Amazon EMR 㧠Apache Spark アプリケーションã®ãƒ¡ãƒ¢ãƒªã‚’ã†ã¾ã管ç†ã™ã‚‹ãŸã‚ã®ãƒ™ã‚¹ãƒˆãƒ—ラクティス | Amazon Web Services
Amazon Web Services ブãƒã‚° Amazon EMR 㧠Apache Spark アプリケーションã®ãƒ¡ãƒ¢ãƒªã‚’ã†ã¾ã管ç†ã™ã‚‹ãŸã‚ã®ãƒ™ã‚¹ãƒˆãƒ—ラクティス ビッグデータã®ä¸–ç•Œã«ãŠã‘る一般的ãªãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã¯ã€ã•ã¾ã–ã¾ãªãƒ‡ãƒ¼ã‚¿ã‚½ãƒ¼ã‚¹ã‹ã‚‰ã®å¤§é‡ã®ãƒ‡ãƒ¼ã‚¿ã«ãŠã‘る抽出/å¤‰æ› (ET) ã¨ãƒ‡ãƒ¼ã‚¿åˆ†æžã®å®Ÿè¡Œã§ã™ã€‚多ãã®å ´åˆã€ã“ã®å¾Œã§ãƒ‡ãƒ¼ã‚¿ã‚’分æžã—ã¦ã‚¤ãƒ³ã‚µã‚¤ãƒˆã‚’å–å¾—ã—ã¾ã™ã€‚ã“ã®ã‚ˆã†ãªå¤§é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’処ç†ã™ã‚‹ãŸã‚ã®æœ€ã‚‚人気ã®ã‚るクラウドベースソリューションã®ã²ã¨ã¤ãŒ Amazon EMR ã§ã™ã€‚ Amazon EMR ã¯ã€AWS ã§ã® Apache Hadoop ãŠã‚ˆã³ Apache Spark ãªã©ã®ãƒ“ッグデータフレームワークã®å®Ÿè¡Œã‚’シンプル化ã™ã‚‹ãƒžãƒãƒ¼ã‚¸ãƒ‰ã‚¯ãƒ©ã‚¹ã‚¿ãƒ¼ãƒ—ラットフォームã§ã™ã€‚Amazon EMR ã¯ã€çµ„ç¹”ãŒè¤‡æ•°ã®ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã‚’æŒã¤ã‚¯ãƒ©ã‚¹ã‚¿ãƒ¼ã‚’ã»ã‚“ã®æ•°åˆ†ã§ã‚¹ãƒ”ンアップã™ã‚‹ã“ã¨ã‚’å¯èƒ½ã«ã—
Clouderaã€AWS上ã§PaaS「Cloudera Altusã€æ供を発表。ビッグデータã®åˆ†æžåŸºç›¤ã‚’サービスã¨ã—ã¦æä¾›
Clouderaã€AWS上ã§PaaS「Cloudera Altusã€æ供を発表。ビッグデータã®åˆ†æžåŸºç›¤ã‚’サービスã¨ã—ã¦æä¾› 先月4月28æ—¥ã«ãƒ‹ãƒ¥ãƒ¼ãƒ¨ãƒ¼ã‚¯è¨¼åˆ¸å–引所ã«ä¸Šå ´ã—ãŸã°ã‹ã‚Šã®Clouderaã¯ã€ã‚¯ãƒ©ã‚¦ãƒ‰ã‚µãƒ¼ãƒ“ス「Cloudera Altusã€ã‚’発表ã—ã¾ã—ãŸã€‚ ã“ã‚Œã¾ã§ä¸»è¦ãªHadoop/Sparkã®ãƒ‡ã‚£ã‚¹ãƒˆãƒªãƒ“ューションベンダーã¨ã—ã¦çŸ¥ã‚‰ã‚Œã¦ããŸåŒç¤¾ã«ã‚ˆã‚‹ã‚¯ãƒ©ã‚¦ãƒ‰ã‚µãƒ¼ãƒ“スã®æ供開始ã¯ã€åŒç¤¾ã«ã¨ã£ã¦æ–°ãŸãªãƒ“ジãƒã‚¹å±•é–‹ã§ã™ã€‚ Cloudera Altusã¯ã€ä¸‹è¨˜ã®ã‚ˆã†ã«AWSã®ã†ãˆã«æ§‹ç¯‰ã•ã‚ŒãŸSpark/MapReduce2/Hiveã‚’æä¾›ã™ã‚‹ã‚¯ãƒ©ã‚¦ãƒ‰ã‚µãƒ¼ãƒ“スã€ã„ã‚ゆるPlatform as a Service（PaaS）ã®ä¸€ç¨®ã§ã€ãƒ“ッグデータã®åˆ†æžãªã©å®Ÿè¡Œã™ã‚‹åŸºç›¤æ©Ÿèƒ½ã‚’サービスã¨ã—ã¦æä¾›ã—ã¾ã™ã€‚ ユーザーã¯ã‚¯ãƒ©ã‚¹ã‚¿ã®ç®¡ç†ã‚„é‹ç”¨ã®æ‰‹é–“ã‚’ã‹ã‘ã‚‹ã“ã¨ãªãã€ãã®ã†ãˆã§å®Ÿè¡Œã™ã‚‹ã‚¢ãƒ—リケーションã®
