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mogwaingã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (7,195)

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Alpacaã®å‹‰å¼·ä¼šã®ãŠçŸ¥ã‚‰ã›: Orb / Alpaca Tech Talk 2017/06/26(æœˆ) 18:30ã€œ - FutureInsight.info
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mogwaing 2017/06/18
alpaca

orb

database

ai
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http://www.hydromatic.net/wiki/FarragoHeuristicPlanner
mogwaing 2016/10/16
optimizer

database

hep

volcano
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ç¬¬22å›žï¼»æœ€çµ‚å›žï¼½ã€€ã¾ã¨ã‚ã¨æœ¬é€£è¼‰ã§è§£èª¬ã§ããªã‹ã£ãŸã“ã¨ | gihyo.jp
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mogwaing 2016/07/08
hadoop
ãƒªãƒ³ã‚¯
sublime text 3ã§golangé–‹ç™ºã™ã‚‹ãŸã‚ã®ãƒ—ãƒ©ã‚°ã‚¤ãƒ³ãªã© - Qiita
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mogwaing 2016/06/20
go

sublime
ãƒªãƒ³ã‚¯
Kubernetesã¨ã¯ä½•ã‹ã‚’åˆ†ã‹ã‚Šã‚„ã™ãç´¹ä»‹ã™ã‚‹ã€Œã‚ˆã„åã®ãŸã‚ã®Kubernetesçµµæœ¬ã€å‹•ç”»ãŒå…¬é–‹
ã€ŒKubernetesã£ã¦ãªã‚ã«ï¼Ÿã€ã¨å¨˜ã«èžã‹ã‚ŒãŸãŠçˆ¶ã•ã‚“ã€‚ã€ŒKuberenetsã¯ã€Dockerã‚³ãƒ³ãƒ†ãƒŠç¾¤ã‚’ã‚ªãƒ¼ã‚±ã‚¹ãƒˆãƒ¬ãƒ¼ã‚·ãƒ§ãƒ³ã™ã‚‹ãŸã‚ã®ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®ã‚·ã‚¹ãƒ†ãƒ ã ã‚ˆã€ã¨ç”ãˆãŸãŠçˆ¶ã•ã‚“ãŒä½œã£ãŸã®ãŒã€ã“ã®çµµæœ¬ã§ã™ã€‚ Dockerã‚³ãƒ³ãƒ†ãƒŠã®æ™®åŠã¨ã¨ã‚‚ã«ã€ã‚³ãƒ³ãƒ†ãƒŠã‚’ã‚¯ãƒ©ã‚¹ã‚¿åŒ–ã—ãŸéš›ã®é‹ç”¨ç®¡ç†ãƒ„ãƒ¼ãƒ«ã®é‡è¦æ€§ã‚‚é«˜ã¾ã£ã¦ãã¦ã„ã¾ã™ã€‚Kubernetesã¯ãã†ã—ãŸé‹ç”¨ç®¡ç†ãƒ„ãƒ¼ãƒ«ã®1ã¤ã§ã™ã€‚ ã—ã‹ã—Kubernetesã®ä»•çµ„ã¿ã¨æ©Ÿèƒ½ã¯ã‚„ã‚„è¤‡é›‘ã§ã™ã€‚ãã“ã§ã€åˆ†ã‹ã‚Šã‚„ã™ãKubernetesã‚’ç´¹ä»‹ã—ã‚ˆã†ã¨ã€Kubernetesã‚’ãƒ™ãƒ¼ã‚¹ã«ã—ãŸPaaSåŸºç›¤ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã‚’é–‹ç™ºã—ã¦ã„ã‚‹Deis,IncãŒå…¬é–‹ã—ãŸå‹•ç”»ãŒã€ŒThe Illustrated Children's Guide to Kubernetesã€ï¼ˆã‚ˆã„åã®ãŸã‚ã®Kubernetesçµµæœ¬ï¼‰ã§ã™ã€‚ ã‚‚ã¡ã‚ã‚“é¡Œåã¯ã‚ã‚‹ç¨®ã®ã‚¸ãƒ§ãƒ¼ã‚¯ã§ã€ä¸èº«ã¯ãã¡ã‚“ã¨Kube
mogwaing 2016/06/16
kubernetes

