{"id":11692,"date":"2026-06-02T09:02:49","date_gmt":"2026-06-02T02:02:49","guid":{"rendered":"https:\/\/microsoft.cmctelecom.vn\/microsoft-fabric-ra-mat-incremental-liquid-clustering-toi-uu-chi-phi-va-hieu\/"},"modified":"2026-06-03T11:13:40","modified_gmt":"2026-06-03T04:13:40","slug":"microsoft-fabric-ra-mat-incremental-liquid-clustering-toi-uu-chi-phi-va-hieu","status":"publish","type":"post","link":"https:\/\/microsoft.cmctelecom.vn\/ja\/microsoft-fabric-ra-mat-incremental-liquid-clustering-toi-uu-chi-phi-va-hieu\/","title":{"rendered":"Microsoft Fabric ra m\u1eaft Incremental Liquid Clustering: T\u1ed1i \u01b0u chi ph\u00ed v\u00e0 hi\u1ec7u n\u0103ng Lakehouse"},"content":{"rendered":"<p>Ng\u00e0y 27\/05\/2026, Microsoft Fabric \u0111\u00e3 gi\u1edbi thi\u1ec7u Incremental Liquid Clustering, m\u1ed9t t\u00ednh n\u0103ng \u0111\u1ed9t ph\u00e1 gi\u00fap gi\u1ea3m \u0111\u00e1ng k\u1ec3 chi ph\u00ed v\u00e0 th\u1eddi gian cho vi\u1ec7c t\u1ed5 ch\u1ee9c d\u1eef li\u1ec7u trong m\u00f4i tr\u01b0\u1eddng lakehouse. Gi\u1ea3i ph\u00e1p n\u00e0y gi\u1ea3i quy\u1ebft th\u00e1ch th\u1ee9c l\u1edbn khi vi\u1ec7c duy tr\u00ec layout d\u1eef li\u1ec7u tr\u1edf n\u00ean t\u1ed1n k\u00e9m khi quy m\u00f4 b\u1ea3ng t\u0103ng l\u00ean, gi\u00fap doanh nghi\u1ec7p t\u1ed1i \u01b0u h\u00f3a chi ph\u00ed v\u1eadn h\u00e0nh m\u00e0 kh\u00f4ng \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn hi\u1ec7u n\u0103ng truy v\u1ea5n.<\/p>\n<h2>Th\u00e1ch th\u1ee9c t\u1eeb c\u01a1 ch\u1ebf clustering truy\u1ec1n th\u1ed1ng<\/h2>\n<p>Liquid Clustering gi\u00fap gi\u1ea3m chi ph\u00ed truy v\u1ea5n b\u1eb1ng c\u00e1ch s\u1eafp x\u1ebfp d\u1eef li\u1ec7u \u0111\u1ec3 c\u00e1c query ch\u1ec9 c\u1ea7n \u0111\u1ecdc nh\u1eefng file c\u1ea7n thi\u1ebft. Tuy nhi\u00ean, vi\u1ec7c duy tr\u00ec layout n\u00e0y l\u1ea1i t\u1ed1n k\u00e9m: m\u1ed7i l\u1ec7nh <code>OPTIMIZE<\/code> \u0111\u1ec1u ti\u00eau t\u1ed1n t\u00e0i nguy\u00ean t\u00ednh to\u00e1n \u0111\u1ec3 ghi l\u1ea1i file.<\/p>\n<p>Thu\u1eadt to\u00e1n ti\u00eau chu\u1ea9n t\u1eeb m\u00e3 ngu\u1ed3n m\u1edf Delta Lake ghi l\u1ea1i to\u00e0n b\u1ed9 d\u1eef li\u1ec7u trong c\u00e1c nh\u00f3m file \u0111\u00e3 \u0111\u01b0\u1ee3c cluster m\u1ed7i khi ch\u1ea1y <code>OPTIMIZE<\/code>, b\u1ea5t k\u1ec3 c\u00e1c file \u0111\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c s\u1eafp x\u1ebfp t\u1ed1t hay ch\u01b0a. V\u00ed d\u1ee5, vi\u1ec7c th\u00eam 1 KB d\u1eef li\u1ec7u v\u00e0o m\u1ed9t b\u1ea3ng 99 GB c\u0169ng s\u1ebd k\u00edch ho\u1ea1t