data to these tables and read/enrich the streaming sensor data.</p>
-<p>By the end, you'll see how a sensor reading can be ingested into a log table and immediately enriched with information from a primary key table (essentially performing a real-time lookup join for streaming data enrichment).</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="preflight-check">Preflight Check<a href="https://fluss.apache.org/blog/fluss-java-client/#preflight-check" class="hash-link" aria-label="Direct link to Preflight Check" title="Direct link to Preflight Check" translate="no"></a></h2>
-<p>The full source code can be found <a href="https://github.com/ververica/ververica-fluss-examples/tree/main/fluss-java-client" target="_blank" rel="noopener noreferrer" class="">here</a>.</p>
-<div class="language-shell codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-shell codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token function" style="color:#7C3AED">docker</span><span class="token plain"> compose up</span><br></di [...]
-<p>The first thing we need to do is establish a connection to the Fluss cluster.
-The <code>Connection</code> is the main entry point for the Fluss client, from which we obtain an <code>Admin</code> (for metadata operations) and Table instances (for data operations)</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token comment" style="color:#64748B;font-style:italic">// Configure connection to Fluss cluster</span><sp [...]
-<p>The above code snippet shows the bare minimum requirements for connecting and interacting with a Fluss Cluster.
-For our example we will use the following mock data - to keep things simple - which you can find below:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="operating-the-cluster">Operating The Cluster<a href="https://fluss.apache.org/blog/fluss-java-client/#operating-the-cluster" class="hash-link" aria-label="Direct link to Operating The Cluster" title="Direct link to Operating The Cluster" translate="no"></a></h2>
-<p>Let's create a database for our IoT data, and within it define two tables:</p>
-<ul>
-<li class=""><strong>Sensor Readings Table:</strong> A log table that will collect time-series readings from sensors (like temperature and humidity readings). This table is append-only (new records are added continuously, with no updates/deletes) which is ideal for immutable event streams</li>
-<li class=""><strong>Sensor Information Table:</strong> A primary key table that stores metadata for each sensor (like sensor ID, location, type). Each <code>sensorId</code> will be unique and acts as the primary key. This table can be updated as sensor info changes (e.g., sensor relocated or reconfigured).</li>
-</ul>
-<p>Using the Admin client, we can programmatically create these tables.</p>
-<p>First, we'll ensure the database exists (creating it if not), then define schemas for each table and create them:</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="schema-definitions">Schema Definitions<a href="https://fluss.apache.org/blog/fluss-java-client/#schema-definitions" class="hash-link" aria-label="Direct link to Schema Definitions" title="Direct link to Schema Definitions" translate="no"></a></h3>
-<h4 class="anchor anchorTargetStickyNavbar_Vzrq" id="log-table-sensor-readings">Log table (sensor readings)<a href="https://fluss.apache.org/blog/fluss-java-client/#log-table-sensor-readings" class="hash-link" aria-label="Direct link to Log table (sensor readings)" title="Direct link to Log table (sensor readings)" translate="no"></a></h4>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<h4 class="anchor anchorTargetStickyNavbar_Vzrq" id="primary-key-table-sensor-information">Primary Key table (sensor information)<a href="https://fluss.apache.org/blog/fluss-java-client/#primary-key-table-sensor-information" class="hash-link" aria-label="Direct link to Primary Key table (sensor information)" title="Direct link to Primary Key table (sensor information)" translate="no"></a></h4>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="table-creation">Table Creation<a href="https://fluss.apache.org/blog/fluss-java-client/#table-creation" class="hash-link" aria-label="Direct link to Table Creation" title="Direct link to Table Creation" translate="no"></a></h3>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<p>We specify a distribution with <code>.distributedBy(3, "sensorId")</code>.
-Fluss tables are partitioned into buckets (similar to partitions in Kafka topics) for scalability.
-Here we use 3 buckets, meaning data gets distributed across 3 buckets. Multiple buckets allow for higher throughput or to parallelize reads/writes.
-If using multiple buckets, Fluss would hash on the bucket key (<code>sensorId</code> in our case) to assign records to buckets.</p>
-<p>For the <code>sensor_readings</code> table, we define a schema without any primary key. In Fluss, a table created without a primary key clause is a Log Table.
-A log table only supports appending new records (no updates or deletes), making it perfect for immutable time-series data or logs.</p>
-<p>In the log table, specifying a bucket key like <code>sensorId</code> ensures all readings from the same sensor end up to the same bucket providing strict ordering guarantees.</p>
-<p>With our tables created let's go and write some data.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="table-writes">Table Writes<a href="https://fluss.apache.org/blog/fluss-java-client/#table-writes" class="hash-link" aria-label="Direct link to Table Writes" title="Direct link to Table Writes" translate="no"></a></h2>
-<p>With our tables in place, let's insert some data using the Fluss Java API.
-The client allows us to write or read data from it.
-We'll demonstrate two patterns:</p>
-<ul>
-<li class=""><strong>Upserting</strong> into the primary key table (sensor information).</li>
-<li class=""><strong>Appending</strong> to the log table (sensor readings).</li>
-</ul>
-<p>Fluss provides specialized writer interfaces for each table type: an <strong>UpsertWriter</strong> for primary key tables and an <strong>AppendWriter</strong> for log tables.
-Under the hood, the Fluss client currently expects data as <strong>GenericRow</strong> objects (a generic row data format).</p>
-<blockquote>
-<p><strong>Note:</strong> Internally Fluss uses <strong>InternalRow</strong> as an optimized, binary representation of data for better performance and memory efficiency.
-<strong>GenericRow</strong> is a generic implementation of InternalRow. This allows developers to interact with data easily while Fluss processes it efficiently using the underlying binary format.</p>
-</blockquote>
-<p>Since we are creating <strong>Pojos</strong> though this means that we need to convert these into a GenericRow in order to write them into Fluss.</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<p><strong>Note:</strong> For certain data types like <code>String</code> or <code>LocalDateTime</code> we need to use certain functions like
-<code>BinaryString.fromString("string_value")</code> or <code>TimestampNtz.fromLocalDateTime(datetime)</code> otherwise you might
-come across some conversion exceptions.</p>
-<p>Let's start by writing data to the <code>Log Table</code>. This requires getting an <code>AppendWriter</code> as follows:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">logger</span><span class="token punctuation" style="color:#475569">.</span><span class="toke [...]
-<p>At this point we have successfully written 10 sensor readings to our table.</p>
-<p>Next, let's write data to the <code>Primary Key Table</code>. This requires getting an <code>UpsertWriter</code> as follows:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">logger</span><span class="token punctuation" style="color:#475569">.</span><span class="toke [...]
-<p>At this point we have successfully written 10 sensor information records to our table, because
-updates will be handled on the primary key and merged.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="scans--lookups">Scans & Lookups<a href="https://fluss.apache.org/blog/fluss-java-client/#scans--lookups" class="hash-link" aria-label="Direct link to Scans & Lookups" title="Direct link to Scans & Lookups" translate="no"></a></h2>
-<p>Now comes the real-time data enrichment part of our example.
-We want to simulate a process where each incoming sensor reading is immediately looked up against the sensor information table to add context (like location and type) to the raw reading.
-This is a common pattern in streaming systems, often achieved with lookup joins.</p>
-<p>With the Fluss Java client, we can do this by combining a <strong>log scanner on the readings table</strong> with <strong>point lookups on the sensor information table</strong>.</p>
-<p>To consume data from a Fluss table, we use a *<em>Scanner</em>.
-For a log table, Fluss provides a <strong>LogScanner</strong> that allows us to <strong>subscribe to one or more buckets</strong> and poll for new records.</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token class-name" style="color:#7C3AED">LogScanner</span><span class="token plain"> logScanner </span><sp [...]
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token class-name" style="color:#7C3AED">Lookuper</span><span class="token plain"> sensorInfoLookuper </sp [...]
-<p>We set up a scanner on the <code>sensor_readings</code> table, and next we need to subscribe to all its buckets, and then poll for any available records:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">int</span><span class="token plain"> numBuckets </span><span class=" [...]
-<p>Start polling for records. For each incoming record we will use the <strong>Lookuper</strong> to <code>lookup</code> sensor information from the primary key table,
-and creating a <strong>SensorReadingEnriched</strong> record.</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain"> </span><span class="token keyword" style="color:#194670">while</span><span class="token pla [...]
-<p>Let's summarize what's happening here:</p>
-<ul>
-<li class="">We create a LogScanner for the <code>sensor_readings</code> table using <em>table.newScan().createLogScanner()</em>.</li>
-<li class="">We subscribe to each bucket of the table from the beginning (offset 0). Subscribing <code>from beginning</code> means we'll read all existing data from the start; alternatively, one could subscribe from the latest position to only get new incoming data or based on other attributes like time. In our case, since we just inserted data, from-beginning will capture those inserts.</li>
-<li class="">We then call <code>poll(Duration)</code> on the scanner to retrieve available records, waiting up to the given timeout (1 second here). This returns a <code>ScanRecords</code> batch containing any records that were present. We iterate over each <code>TableBucket</code> and then over each <code>ScanRecord</code> within that bucket.</li>
-<li class="">For each record, we extract the fields via the InternalRow interface (which provides typed access to each column in the row) and <strong>convert them into a Pojo</strong>.</li>
-<li class="">Next, for each reading, we perform a <strong>lookup</strong> on the <strong>sensor_information</strong> table to get the sensor's info. We construct a key (GenericRow with just the sensor_id) and use <strong>sensorTable.newLookup().createLookuper().lookup(key)</strong>. This performs a point lookup by primary key and returns a <code>LookupResult future</code>; we call <code>.get()</code> to get the result synchronously. If present, we retrieve the InternalRow of the sensor i [...]
-<li class="">We then combine the data: logging an enriched message that includes the sensor's information alongside the reading values.</li>
-</ul>
-<p>Fluss's lookup API gives us quick primary-key retrieval from a table, which is exactly what we need to enrich the streaming data.
-In a real application, this enrichment could be done on the fly in a streaming job (and indeed <strong>Fluss is designed to support high-QPS lookup joins in real-time pipelines</strong>), but here we're simulating it with client calls for clarity.</p>
-<p>If you run the above code found <a href="https://github.com/ververica/ververica-fluss-examples" target="_blank" rel="noopener noreferrer" class="">here</a>, you should see an output like the following:</p>
-<div class="language-shell codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-shell codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token number" style="color:#B45309">16</span><span class="token plain">:07:13.594 INFO </span><span cl [...]