docker
ãƒªãƒ³ã‚¯
Are you really doing continuous integration? Here's how to tell | TechBeacon
OpenText World 2024 is just around the corner! With help from our sponsors, weâ€™re making this an event to rem ember
mogwaing 2016/06/15
kubernetes

docker
ãƒªãƒ³ã‚¯
ç ”ç©¶æ³•ï¼ˆClaimã¨ã¯ï¼‰
3. ã‚¯ãƒ¬ãƒ¼ãƒ claim â€¢ ç§‘å¦æŠ€è¡“è«–æ–‡ã§ã¯ã€æ£èª¤ãŒå®¢è¦³çš„ã«åˆ¤å®šã§ãã‚‹è¨€æ˜Žã€‚ â€¢ ç ”ç©¶ã¨ã¯ã€Œæ–°ã—ã„claimã‚’æç¤ºã—ã€ãã‚Œã‚’ç«‹è¨¼ã™ã‚‹ã€ã“ã¨ã€‚ â€¢ ç«‹è¨¼ã¨ã¯ã€ç‹é“ã‚’ãŸã¦ã¦ç†è§£ã™ã‚Œã°ã€ãã‚ŒãŒæ£ã—ã„ã¨ç´å¾—ã§ãã‚‹ ã‚ˆã†ã«è°è«–ã‚’æ§‹æˆã™ã‚‹ã“ã¨ï¼ˆæ ¹æ‹ ã¨è«–ç†ï¼‰ã€‚ â€¢ Claim ã®ä¾¡å€¤ã¨ã„ã†ã‚‚ã®ã‚‚ã‚ã‚‹ï¼ˆclaimã§ã‚ã‚‹ãŒä¾¡å€¤ã®ãªã„ã‚‚ã® ã‚‚ã‚ã‚‹ï¼‰ã€‚ â€¢ è«–æ–‡ã‚„ç ”ç©¶ç™ºè¡¨ã¯claimãŒä¼ã‚ã‚‹ã‚ˆã†ã«æ§‹æˆã—ãªã‘ã‚Œã°ãªã‚‰ãªã„ã€‚ â€¢ ç ”ç©¶éŽç¨‹ã§ã‚‚å¸¸ã«claimã‚’è¨å®šã—ã¦ãŠãã“ã¨ã€‚ç ”ç©¶ãŒé€²å±•ã™ã‚‹éŽ ç¨‹ã§å¤‰æ›´ã™ã‚‹ã“ã¨ã¯ã‹ã¾ã‚ãªã„ã€‚ã—ã‹ã—ã€ã€Œç«‹è¨¼ã—ãŸã„ä¸»å¼µã€ãŒ ãªã„çŠ¶æ…‹ã§æ‰‹ã ã‘å‹•ã‹ã—ã¦ã„ã‚‹ã“ã¨ã®ãªã„ã‚ˆã†ã«ã€‚ 4. Claimã§ã‚ã‚‹ä¾‹ â€¢ DNAã®ç«‹ä½“æ§‹é€ ã¯ï¼’é‡èžºæ—‹ã§ã‚ã‚‹ã€‚ â€¢ ç´°èƒžã«é…¸ã§ã‚¹ãƒˆãƒ¬ã‚¹ã‚’ä¸Žãˆã‚‹ã¨ä¸‡èƒ½ç´°èƒžåŒ–ã™ã‚‹ã€‚ â€“ Claimã®ç«‹ã¦æ–¹ã¯ç´ æ™´ã‚‰ã—ã„ã€‚ãŸã ç«‹è¨¼ã§ããªã‹ã£ãŸ.. â€¢ ä¸Šè…•ç‹è‚‰ã«é›»æ°—åˆºæ¿€ã‚’ä¸Žãˆã‚‹ã¨æŒ‡ãŒå‹•
mogwaing 2016/06/08
research
ãƒªãƒ³ã‚¯
Monitoring Cassandra at Scale
At Yelp we leverage Cassandra to fulfill a diverse workload that seems to combine every consistency and availability tradeoff imaginable. It is a fantastically versatile datastore, and a great complement for our developers to our MySQL and Elasticsearch offerings. However, our infrastructure is not done until it ships and is monitored. When we started deploying Cassandra we immediately started loo
mogwaing 2016/06/08
cassandra