vi\u1ec7c ghi l\u1ea1i to\u00e0n b\u1ed9 h\u01a1n 99 GB d\u1eef li\u1ec7u \u0111\u00f3. \u0110i\u1ec1u n\u00e0y khi\u1ebfn chi ph\u00ed clustering t\u0103ng tuy\u1ebfn t\u00ednh theo k\u00edch th\u01b0\u1edbc c\u1ee7a b\u1ea3ng ch\u1ee9 kh\u00f4ng ph\u1ea3i theo l\u01b0\u1ee3ng d\u1eef li\u1ec7u m\u1edbi, l\u00e0m cho vi\u1ec7c duy tr\u00ec cluster cho c\u00e1c b\u1ea3ng l\u1edbn tr\u1edf n\u00ean kh\u00f4ng hi\u1ec7u qu\u1ea3.<\/p>\n<h2>Incremental Liquid Clustering: T\u1ed1i \u01b0u th\u00f4ng minh v\u00e0 ti\u1ebft ki\u1ec7m<\/h2>\n<p>T\u00ednh n\u0103ng Incremental Liquid Clustering c\u1ee7a Microsoft Fabric \u0111\u00e3 kh\u1eafc ph\u1ee5c \u0111\u01b0\u1ee3c s\u1ef1 m\u1ea5t c\u00e2n b\u1eb1ng n\u00e0y: gi\u1eef nguy\u00ean hi\u1ec7u n\u0103ng truy v\u1ea5n nh\u01b0ng v\u1edbi chi ph\u00ed duy tr\u00ec ch\u1ec9 b\u1eb1ng m\u1ed9t ph\u1ea7n nh\u1ecf.<\/p>\n<p>Thay v\u00ec ghi l\u1ea1i t\u1ea5t c\u1ea3 m\u1ecdi th\u1ee9, l\u1ec7nh <code>OPTIMIZE<\/code> gi\u1edd \u0111\u00e2y ch\u1ec9 x\u00e1c \u0111\u1ecbnh v\u00e0 x\u1eed l\u00fd c\u00e1c file th\u1ef1c s\u1ef1 c\u1ea7n clustering, bao g\u1ed3m:<\/p>\n<ul>\n<li><strong>File ch\u01b0a \u0111\u01b0\u1ee3c cluster:<\/strong> D\u1eef li\u1ec7u m\u1edbi \u0111\u01b0\u1ee3c th\u00eam v\u00e0o m\u00e0 kh\u00f4ng c\u00f3 metadata v\u1ec1 clustering.<\/li>\n<li><strong>File nh\u1ecf:<\/strong> C\u00e1c file c\u00f3 k\u00edch th\u01b0\u1edbc d\u01b0\u1edbi ng\u01b0\u1ee1ng m\u1ee5c ti\u00eau c\u1ea7n \u0111\u01b0\u1ee3c g\u1ed9p l\u1ea1i.<\/li>\n<li><strong>File c\u00f3 vector x\u00f3a:<\/strong> C\u00e1c file c\u00f3 s\u1ed1 l\u01b0\u1ee3ng b\u1ea3n ghi b\u1ecb x\u00f3a v\u01b0\u1ee3t qu\u00e1 ng\u01b0\u1ee1ng d\u1ecdn d\u1eb9p.<\/li>\n<\/ul>\n<p>C\u00e1c file \u0111\u00e3 \u0111\u01b0\u1ee3c cluster t\u1ed1t v\u00e0 c\u00f3 k\u00edch th\u01b0\u1edbc ph\u00f9 h\u1ee3p s\u1ebd \u0111\u01b0\u1ee3c b\u1ecf qua ho\u00e0n to\u00e0n. D\u1eef li\u1ec7u m\u1edbi \u0111\u01b0\u1ee3c \u0111\u1ecbnh tuy\u1ebfn v\u00e0o c\u00e1c nh\u00f3m file \u0111\u00e3 cluster s\u1eb5n, duy tr\u00ec t\u00ednh li\u00ean t\u1ee5c c\u1ee7a layout m\u00e0 kh\u00f4ng c\u1ea7n ghi l\u1ea1i d\u1eef li\u1ec7u \u0111\u00e3 \u1edf \u0111\u00fang v\u1ecb tr\u00ed. K\u1ebft qu\u1ea3 l\u00e0 m\u1ed9t ho\u1ea1t \u0111\u1ed9ng clustering c\u00f3 th\u1eddi gian g\u1ea7n nh\u01b0 kh\u00f4ng \u0111\u1ed5i, ch\u1ec9 ph\u1ee5 thu\u1ed9c v\u00e0o k\u00edch th\u01b0\u1edbc d\u1eef li\u1ec7u m\u1edbi ch\u1ee9 kh\u00f4ng ph\u1ea3i k\u00edch th\u01b0\u1edbc c\u1ee7a to\u00e0n b\u1ed9 b\u1ea3ng.