[Blog] New in Confluent Cloud: Queues for Kafka, new migration tooling, & more | Read Now
Database Streaming with ksqlDB | Confluent
[Blog] New in Confluent Cloud: Queues for Kafka, new migration tooling, & more | Read Now
ヤフー発ã®ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ã‚ュー「Pulsarã€ã®ã”紹介
Pulsarã¯ã‚¢ãƒ¡ãƒªã‚«ã®Yahoo! Inc. ã«ã‚ˆã£ã¦é–‹ç™ºã•ã‚Œã€ç¾åœ¨ã¯Apache Software Foundationã«ç§»ç®¡ã•ã‚ŒãŸã‚ªãƒ¼ãƒ—ンソースã®ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ã‚ューã§ã™ã€‚ 日本ã®ãƒ¤ãƒ•ãƒ¼ã§ã‚‚利用ã—ã¦ã„ã‚‹ã“ã®Pulsarã®æ©Ÿèƒ½ã‚„特徴をã”紹介ã—ã¾ã™ã€‚
Kafka
本書ã§ã¯ã€ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã®ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†åŸºç›¤ã§ã‚ã‚‹Kafkaを本番環境ã§å‹•ã‹ã—ã€ãã®ä¸Šã«å …牢ã§é«˜æ€§èƒ½ãªã‚·ã‚¹ãƒ†ãƒ を構築ã™ã‚‹ãŸã‚ã«å¿…è¦ãªæ‰‹é †ã‚’解説ã—ã¾ã™ã€‚ Kafkaã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã‚„è¨å®šæ–¹æ³•ã€Kafka APIを使ã£ãŸé–‹ç™ºã‹ã‚‰ã€ãƒ‡ãƒ¼ã‚¿ãƒ‘イプラインã®æ§‹ç¯‰ã€Kafkaã®ç®¡ç†ã‚„モニタリングã¾ã§ã€Kafkaã‚’å°Žå…¥ã™ã‚‹éš›ã®ä¸€é€£ã®æµã‚Œã‚’豊富ãªã‚µãƒ³ãƒ—ルコードを使ã£ã¦è§£èª¬ã—ã¾ã™ã€‚ã¾ãŸã‚¤ãƒ™ãƒ³ãƒˆé§†å‹•ãƒžã‚¤ã‚¯ãƒã‚µãƒ¼ãƒ“スã®ãŸã‚ã®ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒã‚¹ã‚„ストリーム処ç†ã‚¢ãƒ—リケーションã€å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ãƒ‘イプラインãªã©ã®ãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã‚’詳述。Kafkaã®ãƒ¬ãƒ—リケーションやストレージレイヤãªã©ã€ã‚¢ãƒ¼ã‚テクãƒãƒ£ã¨å†…éƒ¨æ§‹é€ ã«ã¤ã„ã¦è§£èª¬ã—ã¦ã„ã‚‹ãŸã‚ã€Kafkaã®ä»•çµ„ã¿ã‚’ç†è§£ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ Kafkaã®é–‹ç™ºè€…ãŸã¡ãŒåŸ·ç†ã—ãŸæœ¬æ›¸ã¯ã€ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†ã«ã¤ã„ã¦å¦ã³ãŸã„エンジニア必æºã®ä¸€å†Šã§ã™ã€‚ 目　次 監訳者ã¾ãˆãŒã åºæ–‡ ã¯ã˜ã‚㫠本書ã®å¯¾è±¡èªè€…
Apache Pulsar
Pulsar is proven at scale by hundreds of companies of different sizes, serving millions of messages per second. See case studies What is PulsarApache Pulsar is an all-in-one messaging and streaming platform. Messages can be consumed and acknowledged individually or consumed as streams with less than 10ms of latency. Its layered architecture allows rapid scaling across hundreds of nodes, without da