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="column-pruning-scans">Column Pruning Scans<a href="https://fluss.apache.org/blog/fluss-java-client/#column-pruning-scans" class="hash-link" aria-label="Direct link to Column Pruning Scans" title="Direct link to Column Pruning Scans" translate="no"></a></h2>
-<p>Column pruning lets you fetch only the columns you need, <strong>reducing network overhead and improving read performance</strong>. With Fluss’s Java client, you can specify a subset of columns in your scan:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token class-name" style="color:#7C3AED">LogScanner</span><span class="token plain"> logScanner </span><sp [...]
-<p>Let's break this down:</p>
-<ul>
-<li class=""><code>.project(...)</code> instructs the client to request only the specified columns (sensorId,timestamp and temperature) from the server.</li>
-<li class="">Fluss’s columnar storage means non-requested columns (e.g., humidity, etc.) <strong>aren’t transmitted, saving bandwidth and reducing client-side parsing overhead</strong>.</li>
-<li class="">You can combine projection with filters or lookups to further optimize your data access patterns.</li>
-</ul>
-<p>Example output:</p>
-<div class="language-shell codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-shell codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token number" style="color:#B45309">16</span><span class="token plain">:12:35.114 INFO </span><span cl [...]
-<p>Notice, how only the requested columns are returned from the server.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="conclusion">Conclusion<a href="https://fluss.apache.org/blog/fluss-java-client/#conclusion" class="hash-link" aria-label="Direct link to Conclusion" title="Direct link to Conclusion" translate="no"></a></h2>
-<p>In this blog post, we've introduced the Fluss Java Client by guiding you through a full example of creating tables, writing data, and reading/enriching data in real-time.
-We covered how to use the <code>Admin</code> client to define a <strong>Primary Key table</strong> (for reference data that can be updated) and a <strong>Log table</strong> (for immutable event streams), and how to use the Fluss client to upsert and append data accordingly.
-We also demonstrated reading from a log table using a scanner and performing a lookup on a primary key table to enrich the streaming data on the fly.</p>
-<p>This IoT sensor scenario is just one example of Fluss in action and also highlights the <strong>Stream/Table duality</strong> within the same system.
-Fluss's ability to handle high-throughput append streams and fast key-based lookups makes it well-suited for real-time analytics use cases like this and many others.
-With this foundation, you can explore more advanced features of Fluss to build robust real-time data applications. Happy streaming! 🌊</p>
-<p>And before you go 😊 don’t forget to give Fluss 🌊 some ❤️ via ⭐ on <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">GitHub</a></p>]]></content>
- <author>
- <name>Giannis Polyzos</name>
- <uri>https://github.com/polyzos</uri>
- </author>
- </entry>
</feed>
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diff --git a/blog/column-pruning-streaming-storage/index.html b/blog/column-pruning-streaming-storage/index.html
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<p><strong>TL;DR:</strong></p>
<blockquote>
<p>Apache Kafka's "column pruning" is actually pseudo-pruning. All fields still cross the network, and clients discard unwanted ones after the fact.
diff --git a/blog/fluss-for-ai/index.html b/blog/fluss-for-ai/index.html
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<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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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.
diff --git a/blog/fluss-open-source/index.html b/blog/fluss-open-source/index.html
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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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<p><strong>Apache Fluss stores data in three places:</strong> local disk on the tablet server, remote object storage like S3, and the lakehouse. Which place holds which data at any given moment, and what is responsible for moving it between them, is the foundation everything else rests on. Your capacity plan depends on it. Your latency targets depend on it. Your disaster-recovery story depends on it. So does your ability to predict, in advance, that a particular configuration change is g [...]
<p>This post walks through that layering. We'll cover what each tier holds, the two background tasks that move data between them, what changes for primary-key tables, and how recovery actually works when a tablet server loses its disk.</p>
<p>By the end you should be able to look at a Fluss deployment and say, for any given record, where it lives right now and where it will live in an hour.</p>
diff --git a/blog/fluss-tiering-service-deep-dive-part1/index.html b/blog/fluss-tiering-service-deep-dive-part1/index.html
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<p>If you're new to Fluss, the lake-tiering story is one of those topics where every explanation seems to assume you already know how it works.
This three-part walkthrough aims to bring some clarity to the confusing parts of the system, and to help you understand how it works in practice.</p>
<p><strong>Part 1</strong> builds the mental model from scratch and by the end of it you'll be able to describe, step by step, what happens between the moment a tiering timer fires and the moment a lake snapshot is committed.</p>
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<p><a class="" href="/blog/fluss-tiering-service-deep-dive-part1/">Part 1</a> built the mental model. What tiering is, who does what, how the round runs end-to-end.</p>
<p>This part adds the dials. Buckets and splits determine how a round parallelizes.
Log and PK tables behave so differently on round one that the difference deserves its own treatment.
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<p><a class="" href="/blog/fluss-tiering-service-deep-dive-part1/">Part 1</a> and <a class="" href="/blog/fluss-tiering-service-deep-dive-part2/">Part 2</a> built up everything you need to know about how tiering behaves: the mental model, the dials, the queue dynamics, the scale-out story. This part is about what to do with all of that. What breaks at runtime, and which of those failures self-heal versus need operator action. The design mistakes that look fine on day one but come back to [...]
<p><strong>Tiering Service Deep Dive, 3-parts:</strong></p>
<ul>
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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 ec6f78332c..7bfb0a0ded 100644
--- a/blog/fluss_rust_client_release/index.html
+++ b/blog/fluss_rust_client_release/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
-<script src="/assets/js/runtime~main.2bc92054.js" defer="defer"></script>
-<script src="/assets/js/main.665e7276.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
+<script src="/assets/js/main.f3cd8977.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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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 f0008aceac..3dc5b4cac5 100644
--- a/blog/hands-on-fluss-lakehouse/index.html
+++ b/blog/hands-on-fluss-lakehouse/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
-<script src="/assets/js/runtime~main.2bc92054.js" defer="defer"></script>
-<script src="/assets/js/main.665e7276.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
+<script src="/assets/js/main.f3cd8977.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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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>
@@ -170,7 +170,7 @@
<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:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">/tmp/minio-data ❯ tree .</span><br></div><div class="token-line" style="color:#0A0F1C"><span [...]
+<div class="language-text codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-text codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">/tmp/minio-data ❯ tree .</span><br></div><div class="token-line" style="color:#0A0F1C"><span [...]
</li>
<li class="">
<p>View Snapshots</p>
diff --git a/blog/index.html b/blog/index.html
index f7b34cbf48..313c86c0e5 100644
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+++ b/blog/index.html
@@ -3,7 +3,7 @@
<head>
<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.2">
-<title data-rh="true">Blog | Apache Fluss™</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.apache.org/blo [...]
+<title data-rh="true">Blog | Apache Fluss™</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.apache.org/blo [...]
<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache Fluss™ Atom Feed">
@@ -26,14 +26,19 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
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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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<p><em>A production case study in columnar streaming, cold-data isolation, and lakehouse integration</em>
+<em>Presented at Flink Forward Asia 2026.</em></p>
+<p><a href="http://rednote.com/" target="_blank" rel="noopener noreferrer" class="">Rednote</a> (Xiaohongshu) is a lifestyle community platform centered on content discovery and sharing. During the FIFA World Cup, it becomes a hub for live coverage, pre-match analysis, trending posts, and fan discussions.</p>
+<p>At that scale, hundreds of millions of users can hit live streaming, search, and feeds at once. Content must refresh in moments while advertising, search, and recommendation services stay stable—powered by a real-time indexing pipeline that continuously ingests data and updates indexes.</p>
+<p>That pipeline is critical to Rednote's content distribution and user experience, but its Kafka-based wide-table architecture was reaching cost and stability limits. To scale without compromising real-time performance, Rednote introduced <strong>Fluss</strong> into its core production path and began progressively migrating index data from Kafka to Fluss.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left [...]
<p>As <a href="https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-new-top-level-projects-5" target="_blank" rel="noopener noreferrer" class="">officially announced by the Apache Software Foundation</a>, we are thrilled to share that <strong>Apache Fluss</strong> has graduated from the Apache Incubator to become a <strong>Top Level Project (TLP)</strong>.</p>
<p>The project's <a href="https://lists.apache.org/thread/kltvfrklyoqm9dj6dgwdzf82sm097427" target="_blank" rel="noopener noreferrer" class="">graduation proposal</a> received unanimous approval from the Apache Incubator Project Management Committee (IPMC) and was subsequently approved by the ASF Board of Directors. This milestone not only marks a new stage in Fluss's journey, but also further advances the convergence of streaming storage, the real-time Lakehouse, and AI data i [...]
<p><a class="" href="/blog/fluss-tiering-service-deep-dive-part1/">Part 1</a> and <a class="" href="/blog/fluss-tiering-service-deep-dive-part2/">Part 2</a> built up everything you need to know about how tiering behaves: the mental model, the dials, the queue dynamics, the scale-out story. This part is about what to do with all of that. What breaks at runtime, and which of those failures self-heal versus need operator action. The design mistakes that look fine on day one but come back to [...]
@@ -91,9 +96,7 @@ The full combination of four dimensions means <strong>16 grouping methods</stron
<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>
-<p>We took a different path and tried to leverage the lessons of the great.</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/fluss-rust/">Fluss Rust</a></li><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/fluss-rs/">fluss-rs</a></li><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagR [...]
-<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 took a different path and tried to leverage the lessons of the great.</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/fluss-rust/">Fluss Rust</a></li><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagRegular_sFm0" href="/blog/tags/fluss-rs/">fluss-rs</a></li><li class="tag_QGVx"><a rel="tag" class="tag_zVej tagR [...]
<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 4d5edcec25..18993ea12d 100644
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+++ b/blog/page/2/index.html
@@ -3,7 +3,7 @@
<head>
<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.2">
-<title data-rh="true">Blog | Apache Fluss™</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.apache.org/blo [...]
+<title data-rh="true">Blog | Apache Fluss™</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.apache.org/blo [...]