<\/p>\n<figure class=\"wp-block-image size-large aligncenter\" style=\"text-align: center; margin: 1.5em 0;\"><img decoding=\"async\" style=\"border-radius: 8px; border: 1px solid #e5e7eb; max-width: 100%; max-height: 560px; width: auto; height: auto; display: inline-block;\" src=\"https:\/\/microsoft.cmctelecom.vn\/wp-content\/uploads\/2026\/06\/483a671301.png\" alt=\"Bi\u1ec3u \u0111\u1ed3 so s\u00e1nh th\u1eddi gian clustering gi\u1eefa thu\u1eadt to\u00e1n ti\u00eau chu\u1ea9n v\u00e0 Incremental Liquid Clustering\" \/><\/figure>\n<p>Bi\u1ec3u \u0111\u1ed3 tr\u00ean cho th\u1ea5y th\u1eddi gian clustering c\u1ee7a thu\u1eadt to\u00e1n ti\u00eau chu\u1ea9n (Standard) t\u0103ng tuy\u1ebfn t\u00ednh theo k\u00edch th\u01b0\u1edbc b\u1ea3ng, trong khi ch\u1ebf \u0111\u1ed9 gia t\u0103ng (Incremental) duy tr\u00ec \u1edf m\u1ee9c \u1ed5n \u0111\u1ecbnh.<\/p>\n<h2>T\u1ef1 \u0111\u1ed9ng duy tr\u00ec ch\u1ea5t l\u01b0\u1ee3ng v\u1edbi Auto Reclustering<\/h2>\n<p>Incremental Liquid Clustering c\u00f2n bao g\u1ed3m t\u00ednh n\u0103ng <strong>Auto Reclustering<\/strong>. Thu\u1eadt to\u00e1n s\u1ebd t\u1ef1 \u0111\u1ed9ng x\u00e1c \u0111\u1ecbnh c\u00e1c cluster c\u00f3 file b\u1ecb ch\u1ed3ng ch\u00e9o v\u00e0 ch\u1ec9 cluster l\u1ea1i c\u00e1c file b\u1ecb suy gi\u1ea3m ch\u1ea5t l\u01b0\u1ee3ng khi v\u01b0\u1ee3t ng\u01b0\u1ee1ng cho ph\u00e9p, gi\u00fap ch\u1ea5t l\u01b0\u1ee3ng d\u1eef li\u1ec7u lu\u00f4n cao m\u00e0 kh\u00f4ng c\u1ea7n can thi\u1ec7p th\u1ee7 c\u00f4ng.<\/p>\n<h2>Hi\u1ec7u n\u0103ng v\u01b0\u1ee3t tr\u1ed9i qua c\u00e1c k\u1ecbch b\u1ea3n th\u1ef1c t\u1ebf<\/h2>\n<p>Microsoft \u0111\u00e3 th\u1ef1c hi\u1ec7n benchmark tr\u00ean ba k\u1ecbch b\u1ea3n workload ph\u1ed5 bi\u1ebfn:<\/p>\n<ol>\n<li><strong>Streaming Ingest:<\/strong> C\u00e1c l\u01b0\u1ee3t ghi nh\u1ecf li\u00ean t\u1ee5c, d\u1eef li\u1ec7u m\u1edbi kh\u00f4ng ch\u1ed3ng ch\u00e9o v\u1edbi b\u1ea3n ghi hi\u1ec7n c\u00f3 (v\u00ed d\u1ee5: d\u1eef li\u1ec7u IoT, clickstream).<\/li>\n<li><strong>Analytics Table:<\/strong> D\u1eef li\u1ec7u m\u1edbi ch\u1ed3ng ch\u00e9o ho\u00e0n to\u00e0n v\u1edbi b\u1ea3n ghi hi\u1ec7n c\u00f3 (v\u00ed d\u1ee5: b\u1ea3ng dimension).<\/li>\n<li><strong>ETL Pipeline:<\/strong> C\u00e1c ho\u1ea1t \u0111\u1ed9ng <code>MERGE<\/code> n\u01a1i d\u1eef li\u1ec7u \u0111\u1ebfn ch\u1ed3ng ch\u00e9o m\u1ed9t ph\u1ea7n (v\u00ed d\u1ee5: CDC feeds, c\u1eadp nh\u1eadt \u0111\u01a1n h\u00e0ng).