{"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\/en\/blog\/news\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","title":{"rendered":"The code for the BlazingSQL SQL engine, which uses GPU for acceleration, has been opened.","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\">Announced<\/a><\/noindex> about the release of the SQL engine source code  <noindex><a rel=\"nofollow\" href=\"https:\/\/rapids.ai\/blazingsql.html\">BlazingSQL<\/a><\/noindex>, which uses GPU to accelerate data processing. BlazingSQL is not a full-fledged DBMS, but positions itself as an engine for analyzing and processing large datasets, comparable in its tasks to <noindex><a rel=\"nofollow\" href=\"https:\/\/spark.apache.org\/\">Apache Spark<\/a><\/noindex>. The code is written in Python and <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/blazingdb\/pyBlazing\/\">is open<\/a><\/noindex> under the Apache 2.0 license. <\/p>\n<p>BlazingSQL is suitable for executing single analytical queries over large datasets (tens of gigabytes), stored in tabular formats (e.g., logs, NetFlow statistics, etc.). BlazingSQL can execute queries from raw files in formats such as CSV and Apache Parquet, located in network and cloud file systems like HDFS and AWS S3, directly transferring the results to GPU memory. Thanks to parallel processing on the GPU and the use of faster video memory, query execution in BlazingSQL is done up to <noindex><a rel=\"nofollow\" href=\"https:\/\/blog.blazingdb.com\/blazingsql-the-gpu-sql-engine-now-runs-over-20x-faster-than-apache-spark-1b0bffc990a9\">20 times<\/a><\/noindex>  faster than in 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=\"The code for the BlazingSQL SQL engine, which uses GPU for acceleration, has been opened.\" src=\"\/wp-content\/uploads\/2019\/08\/2a3cc86f97daf9ea00fc716ba02fa283.jpeg\" style=\"display:block;margin: 0 auto;\" \/><\/a><\/noindex><\/center><\/p>\n<p>For working with the GPU, a library set developed with the participation of NVIDIA is used <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/rapidsai\/\">open-source<\/a><\/noindex> called <noindex><a rel=\"nofollow\" href=\"https:\/\/rapids.ai\/\">RAPIDS<\/a><\/noindex>, which allows creating applications for data processing and analytics executed entirely on the GPU side (a <noindex><a rel=\"nofollow\" href=\"https:\/\/rapidsai.github.io\/projects\/cudf\/en\/0.8.0\/api.html\">Python interface<\/a><\/noindex> is provided for using low-level CUDA primitives and parallelizing computations). <\/p>\n<p>BlazingSQL provides the ability to use SQL instead of the data processing API <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/rapidsai\/cudf\">cuUDF<\/a><\/noindex> (based on <noindex><a rel=\"nofollow\" href=\"http:\/\/arrow.apache.org\/\">Apache Arrow<\/a><\/noindex>), used in RAPIDS. BlazingSQL is an additional layer working over cuDF and uses the cuIO library to read data from disk. SQL queries are translated into cuUDF function calls, allowing data to be loaded into the GPU and operations like merging, aggregating, and filtering to be performed. Support for creating distributed configurations covering thousands of GPUs is included. <\/p>\n<p> BlazingSQL significantly simplifies data management\u2014rather than making hundreds of cuDF function calls, you can achieve this with a single SQL query. The use of SQL allows for the integration of RAPIDS with existing analytics systems, without the need for writing specific handlers or resorting to intermediate data loading into an additional database, but<br \/>\nmaintaining full compatibility with all components of RAPIDS, translating existing functionality into SQL and providing performance on the level of cuDF. This includes support for integration with libraries <noindex><a rel=\"nofollow\" href=\"https:\/\/xgboost.readthedocs.io\/en\/latest\/\">XGBoost<\/a><\/noindex> and  <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/rapidsai\/cuml\">cuML<\/a><\/noindex> for solving analytics and machine learning tasks. <\/p>\n<p><noindex><a rel=\"nofollow\" name=\"link\"><\/a><\/noindex><\/p>\n<p>Source: <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 5.0.1.1 - 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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