<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache Fluss™ Atom Feed">
@@ -26,14 +26,16 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
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-<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<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><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
@@ -66,7 +68,7 @@ through batch-oriented systems or log-only pipelines.</p>
<li class=""><strong>Operationally robust clusters</strong> with automatic rebalancing and safer maintenance workflows</li>
<li class=""><strong>Apache Spark integration</strong>, enabling unified batch and streaming analytics on Fluss</li>
<li class=""><strong>First-class Azure support</strong>, allowing Fluss to tier and operate seamlessly on Azure Blob Storage and ADLS Gen2</li>
-</ul></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" title="Content related release announcement." class="tag_zVej tagRegular_sFm0" href="/blog/tags/releases/">releases</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Apache Fluss (Incubating) 0.9 Release Announcement" href="/blog/releases/0.9/"><b>Read more</b></a></div></footer></arti [...]
+</ul></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" title="Content related release announcement." class="tag_zVej tagRegular_sFm0" href="/blog/tags/releases/">releases</a></li></ul></div><div class="col text--right col--3"><a aria-label="Read more about Apache Fluss (Incubating) 0.9 Release Announcement" href="/blog/releases/0.9/"><b>Read more</b></a></div></footer></arti [...]
<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></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about A fraud detection pipeline with Streamhouse" href="/blog/fluss_fraud_detection/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="title_f1Hy"><a href="/blog/2025/12/02/fluss-x-iceberg-why-your-lakehou [...]
<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 [...]
@@ -100,13 +102,7 @@ Looking ahead, the team will continue to extend Fluss within a <strong>Lakehouse
<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>
-<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>.
-In practice, this means Fluss can ingest high-throughput event streams <em>(using log tables)</em> while also maintaining <em>up-to-date</em> reference data or state <em>(using primary key tables)</em>, a combination ideal for
-scenarios like IoT, where you might stream sensor readings and look up information for those sensors in real-time, without
-the need for external K/V stores.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Apache Fluss Java Client: A Deep Dive" href="/blog/fluss-java-client/"><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="pagination-nav__label">Newer entries</div></a><a class="pagination-nav__link paginat [...]
+<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><nav class="pagination-nav" aria-label="Blog list page navigation"><a class="pagination-nav__link pagination-nav__link--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/3/index.html b/blog/page/3/index.html
index 415ad3cc1e..e9b11cd41a 100644
--- a/blog/page/3/index.html
+++ b/blog/page/3/index.html
@@ -3,7 +3,7 @@
<head>
<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.2">
-<title data-rh="true">Blog | Apache Fluss™</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.apache.org/blo [...]
+<title data-rh="true">Blog | Apache Fluss™</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.apache.org/blo [...]
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@@ -26,14 +26,20 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
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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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<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>.
+In practice, this means Fluss can ingest high-throughput event streams <em>(using log tables)</em> while also maintaining <em>up-to-date</em> reference data or state <em>(using primary key tables)</em>, a combination ideal for
+scenarios like IoT, where you might stream sensor readings and look up information for those sensors in real-time, without
+the need for external K/V stores.</p></div><footer class="row docusaurus-mt-lg"><div class="col text--right"><a aria-label="Read more about Apache Fluss Java Client: A Deep Dive" href="/blog/fluss-java-client/"><b>Read more</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="title_f1Hy"><a href="/blog/tiering-service/">Tiering Service Deep Dive</a></h2><div class="container_mt6G margin-vert--md"><time datetime="2025-07-01T00:00:00.000Z">July 1, 2025</tim [...]
<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/partial-updates/index.html b/blog/partial-updates/index.html
index 297af8bfee..10c7419b30 100644
--- a/blog/partial-updates/index.html
+++ b/blog/partial-updates/index.html
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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
index 29c6772c16..7d57dbcd28 100644
--- a/blog/pk-key-tables-log-cache-streaming/index.html
+++ b/blog/pk-key-tables-log-cache-streaming/index.html
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
+<script src="/assets/js/main.f3cd8977.js" defer="defer"></script>
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
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>.
diff --git a/blog/rednote-kafka-to-fluss-real-time-indexing/index.html b/blog/rednote-kafka-to-fluss-real-time-indexing/index.html
new file mode 100644
index 0000000000..1b18d06b3f
--- /dev/null
+++ b/blog/rednote-kafka-to-fluss-real-time-indexing/index.html
@@ -0,0 +1,93 @@
+<!doctype html>
+<html lang="en" dir="ltr" class="blog-wrapper blog-post-page plugin-blog plugin-id-default" data-has-hydrated="false">
+<head>
+<meta charset="UTF-8">
+<meta name="generator" content="Docusaurus v3.10.2">
+<title data-rh="true">From Kafka to Fluss: How Rednote Migrated a Core Real-Time Indexing Pipeline | Apache Fluss™</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:url" content="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/"><meta data-rh="true" property="og:locale" content="en"><meta data-rh="true" name="docusaurus_lo [...]
+<link rel="alternate" type="application/atom+xml" href="/blog/atom.xml" title="Apache Fluss™ Atom Feed">
+
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+
+
+<link rel="search" type="application/opensearchdescription+xml" title="Apache Fluss™" href="/opensearch.xml">
+
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+<link rel="icon" href="/img/logo/fluss_favicon.svg">
+<link rel="manifest" href="/manifest.json">
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+<meta name="description" content="Apache Fluss is an open-source columnar streaming storage system. Sub-second freshness, primary-key tables, first-class Apache Flink integration, and native tiering to Apache Iceberg and Apache Paimon.">
+<meta property="og:title" content="Apache Fluss · Streaming Storage for the Real-Time Lakehouse">
+<meta property="og:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
+<meta property="og:type" content="website">
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+<meta name="twitter:title" content="Apache Fluss · Streaming Storage for the Real-Time Lakehouse">
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+<meta name="theme-color" content="#102856">
+<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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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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+<p><em>A production case study in columnar streaming, cold-data isolation, and lakehouse integration</em>
+<em>Presented at Flink Forward Asia 2026.</em></p>
+<p><a href="http://rednote.com/" target="_blank" rel="noopener noreferrer" class="">Rednote</a> (Xiaohongshu) is a lifestyle community platform centered on content discovery and sharing. During the FIFA World Cup, it becomes a hub for live coverage, pre-match analysis, trending posts, and fan discussions.</p>
+<p>At that scale, hundreds of millions of users can hit live streaming, search, and feeds at once. Content must refresh in moments while advertising, search, and recommendation services stay stable—powered by a real-time indexing pipeline that continuously ingests data and updates indexes.</p>
+<p>That pipeline is critical to Rednote's content distribution and user experience, but its Kafka-based wide-table architecture was reaching cost and stability limits. To scale without compromising real-time performance, Rednote introduced <strong>Fluss</strong> into its core production path and began progressively migrating index data from Kafka to Fluss.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="why-the-indexing-pipeline-needed-to-change">Why the indexing pipeline needed to change<a href="#why-the-indexing-pipeline-needed-to-change" class="hash-link" aria-label="Direct link to Why the indexing pipeline needed to change" title="Direct link to Why the indexing pipeline needed to change" translate="no"></a></h2>
+<p>Rednote's search, recommendation, and advertising systems all depend on real-time indexes. Upstream processing platforms continuously produce wide tables, while many downstream online services consume different subsets of those tables. One service may need content features, another may care about user and interaction fields, and a third may build a specialized index for search or ads. The consumers are numerous, their column requirements differ, and their fan-out ratios vary dram [...]
