{"id":12004,"date":"2026-06-23T09:03:02","date_gmt":"2026-06-23T02:03:02","guid":{"rendered":"https:\/\/microsoft.cmctelecom.vn\/native-execution-engine-cua-fabric-tang-toc-python-udf-den-5-76-lan\/"},"modified":"2026-06-24T18:20:32","modified_gmt":"2026-06-24T11:20:32","slug":"native-execution-engine-cua-fabric-tang-toc-python-udf-den-5-76-lan","status":"publish","type":"post","link":"https:\/\/microsoft.cmctelecom.vn\/kr\/native-execution-engine-cua-fabric-tang-toc-python-udf-den-5-76-lan\/","title":{"rendered":"Native Execution Engine c\u1ee7a Fabric: T\u0103ng t\u1ed1c Python UDF \u0111\u1ebfn 5.76 l\u1ea7n"},"content":{"rendered":"

Microsoft v\u1eeba c\u00f4ng b\u1ed1 m\u1ed9t c\u1ea3i ti\u1ebfn quan tr\u1ecdng cho Native Execution Engine (NEE) tr\u00ean n\u1ec1n t\u1ea3ng Microsoft Fabric, gi\u00fap t\u0103ng t\u1ed1c \u0111\u00e1ng k\u1ec3 c\u00e1c workload Spark s\u1eed d\u1ee5ng h\u00e0m do ng\u01b0\u1eddi d\u00f9ng \u0111\u1ecbnh ngh\u0129a (UDF – User-Defined Function<\/em>) b\u1eb1ng Python\/Scala v\u00e0 c\u00e1c ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p. C\u1ea3i ti\u1ebfn n\u00e0y c\u00f3 th\u1ec3 gi\u00fap t\u0103ng hi\u1ec7u n\u0103ng x\u1eed l\u00fd l\u00ean \u0111\u1ebfn 5.76 l\u1ea7n<\/strong> m\u00e0 kh\u00f4ng y\u00eau c\u1ea7u thay \u0111\u1ed5i m\u00e3 ngu\u1ed3n, mang l\u1ea1i l\u1ee3i \u00edch tr\u1ef1c ti\u1ebfp v\u1ec1 chi ph\u00ed v\u00e0 th\u1eddi gian cho c\u00e1c doanh nghi\u1ec7p.<\/p>\n

V\u00ed d\u1ee5, m\u1ed9t pipeline d\u1eef li\u1ec7u (ETL) x\u1eed l\u00fd c\u00e1c s\u1ef1 ki\u1ec7n d\u1ea1ng JSON c\u00f3 ch\u1ee9a c\u00e1c m\u1ea3ng d\u1eef li\u1ec7u l\u1ed3ng nhau. N\u1ebfu pipeline n\u00e0y s\u1eed d\u1ee5ng UDF Python \u0111\u1ec3 \u00e1p d\u1ee5ng logic nghi\u1ec7p v\u1ee5 ri\u00eang, tr\u01b0\u1edbc \u0111\u00e2y n\u00f3 s\u1ebd g\u1eb7p hai n\u00fat th\u1eaft hi\u1ec7u n\u0103ng: UDF bu\u1ed9c ph\u1ea3i tu\u1ea7n t\u1ef1 h\u00f3a d\u1eef li\u1ec7u (serialization) gi\u1eefa JVM v\u00e0 m\u1ed9t Python worker, v\u00e0 d\u1eef li\u1ec7u l\u1ed3ng nhau khi\u1ebfn engine chuy\u1ec3n t\u1eeb x\u1eed l\u00fd theo c\u1ed9t (columnar) t\u1ed1i \u01b0u sang x\u1eed l\u00fd theo h\u00e0ng (row-based) ch\u1eadm h\u01a1n. V\u1edbi NEE \u0111\u01b0\u1ee3c k\u00edch ho\u1ea1t, c\u1ea3 hai ho\u1ea1t \u0111\u1ed9ng n\u00e0y \u0111\u1ec1u ch\u1ea1y tr\u00ean lu\u1ed3ng th\u1ef1c thi g\u1ed1c, gi\u00fap t\u0103ng t\u1ed1c \u0111\u1ed9 t\u1eeb 2 \u0111\u1ebfn 5 l\u1ea7n.<\/p>\n

N\u00fat th\u1eaft hi\u1ec7u n\u0103ng c\u1ee7a UDF v\u00e0 d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p<\/h2>\n

Trong qu\u00e1 tr\u00ecnh th\u1ef1c thi Spark ti\u00eau chu\u1ea9n, c\u00e1c UDF vi\u1ebft b\u1eb1ng Python ho\u1eb7c Scala y\u00eau c\u1ea7u engine ph\u1ea3i th\u1ef1c hi\u1ec7n nhi\u1ec1u b\u01b0\u1edbc t\u1ed1n k\u00e9m:<\/p>\n

    \n
  • Tu\u1ea7n t\u1ef1 h\u00f3a (serialize) c\u00e1c h\u00e0ng t\u1eeb \u0111\u1ecbnh d\u1ea1ng n\u1ed9i b\u1ed9 c\u1ee7a Spark.<\/li>\n
  • Chuy\u1ec3n d\u1eef li\u1ec7u sang m\u1ed9t ti\u1ebfn tr\u00ecnh Python worker ri\u00eang bi\u1ec7t.<\/li>\n
  • Th\u1ef1c thi UDF, sau \u0111\u00f3 tu\u1ea7n t\u1ef1 h\u00f3a k\u1ebft qu\u1ea3 tr\u1edf l\u1ea1i JVM.<\/li>\n<\/ul>\n

    M\u1ed7i v\u00f2ng l\u1eb7p nh\u01b0 v\u1eady l\u00e0m t\u0103ng chi ph\u00ed CPU, \u00e1p l\u1ef1c b\u1ed9 nh\u1edb v\u00e0 ph\u00e1 v\u1ee1 c\u01a1 ch\u1ebf th\u1ef1c thi vector h\u00f3a (vectorized execution). T\u01b0\u01a1ng t\u1ef1, c\u00e1c ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p (m\u1ea3ng, map, struct) c\u00f3 th\u1ec3 bu\u1ed9c engine ph\u1ea3i t\u1eeb b\u1ecf \u0111\u01b0\u1eddng d\u1eabn columnar \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u h\u00f3a \u0111\u1ec3 chuy\u1ec3n sang x\u1eed l\u00fd theo h\u00e0ng, l\u00e0m m\u1ea5t \u0111i l\u1ee3i \u00edch c\u1ee7a vi\u1ec7c th\u1ef1c thi g\u1ed1c cho ph\u1ea7n c\u00f2n l\u1ea1i c\u1ee7a truy v\u1ea5n. Do \u0111\u00f3, c\u00e1c \u0111\u1ed9i ng\u0169 k\u1ef9 s\u01b0 th\u01b0\u1eddng ph\u1ea3i tr\u00e1nh d\u00f9ng UDF ho\u1eb7c l\u00e0m ph\u1eb3ng (flatten) c\u00e1c schema l\u1ed3ng nhau, g\u00e2y t\u1ed1n th\u00eam c\u00f4ng s\u1ee9c ph\u00e1t tri\u1ec3n.<\/p>\n

    Native Execution Engine gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 nh\u01b0 th\u1ebf n\u00e0o<\/h2>\n

    Native Execution Engine (NEE) c\u1ee7a Microsoft Fabric x\u1eed l\u00fd c\u00e1c UDF Python\/Scala v\u00e0 c\u00e1c ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p tr\u1ef1c ti\u1ebfp trong engine columnar g\u1ed1c. C\u1ee5 th\u1ec3:<\/p>\n

