[B! Presto] InoHiroã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

InoHiro id:InoHiro

Prestoã«é–¢ã™ã‚‹InoHiroã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (14)

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Gravitonã§Prestoã‚’èµ°ã‚‰ã›ã‚‹ï½œKai Sasaki
å…ˆæ—¥ã®Re:Inventã§Armãƒ™ãƒ¼ã‚¹ã®ãƒ—ãƒã‚»ãƒƒã‚µGraviton 2ã‚’æè¼‰ã—ãŸã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã‚¿ã‚¤ãƒ—ãŒAWSã‹ã‚‰ç™ºè¡¨ã•ã‚Œã¾ã—ãŸã€‚AWSã¯åŽ»å¹´ã«ã‚‚Armãƒ™ãƒ¼ã‚¹ã®Gravitonãƒ—ãƒã‚»ãƒƒã‚µã‚’ä½¿ã£ãŸA1ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã‚’ç™ºè¡¨ã—ã¦ã„ã¾ã™ãŒã€ã“ã‚Œã¯ãã®æ¬¡ä¸–ä»£æ©Ÿã«ãªã‚Šã¾ã™ã€‚ Graviton 2ã¯Armv8-Aã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã‚’ã‚µãƒãƒ¼ãƒˆã—ãŸNeoverseã¨ã„ã†ãƒžã‚¤ã‚¯ãƒã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã‚’å…ƒã«ä½œã‚‰ã‚ŒãŸã‚«ã‚¹ã‚¿ãƒ ãƒãƒƒãƒ—ã§ã€ä½Žä¾¡æ ¼ã§ã‚ã‚ŠãªãŒã‚‰é«˜ã„æ€§èƒ½ã‚’ç™ºæ®ã™ã‚‹ã“ã¨ãŒã§ãã‚‹ã‚³ã‚¢ã§ã™ã€‚ Graviton 2ã¯ã¾ã previewã¨ã„ã†æ‰±ã„ã§ã™ãã«åˆ©ç”¨ãŒã™ã‚‹ã“ã¨ãŒã§ãã¾ã›ã‚“ã§ã—ãŸã®ã§ã€ã“ã®è¨˜äº‹ã§ã¯Gravitonã‚’æè¼‰ã—ãŸA1ã‚¤ãƒ³ã‚¹ã‚¿ãƒ³ã‚¹ã§åˆ†æ•£SQLã‚¨ãƒ³ã‚¸ãƒ³ã§ã‚ã‚‹Prestoã‚’å‹•ã‹ã—ã¦ã¿ãŸã„ã¨æ€ã„ã¾ã™ã€‚ Armã‚’ã‚µãƒãƒ¼ãƒˆã—ãŸPrestoã‚’ä½œã‚‹Prestoã¯Javaã§å‹•ãã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ãªã®ã§ã€åŸºæœ¬çš„ã«JVMãŒArmã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒ
InoHiro 2020/01/10
Presto

aws
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Presto Experiment with Graviton Processor
This December, AWS announced new instance types powered by Arm-based AWS Graviton2 Processor. M6g, C6g, and R6g are designed to deliver up to 40% improved price/performance compared with the current generation instance types. We can achieve cost-effectiveness by using these instance type series. Presto is just a Java application, so that we should be able to run the workload with this type of cost
InoHiro 2020/01/10
Presto

