Performance Optimizations in Apache Impala
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard componen
CPU Utilization is Wrong
Recent posts: 29 Oct 2024 » AI Flame Graphs 22 Jul 2024 » No More Blue Fridays 24 Mar 2024 » Linux Crisis Tools 17 Mar 2024 » The Return of the Frame Pointers 10 Mar 2024 » eBPF Documentary 28 Apr 2023 » eBPF Observability Tools Are Not Security Tools 01 Mar 2023 » USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon 17 Feb 2023 » USENIX SREcon APAC 2023: CFP 02 May 2022 » Brendan
詳解システム・パフォーマンス 第2ç« ã€Œãƒ¡ã‚½ãƒ‰ãƒã‚¸ã€ãƒ¡ãƒ¢ - ゆã†ã†ãメモ
書ç±ã€Œè©³è§£ã‚·ã‚¹ãƒ†ãƒ ・パフォーマンス〠下èªã¿ - ゆã†ã†ãメモ ã®ç¶šã。今回ã¯ç¬¬2ç« ã€Œãƒ¡ã‚½ãƒ‰ãƒã‚¸ã€ã€‚ ãƒã‚šãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ã‚¢ãƒŠãƒªã‚¹ãƒˆã‹ã‚™è¤‡é›‘ãªã‚·ã‚¹ãƒ†ãƒ ã«ç«‹ã¡å‘ã‹ã†ã¨ãã«ã€ãƒã‚šãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹å•é¡Œã‚’èµ·ã“ ã—ã¦ã„ã‚‹å ´æ‰€ã‚’ç‰¹å®šã—ã€å•é¡Œã‚’分æžã™ã‚‹ãŸã‚ã«ã¨ã‚™ã“ã‹ã‚‰å§‹ã‚ã€ã¨ã‚™ã®ã‚ˆã†ãªæ‰‹é †ã‚’è¸ã‚“ãŸã‚™ã‚‰ã‚ˆã„ã‹ã‚’示ã—ã¦ãれるã®ã‹ã‚™ãƒ¡ã‚½ãƒˆã‚™ãƒã‚·ã‚™ã¦ã‚™ã‚る。 Brendan Gregg,西脇é–ç´˜,長尾高弘「詳解システム・パフォーマンスã€, オライリージャパン p.15 ã“ã®ç« ã§èˆˆå‘³æ·±ã‹ã£ãŸã®ã¯ä»¥ä¸‹ã®7点ã§ã‚る。 Known-Unknowns リソース使用率ã®å®šç¾©ã¨é£½å’Œ(Saturation)ã®æ¦‚念 ワークãƒãƒ¼ãƒˆã‚™åˆ†æž(workload analysis)ã¨ãƒªã‚½ãƒ¼ã‚¹åˆ†æž(resource analysis) USEメソッド 競åˆã¨ã‚³ãƒ’ーレンスã®ã‚¹ã‚±ãƒ¼ãƒ©ãƒ“リティプãƒãƒ•ã‚¡ã‚¤ãƒ«ã®å·® å¾…ã¡è¡Œåˆ—ç†è«– ヒートマップ Known-Un
Kafka 0.9 Configuration Best Practices
This article is applicable for anyone deploying a Kafka cluster for production use. It points out certain high level points that you should be thinking about before deploying your cluster. It is divided into 3 parts: Important configurations for the Producer, Broker, Consumer and tips for Performance tuning. Important Client / Producer Configurations: The most important producer configurations: co
詳解 システム・パフォーマンス
TOPICS System/Network 発行年月日 2017å¹´02月 PRINT LENGTH 784 ISBN 978-4-87311-790-4 原書 Systems Performance FORMAT PDF EPUB 本書ã¯ã‚¨ãƒ³ã‚¿ãƒ¼ãƒ—ライズ環境ã¨ã‚¯ãƒ©ã‚¦ãƒ‰ç’°å¢ƒã®ä¸¡æ–¹ã‚’対象ã¨ã—ãŸã‚ªãƒšãƒ¬ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã‚·ã‚¹ãƒ†ãƒ ãŠã‚ˆã³ã‚ªãƒšãƒ¬ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã‚·ã‚¹ãƒ†ãƒ ã®ã‚³ãƒ³ãƒ†ã‚ストã«ãŠã‘るアプリケーションã®ãƒ‘フォーマンス分æžã¨å‘上ã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚主ã«Linuxã¨Solarisベースã®ã‚ªãƒšãƒ¬ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã‚·ã‚¹ãƒ†ãƒ ã«å«ã¾ã‚Œã‚‹ãƒ„ールã¨ãã®ä½¿ç”¨ä¾‹ã‚„ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°å¯èƒ½ãƒ‘ラメータã®è¨å®šã‚’通ã˜ã¦ã‚·ã‚¹ãƒ†ãƒ パフォーマンスを引ã出ã™æ‰‹æ³•ã‚’å¦ã³ã¾ã™ã€‚CPUやメモリã€ãƒ•ã‚¡ã‚¤ãƒ«ã‚·ã‚¹ãƒ†ãƒ ãªã©å€‹åˆ¥ãƒ†ãƒ¼ãƒžã”ã¨ã«è¨ã‘られãŸå„ç« ã®å‰åŠã§ã¯ã€ç”¨èªžã€è€ƒãˆæ–¹ã€æ–¹æ³•è«–ã«ã¤ã„ã¦è¿°ã¹ã€å¾ŒåŠã§ã¯å®Ÿè£…ã®å…·ä½“例を示ã—ã¤ã¤ã€ã‚¢ãƒ¼ã‚テクãƒãƒ£ã€åˆ†æžãƒ„ールã€ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ãªã©ã‚’解
Dockerホストã®ãƒ‘フォーマンスを引ã出ã™TCPカーãƒãƒ«ãƒ‘ラメータãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚° - tehepero note(・ω<)
2016 - 08 - 12 Dockerホストã®ãƒ‘フォーマンスを引ã出ã™TCPカーãƒãƒ«ãƒ‘ラメータãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚° Docker Linux ã‚‚ã†åŠå¹´ãらã„フルDockerã§microservicesãªã‚µãƒ¼ãƒ“スをé‹ç”¨ã—ã¦ã‚‹ã‚“ã§ã™ãŒã€ã‚¤ãƒžã‚¤ãƒãƒ‘フォーマンスを出ã—切れã¦ã„ãªã„ãªã¨ã„ã†é¢ãŒã‚ã‚Šã¾ã—ã¦ã€ä»Šå›žDockerホスト㮠TCP カーãƒãƒ« パラメータを抜本的ã«è¦‹ç›´ã—ã¾ã—ãŸã€‚ ãã—ãŸã‚‰åŠ‡çš„ã«ç—‡çŠ¶ãŒæ”¹å–„ã—ã¦ã€ インスタンス 数も削減ã§ããŸä¸Šã«å®‰å®šã—㦠メシウマ状態 ã«ãªã£ãŸã®ã§ç´¹ä»‹ã—ã¾ã™ã€‚実際効果ãŒã‚ã£ãŸã®ã§ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ãƒã‚¤ãƒ³ãƒˆã¨ã—ã¦ã¯ã‚る程度æ£è§£ã§ã‚ã£ãŸã¨è€ƒãˆã¦ã„ã¾ã™ãŒã€ã‚‚ã¡ã‚ん扱ã£ã¦ã‚‹ã‚¢ãƒ—リケーションã®ç‰¹æ€§ã«ã‚‚よるã¯ãšãªã®ã§ä¸€ã¤ã® ケーススタディ ã§ã‚ã‚‹ã“ã¨ã‚’ã”äº†æ‰¿é ‚ã‘ã‚Œã°ã¨ã€‚ å‰æ ã¾ãšã¯ä»Šå›žã®ãŠè©±ã®å‰æを。ã“ã‚“ãªç’°å¢ƒã§ã™ã€‚ EC2 c3.xlarge ホスト㯠Ubuntu （EC2 Opt
Performance Comparison of Streaming Big Data Platforms
Flink, Spark, and Storm are three popular streaming platforms compared on performance. A benchmark was created to simulate an advertising analytics pipeline with events streamed to Kafka. Flink and Storm had similar linear latency increases with throughput. Spark had higher latency due to micro-batching, but could handle higher throughput. At very high throughput, Storm performed best with acknowl
Concord.io is For Sale | BrandBucket
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Java Mixed-Mode Flame Graphs 㧠Java ã® CPU ãƒãƒƒã‚¯ã‚’フルスタックã§åˆ†æžã™ã‚‹ - ablog