+<p>Kafka remains a mature and dependable messaging platform. But in this particular workload, row-oriented writes and full-row consumption amplified three structural problems.</p>
+<p><img decoding="async" loading="lazy" alt="Kafka-based wide-table distribution before the migration to Fluss" src="/assets/images/figure1-8f967d87077622a58029eae759abe52a.webp" width="2048" height="836" class="img_ev3q"></p>
+<p><em>Figure 1. Before the migration: Kafka wide-table distribution created read amplification, slow cold reads, and resource contention.</em></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="1-read-amplification-kept-growing">1. Read amplification kept growing<a href="#1-read-amplification-kept-growing" class="hash-link" aria-label="Direct link to 1. Read amplification kept growing" title="Direct link to 1. Read amplification kept growing" translate="no"></a></h3>
+<p>The same wide table was consumed repeatedly by multiple online services, even though each service needed only a fraction of the available columns. In a row-oriented model, a consumer interested in a handful of fields still had to read and process the entire record.</p>
+<p>As fan-out increased from 1x to 10x and 100x—and reached roughly 1,000x in some localized workloads—large volumes of irrelevant data were repeatedly transferred across the network. At Rednote's scale, that unnecessary I/O translated directly into bandwidth and compute costs.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="2-cold-data-replay-took-the-long-way-around">2. Cold-data replay took the long way around<a href="#2-cold-data-replay-took-the-long-way-around" class="hash-link" aria-label="Direct link to 2. Cold-data replay took the long way around" title="Direct link to 2. Cold-data replay took the long way around" translate="no"></a></h3>
+<p>Index building does not consume only the latest events. Batch and incremental jobs frequently need to scan historical data as well. In the previous architecture, cold data lived in object storage. A replay first had to pull that data back through a Kafka broker, which then forwarded it to the client.</p>
+<p>That extra hop lengthened the path and forced brokers designed for real-time delivery to carry substantial historical scan traffic.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="3-historical-scans-competed-with-online-traffic">3. Historical scans competed with online traffic<a href="#3-historical-scans-competed-with-online-traffic" class="hash-link" aria-label="Direct link to 3. Historical scans competed with online traffic" title="Direct link to 3. Historical scans competed with online traffic" translate="no"></a></h3>
+<p>Batch and incremental index builds often scan historical data with high concurrency. Those reads consumed broker network bandwidth and polluted the page cache, creating direct contention with latency-sensitive online consumers.</p>
+<p>For a core indexing pipeline, online stability comes first. If a historical rebuild can degrade real-time delivery, the architecture needs a cleaner boundary between hot-path consumption and cold-data access.</p>
+<p>The real issue was not simply a need to replace one messaging system with another. The organization and access pattern of the data no longer matched the way the data was being consumed. Rednote needed a platform that preserved streaming semantics while adding columnar reads, hot-and-cold tiering, and native integration with lake storage.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="what-changed-with-fluss">What changed with Fluss<a href="#what-changed-with-fluss" class="hash-link" aria-label="Direct link to What changed with Fluss" title="Direct link to What changed with Fluss" translate="no"></a></h2>
+<p>The migration changed how data was written, consumed, and replayed.</p>
+<p><img decoding="async" loading="lazy" alt="Fluss-based real-time indexing architecture and migration benefits" src="/assets/images/figure2-f63b69dfd5c6426aa5ee56376e14eea3.webp" width="2048" height="836" class="img_ev3q"></p>
+<p><em>Figure 2. After the migration: columnar consumption, direct object-store reads, and Paimon integration reshape the real-time indexing path.</em></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="online-services-moved-from-full-row-transfers-to-columnar-reads">Online services moved from full-row transfers to columnar reads<a href="#online-services-moved-from-full-row-transfers-to-columnar-reads" class="hash-link" aria-label="Direct link to Online services moved from full-row transfers to columnar reads" title="Direct link to Online services moved from full-row transfers to columnar reads" translate="no"></a></h3>
+<p>Upstream systems write to Fluss through Apache Arrow. Downstream services can then read only the columns they actually need instead of fetching the full row every time.</p>
+<p>Consumption cost is therefore tied more closely to the data a service uses, rather than to the total width of the source table. In a pipeline with wide records, many consumers, and high fan-out, the value of column projection compounds as the workload grows.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="historical-replay-bypassed-the-broker">Historical replay bypassed the broker<a href="#historical-replay-bypassed-the-broker" class="hash-link" aria-label="Direct link to Historical replay bypassed the broker" title="Direct link to Historical replay bypassed the broker" translate="no"></a></h3>
+<p>Cold-data replay no longer has to flow entirely through the broker path. Index builders can read Fluss log files directly from object storage, separating large historical scans from the online messaging layer.</p>
+<p>This reduces pressure on broker NICs and cache resources. Real-time consumers stay on a low-latency path, while historical builds use a route better suited to parallel scans. The result is stronger isolation between two workloads with very different performance profiles.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="fluss-and-paimon-created-a-shared-lakestream-foundation">Fluss and Paimon created a shared Lakestream foundation<a href="#fluss-and-paimon-created-a-shared-lakestream-foundation" class="hash-link" aria-label="Direct link to Fluss and Paimon created a shared Lakestream foundation" title="Direct link to Fluss and Paimon created a shared Lakestream foundation" translate="no"></a></h3>
+<p>Rednote also uses Fluss's Lakestream to write the same real-time data into Apache Paimon. During migration, the Paimon tables support data exploration and dual-run validation, helping the team compare the new and existing pipelines for correctness. In production, the lake copy can also serve historical replay, offline analytics, and batch index construction.</p>
+<p>Instead of maintaining separate and disconnected real-time and historical datasets, the architecture moves toward a common data foundation that can serve both.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-gains-appeared-across-the-entire-pipeline">The gains appeared across the entire pipeline<a href="#the-gains-appeared-across-the-entire-pipeline" class="hash-link" aria-label="Direct link to The gains appeared across the entire pipeline" title="Direct link to The gains appeared across the entire pipeline" translate="no"></a></h2>
+<p>Because the migration changed data layout, access paths, and workload isolation at the same time, the benefits were not confined to a single benchmark.</p>
+<p><strong>On the write path, CPU usage fell by about 30%, while write traffic fell by roughly 50%.</strong> Arrow-based writes and a layout better suited to the indexing workload reduced resource consumption and created more stability headroom for traffic peaks.</p>
+<p><strong>For index construction, batch and incremental build times fell by roughly 50% to 80%.</strong> Builders could pull data in parallel more efficiently, allowing changes in the business to propagate faster into search, recommendation, and advertising indexes.</p>
+<p><strong>Across online workloads, bandwidth savings ranged from about 30% to 90%, while peak throughput increased by approximately 3x.</strong> Columnar reads eliminated large amounts of unnecessary data transfer, particularly for wide tables with many downstream consumers.</p>
+<p>The significance lies in the combination. The core pipeline gained lower resource overhead, faster index builds, and greater peak capacity at the same time. Together, those improvements create the operating margin a production system needs in order to keep scaling.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-hardest-part-was-proving-production-readiness">The hardest part was proving production readiness<a href="#the-hardest-part-was-proving-production-readiness" class="hash-link" aria-label="Direct link to The hardest part was proving production readiness" title="Direct link to The hardest part was proving production readiness" translate="no"></a></h2>
+<p>Clear performance gains did not make the migration simple. For infrastructure at Rednote's scale, a new system must answer more than whether its features work. It must demonstrate predictable performance, long-running stability, data correctness, and operational maturity.</p>
+<p>The team therefore treated Fluss as production infrastructure from the outset, not as an experimental swap. It ran comprehensive load tests against the critical Kafka baselines, followed by extended dual-run validation to confirm data correctness. Only after the new path had passed those checks did the team progressively connect core workloads and expose it to real production traffic.</p>
+<p>Platform capabilities had to mature alongside the data path: table management, multi-tenancy, monitoring and alerting, data lineage, cluster operations, multi-cloud control, cross-region backup, and lake-ingestion job management. These capabilities are less visible than a throughput chart, but they determine whether a system can move from a successful test to years of reliable operation.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="from-one-critical-pipeline-to-a-shared-real-time-data-platform">From one critical pipeline to a shared real-time data platform<a href="#from-one-critical-pipeline-to-a-shared-real-time-data-platform" class="hash-link" aria-label="Direct link to From one critical pipeline to a shared real-time data platform" title="Direct link to From one critical pipeline to a shared real-time data platform" translate="no"></a></h2>
+<p>Fluss is now running in Rednote's core production indexing path. According to the figures shared in this blog post, a single table is already ingesting roughly 1 billion records and 10 TB per day, and the deployment has completed performance and stability validation.</p>
+<p>In the second half of 2026, Rednote plans to continue the progressive migration of existing Kafka-based index pipelines. The next phase is expected to span more than 100 tables and infrastructure measured in hundreds of thousands of CPU cores. The rollout will remain phased and controlled, allowing the old and new paths to hand off traffic gradually rather than through a high-risk cutover.</p>
+<p>For historical replay, the team plans to make greater use of Paimon's column projection and the unified streaming-and-lakehouse architecture to improve object-storage efficiency. For real-time serving, it will also explore Fluss primary-key tables for enrichment, point lookups, partial updates, and continuously updated state.</p>
+<p><img decoding="async" loading="lazy" alt="Rednote Fluss real-time data platform progress, architecture, and roadmap" src="/assets/images/figure3-d5b49dda5cb1c9f44f3df13b069a8aba.webp" width="2048" height="759" class="img_ev3q"></p>
+<p><em>Figure 3. Rednote's Fluss real-time data platform: current progress, platform capabilities, ecosystem integration, and future plans.</em></p>
+<p>That roadmap changes the role Fluss plays inside Rednote. It is no longer merely an alternative transport layer; it is evolving into a more unified real-time data service. Over time, the platform is expected to integrate more deeply with compute engines such as Flink, Spark, and StarRocks, and with lake technologies including Paimon and Lance, supporting workloads across the community, search and recommendation, advertising, security, commerce, and live streaming.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-takeaway-this-was-bigger-than-a-broker-swap">The takeaway: this was bigger than a broker swap<a href="#the-takeaway-this-was-bigger-than-a-broker-swap" class="hash-link" aria-label="Direct link to The takeaway: this was bigger than a broker swap" title="Direct link to The takeaway: this was bigger than a broker swap" translate="no"></a></h2>
+<p>Rednote's move from Kafka to Fluss was not a rejection of a mature technology, nor was it a component change made for a single headline benchmark. It addressed a connected set of scaling problems: wide tables being read in full by many services, historical replay consuming online resources, a disconnect between streaming and lake data, and a core pipeline that needed more throughput with lower cost and stronger isolation.</p>
+<p>Columnar stream storage, direct object-store reads, and lakehouse integration allowed those issues to be reorganized within one architecture. Just as importantly, Rednote used load testing, dual runs, phased migration, and platform engineering to show that Fluss could move beyond technical promise and carry the pressure of a core production indexing workload.</p>
+<p>For teams building real-time search, recommendation, advertising, or AI data infrastructure, the lesson is straightforward: when data volumes keep growing, scaling is not only about raising a throughput ceiling. It is also about changing how data is organized, how it is accessed, and how safely the platform can absorb the next order of magnitude.</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 c [...]
+ <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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diff --git a/blog/releases/0.6/index.html b/blog/releases/0.6/index.html
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<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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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
index 46aa54e16a..8e780d3158 100644
--- a/blog/releases/0.7/index.html
+++ b/blog/releases/0.7/index.html
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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
index 61b49a60c1..e580b5df83 100644
--- a/blog/releases/0.8/index.html
+++ b/blog/releases/0.8/index.html
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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 0e05dee919..bdb872a73e 100644
--- a/blog/releases/0.9/index.html
+++ b/blog/releases/0.9/index.html
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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
index 323414bf58..229c823e83 100644
--- a/blog/roaringbitmap-uv-deduplication/index.html
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@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
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<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>
diff --git a/blog/rss.xml b/blog/rss.xml
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--- a/blog/rss.xml
+++ b/blog/rss.xml
@@ -4,10 +4,75 @@
<title>Apache Fluss™ Blog</title>
<link>https://fluss.apache.org/blog/</link>
<description>Apache Fluss™ Blog</description>
- <lastBuildDate>Thu, 06 Aug 2026 00:00:00 GMT</lastBuildDate>
+ <lastBuildDate>Tue, 18 Aug 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[From Kafka to Fluss: How Rednote Migrated a Core Real-Time Indexing Pipeline]]></title>
+ <link>https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/</link>
+ <guid>https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/</guid>
+ <pubDate>Tue, 18 Aug 2026 00:00:00 GMT</pubDate>
+ <description><![CDATA[A production case study in columnar streaming, cold-data isolation, and lakehouse integration.]]></description>
+ <content:encoded><![CDATA[<p><img decoding="async" loading="lazy" alt="From Kafka to Fluss: Xiaohongshu&#39;s production-grade migration of its core indexing pipeline" src="https://fluss.apache.org/assets/images/banner-d978c02a35df34891b55b615f9281366.png" width="1202" height="508" class="img_ev3q"></p>
+<p><em>A production case study in columnar streaming, cold-data isolation, and lakehouse integration</em>
+<em>Presented at Flink Forward Asia 2026.</em></p>
+<p><a href="http://rednote.com/" target="_blank" rel="noopener noreferrer" class="">Rednote</a> (Xiaohongshu) is a lifestyle community platform centered on content discovery and sharing. During the FIFA World Cup, it becomes a hub for live coverage, pre-match analysis, trending posts, and fan discussions.</p>
+<p>At that scale, hundreds of millions of users can hit live streaming, search, and feeds at once. Content must refresh in moments while advertising, search, and recommendation services stay stable—powered by a real-time indexing pipeline that continuously ingests data and updates indexes.</p>
+<p>That pipeline is critical to Rednote's content distribution and user experience, but its Kafka-based wide-table architecture was reaching cost and stability limits. To scale without compromising real-time performance, Rednote introduced <strong>Fluss</strong> into its core production path and began progressively migrating index data from Kafka to Fluss.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="why-the-indexing-pipeline-needed-to-change">Why the indexing pipeline needed to change<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#why-the-indexing-pipeline-needed-to-change" class="hash-link" aria-label="Direct link to Why the indexing pipeline needed to change" title="Direct link to Why the indexing pipeline needed to change" translate="no"></a></h2>
+<p>Rednote's search, recommendation, and advertising systems all depend on real-time indexes. Upstream processing platforms continuously produce wide tables, while many downstream online services consume different subsets of those tables. One service may need content features, another may care about user and interaction fields, and a third may build a specialized index for search or ads. The consumers are numerous, their column requirements differ, and their fan-out ratios vary dramatica [...]
+<p>Kafka remains a mature and dependable messaging platform. But in this particular workload, row-oriented writes and full-row consumption amplified three structural problems.</p>
+<p><img decoding="async" loading="lazy" alt="Kafka-based wide-table distribution before the migration to Fluss" src="https://fluss.apache.org/assets/images/figure1-8f967d87077622a58029eae759abe52a.webp" width="2048" height="836" class="img_ev3q"></p>
+<p><em>Figure 1. Before the migration: Kafka wide-table distribution created read amplification, slow cold reads, and resource contention.</em></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="1-read-amplification-kept-growing">1. Read amplification kept growing<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#1-read-amplification-kept-growing" class="hash-link" aria-label="Direct link to 1. Read amplification kept growing" title="Direct link to 1. Read amplification kept growing" translate="no"></a></h3>
+<p>The same wide table was consumed repeatedly by multiple online services, even though each service needed only a fraction of the available columns. In a row-oriented model, a consumer interested in a handful of fields still had to read and process the entire record.</p>
+<p>As fan-out increased from 1x to 10x and 100x—and reached roughly 1,000x in some localized workloads—large volumes of irrelevant data were repeatedly transferred across the network. At Rednote's scale, that unnecessary I/O translated directly into bandwidth and compute costs.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="2-cold-data-replay-took-the-long-way-around">2. Cold-data replay took the long way around<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#2-cold-data-replay-took-the-long-way-around" class="hash-link" aria-label="Direct link to 2. Cold-data replay took the long way around" title="Direct link to 2. Cold-data replay took the long way around" translate="no"></a></h3>
+<p>Index building does not consume only the latest events. Batch and incremental jobs frequently need to scan historical data as well. In the previous architecture, cold data lived in object storage. A replay first had to pull that data back through a Kafka broker, which then forwarded it to the client.</p>
+<p>That extra hop lengthened the path and forced brokers designed for real-time delivery to carry substantial historical scan traffic.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="3-historical-scans-competed-with-online-traffic">3. Historical scans competed with online traffic<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#3-historical-scans-competed-with-online-traffic" class="hash-link" aria-label="Direct link to 3. Historical scans competed with online traffic" title="Direct link to 3. Historical scans competed with online traffic" translate="no"></a></h3>
+<p>Batch and incremental index builds often scan historical data with high concurrency. Those reads consumed broker network bandwidth and polluted the page cache, creating direct contention with latency-sensitive online consumers.</p>
+<p>For a core indexing pipeline, online stability comes first. If a historical rebuild can degrade real-time delivery, the architecture needs a cleaner boundary between hot-path consumption and cold-data access.</p>
+<p>The real issue was not simply a need to replace one messaging system with another. The organization and access pattern of the data no longer matched the way the data was being consumed. Rednote needed a platform that preserved streaming semantics while adding columnar reads, hot-and-cold tiering, and native integration with lake storage.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="what-changed-with-fluss">What changed with Fluss<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#what-changed-with-fluss" class="hash-link" aria-label="Direct link to What changed with Fluss" title="Direct link to What changed with Fluss" translate="no"></a></h2>
+<p>The migration changed how data was written, consumed, and replayed.</p>
+<p><img decoding="async" loading="lazy" alt="Fluss-based real-time indexing architecture and migration benefits" src="https://fluss.apache.org/assets/images/figure2-f63b69dfd5c6426aa5ee56376e14eea3.webp" width="2048" height="836" class="img_ev3q"></p>
+<p><em>Figure 2. After the migration: columnar consumption, direct object-store reads, and Paimon integration reshape the real-time indexing path.</em></p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="online-services-moved-from-full-row-transfers-to-columnar-reads">Online services moved from full-row transfers to columnar reads<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#online-services-moved-from-full-row-transfers-to-columnar-reads" class="hash-link" aria-label="Direct link to Online services moved from full-row transfers to columnar reads" title="Direct link to Online services moved from full- [...]
+<p>Upstream systems write to Fluss through Apache Arrow. Downstream services can then read only the columns they actually need instead of fetching the full row every time.</p>
+<p>Consumption cost is therefore tied more closely to the data a service uses, rather than to the total width of the source table. In a pipeline with wide records, many consumers, and high fan-out, the value of column projection compounds as the workload grows.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="historical-replay-bypassed-the-broker">Historical replay bypassed the broker<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#historical-replay-bypassed-the-broker" class="hash-link" aria-label="Direct link to Historical replay bypassed the broker" title="Direct link to Historical replay bypassed the broker" translate="no"></a></h3>
+<p>Cold-data replay no longer has to flow entirely through the broker path. Index builders can read Fluss log files directly from object storage, separating large historical scans from the online messaging layer.</p>
+<p>This reduces pressure on broker NICs and cache resources. Real-time consumers stay on a low-latency path, while historical builds use a route better suited to parallel scans. The result is stronger isolation between two workloads with very different performance profiles.</p>
+<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="fluss-and-paimon-created-a-shared-lakestream-foundation">Fluss and Paimon created a shared Lakestream foundation<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#fluss-and-paimon-created-a-shared-lakestream-foundation" class="hash-link" aria-label="Direct link to Fluss and Paimon created a shared Lakestream foundation" title="Direct link to Fluss and Paimon created a shared Lakestream foundation" transla [...]
+<p>Rednote also uses Fluss's Lakestream to write the same real-time data into Apache Paimon. During migration, the Paimon tables support data exploration and dual-run validation, helping the team compare the new and existing pipelines for correctness. In production, the lake copy can also serve historical replay, offline analytics, and batch index construction.</p>
+<p>Instead of maintaining separate and disconnected real-time and historical datasets, the architecture moves toward a common data foundation that can serve both.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-gains-appeared-across-the-entire-pipeline">The gains appeared across the entire pipeline<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#the-gains-appeared-across-the-entire-pipeline" class="hash-link" aria-label="Direct link to The gains appeared across the entire pipeline" title="Direct link to The gains appeared across the entire pipeline" translate="no"></a></h2>
+<p>Because the migration changed data layout, access paths, and workload isolation at the same time, the benefits were not confined to a single benchmark.</p>
+<p><strong>On the write path, CPU usage fell by about 30%, while write traffic fell by roughly 50%.</strong> Arrow-based writes and a layout better suited to the indexing workload reduced resource consumption and created more stability headroom for traffic peaks.</p>
+<p><strong>For index construction, batch and incremental build times fell by roughly 50% to 80%.</strong> Builders could pull data in parallel more efficiently, allowing changes in the business to propagate faster into search, recommendation, and advertising indexes.</p>
+<p><strong>Across online workloads, bandwidth savings ranged from about 30% to 90%, while peak throughput increased by approximately 3x.</strong> Columnar reads eliminated large amounts of unnecessary data transfer, particularly for wide tables with many downstream consumers.</p>
+<p>The significance lies in the combination. The core pipeline gained lower resource overhead, faster index builds, and greater peak capacity at the same time. Together, those improvements create the operating margin a production system needs in order to keep scaling.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-hardest-part-was-proving-production-readiness">The hardest part was proving production readiness<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#the-hardest-part-was-proving-production-readiness" class="hash-link" aria-label="Direct link to The hardest part was proving production readiness" title="Direct link to The hardest part was proving production readiness" translate="no"></a></h2>
+<p>Clear performance gains did not make the migration simple. For infrastructure at Rednote's scale, a new system must answer more than whether its features work. It must demonstrate predictable performance, long-running stability, data correctness, and operational maturity.</p>
+<p>The team therefore treated Fluss as production infrastructure from the outset, not as an experimental swap. It ran comprehensive load tests against the critical Kafka baselines, followed by extended dual-run validation to confirm data correctness. Only after the new path had passed those checks did the team progressively connect core workloads and expose it to real production traffic.</p>
+<p>Platform capabilities had to mature alongside the data path: table management, multi-tenancy, monitoring and alerting, data lineage, cluster operations, multi-cloud control, cross-region backup, and lake-ingestion job management. These capabilities are less visible than a throughput chart, but they determine whether a system can move from a successful test to years of reliable operation.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="from-one-critical-pipeline-to-a-shared-real-time-data-platform">From one critical pipeline to a shared real-time data platform<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#from-one-critical-pipeline-to-a-shared-real-time-data-platform" class="hash-link" aria-label="Direct link to From one critical pipeline to a shared real-time data platform" title="Direct link to From one critical pipeline to a shar [...]