ディープラーニングフレームワークをCDHã¨Cloudera Data Science Workbenchã§å‹•ã‹ã™
原著者：Vartika Singh 原文：Deep Learning Frameworks on CDH and Cloudera Data Science Workbench 訳：有賀 「ビッグデータã€ã®å°é ã«ã‚ˆã‚Šã€æ©Ÿæ¢°å¦ç¿’ã¯ãšã£ã¨ç°¡å˜ã«ãªã‚Šã¾ã—ãŸã€‚å°‘é‡ã®ãƒ‡ãƒ¼ã‚¿ã ã‘を観察ã—ãŸå¾Œã«æ–°ã—ã„データを一般化ã™ã‚‹ã€çµ±è¨ˆçš„推定ã®è² æ‹…ãŒå¤§å¹…ã«è»½æ¸›ã•ã‚ŒãŸãŸã‚ã§ã™ã€‚典型的ãªæ©Ÿæ¢°å¦ç¿’タスクã®ã‚´ãƒ¼ãƒ«ã¯ã€è¦³æ¸¬ãƒ‡ãƒ¼ã‚¿ã‚’説明ã™ã‚‹å¤‰å‹•è¦å› を分離ã™ã‚‹ã‚ˆã†ã«ç‰¹å¾´ã‚’è¨è¨ˆã™ã‚‹ã“ã¨ã§ã™ã€‚ ã—ã‹ã—ã€å¤šãã®å®Ÿä¸–ç•Œã®äººå·¥çŸ¥èƒ½ã‚¢ãƒ—リケーションã®é›£ã—ã•ã®ä¸»ãªåŽŸå› ã¯ã€å¤‰å‹•è¦å› ã®å¤šããŒè¦³å¯Ÿã§ãã‚‹ã™ã¹ã¦ã®ãƒ‡ãƒ¼ã‚¿ã«å½±éŸ¿ã‚’与ãˆã‚‹ã“ã¨ã§ã™ã€‚ ディープラーニングã¯ã€ã‚ˆã‚Šå˜ç´”ãªè¡¨ç¾ã‚’å°Žå…¥ã™ã‚‹ã“ã¨ã«ã‚ˆã£ã¦ã€è¡¨ç¾å¦ç¿’を通ã—ã¦ã“ã®ä¸å¿ƒçš„ãªå•é¡Œã‚’解決ã—ã¾ã™ã€‚ ä¼æ¥ã‚„ç ”ç©¶è€…ã¯ã€ç¾åœ¨ãŠã‚ˆã³å°†æ¥ã®ãƒ†ã‚¯ãƒŽãƒã‚¸ãƒ¼ã®é€²åŒ–ã«å½±éŸ¿ã‚’åŠã¼ã™ãŸã‚ã«ã€ã¾ã™ã¾ã™å¤šãã®ãƒ‡ãƒ¼ã‚¿ã‚’分æžã—ã¦
Monitoring Spark Applications
Monitoring Spark Applications Tzach Zohar @ Kenshoo, March/2016 The document discusses monitoring Spark applications. It covers using the Spark UI to monitor jobs, stages and tasks; using the Spark REST API to programmatically access monitoring data; configuring Spark metric sinks like Graphite to export internal Spark metrics; and creating applicative metrics to monitor your own application metri
Apache Sparkã«ãŠã‘るメモリ - アプリケーションをè½ã¨ã•ãªã„メモリè¨è¨ˆæ‰‹æ³• -