monitoring
ãƒªãƒ³ã‚¯
Scalability - Bitcoin Wiki
Note: This page is seriously outdated and largely unmaintained; due to past incidents of edit-warring it has not been subject to much peer review. A Bitcoin full node could be modified to scale to much higher transaction rates than are seen today, assuming that said node is running on a high end servers rather than a desktop. Bitcoin was designed to support lightweight clients that only process sm
mogwaing 2016/05/28
bitcoin

scalability
ãƒªãƒ³ã‚¯
[Japanese] White Paper
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mogwaing 2016/05/22
blockchain

ethereum
ãƒªãƒ³ã‚¯
Cassandra introduction apache con 2014 budapest
mogwaing 2016/05/18
cassandra
ãƒªãƒ³ã‚¯
ç¬¬20å›žã€€Sparkã®è¨è¨ˆã¨å®Ÿè£…ï¼»1ï¼½ï½žç™»å ´ã®èƒŒæ™¯ã¨ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®ç‰¹å¾´ | gihyo.jp
ã¯ã˜ã‚ã« ä»Šå›žã‹ã‚‰2å›žã«æ¸¡ã£ã¦ã€ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã®ã²ã¨ã¤ã§ã‚ã‚‹Sparkã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ã¾ãšã¯ã˜ã‚ã«ã€Sparkã®é–‹ç™ºãŒå§‹ã‚ã‚‰ã‚ŒãŸçµŒç·¯ã‚’ç´¹ä»‹ã—ã€æ¬¡ã«Sparkã®ç‰¹å¾´ã‚’èª¬æ˜Žã—ã¾ã™ã€‚ SparkãŒç™»å ´ã—ãŸèƒŒæ™¯ Sparkã¯ã€Hadoop MapReduceã¨åŒæ§˜ã«ã€è¤‡æ•°ã®è¨ˆç®—æ©Ÿã‚’ç”¨ã„ã¦ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚’è¡Œã†ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã§ã™ã€‚2009å¹´ã«ã€ã‚«ãƒªãƒ•ã‚©ãƒ«ãƒ‹ã‚¢å¤§å¦ãƒãƒ¼ã‚¯ãƒ¬ãƒ¼æ ¡ã®AMPLabã«ã¦ã€Matei Zahariaæ°ã‚’ä¸å¿ƒã¨ã—ã¦é–‹ç™ºãŒå§‹ã¾ã‚Šã¾ã—ãŸã€‚Sparkã®é–‹ç™ºãŒå§‹ã¾ã£ãŸå½“æ™‚ã€ä¸–ã®ä¸ã«ã¯ã™ã§ã«HadoopãŒå˜åœ¨ã—ã¦ãŠã‚Šã€é«˜ã„è€éšœå®³æ€§ã‚’æœ‰ã—ã‹ã¤ã‚¹ã‚±ãƒ¼ãƒ©ãƒ–ãƒ«ãªä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚’ã€ã‚³ãƒ¢ãƒ‡ã‚£ãƒ†ã‚£ãªè¨ˆç®—æ©Ÿã‚’ç”¨ã„ã¦è¡Œã†ã“ã¨ã¯ä¸€èˆ¬çš„ã«ãªã‚Šã¤ã¤ã‚ã‚Šã¾ã—ãŸã€‚ã—ã‹ã—ã€Hadoop MapReduceã¯å¿…ãšã—ã‚‚å€‹ã€…ã®è¨ˆç®—æ©Ÿã®ãƒ¡ãƒ¢ãƒªã‚’åŠ¹çŽ‡çš„ã«æ´»ç”¨ã™ã‚‹è¨è¨ˆã§ã¯ã‚ã‚Šã¾ã›ã‚“ã§ã—ãŸã€‚ Hadoop MapReduceã¯ã€ã‚¸ãƒ§
mogwaing 2016/05/11
spark

