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InoHiro 2018/10/18
cookpad

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StreamProcessing
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åˆ†æ•£ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ã‚¨ãƒ³ã‚¸ãƒ³ã‚ã‚Œã“ã‚Œ - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? #åˆ†æ•£ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ã‚¨ãƒ³ã‚¸ãƒ³ã®ç¾¤é›„å‰²æ‹ ã®æ™‚ä»£ ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ã‚’å®Ÿç¾ã™ã‚‹åˆ†æ•£ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ãŒã€å¤§åˆ†å¢—ãˆã¾ã—ãŸã€‚ ä½•ãŒè‰¯ã„ã‹ï¼æ‚ªã„ã‹ã¯ã€ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ã«æ±‚ã‚ã‚‹å†…å®¹ã‚„ã€é–¢é€£ã™ã‚‹ã‚¨ã‚³ã‚·ã‚¹ãƒ†ãƒ ã«ã‚‚å½±éŸ¿ã™ã‚‹ãŸã‚ã€ä¸€æ¦‚ã«ã¯è¨€ãˆãªã„ã§ã™ãŒã€Apacheã§æä¾›ã•ã‚Œã¦ã„ã‚‹OSSã¨ã—ã¦ã€æƒ…å ±ã‚’ã¾ã¨ã‚ãŸã‚‚ã®ãŒã‚ã£ãŸã®ã§ã€ãƒã‚¤ãƒ³ãƒˆã‚’ã¾ã¨ã‚ãŸã„ã¨æ€ã„ã¾ã™ã€‚ AN OVERVIEW OF APACHE STREAMING TECHNO LOGIES https://database line.wordpress.com/2016/03/12/an-overview-
InoHiro 2017/11/04
StreamProcessing
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Apache Flink ã¨ã¯ï¼Ÿ - Qiita
ã€ŒApache Flinkã€ã¯ã€åˆ†æ•£ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ã®ã²ã¨ã¤ã®OSSã§ã™ã€‚ åŒé¡žã®ãƒ¢ãƒŽã¨ã—ã¦ã¯ã€ã€ŒApache Stormã€ã‚„ã€ŒApache Spark Streamingã€ãªã©ãŒã‚ã‚Šã¾ã™ï¼ˆåŒã˜Apacheã§ã€ã•ã™ãŒã«åŒé¡žã®ãƒ¢ãƒŽã‚’å‡ºã—éŽãŽã ã‚ã†ã€ã¨ç§ã‚‚æ€ã£ã¦ã„ã¾ã™ï¼‰ã€‚ ä»–ã®ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ã¨ã—ã¦ç•°ãªã‚‹ç‰¹é•·ã¯ã€ä»¥ä¸‹ã«ãªã‚Šã¾ã™ã€‚ é«˜ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ï¼†ä½Žãƒ¬ã‚¤ãƒ†ãƒ³ã‚·ï¼ˆçœŸã®ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ãŒå¯èƒ½ï¼‰ è€éšœå®³æ€§ã«å„ªã‚Œã‚‹ï¼ˆè‡ªå‹•ã§ã‚¤ãƒ™ãƒ³ãƒˆå‡¦ç†ç¶™ç¶šï¼‰ ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ã€ãƒãƒƒãƒå‡¦ç†ã®ä¸¡æ–¹ã‚’ã‚µãƒãƒ¼ãƒˆã—ã€é«˜ãƒ¬ãƒ™ãƒ«ã®APIãŒæä¾›ã•ã‚Œã‚‹All-in-Oneæ§‹æˆ Flink Stack Flinkã®å…¨ä½“åƒã§ã™ã€‚ ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ã ã‘ã§ãªãã€ãƒãƒƒãƒå‡¦ç†ã‚‚å¯èƒ½ã§ã‚ã‚Šã€CEPï¼ˆè¤‡åˆã‚¤ãƒ™ãƒ³ãƒˆå‡¦ç†ï¼‰ã‚„MLï¼ˆæ©Ÿæ¢°å¦ç¿’ï¼‰ã€SQLãƒ©ã‚¤ã‚¯ãªAPIã‚‚æä¾›ã—ã¦ã„ã¾ã™ã€‚ ã“ã®è¾ºã‚Šã¯ã€Sparkã¨ä¼¼ã¦ã„ã¾ã™ãã€‚ãŸã ã€Sparkã¯ãƒãƒƒãƒå‡¦ç†ã‹
InoHiro 2017/09/29
Apache-Flink