+<p>Fluss is now running in Rednote's core production indexing path. According to the figures shared in this blog post, a single table is already ingesting roughly 1 billion records and 10 TB per day, and the deployment has completed performance and stability validation.</p>
+<p>In the second half of 2026, Rednote plans to continue the progressive migration of existing Kafka-based index pipelines. The next phase is expected to span more than 100 tables and infrastructure measured in hundreds of thousands of CPU cores. The rollout will remain phased and controlled, allowing the old and new paths to hand off traffic gradually rather than through a high-risk cutover.</p>
+<p>For historical replay, the team plans to make greater use of Paimon's column projection and the unified streaming-and-lakehouse architecture to improve object-storage efficiency. For real-time serving, it will also explore Fluss primary-key tables for enrichment, point lookups, partial updates, and continuously updated state.</p>
+<p><img decoding="async" loading="lazy" alt="Rednote Fluss real-time data platform progress, architecture, and roadmap" src="https://fluss.apache.org/assets/images/figure3-d5b49dda5cb1c9f44f3df13b069a8aba.webp" width="2048" height="759" class="img_ev3q"></p>
+<p><em>Figure 3. Rednote's Fluss real-time data platform: current progress, platform capabilities, ecosystem integration, and future plans.</em></p>
+<p>That roadmap changes the role Fluss plays inside Rednote. It is no longer merely an alternative transport layer; it is evolving into a more unified real-time data service. Over time, the platform is expected to integrate more deeply with compute engines such as Flink, Spark, and StarRocks, and with lake technologies including Paimon and Lance, supporting workloads across the community, search and recommendation, advertising, security, commerce, and live streaming.</p>
+<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="the-takeaway-this-was-bigger-than-a-broker-swap">The takeaway: this was bigger than a broker swap<a href="https://fluss.apache.org/blog/rednote-kafka-to-fluss-real-time-indexing/#the-takeaway-this-was-bigger-than-a-broker-swap" class="hash-link" aria-label="Direct link to The takeaway: this was bigger than a broker swap" title="Direct link to The takeaway: this was bigger than a broker swap" translate="no"></a></h2>
+<p>Rednote's move from Kafka to Fluss was not a rejection of a mature technology, nor was it a component change made for a single headline benchmark. It addressed a connected set of scaling problems: wide tables being read in full by many services, historical replay consuming online resources, a disconnect between streaming and lake data, and a core pipeline that needed more throughput with lower cost and stronger isolation.</p>
+<p>Columnar stream storage, direct object-store reads, and lakehouse integration allowed those issues to be reorganized within one architecture. Just as importantly, Rednote used load testing, dual runs, phased migration, and platform engineering to show that Fluss could move beyond technical promise and carry the pressure of a core production indexing workload.</p>
+<p>For teams building real-time search, recommendation, advertising, or AI data infrastructure, the lesson is straightforward: when data volumes keep growing, scaling is not only about raising a throughput ceiling. It is also about changing how data is organized, how it is accessed, and how safely the platform can absorb the next order of magnitude.</p>]]></content:encoded>
+ <category>Apache Fluss</category>
+ <category>Real-time Analytics</category>
+ <category>Streaming Lakehouse</category>
+ </item>
<item>
<title><![CDATA[Apache Fluss Graduates to a Top Level Project]]></title>
<link>https://fluss.apache.org/blog/apache-fluss-graduates-to-top-level-project/</link>
@@ -2834,129 +2899,5 @@ In key business scenarios such as log collection and analysis, search recommenda
<li class="">Mailing List: <code>dev@fluss.apache.org</code> (by sending any mail to <code>dev-subscribe@fluss.apache.org</code> to subscribe)</li>
</ul>]]></content:encoded>
</item>
- <item>
- <title><![CDATA[Apache Fluss Java Client: A Deep Dive]]></title>
- <link>https://fluss.apache.org/blog/fluss-java-client/</link>
- <guid>https://fluss.apache.org/blog/fluss-java-client/</guid>
- <pubDate>Mon, 07 Jul 2025 00:00:00 GMT</pubDate>
- <description><![CDATA[Banner]]></description>
- <content:encoded><![CDATA[<p><img decoding="async" loading="lazy" alt="Banner" src="https://fluss.apache.org/assets/images/banner-9b4e9683efcde75c4961249525c8f269.png" width="1477" height="975" class="img_ev3q"></p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="introduction">Introduction<a href="https://fluss.apache.org/blog/fluss-java-client/#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>.
-In practice, this means Fluss can ingest high-throughput event streams <em>(using log tables)</em> while also maintaining <em>up-to-date</em> reference data or state <em>(using primary key tables)</em>, a combination ideal for
-scenarios like IoT, where you might stream sensor readings and look up information for those sensors in real-time, without
-the need for external K/V stores.</p>
-<p>In this tutorial, we'll introduce the <strong>Fluss Java Client</strong> by walking through a simple home IoT system example.
-We will use <code>Fluss's Admin client</code> to create a primary key table for sensor information and a log table for sensor readings, then use the client
-to write data to these tables and read/enrich the streaming sensor data.</p>
-<p>By the end, you'll see how a sensor reading can be ingested into a log table and immediately enriched with information from a primary key table (essentially performing a real-time lookup join for streaming data enrichment).</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="preflight-check">Preflight Check<a href="https://fluss.apache.org/blog/fluss-java-client/#preflight-check" class="hash-link" aria-label="Direct link to Preflight Check" title="Direct link to Preflight Check" translate="no"></a></h2>
-<p>The full source code can be found <a href="https://github.com/ververica/ververica-fluss-examples/tree/main/fluss-java-client" target="_blank" rel="noopener noreferrer" class="">here</a>.</p>
-<div class="language-shell codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-shell codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token function" style="color:#7C3AED">docker</span><span class="token plain"> compose up</span><br></di [...]
-<p>The first thing we need to do is establish a connection to the Fluss cluster.
-The <code>Connection</code> is the main entry point for the Fluss client, from which we obtain an <code>Admin</code> (for metadata operations) and Table instances (for data operations)</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token comment" style="color:#64748B;font-style:italic">// Configure connection to Fluss cluster</span><sp [...]
-<p>The above code snippet shows the bare minimum requirements for connecting and interacting with a Fluss Cluster.
-For our example we will use the following mock data - to keep things simple - which you can find below:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="operating-the-cluster">Operating The Cluster<a href="https://fluss.apache.org/blog/fluss-java-client/#operating-the-cluster" class="hash-link" aria-label="Direct link to Operating The Cluster" title="Direct link to Operating The Cluster" translate="no"></a></h2>
-<p>Let's create a database for our IoT data, and within it define two tables:</p>
-<ul>
-<li class=""><strong>Sensor Readings Table:</strong> A log table that will collect time-series readings from sensors (like temperature and humidity readings). This table is append-only (new records are added continuously, with no updates/deletes) which is ideal for immutable event streams</li>
-<li class=""><strong>Sensor Information Table:</strong> A primary key table that stores metadata for each sensor (like sensor ID, location, type). Each <code>sensorId</code> will be unique and acts as the primary key. This table can be updated as sensor info changes (e.g., sensor relocated or reconfigured).</li>
-</ul>
-<p>Using the Admin client, we can programmatically create these tables.</p>
-<p>First, we'll ensure the database exists (creating it if not), then define schemas for each table and create them:</p>
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="schema-definitions">Schema Definitions<a href="https://fluss.apache.org/blog/fluss-java-client/#schema-definitions" class="hash-link" aria-label="Direct link to Schema Definitions" title="Direct link to Schema Definitions" translate="no"></a></h3>
-<h4 class="anchor anchorTargetStickyNavbar_Vzrq" id="log-table-sensor-readings">Log table (sensor readings)<a href="https://fluss.apache.org/blog/fluss-java-client/#log-table-sensor-readings" class="hash-link" aria-label="Direct link to Log table (sensor readings)" title="Direct link to Log table (sensor readings)" translate="no"></a></h4>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<h4 class="anchor anchorTargetStickyNavbar_Vzrq" id="primary-key-table-sensor-information">Primary Key table (sensor information)<a href="https://fluss.apache.org/blog/fluss-java-client/#primary-key-table-sensor-information" class="hash-link" aria-label="Direct link to Primary Key table (sensor information)" title="Direct link to Primary Key table (sensor information)" translate="no"></a></h4>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<h3 class="anchor anchorTargetStickyNavbar_Vzrq" id="table-creation">Table Creation<a href="https://fluss.apache.org/blog/fluss-java-client/#table-creation" class="hash-link" aria-label="Direct link to Table Creation" title="Direct link to Table Creation" translate="no"></a></h3>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<p>We specify a distribution with <code>.distributedBy(3, "sensorId")</code>.
-Fluss tables are partitioned into buckets (similar to partitions in Kafka topics) for scalability.
-Here we use 3 buckets, meaning data gets distributed across 3 buckets. Multiple buckets allow for higher throughput or to parallelize reads/writes.
-If using multiple buckets, Fluss would hash on the bucket key (<code>sensorId</code> in our case) to assign records to buckets.</p>
-<p>For the <code>sensor_readings</code> table, we define a schema without any primary key. In Fluss, a table created without a primary key clause is a Log Table.
-A log table only supports appending new records (no updates or deletes), making it perfect for immutable time-series data or logs.</p>
-<p>In the log table, specifying a bucket key like <code>sensorId</code> ensures all readings from the same sensor end up to the same bucket providing strict ordering guarantees.</p>
-<p>With our tables created let's go and write some data.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="table-writes">Table Writes<a href="https://fluss.apache.org/blog/fluss-java-client/#table-writes" class="hash-link" aria-label="Direct link to Table Writes" title="Direct link to Table Writes" translate="no"></a></h2>
-<p>With our tables in place, let's insert some data using the Fluss Java API.
-The client allows us to write or read data from it.
-We'll demonstrate two patterns:</p>
-<ul>
-<li class=""><strong>Upserting</strong> into the primary key table (sensor information).</li>
-<li class=""><strong>Appending</strong> to the log table (sensor readings).</li>
-</ul>
-<p>Fluss provides specialized writer interfaces for each table type: an <strong>UpsertWriter</strong> for primary key tables and an <strong>AppendWriter</strong> for log tables.
-Under the hood, the Fluss client currently expects data as <strong>GenericRow</strong> objects (a generic row data format).</p>
-<blockquote>
-<p><strong>Note:</strong> Internally Fluss uses <strong>InternalRow</strong> as an optimized, binary representation of data for better performance and memory efficiency.
-<strong>GenericRow</strong> is a generic implementation of InternalRow. This allows developers to interact with data easily while Fluss processes it efficiently using the underlying binary format.</p>
-</blockquote>
-<p>Since we are creating <strong>Pojos</strong> though this means that we need to convert these into a GenericRow in order to write them into Fluss.</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">public</span><span class="token plain"> </span><span class="token ke [...]