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終ã‚ã‚Šãªã増ãˆã¦ã„ãストリームデータ特化ã®åˆ†æ•£ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã€ŒPravegaã€ã€EMCãŒã‚ªãƒ¼ãƒ—ンソースã§å…¬é–‹ã€‚ã“ã‚Œã¾ã§ã®ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã¨ã©ã†é•ã†ï¼Ÿï¼»PRï¼½ ファイルã¸ã®ä¿å˜ã‚„データベースã¸ã®æ ¼ç´ã¨ã„ã£ãŸã€ã“ã‚Œã¾ã§è¦ªã—ã¾ã‚Œã¦ããŸæ–¹æ³•ã§ã¯æ‰±ã„ã«ãã„ã€æ–°ã—ã„å½¢å¼ã®ãƒ‡ãƒ¼ã‚¿ãŒå˜åœ¨æ„Ÿã‚’高ã‚ã¤ã¤ã‚ã‚Šã¾ã™ã€‚ ãã‚Œã¯ç¶™ç¶šçš„ã«å¤§é‡ã®ãƒ‡ãƒ¼ã‚¿ãŒæµã‚Œè¾¼ã‚“ã§ãる「ストリームデータã€ã§ã™ã€‚ 例ãˆã°ã€ã‚·ã‚¹ãƒ†ãƒ 内ã®ã•ã¾ã–ã¾ãªã‚¢ãƒ—リケーションやサーãƒãŒç”Ÿæˆã™ã‚‹ãƒã‚°ã€ã‚½ãƒ¼ã‚·ãƒ£ãƒ«ãƒ¡ãƒ‡ã‚£ã‚¢ã‹ã‚‰æµã‚Œã¦ãる利用者ã®å£°ã‚„自社製å“ã®è©•åˆ¤ã€ã‚ã‚‹ã„ã¯IoTを活用ã—ãŸã‚·ã‚¹ãƒ†ãƒ ã§ã¯ã€å·¥å ´å†…やオフィスã€å·¥ä½œæ©Ÿæ¢°ã‚„自動車ãªã©ã®æ©Ÿå™¨ã«çµ„ã¿è¾¼ã¾ã‚ŒãŸå¤šæ•°ã®ã‚»ãƒ³ã‚µãƒ¼ã‹ã‚‰å¤§é‡ã«é€ã‚‰ã‚Œã¦ãるリアルタイムデータãªã©ãŒãã‚Œã«ã‚ãŸã‚Šã¾ã™ã€‚ Pravega：終ã‚ã‚ŠãŒãªã増ãˆã¦ã„ãストリームデータã®ãŸã‚ã®ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ ストリームデータã®ç‰¹å¾´ã¨ã—ã¦ã€ãã®å†…容ãŒæ¸©åº¦ã‚„ä½ç½®æƒ…å ±ã€ç”»åƒã€å‹•
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IoT時代ã«ãŠã‘るストリームデータ処ç†ã¨æ€¥æˆé•·ã® Apache Flink
ãˆã€ã¾ã£ã¦ã€‚ãã®ä¸¦åˆ—分散処ç†ã€Kafkaã®ã—ãã¿ã§ã‚‚ã§ãã‚‹ã®ï¼Ÿ Apache Kafkaã®æ©Ÿèƒ½ã‚’利用ã—ãŸå¤§è¦æ¨¡ã‚¹ãƒˆãƒªãƒ¼ãƒ データã®ä¸¦åˆ—分散処ç†
最近ã®ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†äº‹æƒ…振り返り
Hadoopソースコードリーディング 第22回 ã§ã®ç™ºè¡¨è³‡æ–™ã§ã™ã€‚ https://www.eventbrite.com/e/hadoop-22-tickets-31987821435
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ストリーム処ç†ã¨ã¯ä½•ã‹ï¼Ÿï¼‹2016å¹´ã®å‡ºæ¥äº‹ - Qiita
ãã®å¯¾å‡¦ã§å…¨éƒ¨ã«å¯¾å¿œã™ã‚‹ã®ã¯ç„¡ç†ãªã‚“ã˜ã‚ƒãªã„ã®ï¼Ÿ Watermarkã€Triggerã€Accumulationã®æ©Ÿæ§‹ãŒå°Žå…¥ã•ã‚Œã‚Œã°ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†ã¯å…¨ã¦å¯¾å¿œå¯èƒ½ã‹ã¨ã„ã†ã¨ã€ ãã‚“ãªã“ã¨ã¯ã‚ã‚Šã¾ã›ã‚“。 何故ãªã‚‰ã€ä¸‹è¨˜ã®ã‚ˆã†ãªå•é¡ŒãŒç™ºç”Ÿã—ã¦ãã‚‹ã‹ã‚‰ã§ã™ã€‚ Watermarkを実時刻ã‹ã‚‰ã©ã‚Œãらã„é…らã›ã¦è¨å®šã™ã‚Œã°ã„ã„ã®ã‹ï¼Ÿ é…れを大ããã™ã‚Œã°æ£ç¢ºæ€§ã¯å¢—ã—ã¾ã™ãŒã€é…延時間ã¯å¤§ãããªã‚Šã¾ã™ã€‚ Accumulationã®ãŸã‚ã«ã‚¦ã‚£ãƒ³ãƒ‰ã‚¦ã®é›†è¨ˆçµæžœã‚’ã©ã‚Œã ã‘ä¿æŒã™ã‚Œã°ã„ã„ã®ã‹ï¼Ÿ ä¿æŒã™ã‚‹æ™‚é–“ãŒé•·ã„ã»ã©ã€ã‚¹ãƒˆãƒªãƒ¼ãƒ 処ç†ã‚’è¡Œã†ã‚·ã‚¹ãƒ†ãƒ ã®ãƒªã‚½ãƒ¼ã‚¹ãŒå¿…è¦ã¨ãªã‚Šã¾ã™ã€‚ データ処ç†ã‚·ã‚¹ãƒ†ãƒ （ãƒãƒƒãƒã€ã‚¹ãƒˆãƒªãƒ¼ãƒ å«ã‚€ï¼‰ã«ã¯ä¸‹è¨˜ã®ï¼“è¦ç´ ã®ãƒˆãƒ¬ãƒ¼ãƒ‰ã‚ªãƒ•ãŒã‚ã‚‹ã¨ã•ã‚Œã¦ã„ã¾ã™ã€‚ 完全性(Completeness) 低é…延(Low Latency) 低コスト(Low Cost) ã“ã®ï¼“è¦ç´ ã‚’å…¨ã¦ã«æº€ãŸã™ã“ã¨ã¯å‡ºæ¥ãšã€å…¨ã¦ã®ãƒ‡ãƒ¼ã‚¿