      \n
    • V\u1edbi UDF Python ho\u1eb7c Scala:<\/strong> Engine gi\u1ea3m thi\u1ec3u c\u00e1c v\u00f2ng l\u1eb7p tu\u1ea7n t\u1ef1 h\u00f3a gi\u1eefa JVM v\u00e0 Python, gi\u1eef d\u1eef li\u1ec7u \u1edf \u0111\u1ecbnh d\u1ea1ng columnar l\u00e2u h\u01a1n. C\u00e1c vectorized UDFs<\/em> (UDF \u0111\u01b0\u1ee3c vector h\u00f3a) nh\u1eadn \u0111\u01b0\u1ee3c l\u1ee3i \u00edch l\u1edbn nh\u1ea5t, nh\u01b0ng c\u00e1c UDF ti\u00eau chu\u1ea9n c\u0169ng \u0111\u01b0\u1ee3c c\u1ea3i thi\u1ec7n.<\/li>\n
    • V\u1edbi ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p:<\/strong> C\u00e1c ki\u1ec3u d\u1eef li\u1ec7u nh\u01b0 m\u1ea3ng (array), map v\u00e0 struct \u0111\u01b0\u1ee3c x\u1eed l\u00fd nguy\u00ean b\u1ea3n trong engine columnar. C\u00e1c to\u00e1n t\u1eed nh\u01b0 explode<\/code>, truy c\u1eadp map, v\u00e0 tr\u00edch xu\u1ea5t tr\u01b0\u1eddng struct kh\u00f4ng c\u00f2n bu\u1ed9c engine ph\u1ea3i chuy\u1ec3n v\u1ec1 ch\u1ebf \u0111\u1ed9 th\u1ef1c thi theo h\u00e0ng.<\/li>\n<\/ul>\n

      \u0110i\u1ec1u quan tr\u1ecdng nh\u1ea5t l\u00e0 c\u00e1c notebook v\u00e0 job Spark hi\u1ec7n c\u00f3 c\u1ee7a doanh nghi\u1ec7p s\u1ebd t\u1ef1 \u0111\u1ed9ng h\u01b0\u1edfng l\u1ee3i khi NEE \u0111\u01b0\u1ee3c b\u1eadt m\u00e0 kh\u00f4ng c\u1ea7n thay \u0111\u1ed5i code<\/strong> hay h\u1ecdc API m\u1edbi.<\/p>\n

      Nh\u1eefng con s\u1ed1 c\u1ea3i thi\u1ec7n hi\u1ec7u n\u0103ng th\u1ef1c t\u1ebf<\/h2>\n

      C\u00e1c b\u00e0i ki\u1ec3m tra n\u1ed9i b\u1ed9 c\u1ee7a Microsoft tr\u00ean c\u00e1c workload ti\u00eau bi\u1ec3u cho th\u1ea5y:<\/p>\n

        \n
      • Vectorized UDFs b\u1eb1ng Python ho\u1eb7c Scala: nhanh h\u01a1n t\u1edbi 5.76 l\u1ea7n<\/strong>.<\/li>\n
      • C\u00e1c UDF ph\u1ee9c t\u1ea1p: nhanh h\u01a1n t\u1eeb 1.08 \u0111\u1ebfn 2.5 l\u1ea7n<\/strong> t\u00f9y thu\u1ed9c v\u00e0o \u0111\u1ed9 ph\u1ee9c t\u1ea1p.<\/li>\n
      • Workload TPC-DS end-to-end (bao g\u1ed3m c\u00e1c truy v\u1ea5n v\u1edbi ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p): th\u1ef1c thi nhanh h\u01a1n t\u1edbi 2.35 l\u1ea7n<\/strong>.<\/li>\n<\/ul>\n

        C\u00e1c b\u00e0i benchmark n\u00e0y \u0111\u01b0\u1ee3c ch\u1ea1y tr\u00ean c\u00e1c dataset quy m\u00f4 s\u1ea3n xu\u1ea5t s\u1eed d\u1ee5ng c\u1ea5u h\u00ecnh cluster \u0111i\u1ec3n h\u00ecnh c\u1ee7a doanh nghi\u1ec7p.<\/p>\n

        \"Bi\u1ec3u<\/figure>\n

        \u0110\u00e2y l\u00e0 nh\u1eefng c\u1ea3i ti\u1ebfn c\u00f3 \u00fd ngh\u0129a \u0111\u1ed1i v\u1edbi k\u1ef9 thu\u1eadt d\u1eef li\u1ec7u quy m\u00f4 doanh nghi\u1ec7p:<\/p>\n

