{"id":36843,"date":"2019-10-31T22:14:11","date_gmt":"2019-10-31T19:14:11","guid":{"rendered":"https:\/\/prohoster.info\/blog\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya\/"},"modified":"2019-10-31T22:14:11","modified_gmt":"2019-10-31T19:14:11","slug":"otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/cs\/blog\/news\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","title":{"rendered":"Byl otev\u0159en k\u00f3d SQL j\u00e1dra BlazingSQL, kter\u00e9 vyu\u017e\u00edv\u00e1 GPU pro akceleraci.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Ozn\u00e1meno<\/a><\/noindex> o otev\u0159en\u00ed zdrojov\u00fdch k\u00f3d\u016f SQL enginu BlazingSQL<\/a><\/noindex>, kter\u00fd pou\u017e\u00edv\u00e1 GPU pro zrychlen\u00ed zpracov\u00e1n\u00ed dat. BlazingSQL nen\u00ed plnohodnotn\u00e1 datab\u00e1zov\u00e1 SYST\u00c9MA, ale je pozicionov\u00e1n jako motor pro anal\u00fdzu a zpracov\u00e1n\u00ed velk\u00fdch datov\u00fdch sad, srovnateln\u00fd sv\u00fdmi \u00fakoly s Apache Spark<\/a><\/noindex>. K\u00f3d je naps\u00e1n v jazyce Python a je otev\u0159en\u00fd<\/a><\/noindex> pod licenc\u00ed Apache 2.0. <\/p>\n

BlazingSQL je vhodn\u00fd pro vykon\u00e1v\u00e1n\u00ed samostatn\u00fdch analytick\u00fdch dotaz\u016f nad velk\u00fdmi datov\u00fdmi sadami (des\u00edtky gigabajt\u016f), kter\u00e9 jsou ulo\u017eeny v tabulkov\u00fdch form\u00e1tech (nap\u0159\u00edklad logy, statistiky NetFlow atd.). BlazingSQL um\u00ed vykon\u00e1vat dotazy z raw soubor\u016f ve form\u00e1tech CSV a Apache Parquet, ulo\u017een\u00fdch v s\u00ed\u0165ov\u00fdch a cloudov\u00fdch \u00falo\u017ei\u0161t\u00edch, jako jsou HDFS a AWS S3, p\u0159\u00edmo p\u0159ed\u00e1vaj\u00edc\u00ed v\u00fdsledek do pam\u011bti GPU. D\u00edky paralelizaci operac\u00ed v GPU a vyu\u017eit\u00ed rychlej\u0161\u00ed videopam\u011bti, vykon\u00e1v\u00e1n\u00ed dotaz\u016f v BlazingSQL prob\u00edh\u00e1 a\u017e 20x<\/a><\/noindex> rychleji ne\u017e v Apache Spark.<\/p>\n

\"Byl<\/a><\/noindex><\/center><\/p>\n

Pro pr\u00e1ci s GPU se pou\u017e\u00edv\u00e1 sadu knihoven rozv\u00edjenou ve spolupr\u00e1ci s firmou NVIDIA otev\u0159en\u00fdch<\/a><\/noindex> RAPIDS , kter\u00e1 umo\u017e\u0148uje vytv\u00e1\u0159et aplikace pro zpracov\u00e1n\u00ed dat a analytiku, prov\u00e1d\u011bn\u00e9 v\u00fdhradn\u011b na stran\u011b GPU (je poskytov\u00e1n<\/a><\/noindex>Python rozhran\u00ed pro vyu\u017eit\u00ed n\u00edzko\u00farov\u0148ov\u00fdch primitiv CUDA a paralelizaci v\u00fdpo\u010dt\u016f).<\/a><\/noindex> BlazingSQL nab\u00edz\u00ed mo\u017enost pou\u017e\u00edv\u00e1n\u00ed SQL m\u00edsto API pro zpracov\u00e1n\u00ed dat <\/p>\n