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æƒ…å ±å‡¦ç†å¦ä¼šã‚¤ãƒ³ã‚¿ãƒ¼ãƒãƒƒãƒˆã¨é‹ç”¨æŠ€è¡“ç ”ç©¶ä¼šãŒä¸»å‚¬ã•ã‚Œã¦ã„ã‚‹IOTS2016ã¨ã„ã†ç ”究会ã§ã€ã€Œã‚µãƒ¼ãƒãƒ¢ãƒ‹ã‚¿ãƒªãƒ³ã‚°å‘ã‘時系列データベースã®æŽ¢ç©¶ã€ã¨ã„ã†ã‚¿ã‚¤ãƒˆãƒ«ã§æ‹›å¾…講演をã—ã¦ãã¾ã—ãŸã€‚ 講演ã®ãã£ã‹ã‘ インターãƒãƒƒãƒˆã¨é‹ç”¨æŠ€è¡“ç ”ç©¶ä¼š(以下IOTç ”ç©¶ä¼š)ã¨ã„ã†ã®ã¯åƒ•ã«ã¨ã£ã¦ã¯ id:matsumoto_r ã•ã‚“ãŒæ‰€å±žã•ã‚Œã¦ã„ã‚‹ç ”ç©¶ä¼šã§ã™ã€‚ matsumotoryã•ã‚“ãŒã€ã¡ã‚‡ã†ã©2å¹´å‰ã®ã‚¢ãƒ‰ãƒ™ãƒ³ãƒˆã‚«ãƒ¬ãƒ³ãƒ€ãƒ¼ã§æ›¸ã„ãŸåƒ•ã®è¨˜äº‹ã«æ—¥æœ¬èªžã ã¨IPSJã®IOTã¯åˆ†é‡Žçš„ã«ã‚‚インターãƒãƒƒãƒˆã®é‹ç”¨æŠ€è¡“ãŒå«ã¾ã‚Œã‚‹ã®ã§èˆˆå‘³æ·±ã„è«–æ–‡ãŒæ²¢å±±ã‚ã‚‹ã¨æ€ã† ã¨ã‚³ãƒ¡ãƒ³ãƒˆã—ã¦ã„ãŸã ã„ãŸã®ãŒæœ€åˆã«ç ”究会ã®å˜åœ¨ã‚’知るãã£ã‹ã‘ã ã£ãŸã¨æ€ã„ã¾ã™ã€‚ ãã®ã¨ãã¯ãã‚“ãªã‚‚ã®ã‚‚ã‚ã‚‹ã®ã‹ã¨æ€ã£ã¦ã¡ã‚‡ã£ã¨ãƒ—ãƒã‚°ãƒ©ãƒ を眺ã‚ãŸç¨‹åº¦ã§ã—ãŸã€‚ ã—ã‹ã—ã€ã¾ã•ã‹ãã®2年後ã«ã“ã†ã—ã¦æ‹›å¾…ã—ã¦ã„ãŸã ãã“ã¨ã«ãªã‚‹ã¨ã¯ã‚‚ã¡ã‚ã‚“æ€ã£ã¦ã„ã¾ã›ã‚“ã§ã—ãŸã€‚ id:MIZZYã•ã‚“
ストリーム処ç†ã‚’支ãˆã‚‹ã‚ューイングシステムã®é¸ã³æ–¹
This document discusses messaging queues and platforms. It begins with an introduction to messaging queues and their core components. It then provides a table comparing 8 popular open source messaging platforms: Apache Kafka, ActiveMQ, RabbitMQ, NATS, NSQ, Redis, ZeroMQ, and Nanomsg. The document discusses using Apache Kafka for streaming and integration with Google Pub/Sub, Dataflow, and BigQuery
分散ストリーム処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? 分散ストリーム処ç†ã‚¨ãƒ³ã‚¸ãƒ³ã®ç¾¤é›„å‰²æ‹ ã®æ™‚代 ストリーム処ç†ã‚’実ç¾ã™ã‚‹åˆ†æ•£ãƒ—ラットフォームãŒã€å¤§åˆ†å¢—ãˆã¾ã—ãŸã€‚ 何ãŒè‰¯ã„ã‹ï¼æ‚ªã„ã‹ã¯ã€ãƒ—ラットフォームã«æ±‚ã‚る内容やã€é–¢é€£ã™ã‚‹ã‚¨ã‚³ã‚·ã‚¹ãƒ†ãƒ ã«ã‚‚影響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 TECHNOLOGIES https://databaseline.wordpress.com/2016/03/12/an-overview-o
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