          \n
        • Pipeline ETL nhanh h\u01a1n.<\/li>\n
        • Gi\u1ea3m th\u1eddi gian t\u00ednh to\u00e1n c\u1ee7a cluster.<\/li>\n
        • C\u00f3 kh\u1ea3 n\u0103ng gi\u1ea3m chi ph\u00ed v\u1eadn h\u00e0nh.<\/li>\n
        • C\u1ea3i thi\u1ec7n hi\u1ec7u n\u0103ng ph\u00e2n t\u00edch t\u01b0\u01a1ng t\u00e1c.<\/li>\n<\/ul>\n

          H\u1ed7 tr\u1ee3 g\u1ed1c cho c\u00e1c ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p<\/h2>\n

          Native Execution Engine c\u1ee7a Fabric c\u0169ng b\u1ed5 sung h\u1ed7 tr\u1ee3 t\u1ed1i \u01b0u h\u00f3a cho c\u00e1c ki\u1ec3u d\u1eef li\u1ec7u Arrays, Maps, v\u00e0 Structs. \u0110i\u1ec1u n\u00e0y cho ph\u00e9p c\u00e1c pipeline duy tr\u00ec t\u1ed1i \u01b0u h\u00f3a ho\u00e0n to\u00e0n m\u00e0 kh\u00f4ng c\u1ea7n chuy\u1ec3n \u0111\u1ed5i ch\u1ebf \u0111\u1ed9 th\u1ef1c thi. C\u00e1c ho\u1ea1t \u0111\u1ed9ng li\u00ean quan \u0111\u1ebfn d\u1eef li\u1ec7u l\u1ed3ng nhau gi\u1edd \u0111\u00e2y c\u00f3 th\u1ec3 h\u01b0\u1edfng l\u1ee3i t\u1eeb c\u00e1c t\u1ed1i \u01b0u h\u00f3a g\u1ed1c trong khi v\u1eabn gi\u1eef \u0111\u01b0\u1ee3c s\u1ef1 linh ho\u1ea1t m\u00e0 c\u00e1c l\u1eadp tr\u00ecnh vi\u00ean mong \u0111\u1ee3i t\u1eeb Spark. \u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t h\u1eefu \u00edch cho c\u00e1c k\u1ecbch b\u1ea3n t\u1ed1i \u01b0u h\u00f3a lakehouse n\u00e2ng cao nh\u01b0 Z-ORDER, liquid clustering v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u b\u00e1n c\u1ea5u tr\u00fac.<\/p>\n

          T\u1ea7m quan tr\u1ecdng cho t\u01b0\u01a1ng lai ng\u00e0nh k\u1ef9 thu\u1eadt d\u1eef li\u1ec7u<\/h2>\n

          C\u00e1c workload ph\u00e2n t\u00edch \u0111ang ph\u00e1t tri\u1ec3n \u0111\u1ec3 bao g\u1ed3m AI, ra quy\u1ebft \u0111\u1ecbnh th\u1eddi gian th\u1ef1c, d\u1eef li\u1ec7u b\u00e1n c\u1ea5u tr\u00fac, v\u00e0 t\u1ea7m quan tr\u1ecdng ng\u00e0y c\u00e0ng t\u0103ng c\u1ee7a Python. Khi c\u00e1c workload v\u01b0\u1ee3t ra ngo\u00e0i BI truy\u1ec1n th\u1ed1ng, c\u00e1c engine hi\u1ec7u n\u0103ng kh\u00f4ng th\u1ec3 ch\u1ec9 t\u1eadp trung v\u00e0o vi\u1ec7c t\u0103ng t\u1ed1c c\u00e1c truy v\u1ea5n SQL \u0111\u01a1n gi\u1ea3n.<\/p>\n