<\/li>\n<\/ol>\n<p>K\u1ebft qu\u1ea3 cho th\u1ea5y t\u1ed1c \u0111\u1ed9 c\u1ea3i thi\u1ec7n \u0111\u00e1ng k\u1ec3:<\/p>\n<ul>\n<li><strong>Streaming Ingest:<\/strong> Nhanh h\u01a1n <strong>8.9 l\u1ea7n<\/strong>.<\/li>\n<li><strong>Analytics Table:<\/strong> Nhanh h\u01a1n <strong>5.5 l\u1ea7n<\/strong>.<\/li>\n<li><strong>ETL Pipeline:<\/strong> Nhanh h\u01a1n <strong>4.9 l\u1ea7n<\/strong>.<\/li>\n<\/ul>\n<p>\u0110\u00e1ng ch\u00fa \u00fd, hi\u1ec7u n\u0103ng c\u1ee7a thu\u1eadt to\u00e1n ti\u00eau chu\u1ea9n suy gi\u1ea3m m\u1ea1nh khi d\u1eef li\u1ec7u t\u0103ng l\u00ean, trong khi Incremental Liquid Clustering gi\u1eef \u0111\u01b0\u1ee3c s\u1ef1 \u1ed5n \u0111\u1ecbnh.<\/p>\n<h2>So s\u00e1nh v\u1edbi c\u00e1c gi\u1ea3i ph\u00e1p kh\u00e1c tr\u00ean th\u1ecb tr\u01b0\u1eddng<\/h2>\n<p>Khi so s\u00e1nh v\u1edbi m\u1ed9t gi\u1ea3i ph\u00e1p liquid clustering kh\u00e1c c\u1ee7a \u0111\u1ed1i th\u1ee7, Incremental Liquid Clustering c\u1ee7a Fabric cho k\u1ebft qu\u1ea3 \u1ea5n t\u01b0\u1ee3ng:<\/p>\n<ul>\n<li>T\u1ed1c \u0111\u1ed9 clustering trung b\u00ecnh nhanh h\u01a1n <strong>1.6 l\u1ea7n<\/strong>.<\/li>\n<li>L\u01b0\u1ee3ng file c\u1ea7n qu\u00e9t cho c\u00e1c truy v\u1ea5n ch\u1ecdn l\u1ecdc \u00edt h\u01a1n <strong>11%<\/strong>.<\/li>\n<li>V\u1edbi c\u00e1c workload n\u1eb7ng v\u1ec1 <code>MERGE<\/code> (m\u00f4 h\u00ecnh ph\u1ed5 bi\u1ebfn nh\u1ea5t c\u1ee7a ng\u01b0\u1eddi d\u00f9ng Apache Spark), Fabric nhanh h\u01a1n <strong>h\u01a1n 3 l\u1ea7n<\/strong>.<\/li>\n<\/ul>\n<p>M\u00e3 ngu\u1ed3n \u0111\u1ec3 t\u00e1i t\u1ea1o c\u00e1c benchmark n\u00e0y \u0111\u01b0\u1ee3c c\u00f4ng b\u1ed1 tr\u00ean <a href=\"https:\/\/github.com\/microsoft\/fabric-spark-benchmarks\/tree\/main\/benchmarks\/liquid-clustering\/incremental-clustering\">GitHub<\/a>.<\/p>\n<h3>Th\u1eddi gian clustering<\/h3>\n<figure class=\"wp-block-image size-large aligncenter\" style=\"text-align: center; margin: 1.5em 0;\"><img decoding=\"async\" style=\"border-radius: 8px; border: 1px solid #e5e7eb; max-width: 100%; max-height: 560px; width: auto; height: auto; display: inline-block;\" src=\"https:\/\/microsoft.cmctelecom.vn\/wp-content\/uploads\/2026\/06\/061311e98c.png\" alt=\"Bi\u1ec3u \u0111\u1ed3 so s\u00e1nh th\u1eddi gian clustering trung b\u00ecnh c\u1ee7a Fabric v\u00e0 \u0111\u1ed1i th\u1ee7\" \/><\/figure>\n<p>\u0110\u1ed1i v\u1edbi c\u00e1c workload <code>MERGE<\/code>, th\u1eddi gian clustering c\u1ee7a \u0111\u1ed1i th\u1ee7 suy gi\u1ea3m \u0111\u00e1ng k\u1ec3 sau kho\u1ea3ng 100 v\u00f2ng l\u1eb7p, trong khi Fabric v\u1eabn duy tr\u00ec t\u1ed1c \u0111\u1ed9 nhanh v\u00e0 \u1ed5n \u0111\u1ecbnh.