- Apache Spark is an open-source cluster computing framework for large-scale data processing. It was originally developed at the University of California, Berkeley in 2009 and is used for distributed tasks like data mining, streaming and machine learning. - Spark utilizes in-memory computing to optimize performance. It keeps data in memory across tasks to allow for faster analytics compared to dis
Spark × Keras × Dockerã§ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングをスケーラブルã«ã—ã¦ã¿ãŸ2　マルãƒãƒ›ã‚¹ãƒˆç·¨ - Qiita
Spark × Keras × Dockerã§ãƒ‡ã‚£ãƒ¼ãƒ—ラーニングをスケーラブルã«ã—ã¦ã¿ãŸ2 以å‰ã€Dist-kerasã‚’Dockerã«è¼‰ã›ã¦ã‚¹ã‚±ãƒ¼ãƒ©ãƒ–ルãªãƒ‡ã‚£ãƒ¼ãƒ—ラーニングを作ã£ã¦ã¿ã¾ã—ãŸã€‚ http://qiita.com/cvusk/items/3e6c3bade8c0e1c0d9bf 当時ã®åçœç‚¹ã¯ãƒ‘フォーマンスãŒå‡ºãªã‹ã£ãŸã“ã¨ã§ã™ãŒã€ã‚ˆã見直ã—ãŸã‚‰ãƒ‘ラメータã®è¨å®šãŒé–“é•ã£ã¦ã„ãŸã‚ˆã†ã§ã™ã€‚ ãã“ã§åçœãŒã¦ã‚‰ã€ã„ã‚ã„ã‚試ã—ã¦ã¿ã¾ã—ãŸã€‚ å‰å›žã¾ã§ã®ã‚らã™ã˜ Dist-Keras自体ã®èª¬æ˜Žã¯ä»¥å‰ã®æŠ•ç¨¿ã‚’ã”å‚ç…§ã„ãŸã ããŸã„ã®ã§ã™ãŒã€è¦ã¯Sparkクラスター上ã§å‹•ä½œã™ã‚‹Kerasã§ã™ã€‚ ç§ã¯ã“れをDockerイメージã«ã—ã¦ã€ã‚¹ã‚±ãƒ¼ãƒ«ã‚¢ã‚¦ãƒˆã‚’ç°¡å˜ã«ã§ãるよã†ã«ã—ã¦ã¿ã¾ã—ãŸã€‚ ãªãŠã€Dockerfileã¯GitHubã§å…¬é–‹ã—ã¦ã„ã¾ã™ã€‚ https://github.com/shibuiwillia
Elasticsearchã®Apache Sparkサãƒãƒ¼ãƒˆæ©Ÿèƒ½ã§éŠã¶ - CLOVERðŸ€
Elasticsearchã«ã€Apache Sparkå‘ã‘ã®ãƒ©ã‚¤ãƒ–ラリãŒã‚ã‚‹ã“ã¨ã¯çŸ¥ã£ã¦ã„ãŸã®ã§ã™ãŒã€é•·ã‚‰ã手をã¤ã‘ã¦ã„ãªã„ã¾ã¾ã ã£ãŸã®ã§ã€1度試ã—ã¦ã¿ã‚‹ã“ã¨ã«ã—ã¾ã—ãŸã€‚ Apache Spark support | Elasticsearch for Apache Hadoop [2.3] | Elastic ã“ã¡ã‚‰ã‚’使ã†ã“ã¨ã§ã€Apache SparkãŒæä¾›ã™ã‚‹APIã‚’Elasticsearchã§ä½¿ã†ã“ã¨ãŒã§ãるよã†ã«ãªã‚‹ã¿ãŸã„ã§ã™ã。内部的ã«ã¯ã€elasticsearch-hadoopã«ä¾å˜ã—ã¦ã„る模様。 日本語記事もã‚るよã†ã§ã™ã€‚ 楽ã—ã„å¯è¦–化 ： elasticsearchã¨Spark Streamingã®å‡ºä¼šã„ | NTTãƒ‡ãƒ¼ã‚¿å…ˆç«¯æŠ€è¡“æ ªå¼ä¼šç¤¾ ã§ã€ä½•ã‚’ã™ã‚‹ã‹ã§ã™ãŒã€ã¾ã‚…Spark Streamingã¨Twitterã§ã™ã‹ã。今回ã¯ã€ä»¥ä¸‹ã®ãƒ†ãƒ¼ãƒžã§ã‚„ã£ã¦ã¿ã‚‹ã“ã¨ã«ã—ã¾ã—ãŸã€‚