Flink

StreamProcessing
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Apache Kafkaã«å¯¾å¿œã—ãŸSQLã‚¨ãƒ³ã‚¸ãƒ³ã€ŒKSQLã€ãƒªãƒªãƒ¼ã‚¹ã€ã‚¹ãƒˆãƒªãƒ¼ãƒ ãƒ‡ãƒ¼ã‚¿ã‚’SQLã§ç¶™ç¶šçš„ã«å‡¦ç†ã€‚ConfluentãŒã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã§
Apache Kafkaã‚’ç”¨ã„ãŸãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®å•†ç”¨ã‚µãƒ¼ãƒ“ã‚¹ã‚’æä¾›ã—ã¦ã„ã‚‹Confluentã¯ã€Apache Kafkaã«å¯¾å¿œã—ãŸã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®SQLã‚¨ãƒ³ã‚¸ãƒ³ã€ŒKSQLã€ã®ãƒ‡ãƒ™ãƒãƒƒãƒ‘ãƒ¼ãƒ—ãƒ¬ãƒ“ãƒ¥ãƒ¼ã‚’ãƒªãƒªãƒ¼ã‚¹ã—ã¾ã—ãŸã€‚ Apache Kafkaã¯ã‚¹ã‚±ãƒ¼ãƒ©ãƒ“ãƒªãƒ†ã‚£ã«å„ªã‚Œã€å¤§é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã«å‡¦ç†ã§ãã‚‹ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã™ã€‚ã•ã¾ã–ã¾ãªã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã‹ã‚‰é€ã‚‰ã‚Œã¦ãã‚‹ãƒã‚°ã‚„å¤§é‡ã®ã‚»ãƒ³ã‚µãƒ¼ãªã©ã‹ã‚‰ç”Ÿæˆã•ã‚Œã‚‹ãƒ‡ãƒ¼ã‚¿ãªã©ã€ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã«é€ä¿¡ã•ã‚Œã¦ãã‚‹ã‚¹ãƒˆãƒªãƒ¼ãƒ ãƒ‡ãƒ¼ã‚¿ã‚’ã„ã£ãŸã‚“Kafkaã§å—ã‘æ¢ã‚ã€ãã‚Œã‚’ã¾ã¨ã‚ã¦Hadoopãªã©ã®åˆ†æžã‚¨ãƒ³ã‚¸ãƒ³ã«æ¸¡ã—ã¦ãƒ‡ãƒ¼ã‚¿ã®åˆ†æžã‚’è¡Œã†ã€ã¨ã„ã£ãŸå½¢ã§ä½¿ã‚ã‚Œã¾ã™ã€‚ KSQLã¯ã€ã“ã®Kafkaã«å¯¾å¿œã—ãŸSQLã‚¨ãƒ³ã‚¸ãƒ³ã€‚ã“ã‚Œã«ã‚ˆã‚ŠJavaã‚„Pythonã§è¤‡é›‘ãªå‡¦ç†ã‚’è¨˜è¿°ã—ãªãã¨ã‚‚ã€ã‚¹ãƒˆãƒªãƒ¼ãƒ ãƒ‡ãƒ¼ã‚¿ã‚’æŸ”è»Ÿã«åŠ å·¥ã§ãã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã™ã€‚ SQLã¨KSQLã®é•ã„ã¨ã¯ï¼Ÿ ä¸€èˆ¬çš„
InoHiro 2017/09/08
sql

Kafka

StreamProcessing
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Introducing KSQL: Streaming SQL for Apache Kafka | Confluent
What does it even mean to query streaming data, and how does this compare to a SQL database? Well, itâ€™s actually quite different to a SQL database. Most databases are used for doing on-demand lookups and modifications to stored data. KSQL doesnâ€™t do lookups (yet), what it does do is continuous transf ormationsâ€” that is, stream processing. For example, imagine that I have a stream of clicks from use
InoHiro 2017/08/29
Kafka

