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jjugccc2018 app review postmortem
3. アジェンダ • 自己紹介 • アーã‚テクãƒãƒ£ã®èª¬æ˜Ž • レビューã«ã¤ã„㦠– ã©ã®ãƒ¬ãƒ™ãƒ«ã§ï¼Ÿ – ã©ã†ã„ã†è¦³ç‚¹ã§ï¼Ÿ – 指摘ãŒå¤šã„ã‚‚ã®/具体的ãªå†…容 • 性能å•é¡Œ – JVMã®è©± – JVMã®å¤–ã®è©± • 障害例 • ボトルãƒãƒƒã‚¯ã®è¦‹ã¤ã‘æ–¹ • è² è·è©¦é¨“ • ãŠã¾ã‘ 3 4. 自己紹介 • Name: Kiyotaka Suzuki • Twitter: @tamtam180 • Main works – SquareEnix (5.5Y) • PlayOnline, FF-XIV, etc. – SmartNews (4Y〜) • Cross-functional Expert Team – Software Engineer – 広告, 公共 4
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
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2rtxaMm. Tyler Akidau explores the relationship between the Beam Model and stream & table theory. He explains what is required to provide robust stream processing support in SQL and discusses concrete efforts that have been made in this area by the Apache Beam, Calcite, and Flink communities, compare to other off
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Vespa 機能紹介 #yjmu
2. ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«è³‡æ–™ https://yahoojapan.github.io/vespa-tutorial/ • æ°å • 矢野 å‹è²´ (や㮠ゆã†ã) • 所属 • ãƒ¤ãƒ•ãƒ¼æ ªå¼ä¼šç¤¾ D&S統括本部 • サイエンス的ãªãªã«ã‹ • æ¥å‹™ • ã‚€ã‹ãƒ¼ã—Vespaã®ã‚µãƒ¼ãƒ“スé‹ç”¨ã—ã¦ãŸ • 検索モデリングã¨ã‹ï¾ï½®ï½¯ï¾„ﾃﾞｷﾙ • 最近ã¯Solrã«æµ®æ°—ä¸ è‡ªå·±ç´¹ä»‹ Copyright (C) 2018 Yahoo Japan Corporation. All Rights Reserved. ç„¡æ–引用・転載ç¦æ¢ 1 3. ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«è³‡æ–™ https://yahoojapan.github.io/vespa-tutorial/ • Vespaã®æ—¥æœ¬èªžå‘ã‘ã®ãƒãƒ¥ãƒ¼ãƒˆãƒªã‚¢ãƒ«è³‡æ–™ã‚’以下ã«å…¬é–‹ã—ã¦ã¾ã™ • 詳ã—ã„使ã„æ–¹ã¯ãã¡ã‚‰ã‚’å‚ç…§ã—ã¦ãã ã•ã„ • ã“ã®ç™ºè¡¨ã§ã¯Vespaã®ç‰¹å¾´çš„ãªæ©Ÿèƒ½ã‚’Solr/E
Introducing the Confluent Operator: Apache Kafka® on Kubernetes
Introducing the Confluent Operator: Apache Kafka on Kubernetes Made Simple 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 c
Kafka vs Pulsar @KafkaMeetup_20180316
2. Copyright (C) 2018 Yahoo Japan Corporation. All Rights Reserved. ç„¡æ–引用・転載ç¦æ¢ 2 自己紹介 æ —åŽŸ 望 経æ´ï¼š â–ª 2012/04 ãƒ¤ãƒ•ãƒ¼æ ªå¼ä¼šç¤¾ã«æ–°å’入社 â–ª 2012/10 ユーザーã®å±žæ€§æƒ…å ±ã«é–¢é€£ã™ã‚‹ç¤¾å†…å‘ã‘プラットフォームã®é–‹ç™º â–ª 2015/07 ヤフオクã®BEシステムå†æ§‹ç¯‰ â–ª 2016/10 「Pulsarã€ã‚’使ã£ãŸç¤¾å†…å‘ã‘メッセージングプラットフォームã®é–‹ç™º â–ª 2017/06~ 「Pulsarã€ã®ã‚³ãƒŸãƒƒã‚¿ãƒ¼ 趣味: â–ª ã·ã‚ˆã·ã‚ˆãƒ†ãƒˆãƒªã‚¹ â–ª ボードゲームã„ã‚ã„ã‚ 3. Copyright (C) 2018 Yahoo Japan Corporation. All Rights Reserved. ç„¡æ–引用・転載ç¦æ¢ 3 Apache Pulsar Yahoo! Inc.（ç¾ï¼šOath）ã§é–‹ç™ºã•ã‚ŒãŸ
Multitenancy: Kafka clusters for everyone at LINE
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 identify slow requests, and changes to the broker code to pre-warm caches and
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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4. ビジãƒã‚¹é¢ã®åˆ¶ç´„æ¡ä»¶ã‚’考ãˆã‚‹ • 「人工知能ã§ä½•ã¨ã‹ã—ã¦ãã ã•ã„〠• ã“ã®æ¡ˆä»¶ã¯ã©ã®ã‚¿ã‚¤ãƒ—ã®åˆ©ç›Šãƒ¢ãƒ‡ãƒ«ã‹ï¼Ÿ • 人間ã®ãƒªãƒ—レイスãŒç›®çš„ãªã®ã§ã€äººé–“より精度ãŒé«˜ã‘ã‚Œã°ã‚ˆã„？ • 今ã®äººé–“ã®ç²¾åº¦ã¯95%ä½ãªã®ã§ã€ãれよりも精度ãŒé«˜ããªã‘ã‚Œã°ä½¿ãˆãªã„ • 今ã®äººé–“ã®ç²¾åº¦ã¯60%ä½ãªã®ã§ã€ãれよりも精度ãŒé«˜ããªã‘ã‚Œã°ä½¿ãˆãªã„ • 60%ã§ã‚ã‚Œã°ã€ç°¡å˜ãªãƒ«ãƒ¼ãƒ«ãƒ™ãƒ¼ã‚¹ã‚„ç”»åƒå‡¦ç†ã§åˆ°é”ã§ãã‚‹å¯èƒ½æ€§ãŒé«˜ã„ • 機械å¦ç¿’を使ã‚ãªãã¦ã‚‚改善ãŒå‡ºæ¥ã‚‹ • è¦æ±‚ã•ã‚Œã‚‹ç²¾åº¦æ¬¡ç¬¬ã§ã€ä½¿ã†æŠ€è¡“ãŒç•°ãªã‚‹ • 自らã®ç«‹ã¡ä½ç½®ã«ã‚ˆã£ã¦ã€ç²¾åº¦å£²ä¸Šæ›²ç·šã®æ„味ãŒå¤‰ã‚ã£ã¦ãã‚‹ • 内製ã¨ä¸‹è«‹ã‘ 5. Yahooã¨Google • Yahooã¯è‡ªç¤¾ã®æ¤œç´¢ãƒ“ジãƒã‚¹ã‚’ãƒã‚¸ã‚¹ãƒ†ã‚£ãƒƒã‚¯åž‹ã ã¨æ€ã„込んã§ã„㟠• ã“れ以上投資ã—ã¦ã‚‚売上ãŒå¢—ãˆãªã„ã¨æ€ã£ã¦ã„㟠• http://blog.livedoor.jp/lionfan/archiv
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Instaclustr Kafka Meetup Sydney Presentation
Why is My Stream Processing Job Slow? with Xavier Leaute
RealTime Recommendations @Netflix - Spark
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
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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