-<p><strong>Note:</strong> For certain data types like <code>String</code> or <code>LocalDateTime</code> we need to use certain functions like
-<code>BinaryString.fromString("string_value")</code> or <code>TimestampNtz.fromLocalDateTime(datetime)</code> otherwise you might
-come across some conversion exceptions.</p>
-<p>Let's start by writing data to the <code>Log Table</code>. This requires getting an <code>AppendWriter</code> as follows:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">logger</span><span class="token punctuation" style="color:#475569">.</span><span class="toke [...]
-<p>At this point we have successfully written 10 sensor readings to our table.</p>
-<p>Next, let's write data to the <code>Primary Key Table</code>. This requires getting an <code>UpsertWriter</code> as follows:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain">logger</span><span class="token punctuation" style="color:#475569">.</span><span class="toke [...]
-<p>At this point we have successfully written 10 sensor information records to our table, because
-updates will be handled on the primary key and merged.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="scans--lookups">Scans & Lookups<a href="https://fluss.apache.org/blog/fluss-java-client/#scans--lookups" class="hash-link" aria-label="Direct link to Scans & Lookups" title="Direct link to Scans & Lookups" translate="no"></a></h2>
-<p>Now comes the real-time data enrichment part of our example.
-We want to simulate a process where each incoming sensor reading is immediately looked up against the sensor information table to add context (like location and type) to the raw reading.
-This is a common pattern in streaming systems, often achieved with lookup joins.</p>
-<p>With the Fluss Java client, we can do this by combining a <strong>log scanner on the readings table</strong> with <strong>point lookups on the sensor information table</strong>.</p>
-<p>To consume data from a Fluss table, we use a *<em>Scanner</em>.
-For a log table, Fluss provides a <strong>LogScanner</strong> that allows us to <strong>subscribe to one or more buckets</strong> and poll for new records.</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token class-name" style="color:#7C3AED">LogScanner</span><span class="token plain"> logScanner </span><sp [...]
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token class-name" style="color:#7C3AED">Lookuper</span><span class="token plain"> sensorInfoLookuper </sp [...]
-<p>We set up a scanner on the <code>sensor_readings</code> table, and next we need to subscribe to all its buckets, and then poll for any available records:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token keyword" style="color:#194670">int</span><span class="token plain"> numBuckets </span><span class=" [...]
-<p>Start polling for records. For each incoming record we will use the <strong>Lookuper</strong> to <code>lookup</code> sensor information from the primary key table,
-and creating a <strong>SensorReadingEnriched</strong> record.</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token plain"> </span><span class="token keyword" style="color:#194670">while</span><span class="token pla [...]
-<p>Let's summarize what's happening here:</p>
-<ul>
-<li class="">We create a LogScanner for the <code>sensor_readings</code> table using <em>table.newScan().createLogScanner()</em>.</li>
-<li class="">We subscribe to each bucket of the table from the beginning (offset 0). Subscribing <code>from beginning</code> means we'll read all existing data from the start; alternatively, one could subscribe from the latest position to only get new incoming data or based on other attributes like time. In our case, since we just inserted data, from-beginning will capture those inserts.</li>
-<li class="">We then call <code>poll(Duration)</code> on the scanner to retrieve available records, waiting up to the given timeout (1 second here). This returns a <code>ScanRecords</code> batch containing any records that were present. We iterate over each <code>TableBucket</code> and then over each <code>ScanRecord</code> within that bucket.</li>
-<li class="">For each record, we extract the fields via the InternalRow interface (which provides typed access to each column in the row) and <strong>convert them into a Pojo</strong>.</li>
-<li class="">Next, for each reading, we perform a <strong>lookup</strong> on the <strong>sensor_information</strong> table to get the sensor's info. We construct a key (GenericRow with just the sensor_id) and use <strong>sensorTable.newLookup().createLookuper().lookup(key)</strong>. This performs a point lookup by primary key and returns a <code>LookupResult future</code>; we call <code>.get()</code> to get the result synchronously. If present, we retrieve the InternalRow of the sensor i [...]
-<li class="">We then combine the data: logging an enriched message that includes the sensor's information alongside the reading values.</li>
-</ul>
-<p>Fluss's lookup API gives us quick primary-key retrieval from a table, which is exactly what we need to enrich the streaming data.
-In a real application, this enrichment could be done on the fly in a streaming job (and indeed <strong>Fluss is designed to support high-QPS lookup joins in real-time pipelines</strong>), but here we're simulating it with client calls for clarity.</p>
-<p>If you run the above code found <a href="https://github.com/ververica/ververica-fluss-examples" target="_blank" rel="noopener noreferrer" class="">here</a>, you should see an output like the following:</p>
-<div class="language-shell codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-shell codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token number" style="color:#B45309">16</span><span class="token plain">:07:13.594 INFO </span><span cl [...]
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="column-pruning-scans">Column Pruning Scans<a href="https://fluss.apache.org/blog/fluss-java-client/#column-pruning-scans" class="hash-link" aria-label="Direct link to Column Pruning Scans" title="Direct link to Column Pruning Scans" translate="no"></a></h2>
-<p>Column pruning lets you fetch only the columns you need, <strong>reducing network overhead and improving read performance</strong>. With Fluss’s Java client, you can specify a subset of columns in your scan:</p>
-<div class="language-java codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-java codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token class-name" style="color:#7C3AED">LogScanner</span><span class="token plain"> logScanner </span><sp [...]
-<p>Let's break this down:</p>
-<ul>
-<li class=""><code>.project(...)</code> instructs the client to request only the specified columns (sensorId,timestamp and temperature) from the server.</li>
-<li class="">Fluss’s columnar storage means non-requested columns (e.g., humidity, etc.) <strong>aren’t transmitted, saving bandwidth and reducing client-side parsing overhead</strong>.</li>
-<li class="">You can combine projection with filters or lookups to further optimize your data access patterns.</li>
-</ul>
-<p>Example output:</p>
-<div class="language-shell codeBlockContainer_Ckt0 theme-code-block" style="--prism-color:#0A0F1C;--prism-background-color:#F8FAFC"><div class="codeBlockContent_QJqH"><pre tabindex="0" class="prism-code language-shell codeBlock_bY9V thin-scrollbar" style="color:#0A0F1C;background-color:#F8FAFC"><code class="codeBlockLines_e6Vv"><div class="token-line" style="color:#0A0F1C"><span class="token number" style="color:#B45309">16</span><span class="token plain">:12:35.114 INFO </span><span cl [...]
-<p>Notice, how only the requested columns are returned from the server.</p>
-<h2 class="anchor anchorTargetStickyNavbar_Vzrq" id="conclusion">Conclusion<a href="https://fluss.apache.org/blog/fluss-java-client/#conclusion" class="hash-link" aria-label="Direct link to Conclusion" title="Direct link to Conclusion" translate="no"></a></h2>
-<p>In this blog post, we've introduced the Fluss Java Client by guiding you through a full example of creating tables, writing data, and reading/enriching data in real-time.
-We covered how to use the <code>Admin</code> client to define a <strong>Primary Key table</strong> (for reference data that can be updated) and a <strong>Log table</strong> (for immutable event streams), and how to use the Fluss client to upsert and append data accordingly.
-We also demonstrated reading from a log table using a scanner and performing a lookup on a primary key table to enrich the streaming data on the fly.</p>
-<p>This IoT sensor scenario is just one example of Fluss in action and also highlights the <strong>Stream/Table duality</strong> within the same system.
-Fluss's ability to handle high-throughput append streams and fast key-based lookups makes it well-suited for real-time analytics use cases like this and many others.
-With this foundation, you can explore more advanced features of Fluss to build robust real-time data applications. Happy streaming! 🌊</p>
-<p>And before you go 😊 don’t forget to give Fluss 🌊 some ❤️ via ⭐ on <a href="https://github.com/apache/fluss" target="_blank" rel="noopener noreferrer" class="">GitHub</a></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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@@ -3,7 +3,7 @@
<head>
<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.2">
-<title data-rh="true">2 posts tagged with "Apache Fluss" | Apache Fluss™</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:ur [...]
+<title data-rh="true">3 posts tagged with "Apache Fluss" | Apache Fluss™</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:ur [...]
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@@ -26,14 +26,19 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
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+<p><em>A production case study in columnar streaming, cold-data isolation, and lakehouse integration</em>
+<em>Presented at Flink Forward Asia 2026.</em></p>
+<p><a href="http://rednote.com/" target="_blank" rel="noopener noreferrer" class="">Rednote</a> (Xiaohongshu) is a lifestyle community platform centered on content discovery and sharing. During the FIFA World Cup, it becomes a hub for live coverage, pre-match analysis, trending posts, and fan discussions.</p>
+<p>At that scale, hundreds of millions of users can hit live streaming, search, and feeds at once. Content must refresh in moments while advertising, search, and recommendation services stay stable—powered by a real-time indexing pipeline that continuously ingests data and updates indexes.</p>
+<p>That pipeline is critical to Rednote's content distribution and user experience, but its Kafka-based wide-table architecture was reaching cost and stability limits. To scale without compromising real-time performance, Rednote introduced <strong>Fluss</strong> into its core production path and began progressively migrating index data from Kafka to Fluss.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left [...]
<p>As <a href="https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-new-top-level-projects-5" target="_blank" rel="noopener noreferrer" class="">officially announced by the Apache Software Foundation</a>, we are thrilled to share that <strong>Apache Fluss</strong> has graduated from the Apache Incubator to become a <strong>Top Level Project (TLP)</strong>.</p>
<p>The project's <a href="https://lists.apache.org/thread/kltvfrklyoqm9dj6dgwdzf82sm097427" target="_blank" rel="noopener noreferrer" class="">graduation proposal</a> received unanimous approval from the Apache Incubator Project Management Committee (IPMC) and was subsequently approved by the ASF Board of Directors. This milestone not only marks a new stage in Fluss's journey, but also further advances the convergence of streaming storage, the real-time Lakehouse, and AI data i [...]
<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 [...]
