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katotaku id:katotaku

emrã«é–¢ã™ã‚‹katotakuã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (5)

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ãƒªã‚¯ãƒ«ãƒ¼ãƒˆãƒ†ã‚¯ãƒŽãƒã‚·ã‚™ãƒ¼ã‚¹ã‚™ ã«ãŠã‘ã‚‹ EMR ã®æ´»ç”¨ã¨ã‚³ã‚¹ãƒˆåœ§ç¸®æ–¹æ³•
Convert to study materialsBETATransf orm any presentation into ready-made study materialâ€”select from outputs like summaries, definitions, and practice questions. Convert
katotaku 2017/07/13
aws

emr
ãƒªãƒ³ã‚¯
Amazon EMR Masterclass
Amazon EMR enables fast processing of large structured or unstructured datasets, and in this presentation we'll show you how to setup an Amazon EMR job flow to analyse application logs, and perform Hive queries against it. We also review best practices around data file organisation on Amazon Simple Storage Service (S3), how clusters can be started from the AWS web console and command line, and how
katotaku 2016/09/20
aws

emr
ãƒªãƒ³ã‚¯
Map Reduce ã€œå…¥é–€ç·¨ï¼šä»•çµ„ã¿ã®ç†è§£ã¨ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ãƒ‡ã‚¶ã‚¤ãƒ³ã€œ
This document discusses using sliding windows to aggregate streaming data in MapReduce. It proposes buffering input tuples in mappers until a window is full, then em itting the aggregate. Combiners and reducers combine partial aggregates across windows. Window ranges are initialized and updated during merging to remove outdated data and handle late arrivals. This approach allows streaming aggregati
katotaku 2016/09/20
aws

emr
ãƒªãƒ³ã‚¯
Amazon EMRã§CustomJarã‚’ä½œæˆã—ã¦å®Ÿè¡Œã™ã‚‹æ–¹æ³• - Qiita
ä»Šæ›´ã¨ã„ãˆã°ä»Šæ›´ã ãŒã€æ„å¤–ã¨æƒ…å ±ãŒå°‘ãªã‹ã£ãŸã®ã§ã€Amazon EMRã«ã¦ã€CustomJarã‚’ä½œæˆã—ã¦å®Ÿè¡Œã™ã‚‹æ–¹æ³•ã‚’ã¾ã¨ã‚ã¦ã¿ã‚‹ã€‚ å®Ÿè£…ã™ã‚‹å‡¦ç†ã¯ã€ã¿ã‚“ãªå¤§å¥½ãWord Countã§ã™ã€‚ #æ‰‹é †æ¦‚è¦ MapReduceã®å‡¦ç†ã‚’å®Ÿè£…ã—ãŸjarãƒ•ã‚¡ã‚¤ãƒ«ã‚’ä½œã‚‹ ä½œæˆã—ãŸJarãƒ•ã‚¡ã‚¤ãƒ«ã‚’Amazon S3ã¸ã‚¢ãƒƒãƒ—ãƒãƒ¼ãƒ‰ã™ã‚‹ ãƒã‚°ãƒ•ã‚¡ã‚¤ãƒ«å‡ºåŠ›ãƒ‡ã‚£ãƒ¬ã‚¯ãƒˆãƒªã€å…¥åŠ›ãƒ•ã‚¡ã‚¤ãƒ«ã‚’ç”¨æ„ã™ã‚‹ EMRã‚¯ãƒ©ã‚¹ã‚¿ãƒ¼ã‚’ç«‹ã¡ä¸Šã’ã‚‹ ã‚¸ãƒ§ãƒ–ã‚’å®Ÿè¡Œã™ã‚‹ #Hadoopã®å‡¦ç†ã‚’å®Ÿè£…ã—ãŸjarãƒ•ã‚¡ã‚¤ãƒ«ã‚’ä½œã‚‹ Jobã®å†…å®¹ã‚’è¨å®šã™ã‚‹Mainãƒ¡ã‚½ãƒƒãƒ‰ã€Mapperã‚¯ãƒ©ã‚¹ã€Reducerã‚¯ãƒ©ã‚¹ã‚’å®Ÿè£…ã—ã€Jarã‚’ä½œæˆã™ã‚‹ã€‚ Jarã‚’ä½œã‚‹ãŸã‚ã«ã€ä»Šå›žã¯Mavenã‚’ä½¿ç”¨ã™ã‚‹ã€‚ ãƒã‚¤ãƒ³ãƒˆã¨ãªã‚‹ã®ã¯ã€hadoop-coreä»¥å¤–ã§è¿½åŠ ã§ä½¿ç”¨ã™ã‚‹JarãŒã‚ã‚‹å ´åˆã«ã¯ã€ã€Œmaven-assem bly-pluginã€ãªã©ã‚’ä½¿ã£ã¦ã€ä¾å˜ã™ã‚‹Jarã‚’ä¸€ã¤ã«
katotaku 2016/09/20
aws