Kafka+Spark Streaming+Elasticserachã«ã‚ˆã‚‹ã‚·ã‚¹ãƒ†ãƒ 構築ã¨æ¤œè¨¼ã®é€²ã‚æ–¹
ã¯ã˜ã‚ã« å‰å›žã¯Spark Streamingã®æ¦‚è¦ã¨æ¤œè¨¼ã‚·ãƒŠãƒªã‚ªã€ãŠã‚ˆã³æ§‹ç¯‰ã™ã‚‹ã‚·ã‚¹ãƒ†ãƒ ã®æ¦‚è¦ã‚’解説ã—ã¾ã—ãŸã€‚今回ã¯ã‚·ã‚¹ãƒ†ãƒ ã®è©³ç´°æ§‹æˆã¨æ¤œè¨¼ã®é€²ã‚æ–¹ã€ãŠã‚ˆã³åˆæœŸè¨å®šã«ãŠã‘る性能測定çµæžœã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ ã“ã®æ¤œè¨¼ã§ã¯ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ã‚ューã®Kafkaã€ã‚¹ãƒˆãƒªãƒ¼ãƒ データ処ç†ã®Spark Streamingã€æ¤œç´¢ã‚¨ãƒ³ã‚¸ãƒ³ã®Elasticsearchを組ã¿åˆã‚ã›ãŸãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã®ã‚»ãƒ³ã‚µãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚·ã‚¹ãƒ†ãƒ を構築ã—ã¦ã„ã¾ã™ã€‚今回ã¯Kafkaã¨Elasticsearchã®è©³ç´°ãªã‚¢ãƒ¼ã‚テクãƒãƒ£ã‚„Kafkaã¨Sparkã®æŽ¥ç¶šæ™‚ã®æ³¨æ„点も解説ã—ã¾ã™ã€‚ システムã®è©³ç´°æ§‹æˆ マシン構æˆã¨ãƒžã‚·ãƒ³ã‚¹ãƒšãƒƒã‚¯ 評価ã«å‘ã‘ãŸãƒžã‚·ãƒ³ã®åˆæœŸæ§‹æˆã‚’図1ã«ç¤ºã—ã¾ã™ã€‚本システムã¯ä»¥ä¸‹ã®ãƒŽãƒ¼ãƒ‰ã‹ã‚‰æ§‹æˆã•ã‚Œã¾ã™ã€‚ センサデータをåŽé›†ã—ã¦Kafkaã«é€ä¿¡ã™ã‚‹åŽé›†ãƒ»é…信ノード Kafkaクラスタを構æˆã—ã¦ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ã®å—ã‘渡ã—ã‚’è¡Œã†ã‚ューã¨ã—ã¦
Spark 2.0ã®æ€§èƒ½æ¤œè¨¼ã®çµæžœã¨ãƒœãƒˆãƒ«ãƒãƒƒã‚¯ã®è€ƒå¯Ÿ
ã¯ã˜ã‚ã« å‰å›žã¯ã€Spark 2.0ã®ä¸»ãªå¤‰æ›´ç‚¹ã¨ã—ã¦Spark 1.6よりも性能ãŒå‘上ã—ã€ã‚¢ãƒ—リケーションã®å®Ÿè£…ãŒå®¹æ˜“ã«ãªã£ãŸã“ã¨ã‚’解説ã—ã¾ã—ãŸã€‚ã¾ãŸã€ãã®æ€§èƒ½æ¤œè¨¼ã®ã‚·ãƒŠãƒªã‚ªã¨ã—ã¦ã€é›»åŠ›æ¶ˆè²»é‡ãƒ‡ãƒ¼ã‚¿ã‚’集計ã—å¯è¦–化ã™ã‚‹ã‚±ãƒ¼ã‚¹ã‚’想定ã™ã‚‹ã“ã¨ã‚’解説ã—ã¾ã—ãŸã€‚今回ã¯ã€ã‚·ãƒŠãƒªã‚ªã«åŸºã¥ã„ãŸæ¤œè¨¼ã‚’è¡Œã†ãŸã‚ã®ç’°å¢ƒï¼ˆã‚·ã‚¹ãƒ†ãƒ 構æˆã€ãƒ‘ラメータ）ã¨ãã®æ¤œè¨¼çµæžœã‚’解説ã—ã¾ã™ã€‚ ã‚·ã‚¹ãƒ†ãƒ æ§‹æˆ ãƒ‡ãƒ¼ã‚¿åˆ†æžã‚·ã‚¹ãƒ†ãƒ ã®æ¦‚è¦ ãƒ‡ãƒ¼ã‚¿åˆ†æžã‚·ã‚¹ãƒ†ãƒ ã¯ã€å›³1ã®ã‚ˆã†ã«ç®¡ç†ç”»é¢ã¨ãƒ‡ãƒ¼ã‚¿åˆ†æžã‚¢ãƒ—リケーションã€ãƒ‡ãƒ¼ã‚¿å‡¦ç†åŸºç›¤ã®3ã¤ã‹ã‚‰æˆã‚Šã¾ã™ã€‚è¨å‚™ä¼ç”»æ‹…当者ã¯ç®¡ç†ç”»é¢ã‚’介ã—ã¦ãƒ‰ãƒªãƒ«ãƒ€ã‚¦ãƒ³åˆ†æžã‚’è¡Œã„ã¾ã™ã€‚予ã‚データ分æžã‚¢ãƒ—リケーションã§è¨å‚™ã®è² è·ã‚’集計ã—ã€ãã®æ¼”算処ç†ã‚’実行ã™ã‚‹ã®ãŒãƒ‡ãƒ¼ã‚¿å‡¦ç†åŸºç›¤ã§ã™ã€‚本連載ã§å–り上ã’るデータ処ç†åŸºç›¤ã«ã¯HadoopãŠã‚ˆã³Sparkã‚’å°Žå…¥ã—ã¦ã„ã¾ã™ã€‚ ãƒãƒ¼ãƒ‰ã‚¦ã‚§ã‚¢æ§‹æˆ データ処ç†åŸºç›¤ã¯ä»®æƒ³ã‚µãƒ¼ãƒ3å°ã€ç‰©ç†
Spark Internals - Hadoop Source Code Reading #16 in Japan
The document discusses Spark internals and provides an overview of key components such as the Spark code base size and growth over time, core developers, Scala basics used in Spark, RDDs, tasks, caching/block management, and schedulers for running Spark on clusters including Mesos and YARN. It also includes tips for using IntelliJ IDEA to work with Spark's Scala code base.Read less
MapReduce以外ã®åˆ†æ•£å‡¦ç†åŸºç›¤BSP, Piccolo, Sparkã®ç´¹ä»‹ - Preferred Networks Research & Development
ã©ã†ã‚‚，実ã¯ä»Šå¹´ã‹ã‚‰é–‹ç™ºãƒãƒ¼ãƒ ã«joinã—ã¦ã„ãŸä¸å·ã§ã™ï¼Žå¯æ„›ã„犬ã®å†™çœŸãŒãªã‹ã£ãŸã®ã§ï¼Œå¯æ„›ã„マスコットã®ç”»åƒã‚’è²¼ã£ã¦ãŠãã¾ã™ï¼Ž 最近MapReduceã¨ã‹ãã®å®Ÿè£…ã§ã‚ã‚‹Hadoopã¨ã‹ã‚’よãèžãよã†ã«ãªã‚Šã¾ã—ãŸï¼Žã“ã‚Œã¯ã¤ã¾ã‚Šï¼Œãã‚Œã ã‘大é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’ãªã‚“ã¨ã‹å‡¦ç†ã—ãŸã„ã¨ã„ã†è¦æœ›ãŒã‚ã‚‹ã‹ã‚‰ã ã¨æ€ã„ã¾ã™ï¼Žã—ã‹ã—当ãŸã‚Šå‰ã§ã™ãŒï¼ŒMapReduceã¯éŠ€ã®å¼¾ä¸¸ã§ã¯ã‚ã‚Šã¾ã›ã‚“． ã¨ã„ã†ã“ã¨ã§ï¼Œæœ€è¿‘æ°—ã«ãªã£ã¦ã„ã‚‹MapReduceã¨ã¯é•ã£ãŸã‚¢ãƒ—ãƒãƒ¼ãƒã‚’å–ã£ã¦ã„る分散処ç†åŸºç›¤ã«ã¤ã„ã¦ï¼Œç¤¾å†…ã®TechTalkã§è©±ã—ãŸå†…容を簡å˜ã«ã¾ã¨ã‚ã¦ç´¹ä»‹ã—ãŸã„ã¨æ€ã„ã¾ã™ï¼Ž Bulk Sychronous Parallel ã“ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ 自体ã¯1990å¹´ã«èª•ç”Ÿã—ãŸã‚‚ã®ã§ã™ï¼Žé•·ã„ã®ã§BSPã¨æ›¸ãã¾ã™ï¼Žã•ã¦ï¼Œã‚°ãƒ©ãƒ•ã‹ã‚‰æœ€çŸçµŒè·¯ã‚’求ã‚る時，MapReduceã¯ä½¿ãˆã‚‹ã§ã—ょã†ã‹ï¼Ÿã“ã®ã‚ˆã†ãªè«–æ–‡ãŒå‡ºã‚‹ãらã„ã§ã™ã‹ã‚‰å‡ºæ¥ãªã„ã“ã¨ã¯ã‚