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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 [...]
diff --git a/blog/tags/arrow/index.html b/blog/tags/arrow/index.html
index 640c8dee22..3b48a11f4f 100644
--- a/blog/tags/arrow/index.html
+++ b/blog/tags/arrow/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<p><strong>TL;DR:</strong></p>
<blockquote>
<p>Apache Kafka's "column pruning" is actually pseudo-pruning. All fields still cross the network, and clients discard unwanted ones after the fact.
diff --git a/blog/tags/column-pruning/index.html b/blog/tags/column-pruning/index.html
index 4c533bd7f7..ce54c497a1 100644
--- a/blog/tags/column-pruning/index.html
+++ b/blog/tags/column-pruning/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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<svg style="display: none;"><defs>
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</defs></svg>
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<p><strong>TL;DR:</strong></p>
<blockquote>
<p>Apache Kafka's "column pruning" is actually pseudo-pruning. All fields still cross the network, and clients discard unwanted ones after the fact.
diff --git a/blog/tags/fluss-cpp/index.html b/blog/tags/fluss-cpp/index.html
index 2c05f8347c..d7d86c7ba7 100644
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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
index 25fb6487fa..f551e2c83c 100644
--- a/blog/tags/fluss-rs/index.html
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@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script src="/assets/js/main.f3cd8977.js" defer="defer"></script>
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<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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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
index 3ff173eae8..eca24af321 100644
--- a/blog/tags/fluss-rust/index.html
+++ b/blog/tags/fluss-rust/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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/index.html b/blog/tags/fluss/index.html
index c131fd3193..3b652333b6 100644
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
<script src="https://widget.kapa.ai/kapa-widget.bundle.js" async data-website-id="40ccde97-65ed-46d8-81f2-fe8a8a31f9d9" data-project-name="Apache Fluss" data-project-color="#06b6d4" data-project-logo="/img/logo/svg/colored_logo_notext.svg" data-modal-title="Ask Apache Fluss AI" data-modal-image="/img/logo/svg/colored_logo_notext.svg" data-modal-disclaimer="This is a custom LLM with access to the [Apache Fluss documentation](https://fluss.apache.org/docs/). Answers may be inaccurate — alw [...]
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<p><strong>TL;DR:</strong></p>
<blockquote>
<p>Apache Kafka's "column pruning" is actually pseudo-pruning. All fields still cross the network, and clients discard unwanted ones after the fact.
diff --git a/blog/tags/index.html b/blog/tags/index.html
index 4b2fc697d9..2d2fd413a4 100644
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
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diff --git a/blog/tags/kafka/index.html b/blog/tags/kafka/index.html
index 18ad65e92e..49cc2e1b20 100644
--- a/blog/tags/kafka/index.html
+++ b/blog/tags/kafka/index.html
@@ -26,14 +26,14 @@
<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
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-<script src="/assets/js/runtime~main.2bc92054.js" defer="defer"></script>
-<script src="/assets/js/main.665e7276.js" defer="defer"></script>
+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
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<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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<p><strong>TL;DR:</strong></p>
<blockquote>
<p>Apache Kafka's "column pruning" is actually pseudo-pruning. All fields still cross the network, and clients discard unwanted ones after the fact.
diff --git a/blog/tags/lakestream/index.html b/blog/tags/lakestream/index.html
index 22f83f8829..3ea9c0c5c0 100644
--- a/blog/tags/lakestream/index.html
+++ b/blog/tags/lakestream/index.html
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<p>As <a href="https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-new-top-level-projects-5" target="_blank" rel="noopener noreferrer" class="">officially announced by the Apache Software Foundation</a>, we are thrilled to share that <strong>Apache Fluss</strong> has graduated from the Apache Incubator to become a <strong>Top Level Project (TLP)</strong>.</p>
<p>The project's <a href="https://lists.apache.org/thread/kltvfrklyoqm9dj6dgwdzf82sm097427" target="_blank" rel="noopener noreferrer" class="">graduation proposal</a> received unanimous approval from the Apache Incubator Project Management Committee (IPMC) and was subsequently approved by the ASF Board of Directors. This milestone not only marks a new stage in Fluss's journey, but also further advances the convergence of streaming storage, the real-time Lakehouse, and AI data i [...]
<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>
diff --git a/blog/tags/performance/index.html b/blog/tags/performance/index.html
index 9453d05485..7d9d8010eb 100644
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<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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<p><strong>TL;DR:</strong></p>
<blockquote>
<p>Apache Kafka's "column pruning" is actually pseudo-pruning. All fields still cross the network, and clients discard unwanted ones after the fact.
diff --git a/blog/tags/pyfluss/index.html b/blog/tags/pyfluss/index.html
index fbceafcf6c..023a36e4bf 100644
--- a/blog/tags/pyfluss/index.html
+++ b/blog/tags/pyfluss/index.html
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
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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>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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 5c847f3fcd..fc6c82c1c9 100644
--- a/blog/tags/real-time-analytics/index.html
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@@ -3,7 +3,7 @@
<head>
<meta charset="UTF-8">
<meta name="generator" content="Docusaurus v3.10.2">
-<title data-rh="true">2 posts tagged with "Real-time Analytics" | Apache Fluss™</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 [...]
+<title data-rh="true">3 posts tagged with "Real-time Analytics" | Apache Fluss™</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 [...]
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
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+<script src="/assets/js/main.f3cd8977.js" defer="defer"></script>
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
+<p><em>A production case study in columnar streaming, cold-data isolation, and lakehouse integration</em>
+<em>Presented at Flink Forward Asia 2026.</em></p>
+<p><a href="http://rednote.com/" target="_blank" rel="noopener noreferrer" class="">Rednote</a> (Xiaohongshu) is a lifestyle community platform centered on content discovery and sharing. During the FIFA World Cup, it becomes a hub for live coverage, pre-match analysis, trending posts, and fan discussions.</p>
+<p>At that scale, hundreds of millions of users can hit live streaming, search, and feeds at once. Content must refresh in moments while advertising, search, and recommendation services stay stable—powered by a real-time indexing pipeline that continuously ingests data and updates indexes.</p>
+<p>That pipeline is critical to Rednote's content distribution and user experience, but its Kafka-based wide-table architecture was reaching cost and stability limits. To scale without compromising real-time performance, Rednote introduced <strong>Fluss</strong> into its core production path and began progressively migrating index data from Kafka to Fluss.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left [...]
<p>As <a href="https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-new-top-level-projects-5" target="_blank" rel="noopener noreferrer" class="">officially announced by the Apache Software Foundation</a>, we are thrilled to share that <strong>Apache Fluss</strong> has graduated from the Apache Incubator to become a <strong>Top Level Project (TLP)</strong>.</p>
<p>The project's <a href="https://lists.apache.org/thread/kltvfrklyoqm9dj6dgwdzf82sm097427" target="_blank" rel="noopener noreferrer" class="">graduation proposal</a> received unanimous approval from the Apache Incubator Project Management Committee (IPMC) and was subsequently approved by the ASF Board of Directors. This milestone not only marks a new stage in Fluss's journey, but also further advances the convergence of streaming storage, the real-time Lakehouse, and AI data i [...]
<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 [...]
diff --git a/blog/tags/releases/index.html b/blog/tags/releases/index.html
index 4ede5b5700..6bc0b56a86 100644
--- a/blog/tags/releases/index.html
+++ b/blog/tags/releases/index.html
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<meta name="twitter:description" content="Open-source columnar streaming storage with sub-second freshness, primary-key tables, Flink integration, and native tiering to Iceberg and Paimon.">
<meta name="theme-color" content="#102856">
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-<script src="/assets/js/runtime~main.2bc92054.js" defer="defer"></script>
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+<script src="/assets/js/runtime~main.a0e571b5.js" defer="defer"></script>
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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
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-<title data-rh="true">One post tagged with "Streaming Lakehouse" | Apache Fluss™</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" propert [...]
+<title data-rh="true">2 posts tagged with "Streaming Lakehouse" | Apache Fluss™</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 [...]
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+<p><em>A production case study in columnar streaming, cold-data isolation, and lakehouse integration</em>
+<em>Presented at Flink Forward Asia 2026.</em></p>
+<p><a href="http://rednote.com/" target="_blank" rel="noopener noreferrer" class="">Rednote</a> (Xiaohongshu) is a lifestyle community platform centered on content discovery and sharing. During the FIFA World Cup, it becomes a hub for live coverage, pre-match analysis, trending posts, and fan discussions.</p>
+<p>At that scale, hundreds of millions of users can hit live streaming, search, and feeds at once. Content must refresh in moments while advertising, search, and recommendation services stay stable—powered by a real-time indexing pipeline that continuously ingests data and updates indexes.</p>
+<p>That pipeline is critical to Rednote's content distribution and user experience, but its Kafka-based wide-table architecture was reaching cost and stability limits. To scale without compromising real-time performance, Rednote introduced <strong>Fluss</strong> into its core production path and began progressively migrating index data from Kafka to Fluss.</p></div><footer class="row docusaurus-mt-lg"><div class="col col--9"><b>Tags:</b><ul class="tags_jXut padding--none margin-left [...]
<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/streaming/index.html b/blog/tags/streaming/index.html
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<p><strong>TL;DR:</strong></p>
<blockquote>
<p>Apache Kafka's "column pruning" is actually pseudo-pruning. All fields still cross the network, and clients discard unwanted ones after the fact.
diff --git a/blog/taobao-instant-commerce-real-time-decision/index.html b/blog/taobao-instant-commerce-real-time-decision/index.html
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<p>Every autumn in China, social media floods with posts about "The First Cup of Milk Tea in Autumn." With a tap on their phone, consumers expect their order delivered within 30 minutes. That effortless experience is no accident: it is the result of Taobao Instant Commerce making thousands of data-driven decisions every second.</p>
<p>Taobao Instant Commerce has scaled from a single-category food delivery service into a high-frequency platform spanning fresh produce, consumer electronics (3C), and beauty products. It operates under two very different modes: steady high-frequency daily transactions, and explosive traffic surges during promotional events where order volumes can multiply within minutes. Both demand the same thing: real-time responsiveness across hundreds of millions of SKUs.</p>
<p><strong>Real-time</strong> is not a nice-to-have here; it is the lifeline for three critical functions:</p>
diff --git a/blog/taobao-practice/index.html b/blog/taobao-practice/index.html
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<meta name="theme-color" content="#102856">
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+<script>!function(){var t=function(){try{return new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}}()||function(){try{return window.localStorage.getItem("theme")}catch(t){}}();document.documentElement.setAttribute("data-theme",t||"light"),document.documentElement.setAttribute("data-theme-choice",t||"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("docu [...]
<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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<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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<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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<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>
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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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