emr
ãƒªãƒ³ã‚¯
30åˆ†ã§å®Ÿè¡Œã™ã‚‹Amazon Elastic MapReduceï¼ˆAmazon EMRï¼‰
ä»Šå¹´ã®ãƒ†ãƒ¼ãƒžã®ä¸€ã¤ã¯ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿ã¨ã„ã†ã“ã¨ã§ã€ãã‚ãã‚æœ¬æ ¼çš„ã«MapReduceã«æ‰‹ã‚’å‡ºãã†ã¨æ€ã„ã¾ã™ã€‚ æ‰‹è»½ã«MapReduceã‚’è©¦ã—ã¦ã¿ã‚‹ã®ã§ã‚ã‚Œã°ã€ã‚„ã¯ã‚ŠAWSã§ã—ã‚‡ã†ã€‚ä»Šå›žã¯Amazon Elastic MapReduceï¼ˆAmazon EMRï¼‰ã‚’å‹•ã‹ã™æ–¹æ³•ã‚’ç°¡å˜ã«ã¾ã¨ã‚ã¦ãŠãã¾ã™ã€‚ å®Ÿè¡Œã™ã‚‹å‡¦ç†ã®ä¾‹ã¨ã—ã¦ã¯ã€Hadoopã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«ã«ã‚ã‚‹å‡¦ç†ã‚’ãã®ã¾ã¾ä½¿ã‚ã›ã¦ã‚‚ã‚‰ã„ã¾ã—ã‚‡ã†ã€‚ä¸Žãˆã‚‰ã‚ŒãŸãƒ†ã‚ã‚¹ãƒˆï¼ˆè‹±æ–‡ï¼‰ã«å‡ºç¾ã™ã‚‹å˜èªžã‚’æ•°ãˆã‚‹ã¨ã„ã†ã‚‚ã®ã§ã™ã€‚ å®Ÿè¡Œã™ã‚‹å‡¦ç† ä¸Žãˆã‚‰ã‚ŒãŸãƒ†ã‚ã‚¹ãƒˆã«å‡ºç¾ã™ã‚‹å˜èªžã‚’æ•°ãˆã‚‹ å®Ÿè¡Œã®æ‰‹é † Amazon EMRã«ã¯Apache HadoopãŒä½¿ã‚ã‚Œã¦ã„ã¾ã™ã€‚æœ¬æ¥Hadoopã‚’ä½¿ã†ãŸã‚ã«ã¯ã€Hadoopã®ç’°å¢ƒãã®ã‚‚ã®ã‚’ã‚»ãƒƒãƒˆã‚¢ãƒƒãƒ—ã™ã‚‹å¿…è¦ãŒã‚ã‚‹ã®ã§ã™ãŒã€ãã“ã‚’AWSãŒæ—¢ã«ã‚„ã£ã¦ãã‚Œã¦ã„ã‚‹ã‚ã‘ã§ã™ã€‚ ãã®ãŸã‚ã€æ‰‹é †ã¨ã—ã¦ã¯ Hadoopã®ã‚¸ãƒ§ãƒ–ã¨ãªã‚‹jarãƒ•ã‚¡ã‚¤ãƒ«
katotaku 2016/09/20
emr

aws
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