<\/p>\n<figure class=\"wp-block-image size-large aligncenter\" style=\"text-align: center; margin: 1.5em 0;\"><img decoding=\"async\" style=\"border-radius: 8px; border: 1px solid #e5e7eb; max-width: 100%; max-height: 560px; width: auto; height: auto; display: inline-block;\" src=\"https:\/\/microsoft.cmctelecom.vn\/wp-content\/uploads\/2026\/06\/bf5a6be91c.png\" alt=\"Bi\u1ec3u \u0111\u1ed3 so s\u00e1nh th\u1eddi gian clustering theo t\u1eebng v\u00f2ng l\u1eb7p cho workload ETL\" \/><\/figure>\n<h3>Ch\u1ea5t l\u01b0\u1ee3ng b\u1ecf qua file (File skipping)<\/h3>\n<figure class=\"wp-block-image size-large aligncenter\" style=\"text-align: center; margin: 1.5em 0;\"><img decoding=\"async\" style=\"border-radius: 8px; border: 1px solid #e5e7eb; max-width: 100%; max-height: 560px; width: auto; height: auto; display: inline-block;\" src=\"https:\/\/microsoft.cmctelecom.vn\/wp-content\/uploads\/2026\/06\/9bf37d0a86.png\" alt=\"Bi\u1ec3u \u0111\u1ed3 so s\u00e1nh s\u1ed1 l\u01b0\u1ee3ng file \u0111\u01b0\u1ee3c qu\u00e9t trung b\u00ecnh c\u1ee7a Fabric v\u00e0 \u0111\u1ed1i th\u1ee7\" \/><\/figure>\n<p>Fabric \u0111\u1ea1t \u0111\u01b0\u1ee3c kh\u1ea3 n\u0103ng <em>file skipping<\/em> (b\u1ecf qua file kh\u00f4ng li\u00ean quan khi truy v\u1ea5n) t\u01b0\u01a1ng \u0111\u01b0\u01a1ng ho\u1eb7c t\u1ed1t h\u01a1n. \u0110\u1eb7c bi\u1ec7t v\u1edbi workload ghi ch\u1ed3ng ch\u00e9o ho\u00e0n to\u00e0n, Fabric qu\u00e9t \u00edt h\u01a1n <strong>11%<\/strong> s\u1ed1 file cho m\u1ed7i truy v\u1ea5n, cho th\u1ea5y kh\u1ea3 n\u0103ng t\u1ea1o ra layout d\u1eef li\u1ec7u ch\u1ea5t l\u01b0\u1ee3ng cao h\u01a1n.<\/p>\n<h2>L\u1ee3i \u00edch cho doanh nghi\u1ec7p v\u00e0 t\u00ednh s\u1eb5n s\u00e0ng<\/h2>\n<p>Incremental Liquid Clustering \u0111\u00e3 c\u00f3 s\u1eb5n trong <strong>Fabric Runtime 2.0<\/strong> v\u1edbi Delta 4.1 v\u00e0 Spark 4.1, v\u00e0 \u0111\u01b0\u1ee3c b\u1eadt m\u1eb7c \u0111\u1ecbnh. Doanh nghi\u1ec7p ch\u1ec9 c\u1ea7n ch\u1ea1y l\u1ec7nh <code>OPTIMIZE<\/code> tr\u00ean c\u00e1c b\u1ea3ng Liquid Clustered \u0111\u1ec3 thu\u1eadt to\u00e1n m\u1edbi t\u1ef1 \u0111\u1ed9ng \u0111\u01b0\u1ee3c \u00e1p d\u1ee5ng.<\/p>\n<p>C\u00e1c l\u1ee3i \u00edch ch\u00ednh bao g\u1ed3m:<\/p>\n<ul>\n<li><strong>Pipeline streaming v\u00e0 g\u1ea7n th\u1eddi gian th\u1ef1c:<\/strong> L\u1ec7nh <code>OPTIMIZE<\/code> ch\u1ea1y trong v\u00e0i gi\u00e2y thay v\u00ec v\u00e0i ph\u00fat, \u0111\u1ea3m b\u1ea3o SLA.