{"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\/nl\/blog\/news\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","title":{"rendered":"De SQL-engine BlazingSQL is geopend, die GPU gebruikt voor versnelling","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><noindex><a rel=\"nofollow\" href=\"https:\/\/blog.blazingdb.com\/blazingsql-is-now-open-source-b859d342ec20\">Aangekondigd<\/a><\/noindex> over de openstelling van de SQL-engine  <noindex><a rel=\"nofollow\" href=\"https:\/\/rapids.ai\/blazingsql.html\">BlazingSQL<\/a><\/noindex>, die GPU gebruikt voor het versnellen van gegevensverwerking. BlazingSQL is geen volledige database, maar wordt gepositioneerd als een engine voor het analyseren en verwerken van grote datasets, vergelijkbaar met <noindex><a rel=\"nofollow\" href=\"https:\/\/spark.apache.org\/\">Apache Spark<\/a><\/noindex>. De code is geschreven in Python en <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/blazingdb\/pyBlazing\/\">is er onbeperkt en gratis toegang geopend tot de yum-repository met binaire pakketupdates met bugfixes (errata) en beveiligingsproblemen.<\/a><\/noindex> onder de Apache 2.0-licentie. <\/p>\n<p>BlazingSQL is geschikt voor het uitvoeren van enkele analytische queries op grote datasets (tientallen gigabytes) die zijn opgeslagen in tabelvormaten (bijvoorbeeld logboeken, NetFlow-statistieken, enz.). BlazingSQL kan queries uitvoeren vanuit raw-bestanden in CSV- en Apache Parquet-indelingen, opgeslagen in netwerk- en cloudbestandenystemen zoals HDSF en AWS S3, waarbij het resultaat rechtstreeks naar het GPU-geheugen wordt gestuurd. Door het parallel verwerken van taken op de GPU en het gebruik van snellere videogeheugen, voert BlazingSQL queries tot <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.blazingdb.com\/blazingsql-the-gpu-sql-engine-now-runs-over-20x-faster-than-apache-spark-1b0bffc990a9\">20 keer<\/a><\/noindex>  sneller uit dan Apache Spark.<\/p>\n<p><center><noindex><a rel=\"nofollow\" href=\"https:\/\/miro.medium.com\/max\/1000\/1*Pnz_GqmihBX8Hyrxdwgj8w.png\"><img decoding=\"async\" alt=\"De SQL-engine BlazingSQL is geopend, die GPU gebruikt voor versnelling\" src=\"\/wp-content\/uploads\/2019\/08\/2a3cc86f97daf9ea00fc716ba02fa283.jpeg\" style=\"display:block;margin: 0 auto;\" \/><\/a><\/noindex><\/center><\/p>\n<p>Voor het werken met GPU wordt een door NVIDIA ondersteund framework gebruikt, <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/rapidsai\/\">open-source<\/a><\/noindex> bibliotheken <noindex><a rel=\"nofollow\" href=\"https:\/\/rapids.ai\/\">RAPIDS<\/a><\/noindex>, die het mogelijk maakt om applicaties voor gegevensverwerking en analytics te ontwikkelen, die geheel aan de GPU-zijde worden uitgevoerd (een <noindex><a rel=\"nofollow\" href=\"https:\/\/rapidsai.github.io\/projects\/cudf\/en\/0.8.0\/api.html\">Python-interface<\/a><\/noindex> voor het gebruik van lage-level CUDA-primitieven en het parallel verwerken van berekeningen). <\/p>\n<p>BlazingSQL biedt de mogelijkheid om SQL te gebruiken in plaats van de data-verwerkings-API <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/rapidsai\/cudf\">cuUDF<\/a><\/noindex> (gebaseerd op <noindex><a rel=\"nofollow\" href=\"http:\/\/arrow.apache.org\/\">Apache Arrow<\/a><\/noindex>), gebruikt in RAPIDS. BlazingSQL is een extra laag die bovenop cuDF werkt en gebruikt de cuIO-bibliotheek voor het lezen van gegevens van schijven. SQL-queries worden omgezet in aanroepen van cuUDF-functies, die het mogelijk maken om gegevens naar de GPU te laden en bewerkingen voor samenvoegen, aggregatie en filtering uit te voeren. Ondersteuning voor het cre\u00ebren van gedistribueerde configuraties die duizenden GPU's omvatten, wordt ondersteund. <\/p>\n<p> BlazingSQL vereenvoudigt het werken met gegevens aanzienlijk \u2014 in plaats van honderden aanroepen van cuDF-functies, kan men volstaan met \u00e9\u00e9n SQL-query. Het gebruik van SQL maakt het mogelijk om RAPIDS te integreren met bestaande analysystemen, zonder dat er specifieke verwerkers geschreven hoeven te worden en zonder dat er tussentijdse gegevensoverdracht naar een extra DBMS nodig is, maar<br \/>\nhet behoudt volledige compatibiliteit met alle delen van RAPIDS, zet de beschikbare functionaliteit om naar SQL en garandeert prestaties op het niveau van cuDF. Inclusief ondersteuning voor integratie met de bibliotheken <noindex><a rel=\"nofollow\" href=\"https:\/\/xgboost.readthedocs.io\/en\/latest\/\">XGBoost<\/a><\/noindex> en  <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/rapidsai\/cuml\">cuML<\/a><\/noindex> voor analytische en machine learning-taken. <\/p>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Bron: <a \ncontent=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=51222\">opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\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 [&hellip;]<\/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<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041e\u0431\u044a\u044f\u0432\u043b\u0435\u043d\u043e \u043e\u0431 \u043e\u0442\u043a\u0440\u044b\u0442\u0438\u0438 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