CentOS7.5 ã® fsnotify ãŒãƒ¬ãƒ¼ã‚¹ã‚³ãƒ³ãƒ‡ã‚£ã‚·ãƒ§ãƒ³ã‚’èµ·ã“ã™ãƒã‚°ã‚’ nginx + td-agent + in_tail プラグインã§è¸ã‚€ - hibomaã®æ—¥è¨˜
2018/7/18 追記 3.10.0-862.9.1.el7 㧠fix ã•ã‚Œã¾ã—㟠hiboma.hatenadiary.jp 2018/7/4 追記 最新ã®æƒ…å ±ã¯ã“ã¡ã‚‰ã«ã¾ã¨ã‚ã¦ã„ã¾ã™ hiboma.hatenadiary.jp 2018/6/16 追記 CentOS Plus ã® kernel-plus ã§ã¯ä¿®æ£ãŒå…¥ã£ã¦ã„ã¾ã™. 詳ã—ãã¯ã“ã¡ã‚‰ã‚’ã”覧ãã ã•ã„ hiboma.hatenadiary.jp エントリã®æ¦‚è¦ CentOS7.5 ã® fsnotify() ãŒãƒ¬ãƒ¼ã‚¹ã‚³ãƒ³ãƒ‡ã‚£ã‚·ãƒ§ãƒ³ã‚’èµ·ã“ã™ãƒã‚°ã‚’〠nginx + td-agent (fluentd) + in_tail プラグインã§è¸ã‚“ã éš›ã®èª¿æŸ»å†…容を記ã—ã¦ã„ãã¾ã™. イントãƒãƒ€ã‚¯ã‚·ãƒ§ãƒ³ ã“ã®ã‚¨ãƒ³ãƒˆãƒªã‚’書ã„ãŸæ™‚点ã§ã¯ã€CentOS 7.5.1804 以é™ã§ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¦ã„るカーãƒãƒ«ã¯ 3ã¤ã‚ã‚Šã¾ã™ãŒã€ã‚«ãƒ¼ãƒãƒ«ã® fsnotif
Instant Netty Startup using GraalVM Native Image Generation
In this article we demonstrate how you can achieve instant startup for Netty, a non-blocking I/O Java networking framework. We do this by compiling the Netty application into a native executable with GraalVM. First we discuss why we think this is important, then we detail what steps are necessary to enable ahead-of-time compilation of Java bytecode, and finally we show how to do it on a sample Net
Streaming SQL Foundations: Why I ⤠Streams+Tables
Streaming SQL Foundations: Why I ⤠Streams+TablesAI-enhanced description The document presents a discussion on streaming SQL and the associated theory of streams and tables, highlighting the relationship between data at rest (tables) and data in motion (streams). It emphasizes the contributions from various communities like Apache Beam and Apache Kafka, and covers important concepts such as the Be