hadoop

parallel processing

distributed system
ãƒªãƒ³ã‚¯
Vitess | Overview
Pages in this sectionWhat Is VitessArchitectureSupported DatabasesScalability PhilosophyCloud NativeHistoryThe Vitess overview documentation provides general information about Vitess that's less immediately practical than what you'll find in Get Started section and the User Guides.
mogwaing 2016/05/11
mysql

distributed system

transaction
ãƒªãƒ³ã‚¯
Scaling Raft
In CockroachDB, we use the Raft consensus algorithm to ensure that your data rem ains consistent even when machines fail. In most systems that use Raft, such as etcd and Consul, the entire system is one Raft consensus group. In CockroachDB, however, the data is divided into ranges, each with its own consensus group. This means that each node may be participating in hundreds of thousands of consensu
mogwaing 2016/04/26
raft

consensus

distributed system
ãƒªãƒ³ã‚¯
ç¬¬19å›žã€€Impalaã®è¨è¨ˆã¨å®Ÿè£…ï¼»3ï¼½ | gihyo.jp
ã¯ã˜ã‚ã« ä»Šå›žã¯ã€Impalaã«ãŠã‘ã‚‹I/Oã®é«˜é€ŸåŒ–æŠ€æ³•ã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ã€‚ å‰å›žèª¬æ˜Žã—ãŸã‚ˆã†ã«ã€Impalaã®å®Ÿè¡Œã‚¨ãƒ³ã‚¸ãƒ³ã¯å¯èƒ½ãªé™ã‚Šãƒ¡ãƒ¢ãƒªä¸Šã§å‡¦ç†ã‚’ã™ã‚‹ã“ã¨ã§ã‚¢ãƒ‰ãƒ›ãƒƒã‚¯ã‚¯ã‚¨ãƒªã®ãƒ¬ã‚¤ãƒ†ãƒ³ã‚·ã‚’ä¸‹ã’ã€ã‚¹ãƒ«ãƒ¼ãƒ—ãƒƒãƒˆã‚’å‘ä¸Šã•ã›ã‚‹ã€ã¨ã„ã†è¨è¨ˆæ–¹é‡ã§é–‹ç™ºã•ã‚Œã¦ã„ã¾ã™ã€‚ ã—ã‹ã—ã€ãƒ‡ãƒ¼ã‚¿ã¯ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ï¼ˆäºŒæ¬¡è¨˜æ†¶è£…ç½®ï¼‰ã«æ ¼ç´ã•ã‚Œã¦ã„ã‚‹ãŸã‚ã€å½“ç„¶ã€ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã¸ã®I/Oã‚’å›žé¿ã™ã‚‹ã“ã¨ã¯ã§ãã¾ã›ã‚“ã€‚ã¾ãŸã€Impalaã¯å®Ÿè¡Œæ™‚ã«ååˆ†ãªãƒ¡ãƒ¢ãƒªã‚’ç¢ºä¿ã™ã‚‹ã¹ãã€ãƒ‡ãƒ¼ã‚¿ã‚’ãƒ¡ãƒ¢ãƒªä¸Šã«ä¿æŒï¼ˆã‚ãƒ£ãƒƒã‚·ãƒ¥ï¼‰ã—ãªã„ãŸã‚ã€ã‚¯ã‚¨ãƒªã‚’å®Ÿè¡Œã™ã‚‹ãŸã³ã«ãƒ‡ãƒ¼ã‚¿ã‚’ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã‹ã‚‰èªã¿å‡ºã™ã“ã¨ã‚’å‰æã¨ã—ã¦è¨è¨ˆã•ã‚Œã¦ã„ã¾ã™[1]â ã€‚ ä»Šå›žã¯ã€ã“ã®ã‚ˆã†ãªãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã‚’è€ƒæ…®ã—ã¤ã¤ã€é«˜é€Ÿãƒ»é«˜åŠ¹çŽ‡ãªã‚¢ãƒ‰ãƒ›ãƒƒã‚¯ã‚¯ã‚¨ãƒªã‚’å®Ÿè¡Œã™ã‚‹ãŸã‚ã®I/Oå‡¦ç†æ–¹å¼ã¨ãƒ‡ãƒ¼ã‚¿ãƒ¬ã‚¤ã‚¢ã‚¦ãƒˆã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ Short-Circ uit Local Readsã«ã‚ˆã‚‹I/Oã®é«˜åŠ¹çŽ‡åŒ– Sh
mogwaing 2016/04/13
impala