          Native Execution Engine c\u1ee7a Fabric ph\u1ea3n \u00e1nh s\u1ef1 thay \u0111\u1ed5i n\u00e0y. N\u00f3 \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf kh\u00f4ng ch\u1ec9 \u0111\u1ec3 t\u0103ng t\u1ed1c SQL c\u1ed5 \u0111i\u1ec3n, m\u00e0 c\u00f2n cho th\u1ebf h\u1ec7 workload ph\u00e2n t\u00edch ti\u1ebfp theo \u0111\u00f2i h\u1ecfi s\u1ef1 linh ho\u1ea1t, quy m\u00f4 v\u00e0 hi\u1ec7u n\u0103ng th\u1ef1c thi cao tr\u00ean c\u00e1c m\u00f4 h\u00ecnh l\u1eadp tr\u00ecnh \u0111a d\u1ea1ng. Doanh nghi\u1ec7p kh\u00f4ng c\u00f2n ph\u1ea3i \u0111\u00e1nh \u0111\u1ed5i s\u1ef1 linh ho\u1ea1t \u0111\u1ec3 l\u1ea5y hi\u1ec7u n\u0103ng.<\/p>\n

          B\u1eaft \u0111\u1ea7u s\u1eed d\u1ee5ng<\/h2>\n

          \u0110\u1ec3 t\u1eadn d\u1ee5ng nh\u1eefng c\u1ea3i ti\u1ebfn n\u00e0y, doanh nghi\u1ec7p c\u00f3 th\u1ec3 th\u1ef1c hi\u1ec7n c\u00e1c b\u01b0\u1edbc sau:<\/p>\n

            \n
          1. B\u1eadt Native Execution Engine:<\/strong> \u0110i t\u1edbi Workspace Settings > Data Engineering\/Science > Spark Settings > Environment. Thay \u0111\u1ed5i m\u00f4i tr\u01b0\u1eddng m\u1eb7c \u0111\u1ecbnh sang m\u00f4i tr\u01b0\u1eddng \u0111\u00e3 b\u1eadt Native Execution Engine.<\/li>\n
          2. Ch\u1ea1y workload c\u1ee7a b\u1ea1n:<\/strong> Th\u1ef1c thi m\u1ed9t notebook ho\u1eb7c Spark job hi\u1ec7n c\u00f3 \u0111ang s\u1eed d\u1ee5ng UDF ho\u1eb7c c\u00e1c ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p.<\/li>\n
          3. X\u00e1c minh th\u1ef1c thi g\u1ed1c:<\/strong> Ki\u1ec3m tra c\u00e1c c\u1ea3nh b\u00e1o n\u1ed9i tuy\u1ebfn \u0111\u1ec3 x\u00e1c nh\u1eadn kh\u00f4ng c\u00f3 c\u1ea3nh b\u00e1o v\u1ec1 vi\u1ec7c chuy\u1ec3n \u0111\u1ed5i (fallback), cho th\u1ea5y c\u00e1c ho\u1ea1t \u0111\u1ed9ng \u0111ang ch\u1ea1y tr\u00ean engine g\u1ed1c.<\/li>\n<\/ol>","protected":false},"excerpt":{"rendered":"

            Microsoft v\u1eeba c\u00f4ng b\u1ed1 m\u1ed9t c\u1ea3i ti\u1ebfn quan tr\u1ecdng cho Native Execution Engine (NEE) tr\u00ean n\u1ec1n t\u1ea3ng Microsoft Fabric, gi\u00fap t\u0103ng t\u1ed1c \u0111\u00e1ng k\u1ec3 c\u00e1c workload Spark s\u1eed d\u1ee5ng h\u00e0m do ng\u01b0\u1eddi d\u00f9ng \u0111\u1ecbnh ngh\u0129a (UDF – User-Defined Function) b\u1eb1ng Python\/Scala v\u00e0 c\u00e1c ki\u1ec3u d\u1eef li\u1ec7u ph\u1ee9c t\u1ea1p. C\u1ea3i ti\u1ebfn n\u00e0y c\u00f3 th\u1ec3 gi\u00fap t\u0103ng…<\/p>","protected":false},"author":15,"featured_media":12002,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-12004","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ms-blogs"],"acf":[],"yoast_head":"\nNative Execution Engine c\u1ee7a Fabric: T\u0103ng t\u1ed1c Python UDF \u0111\u1ebfn 5.76 l\u1ea7n | CMC Telecom<\/title>\n<meta name=\"description\" content=\"\u0110\u1ed1i t\u00e1c Tier-1 Solutions Provider c\u1ee7a Microsoft t\u1ea1i Vi\u1ec7t Nam\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Native Execution Engine 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