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システムã€ãƒ—ãƒã‚»ã‚¹ã€ã‚«ãƒ«ãƒãƒ£ãƒ¼ã‚’ã„ã‹ã«ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ãƒªãƒ³ã‚°ã™ã‚‹ã‹ 本連載『開発ç¾å ´ã«â€œãƒ‡ãƒ¼ã‚¿æ–‡åŒ–â€ã‚’浸é€ã•ã›ã‚‹ã€Œãƒ‡ãƒ¼ã‚¿åŸºç›¤ã€å¤§è§£å‰–ã€ã§ã¯ã€Œãƒ‡ãƒ¼ã‚¿åŸºç›¤ã€ã®æ§‹ç¯‰äº‹ä¾‹ã‚’紹介ã—ã¾ã™ã€‚具体的ã«ã¯ã€ã‚ªãƒ³ãƒ©ã‚¤ãƒ³å©šæ´»ã‚µãƒ¼ãƒ“ス「ゼクシィç¸çµã³ã€ãªã‚‰ã³ã«ãã®å§‰å¦¹ã‚µãƒ¼ãƒ“ス「ゼクシィæ‹çµã³ã€ã®é–‹ç™ºç¾å ´ã«ãŠã„ã¦ã€ç†è€…ãŒå®Ÿéš›ã«è¡Œã£ãŸã“ã¨ã‚’é¡Œæã¨ã—ã¦ã„ã¾ã™ã€‚ データ基盤を実際ã«æ§‹ç¯‰ã™ã‚‹ã®ã¯å®¹æ˜“ã§ã¯ã‚ã‚Šã¾ã›ã‚“。構築ã—ãŸãƒ‡ãƒ¼ã‚¿åŸºç›¤ã‚’実際ã«åˆ©ç”¨ã—続ã‘ã¦ã‚‚らã†ã®ã¯ã•ã‚‰ã«é›£ã—ã„ã“ã¨ã§ã™ã€‚ 多ãã®é–¢ä¿‚者ãŒãƒ‡ãƒ¼ã‚¿ã‚’åŠ å·¥ã™ã‚‹ã¨ã€ä¼¼ã¦ã„ã‚‹æ„味をæŒã£ã¦ã„ã¦ã‚‚微妙ã«ç•°ãªã‚‹ãƒ‡ãƒ¼ã‚¿ãŒç”Ÿæˆã•ã‚Œã¦ã—ã¾ã„ã€ã©ã®ãƒ‡ãƒ¼ã‚¿ãŒæ£ã—ã„ã®ã‹èª°ã‚‚分ã‹ã‚‰ãªããªã£ã¦ã—ã¾ã„ã¾ã™ã€‚ãã¡ã‚“ã¨å…¨å“¡ã«ä½¿ã‚れるãŸã‚ã«ã¯ãƒ‡ãƒ¼ã‚¿ã®æŒã¤æ„å‘³ã‚„åŠ å·¥ãƒã‚¸ãƒƒã‚¯ã‚’誰ã‹ãŒæ•´ç†ã—ãªã‘ã‚Œã°ã„ã‘ã¾ã›ã‚“。 ã¾ãŸã€ãƒ¢ãƒ€ãƒ³ãªãƒ„ールを使ã£ã¦æ´¾æ‰‹ãªãƒ€ãƒƒã‚·ãƒ¥ãƒœãƒ¼ãƒ‰ã‚’構築ã—ã¦ã‚‚ã€ãã‚Œã ã‘ã§ã¯1週間後ã«ã¯èª°ã‚‚見ãªããªã£ã¦
Vespa 機能紹介 #yjmu
第17回Lucene/Solr勉強会 #SolrJP – Apache Lucene Solrã«ã‚ˆã‚‹å½¢æ…‹ç´ 解æžã®èª²é¡Œã¨N-bestã®æ案
Introducing the Confluent Operator: Apache Kafka® on Kubernetes
At Confluent, our mission is to put a Streaming Platform at the heart of every digital company in the world. This means, making it easy to deploy and use Apache Kafka and Confluent Platform—the de-facto Streaming Platform—across a variety of infrastructure environments. In the last few years, the rise of Kubernetes as the common operations runtime across a myriad of platforms in the enterprise can
Multitenancy: Kafka clusters for everyone at LINE
Multitenancy: Kafka clusters for everyone at LINEAI-enhanced description Yuto Kawamura from LINE Corporation presented on their use of Apache Kafka clusters to provide multitenancy for different internal teams. They face challenges in ensuring isolation between client workloads and preventing abusive clients. Their solutions include request quotas to limit client resource usage, slow logs to ident
データã®æ°‘主化ã¨ã‚µãƒ¼ãƒ“スレベルã«ã¤ã„ã¦è©±ã—ã¾ã—ãŸï¼ 分æžåŸºç›¤Meetup #shinjukugl - 下町柚å黄æ˜è¨˜ by @yuzutas0