hadoop

parallel processing

parallel db
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ç¬¬52å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬( #TokyoWebmining 52nd )ãƒ¼æ©Ÿæ¢°å¦ç¿’æ´»ç”¨ ç¥ã‚Šãƒ¼ã‚’é–‹å‚¬ã—ã¾ã—ãŸ - hamadakoichi blog
2016/3/26 "ç¬¬52å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬( #TokyoWebmining 52nd ) ãƒ¼æ©Ÿæ¢°å¦ç¿’æ´»ç”¨ ç¥ã‚Šãƒ¼" ã‚’é–‹å‚¬ã—ã¾ã—ãŸã€‚ ç¬¬52å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬( #TokyoWebmining 52nd ) ãƒ¼æ©Ÿæ¢°å¦ç¿’æ´»ç”¨ ç¥ã‚Šãƒ¼Eventbrite Google ã‚°ãƒ«ãƒ¼ãƒ— ä¼šå ´æä¾›ã—ã¦ä¸‹ã•ã£ãŸFreakOutã•ã‚“ã€ã©ã†ã‚‚ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã—ãŸã€‚ç´ æ•µãªãƒˆãƒ¼ã‚¯ã‚’æä¾›ã—ã¦ãã‚ŒãŸè¬›å¸«ãƒ¡ãƒ³ãƒãƒ¼ã«æ„Ÿè¬ã—ã¾ã™ã€‚å¤šãã®æ–¹ã€…ã®å‚åŠ ã‚’å¬‰ã—ãæ€ã£ã¦ã„ã¾ã™ã€‚ å‚åŠ è€…IDãƒ»ãƒãƒƒã‚¯ã‚°ãƒ©ã‚¦ãƒ³ãƒ‰ä¸€è¦§ å‚åŠ è€…ã‚»ã‚ã‚³ã‚³ï¼šç¬¬52å›ž ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB ï¼ æ±äº¬ ã‚»ã‚ã‚³ã‚³ (ä½œæˆã—ã¦ãã‚ŒãŸ [Twitter:@komiya_atsushi] ã•ã‚“ã«æ„Ÿè¬ï¼‰ ä»¥ä¸‹ã€å…¨è¬›å¸«è³‡æ–™ã€é–¢é€£è³‡æ–™ã€ãƒ„ã‚¤ãƒ¼ãƒˆã¾ã¨ã‚ã§ã™ã€‚ AGENDAï¼š â– Opening Talkï¼š O1.ã€Œãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°+WEB
mogwaing 2016/03/27
machine learning