StreamProcessing

sql
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ã€æ©Ÿæ¢°å¦ç¿’ã‚¢ãƒ‰ãƒ™ãƒ³ãƒˆã‚«ãƒ¬ãƒ³ãƒ€ãƒ¼2015 8æ—¥ç›®ã€‘ ã‚¹ãƒˆãƒªãƒ¼ãƒ ãƒ‡ãƒ¼ã‚¿è§£æž ã¨ã„ã†åˆ†é‡ŽãŒã‚ã‚‹ã€‚ã‚ã‚‹ç”Ÿæˆå…ƒã‹ã‚‰çµ¶ãˆãšãƒ‡ãƒ¼ã‚¿ãŒåˆ°æ¥ã™ã‚‹ç’°å¢ƒã§ã€ã„ã‹ã«ãã‚Œã‚‰ã‚’æŒãã‹ã¨ã„ã†è©±ã€‚ã€Œæ™‚é–“è¨ˆç®—é‡ã¯ã»ã¼ç·šå½¢ã§ã‚ã£ã¦æ¬²ã—ã„ã—ã€ç©ºé–“è¨ˆç®—é‡ã‚‚å°ã•ãæŠ‘ãˆã¤ã¤ç²¾åº¦ã‚’æ‹…ä¿ã—ãŸã„ã‚ˆãã€ã¨ã„ã†ã“ã¨ã‚’è€ƒãˆã‚‹ä¸–ç•Œã€‚å€‹äººçš„ã«æœ€è¿‘ã¯ãã®ã‚ãŸã‚Šã®æƒ…å ±ã‚’è¿½ã„ã‹ã‘ã¦ã„ãŸã®ã§ã€æ•´ç†ã‚‚å…¼ãã¦ãã®ä¸–ç•Œã‚’ä¿¯çž°ã—ãŸã„ã€‚ ã™ã”ã„ãƒªãƒ³ã‚¯é›† ã¯ã˜ã‚ã«ã€ã“ã®åˆ†é‡Žã§å¤–ã›ãªã„ã¨æ€ã†ãƒªãƒ³ã‚¯ã‚’3ã¤æŒ™ã’ã¦ãŠãã€‚ â– SML: Data Streams Yahooã‚„Googleã®ç ”ç©¶æ‰€ã‚’çµŒã¦CMUã®æ•™æŽˆã‚’ã—ã¦ã„ã‚‹Alex Smolaå…ˆç”Ÿã®è¬›ç¾©ã®ä¸€éƒ¨ï¼ˆã‚¹ãƒ©ã‚¤ãƒ‰ï¼‹å‹•ç”»ã‚ã‚Šï¼‰ã€‚ç†è«–ã‹ã‚‰ã‚·ã‚¹ãƒ†ãƒ ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã¾ã§åŒ…æ‹¬ã—ãŸå®Ÿéš›çš„ãªæ©Ÿæ¢°å¦ç¿’ãªã‚‰ã“ã®äººã€‚ã“ã®äººã®æ©Ÿæ¢°å¦ç¿’ã‚µãƒžãƒ¼ã‚¹ã‚¯ãƒ¼ãƒ«ã®è¬›ç¾©ã¯æœ€é«˜ã ã£ãŸã€‚ å¤å…¸çš„ãªã‚‚ã®ã‹ã‚‰æœ€è¿‘ã®ã‚‚ã®ã¾ã§ã€ä»£è¡¨çš„ãªã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã«ã¤ã„ã¦ç›´æ„Ÿçš„ãªèª¬æ˜Žã¨ã„ã„
InoHiro 2016/02/03
StreamProcessing