<\/li>\n<li><strong>Pipeline ETL v\u00e0 batch:<\/strong> Gi\u1ea3m \u0111\u00e1ng k\u1ec3 chi ph\u00ed t\u00ednh to\u00e1n cho vi\u1ec7c t\u1ed1i \u01b0u h\u00f3a sau khi t\u1ea3i d\u1eef li\u1ec7u.<\/li>\n<li><strong>B\u1ea3ng ph\u00e2n t\u00edch:<\/strong> Hi\u1ec7u n\u0103ng truy v\u1ea5n cao \u0111\u01b0\u1ee3c duy tr\u00ec t\u1ef1 \u0111\u1ed9ng khi d\u1eef li\u1ec7u thay \u0111\u1ed5i, kh\u00f4ng c\u1ea7n tinh ch\u1ec9nh th\u1ee7 c\u00f4ng.<\/li>\n<\/ul>\n<p>\u0110\u00e2y l\u00e0 m\u1ed9t b\u01b0\u1edbc ti\u1ebfn l\u1edbn h\u01b0\u1edbng t\u1edbi m\u1ed9t lakehouse c\u00f3 kh\u1ea3 n\u0103ng t\u1ef1 t\u1ed1i \u01b0u h\u00f3a, lo\u1ea1i b\u1ecf s\u1ef1 \u0111\u00e1nh \u0111\u1ed5i gi\u1eefa ch\u1ea5t l\u01b0\u1ee3ng v\u00e0 chi ph\u00ed clustering. \u0110\u1ec3 t\u00ecm hi\u1ec3u th\u00eam, doanh nghi\u1ec7p c\u00f3 th\u1ec3 tham kh\u1ea3o <a href=\"https:\/\/learn.microsoft.com\/fabric\/data-engineering\/liquid-clustering?tabs=sparksql\">t\u00e0i li\u1ec7u ch\u00ednh th\u1ee9c t\u1eeb Microsoft<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Ng\u00e0y 27\/05\/2026, Microsoft Fabric \u0111\u00e3 gi\u1edbi thi\u1ec7u Incremental Liquid Clustering, m\u1ed9t t\u00ednh n\u0103ng \u0111\u1ed9t ph\u00e1 gi\u00fap gi\u1ea3m \u0111\u00e1ng k\u1ec3 chi ph\u00ed v\u00e0 th\u1eddi gian cho vi\u1ec7c t\u1ed5 ch\u1ee9c d\u1eef li\u1ec7u trong m\u00f4i tr\u01b0\u1eddng lakehouse. Gi\u1ea3i ph\u00e1p n\u00e0y gi\u1ea3i quy\u1ebft th\u00e1ch th\u1ee9c l\u1edbn khi vi\u1ec7c duy tr\u00ec layout d\u1eef li\u1ec7u tr\u1edf n\u00ean t\u1ed1n k\u00e9m khi quy m\u00f4 b\u1ea3ng t\u0103ng l\u00ean, gi\u00fap doanh nghi\u1ec7p t\u1ed1i \u01b0u h\u00f3a chi ph\u00ed v\u1eadn h\u00e0nh m\u00e0 kh\u00f4ng \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn hi\u1ec7u n\u0103ng truy v\u1ea5n. Th\u00e1ch th\u1ee9c t\u1eeb c\u01a1 ch\u1ebf clustering truy\u1ec1n th\u1ed1ng Liquid Clustering gi\u00fap gi\u1ea3m chi ph\u00ed truy v\u1ea5n b\u1eb1ng c\u00e1ch s\u1eafp x\u1ebfp d\u1eef li\u1ec7u \u0111\u1ec3 c\u00e1c query ch\u1ec9 c\u1ea7n \u0111\u1ecdc nh\u1eefng file c\u1ea7n thi\u1ebft&#8230;.<\/p>","protected":false},"author":15,"featured_media":11688,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-11692","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ms-blogs"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Microsoft Fabric ra m\u1eaft Incremental Liquid Clustering: T\u1ed1i \u01b0u chi ph\u00ed v\u00e0 hi\u1ec7u n\u0103ng Lakehouse | CMC Telecom<\/title>\n<meta name=\"description\" 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