data mining
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ç¬¬18å›žã€€Impalaã®è¨è¨ˆã¨å®Ÿè£…ï¼»2ï¼½ | gihyo.jp
ã¯ã˜ã‚ã« ä»Šå›žã¯ã€Impalaã®SQLå‡¦ç†ã®é«˜é€ŸåŒ–ã«ãŠã„ã¦é‡è¦ãªå½¹å‰²ã‚’å ã‚ã‚‹ã‚¯ã‚¨ãƒªå‡¦ç†ã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ã€‚ Impalaã®ã‚¯ã‚¨ãƒªå‡¦ç†ã®ç‰¹å¾´ Impalaã¯ã€MapReduceã‚„Sparkã‚’ã¯ã˜ã‚ã¨ã™ã‚‹æ—¢å˜ã®æ‰‹ç¶šãåž‹ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚¨ãƒ³ã‚¸ãƒ³ã‚’ä½¿ç”¨ã›ãšã€ã‚¢ãƒ‰ãƒ›ãƒƒã‚¯ãªSQLã‚¯ã‚¨ãƒªã®å‡¦ç†ã®é«˜åŠ¹çŽ‡åŒ–ã«ç„¦ç‚¹ã‚’ç½®ã„ãŸè¨è¨ˆã¨å®Ÿè£…ãŒç‰¹å¾´ã§ã™ã€‚ãŸã¨ãˆã°ã€çµåˆæ–¹æ³•ã‚’è¦‹ã¦ã¿ã‚‹ã¨ã€Mapã‚„Reduceã‚‚ã—ãã¯MapReduceã‚¸ãƒ§ãƒ–ãªã©ã®ãƒ–ãƒãƒƒã‚ãƒ³ã‚°ã‚ªãƒšãƒ¬ãƒ¼ã‚¿ï¼ˆç¬¬16å›žï¼‰ã‚’çµ„ã¿åˆã‚ã›ã¦ã„ãå‡¦ç†ã‚¨ãƒ³ã‚¸ãƒ³ã«ãŠã„ã¦ã¯ã€Impalaã«ãŠã‘ã‚‹ãƒ‘ã‚¤ãƒ—ãƒ©ã‚¤ãƒ³çµåˆå‡¦ç†ãªã©ã‚’å®Ÿç¾ã™ã‚‹ã“ã¨ã¯å¿…ãšã—ã‚‚å®¹æ˜“ã§ã¯ã‚ã‚Šã¾ã›ã‚“ï¼ˆç¬¬8å›žã€ŒImpalaï¼Prestoã«ãŠã‘ã‚‹çµåˆå‡¦ç†ã€â ï¼‰â ã€‚ ã¾ãŸã€MapReduceã‚„Sparkã§ã¯ä¸é–“ãƒ‡ãƒ¼ã‚¿ã‚’ãƒ‡ã‚£ã‚¹ã‚¯ã«æ›¸ãè¾¼ã‚€ã“ã¨ã«ã‚ˆã‚Šé«˜ã„è€éšœå®³æ€§ã‚’å®Ÿç¾ã—ã¾ã™ãŒã€Impalaã§ã¯è€éšœå®³æ€§ã‚’å¤šå°‘çŠ ç‰²ã«ã—ã¦ãƒ¡ãƒ¢ãƒªä¸Šã§å‡¦ç†
mogwaing 2016/03/23
impala

parallel processing

hadoop
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How to recommend top 10 products in Spark ALS for all the users?
How can we get top 10 recommended products in PySpark. I understand there are methods like recommendProducts to recommend products for a single user and predictAll to predict rating for the {user,it em} pair. But is there a efficient way i can output the top 10 it ems for each user for all the users?
mogwaing 2016/03/21
spark

mllib
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26 Trillion App Recomendations using 100 Lines of Spark Code - Ayman Farahat
mogwaing 2016/03/21
spark

mllib
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PySpark: Top N Records In Each Group
mogwaing 2016/03/16
spark

tips
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