BeInteractive! [Spark project 勉強会 #03 ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸ]
今月も無事ã€Spark project 勉強会 #03 を終ãˆã‚‹ã“ã¨ãŒå‡ºæ¥ã¾ã—ãŸã€‚今回 100 åå‚åŠ ã¨ã„ã†ã“ã¨ã§ã€ã‚‚ã†æœ¬å½“ã«è‰²ã€…スパークã—ã¦ã„ãŸã‚ˆã†ãªæ°—ãŒã—ã¾ã™ã€‚æ¥æœˆã‚‚スパークï¼å‘ŠçŸ¥ã¯ å…¬å¼ãƒ–ãƒã‚° ã‚’ãƒã‚§ãƒƒã‚¯ã—ã¦ä¸‹ã•ã„ã。 以下ã€åƒ•ã®ç™ºè¡¨è³‡æ–™ã§ã™ã€‚ Spark project 勉強会 #03 Keynote (è¿‘æ³å ±å‘Š)http://www.be-interactive.org/works/20080930/keynote.pdf マルãƒãƒ—レイ Flash ゲームã®ã¤ãã‚Šã‹ãŸhttp://www.be-interactive.org/works/20080930/be-lt02-multiplay.pdf ã‚ã€ã¡ãªã¿ã«ã“ã®åº¦ã€äºŒåæ³ã®èª•ç”Ÿæ—¥ã‚’è¿Žãˆã¾ã—ã¦ã€å‹‰å¼·ä¼šã§ã‚‚ (ã™ã„ã¾ã›ã‚“) ã€æ²¢å±±ã®æ–¹ã«ç¥ã£ã¦é ‚ãã€æœ¬å½“ã«ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸã€‚20 代も三度ã®é£¯ã‚ˆã‚Š ActionScrip
1
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
blog/dedup.md at main · huggingface/blog
Large-scale Near-deduplication Behind BigCode
How We Optimise Apache Spark Jobs | REA Group Ltd
https://events.static.linuxfound.org/sites/events/files/slides/Real%20Time%20Aggregation%20with%20Kafka%20%2CSpark%20Streaming%20and%20ElasticSearch%20%2C%20scalable%20beyond%20Million%20RPS.pdf
https://jp.techcrunch.com/2015/06/16/20150615ibm-pours-researchers-and-resources-into-apache-spark-project/
https://jp.techcrunch.com/2014/11/14/20141113the-19-spark-photon-can-turn-anything-into-a-web-connected-whatsit/
{{#tags}}- {{label}}
{{/tags}}