セプテーニ・オリジナル様ãŒä¸»å‚¬ã™ã‚‹æ–°å®¿Geek Lounge#4 分æžåŸºç›¤Meetupã§LTã‚’ã—ã¾ã—ãŸã€‚ スライド 『データ基盤を支ãˆã‚‹æ°‘主化ã¨ã‚µãƒ¼ãƒ“スレベル〠「ã„ã‹ã«ãƒ“ジãƒã‚¹ä¾¡å€¤ã‚’最大化ã—続ã‘ã‚‹ã‹ã€ã¨ã„ã†æœ¬æ¥ã®ç›®çš„ã‹ã‚‰ã€ãƒ‡ãƒ¼ã‚¿åŸºç›¤1を見直ã™ã‚ッカケã«ãªã‚Œã°ã¨æ€ã„ã¾ã™ã€‚ PyCon JP 2017ã§ãƒ™ã‚¹ãƒˆãƒˆãƒ¼ã‚¯ã‚¢ãƒ¯ãƒ¼ãƒ‰å„ªç§€è³žã‚’å—賞ã—ãŸç™ºè¡¨ï¼ˆæ§‹ç¯‰ç·¨ï¼‰ã®ç¶šã（é‹ç”¨ç·¨ï¼‰ã‚’ãƒãƒ©è¦‹ã›ã§ã™ã€‚ データ基盤ã¯ä½¿ã‚ã‚Œã¦ã“ãæ„味ãŒã‚ã‚‹ 世ã®æµã‚Œã¯ã€Œã‚„ã£ã¦ã¿ãŸã€ã‹ã‚‰ã€Œä¾¡å€¤å‰µå‡ºãƒ»é‹ç”¨ã€å¿—å‘ã«æŽ¨ç§»ã—ã¦ã„ã¾ã™ï¼ˆä¾‹ï¼šDataOpsã€æ©Ÿæ¢°å¦ç¿’å·¥å¦ã€MLOps） 「俺ã®è€ƒãˆãŸæœ€å¼·ã®ãƒ€ãƒƒã‚·ãƒ¥ãƒœãƒ¼ãƒ‰ã€ã§ã¯1週間ã§èª°ã‚‚見ãªããªã‚Šã¾ã™ データã®æ°‘主化 事務スタッフ（éžã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ï¼‰ãŒBigQueryã‚’å©ã„ã¦ã„ã¾ã™ï¼ã™ã”ã„浸é€ï¼ ãƒãƒ¼ãƒ ã”ã¨ã®æ°‘主化状æ³ã‚’モニタリングã—ã¦å¿…è¦ãªã‚¢ã‚¯ã‚·ãƒ§ãƒ³ã‚’実施ã—ã¦ã„ã¾ã™ 民主化ã«ã¯3ã¤ã®å£ãŒã‚ã‚‹ã“ã¨ãŒ
Outshift | Kafka on Kubernetes - using etcd
Get emerging insights on innovative technology straight to your inbox. At Banzai Cloud we are building a cloud agnostic, open source next generation CloudFoundry/Heroku-like PaaS, Pipeline, while running several big data workloads natively on Kubernetes. Apache Kafka is one of the cloud native workloads we support out-of-the-box, alongside Apache Spark and Apache Zeppelin. If you’re interested in
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Instaclustr Kafka Meetup Sydney Presentation
Why is My Stream Processing Job Slow? with Xavier Leaute
RealTime Recommendations @Netflix - Spark
jjugccc2018 app review postmortem
データ集計æ¥å‹™ã‚’åŠå¹´ã§300案件æŒã„ã¦è¦‹ãˆã¦ããŸå‹˜æ‰€ #ãƒ‡ãƒ¼ã‚¿è§£æž #willgate / 20180425
Kapacitor - Real Time Data Processing Engine
Kafka vs Pulsar @KafkaMeetup_20180316
How to Avoid Drowning in GDPR Data Subject Requests in a Data Lake
https://blogs.oracle.com/developers/post/announcing-graalvm-run-programs-faster-anywhere
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