StreamData
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Andrew BrustÂ ï¼ˆSpecial to ZDNET.comï¼‰Â ç¿»è¨³æ ¡æ£ï¼šÂ çŸ³æ©‹å•“ä¸€éƒŽ 2015-09-25 06:30 èªè€…ã®ä¸ã«ã¯ã€Apache Software Foundationã®ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆãƒªã‚¹ãƒˆã‚’è¦‹ã¦ã€æ°—ãŒé ããªã£ãŸäººã‚‚ã„ã‚‹ã®ã§ã¯ãªã„ã ã‚ã†ã‹ã€‚ã“ã®ãƒªã‚¹ãƒˆã«ã¯ãƒˆãƒƒãƒ—ãƒ¬ãƒ™ãƒ«ã®é‡è¦ãªãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã‹ã‚‰ã€ç«‹ã¡ä¸ŠãŒã£ãŸã°ã‹ã‚Šã®ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã¾ã§ã€æ•°å¤šãã®ã‚‚ã®ãŒä¸¦ã‚“ã§ã„ã‚‹ãŒã€ã™ã¹ã¦ã‚’æŠŠæ¡ã™ã‚‹ã«ã¯æ•°ãŒå¤šã™ãŽã‚‹ã€‚ãƒªã‚¹ãƒˆã‚’ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿é–¢é€£ã®ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã«é™ã£ã¦ã•ãˆã€ãã®æ•°ã¯ã‹ãªã‚Šå¤šã„ã€‚ãã®ä¸Šã€Apache Software Foundationã®å‚˜ä¸‹ã«ã¯ãªã„ãŒã€æŠŠæ¡ã—ã¦ãŠãã¹ããƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã‚‚ã‚ã‚‹ã€‚ é€”æ–¹ã«æš®ã‚Œã‚‹èªè€…ã®ãŸã‚ã«ã€ã“ã®è¨˜äº‹ã§ã¯ã€æŠŠæ¡ã—ã¦ãŠãä¾¡å€¤ã®ã‚ã‚‹5ã¤ã®ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã‚’ç´¹ä»‹ã™ã‚‹ã€‚ Apache Flink ã€ŒFlinkã€ã¯ã€è€éšœå®³æ€§ã®ã‚ã‚‹ã€Œexactly onceã€å“è³ªã®ãƒ‡ãƒ¼ã‚¿ã‚¹
InoHiro 2015/09/26
ã‚¹ãƒˆãƒªãƒ¼ãƒ å‡¦ç†ç³»ãŒã©ã‚“ã©ã‚“å‡ºã¦ãã‚‹ãª

StreamProcessing
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PipelineDBâ€”The Streaming SQL Database
Mencari slot gacor hari ini? Anda berada di tempat yang tepat! Dalam dunia perjudian online, istilah "gacor" sering digunakan untuk menggambarkan slot yang sedang "panas" atau memiliki peluang tinggi untuk memberikan kemenangan besar kepada pem ain. Dalam artikel panjang ini, kita akan membahas segala sesuatu tentang slot gacor: cara menemukannya, strategi untuk bermain, dan tentu saja, rekomendasi
InoHiro 2015/09/09
PipelineDB