cuUDF (na z\u00e1klad\u011b<\/a><\/noindex> ), pou\u017e\u00edv\u00e1 se v RAPIDS. BlazingSQL je dodate\u010dn\u00e1 vrstva, kter\u00e1 pracuje nad cuDF a vyu\u017e\u00edv\u00e1 knihovnu cuIO pro \u010dten\u00ed dat z disku. SQL dotazy jsou p\u0159evedeny na vol\u00e1n\u00ed funkc\u00ed cuUDF, kter\u00e9 umo\u017e\u0148uj\u00ed na\u010d\u00edt\u00e1n\u00ed dat do GPU a prov\u00e1d\u011bn\u00ed nad nimi operac\u00ed slou\u010den\u00ed, agregace a filtrov\u00e1n\u00ed. Podporuje se vytv\u00e1\u0159en\u00ed distribuovan\u00fdch konfigurac\u00ed zahrnuj\u00edc\u00edch tis\u00edce GPU. Apache Arrow<\/a><\/noindex>BlazingSQL z\u00e1sadn\u011b zjednodu\u0161uje pr\u00e1ci s daty \u2014 m\u00edsto stovky vol\u00e1n\u00ed funkc\u00ed cuDF lze pou\u017e\u00edt jedin\u00fd SQL dotaz. Pou\u017eit\u00ed SQL umo\u017e\u0148uje integrov\u00e1n\u00ed RAPIDS s existuj\u00edc\u00edmi analytick\u00fdmi syst\u00e9my, ani\u017e by bylo t\u0159eba ps\u00e1t specifick\u00e9 zpracovatele a ani\u017e bychom se uchylovali k meziloadov\u00e1n\u00ed dat do dal\u0161\u00ed datab\u00e1ze, av\u0161ak <\/p>\n

BlazingSQL v\u00fdrazn\u011b zjednodu\u0161uje pr\u00e1ci s daty \u2014 nam\u00edsto stovek vol\u00e1n\u00ed funkc\u00ed cuDF sta\u010d\u00ed pouze jeden SQL dotaz. Pou\u017eit\u00ed SQL umo\u017e\u0148uje zajistit integraci RAPIDS se st\u00e1vaj\u00edc\u00edmi analytick\u00fdmi syst\u00e9my, ani\u017e by bylo nutn\u00e9 ps\u00e1t specifick\u00e9 zpracovatele a bez pot\u0159eby mezijak\u00e9ho na\u010d\u00edt\u00e1n\u00ed dat do dodate\u010dn\u00e9 DBMS, ale
\nXGBoost cuML<\/a><\/noindex> a pro \u0159e\u0161en\u00ed analytick\u00fdch a strojov\u011b u\u010den\u00fdch \u00faloh.<\/a><\/noindex> Bylo ohl\u00e1\u0161eno otev\u0159en\u00ed zdrojov\u00fdch k\u00f3d\u016f SQL enginu <\/p>\n

<\/a><\/noindex><\/p>\n

Zdroj: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041e\u0431\u044a\u044f\u0432\u043b\u0435\u043d\u043e \u043e\u0431 \u043e\u0442\u043a\u0440\u044b\u0442\u0438\u0438 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0445 \u0442\u0435\u043a\u0441\u0442\u043e\u0432 SQL-\u0434\u0432\u0438\u0436\u043a\u0430 BlazingSQL, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0449\u0435\u0433\u043e GPU \u0434\u043b\u044f \u0443\u0441\u043a\u043e\u0440\u0435\u043d\u0438\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0434\u0430\u043d\u043d\u044b\u0445. BlazingSQL \u043d\u0435 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043f\u043e\u043b\u043d\u043e\u0446\u0435\u043d\u043d\u043e\u0439 \u0421\u0423\u0411\u0414, \u0430 \u043f\u043e\u0437\u0438\u0446\u0438\u043e\u043d\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0434\u0432\u0438\u0436\u043e\u043a \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0438 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u043d\u0430\u0431\u043e\u0440\u043e\u0432 \u0434\u0430\u043d\u043d\u044b\u0445, \u0441\u0440\u0430\u0432\u043d\u0438\u043c\u044b\u0439 \u043f\u043e \u0441\u0432\u043e\u0438\u043c \u0437\u0430\u0434\u0430\u0447\u0430\u043c \u0441 Apache Spark. \u041a\u043e\u0434 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0438 \u043e\u0442\u043a\u0440\u044b\u0442 \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 Apache 2.0. BlazingSQL \u043f\u043e\u0434\u0445\u043e\u0434\u0438\u0442 \u0434\u043b\u044f \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0435\u0434\u0438\u043d\u0438\u0447\u043d\u044b\u0445 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432 \u043d\u0430\u0434 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":27603,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-36843","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Otev\u0159en\u00fd k\u00f3d SQL enginu BlazingSQL, kter\u00fd 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