Brendan Gregg (NETFLIX ã® Senior Performance Architect) 作㮠Java Mixed-Mode Flame Graphs を使ã†ã¨ Java ã®ãƒ—ãƒã‚»ã‚¹ãŒ CPU ãƒãƒƒã‚¯ã®ã‚±ãƒ¼ã‚¹ã§ã€Java アプリケーションコードã€JVM(HotSpot VM)ã€Linux Kernel ã®ã©ã®ãƒ¬ã‚¤ãƒ¤ãƒ¼ã®ã©ã®é–¢æ•°ãŒãƒœãƒˆãƒ«ãƒãƒƒã‚¯ã«ãªã£ã¦ã„ã‚‹ã‹ã‚’ç°¡å˜ã«ç‰¹å®šã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ 以下㯠GitHub - martint/jittest: Demonstrate JIT compiler issue in java 7 ã®ãƒ¯ãƒ¼ã‚¯ãƒãƒ¼ãƒ‰ã‚’実行ã—㦠Flame Graphs ã§å¯è¦–化ã—ãŸã‚‚ã®ã§ã™ã€‚ 以下㯠Java One 2015 ã§ã® Brendan Gregg ã®ã‚¹ãƒ©ã‚¤ãƒ‰ã§ã™(YouTube ã‚‚ã‚ã‚Šã¾ã™)。 JavaOne 2015 Java Mixed-Mo
Systems Performance Book
Systems Performance: Enterprise and the Cloud, 1st Edition (2013) This is the official site for the book Systems Performance: Enterprise and the Cloud, 1st Edition, published by Prentice Hall (2013). Here I'll describe the book, link to related content, and list errata. Also see the preface for a detailed description of the book, which is included in the sample chapter from the InformIT site. Upda
Performance Management | 6.3.x | Cloudera Documentation
© 2021 Cloudera, Inc. All rights reserved. Apache Hadoop and associated open source project names are trademarks of the Apache Software Foundation. For a complete list of trademarks, click here. If this documentation includes code, including but not limited to, code examples, Cloudera makes this available to you under the terms of the Apache License, Version 2.0, including any required notices. A
SmartNews TechNight Vol5 : SmartNews AdServer 解体新書 / ãƒã‚¹ãƒˆãƒ¢ãƒ¼ãƒ†ãƒ
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GitHub - linkedin/dr-elephant: Dr. Elephant is a job and flow-level performance monitoring and tuning tool for Apache Hadoop and Apache Spark
Compression vs. Performance · Apache Big_Data Notes: Hadoop, Spark, Flink, etc.
Spark Summit EU talk by Qifan Pu
Voice from CERN: Apache Spark 2.0 Performance Improvements Investigated With Flame Graphs
Mackerelã«ãŠã‘る時系列データベースã®æ€§èƒ½æ”¹å–„ / Performance Improvement of TSDB in Mackerel
ã¯ã¦ãªã«ãŠã‘ã‚‹Linuxãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã‚¹ã‚¿ãƒƒã‚¯ãƒ‘フォーマンス改善 / Linux network performance improvement at hatena
Apache Spark as a Compiler: Joining a Billion Rows per Second on a Laptop
SparkInternals/EnglishVersion/1-Overview.md at master · JerryLead/SparkInternals
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