StreamProcessing
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PipelineDBã‚’Productionã«å°Žå…¥ã—ã¦ã¿ã¾ã—ãŸã€‚ How SmartNews Utilizes PipelineDB - SmartNews Engineering Blog
ENGLISH FOLLOWS BELOW ã“ã‚“ã«ã¡ã¯ã€ãŸã‚€ãŸã‚€(@tamtam180)ã§ã™ã€‚ SmartNewsã§ã¯ä¸»ã«åºƒå‘Šã®é…ä¿¡ã‚µãƒ¼ãƒã‚’æ›¸ã„ã¦ã„ã¾ã™ã€‚ æœ€è¿‘ã€Pipeline DBã¨ã„ã†Continuous Queryã‚’ã‚µãƒãƒ¼ãƒˆã™ã‚‹è£½å“ã‚’å°Žå…¥ã—ãŸã®ã§ãã®ãƒ¬ãƒãƒ¼ãƒˆã‚’æ›¸ãã¾ã™ã€‚ ãªãŠã€ã‚»ãƒƒãƒˆã‚¢ãƒƒãƒ—æ–¹æ³•ã¯æœ¬å®¶ã®ã‚µã‚¤ãƒˆã‚’è¦‹ã‚Œã°èª°ã§ã‚‚å‡ºæ¥ã‚‹ã®ã§ã€ãã®ã‚ãŸã‚Šã¯å‰²æ„›ã—ã¾ã™ã€‚ ç«¶åˆã«ã‚ãŸã‚‹è£½å“ã¨ã—ã¦ã¯ã€Norikraã‚„Azureã®Stream AnalyticsãŒã‚ã‚Šã€æ—¥æœ¬ã®ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ç•Œéšˆã§ã¯Norikraã‚’å¥½ã‚“ã§ä½¿ç”¨ã—ã¦ã„ã‚‹ã¨æ€ã„ã¾ã™ã€‚ ç§ãŒPipeline DBã‚’å°Žå…¥ã—ãŸç†ç”±ã¯ã€Pipeline DBãŒPostgreSQL(9.4)ã‚’ãƒ™ãƒ¼ã‚¹ã«ã—ã¦ã„ã‚‹ãŸã‚ã€å¼Šç¤¾ãŒå°Žå…¥ã—ã¦ã„ã‚‹ãƒ€ãƒƒã‚·ãƒ¥ãƒœãƒ¼ãƒ‰ãƒ„ãƒ¼ãƒ«ã€Chartioã¨ã®ç›¸æ€§ãŒè‰¯ã‹ã£ãŸã‹ã‚‰ã§ã™ã€‚ Pipeline DBã¨ã¯ Pipeline DBã¯ã€PostgreS
InoHiro 2015/09/09
PipelineDB

StreamProcessing
ãƒªãƒ³ã‚¯
Apache FlinkÂ® â€” Stateful Computations over Data Streams
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
InoHiro 2015/06/12
Flink

Apache-Flink

StreamProcessing
ãƒªãƒ³ã‚¯
Difference bloom filters and FM-sketches
InoHiro 2014/07/15
algorithm

StreamProcessing
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Charming Python: Functional programming in Python, Part 3
IBM Developer is your one-stop location for getting hands-on training and learning in-demand skills on relevant techno logies such as generative AI, data science, AI, and open source.
InoHiro 2014/06/10
developer_works

StreamProcessing
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GitHub - streamreasoning/CSPARQL-engine: reasoning over RDF stream made easy
InoHiro 2014/03/04
SPARQL

C-SPARQL

CEP

StreamProcessing
ãƒªãƒ³ã‚¯
Streaming Distributed Data Processing with Silk #deim2014
Silk is a framework for building and running complex workflows of distributed data processing. It allows describing dataflows in Scala in a type safe and concise syntax. Silk translates Scala programs into logical plans and schedules the distributed execution through various "weavers" like an in-memory weaver or Hadoop weaver. It performs static and run-time optimizations of dataflows and supports
InoHiro 2014/03/03
deim2014

StreamProcessing
ãƒªãƒ³ã‚¯
Norikra in Action (ver. 2014 spring)
Wprowadzenie do techno logii Big Data / Intro to Big Data Ecosystem
InoHiro 2014/01/31
StreamProcessing

CEP
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Stream(SQL) Event Processing with Esper - igvita.com
By Ilya Grigorik on May 27, 2011 The growth of both the types and the amount of data generated by servers, users, and applications have resulted in a number of recent trends and innovations: NoSQL, rise of popularity of Hadoop, and dozens of higher-level map-reduce frameworks. However, the batch-processing model imposed by map-reduce style of processing is not always a great fit either, especially
InoHiro 2014/01/31
StreamData

StreamProcessing
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Esper - Complex Event Processing
InoHiro 2014/01/31
Esper for .NET

StreamProcessing

.net
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Esper
Where Complex Event Processing meets Open Source: Esper & NEsper Make no compromise on enterprise readiness: discover EsperHA, EsperJDBC and our support services. Embed mainstream CEP in your products and deployments: Learn about our licensing options. Event Stream Intelligence with Esper and NEsper Esper is a component for CEP and ESP applications, available for Java as Esper, and for .NET as
InoHiro 2014/01/31
CEP

StreamProcessing
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