AWS
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https://techcrunch.com/2019/11/20/starburst-raises-22m-to-modernize-data-analytics-with-presto/
InoHiro 2019/11/21
Presto
ãƒªãƒ³ã‚¯
Reading The Source Code of Presto
This document provides an overview of reading the source code of Presto, an open source distributed SQL query engine. It recommends starting on GitHub at prestosql/presto and exploring areas of interest like SQL interfaces to different data sources via connectors, the query engine core, distributed systems implementation, or extending Presto. Useful techniques for navigating the code with IntelliJ
InoHiro 2019/09/25
Presto
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Treasure Data Update 2015 - Presto - Qiita
Treasure Dataã§ã¯fluentd, å„ç¨®SDK, Data Connectorãªã©ã§åŽé›†ã•ã‚ŒãŸãƒ‡ãƒ¼ã‚¿ã«å¯¾ã—ã¦ã€Hive, Prestoã«ã‚ˆã‚‹åˆ†æ•£SQLã‚¯ã‚¨ãƒªãŒå®Ÿè¡Œã§ãã¾ã™ã€‚ç‰¹ã«Prestoã¯ã“ã®1å¹´ã§å¤§ããé€²åŒ–ã—ã¾ã—ãŸã®ã§ã€ã“ã“ã§ãã®å†…å®¹ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¦ã„ããŸã„ã¨æ€ã„ã¾ã™ã€‚ Prestoã‚¯ã‚¨ãƒªã®åˆ©ç”¨é‡ã¯å¢—ãˆç¶šã‘ã¦ã„ã¦ã€2015å¹´12æœˆç¾åœ¨ã€Treasure Dataã®åˆ©ç”¨çµ±è¨ˆã§ã¯ã€ ï¼‘æ—¥ã‚ãŸã‚Š5ä¸‡ã‚¯ã‚¨ãƒª (æœˆæ›ç®—ã§150ä¸‡ã‚¯ã‚¨ãƒª) ï¼‘æ—¥ã‚ãŸã‚Š10å…† (10 trillion) ãƒ¬ã‚³ãƒ¼ãƒ‰ ã‚’å‡¦ç†ã—ã¦ã„ã¾ã™ã€‚2015å¹´ã®å§‹ã¾ã‚Šã®æ™‚ç‚¹ã§ã¯ã€1æ—¥ã‚ãŸã‚ŠãŠã‚ˆã5000ã‚¯ã‚¨ãƒªã€1å…†ãƒ¬ã‚³ãƒ¼ãƒ‰ã¨ã„ã†æ•°å—ã§ã—ãŸã®ã§ã€ã“ã®ï¼‘å¹´ã§ã»ã¼10å€ã«ãªã£ãŸè¨ˆç®—ã«ãªã‚Šã¾ã™ã€‚æ˜¨å¹´æœ«ã®Prestoã‚µãƒ¼ãƒ“ã‚¹ã®é–‹å§‹ã«ã‚ãŸã‚Šã€CTOã®å¤ªç”°ã¨ç›¸è«‡ã—ã¦10å€ã‚¹ã‚±ãƒ¼ãƒ«ã§ãã‚‹ã‚ˆã†ã«æº–å‚™ã‚’ã—ã¦ã„ãŸã®ã§ã™ãŒã€æƒ³å®šã—ã¦ã„ãŸã‚ˆã‚Šæ—©ãã“
InoHiro 2015/12/07
ã™ã”ã„ãƒ‡ãƒ¼ã‚¿é‡ã

TD

Presto
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Hardware requirements for Presto
Most people are running Trino (formerly PrestoSQL) on the Hadoop nodes they already have. At Facebook we typically run Presto on a few nodes within the Hadoop cluster to spread out the network load. Generally, I'd go with the industry standard ratios for a new cluster: 2 cores and 2-4 gig of memory for each disk, with 10 gigabit networking if you can afford it. After you have a few machines (4+),
InoHiro 2015/11/10
Facebookã®ä¸ã®äººãŒå›žç”ã—ã¦ã‚‹

Presto
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Presto @ Treasure Data - Presto Meetup Boston 2015
InoHiro 2015/10/14
Presto

TD
ãƒªãƒ³ã‚¯
Presto meetup #1 - Go ahead!
Presto meetup 1æœˆ20æ—¥ã«ç¬¬ä¸€å›žã‚’ã‚„ã‚Šã¾ã—ãŸï¼ã‚ãƒ£ãƒ³ã‚»ãƒ«ã‚‚å°‘ãªã‚ã§ï¼Œã‚¤ãƒ™ãƒ³ãƒˆå‚åŠ 100äººå¼·ï¼Œæ‡‡è¦ªä¼šãŒ80äººãã‚‰ã„ã§ã—ãŸï¼Žäºˆæƒ³ã®å€ä»¥ä¸Šé›†ã¾ã£ãŸæ„Ÿã˜ã§ï¼ŒPrestoã‚‚æ³¨ç›®ã•ã‚Œã¦ã‚‹ã‚“ã ãªã¨å®Ÿæ„Ÿã—ãŸã‚¤ãƒ™ãƒ³ãƒˆã§ã—ãŸï¼Ž ä»Šå›žã¯åŸºæœ¬ã‹ã‚‰å§‹ã¾ã‚Šï¼Œãã‚Œãžã‚Œä»Šä½¿ã£ã¦ã„ã‚‹äººé”ã«ï¼Œé‹ç”¨ã®è©±ï¼ŒBIãƒ„ãƒ¼ãƒ«ã¨ã®é€£æºã®è©±ï¼Œã©ã†ã„ã†çµ„ã¿åˆã‚ã›ã§ä½¿ã£ã¦ã„ã‚‹ã‹ãªã©ç™ºè¡¨ã—ã¦ã‚‚ã‚‰ã„ã¾ã—ãŸï¼Žã€ŒPrestoã®DynamoDBã‚³ãƒã‚¯ã‚¿ã‚’ä½œã‚‹ï¼ã€ã¿ãŸã„ãªè©±ã‚‚ã‚ã£ãŸã‚Šã—ã¦ï¼Œãƒªãƒªãƒ¼ã‚¹ãŒå¾…ã¡é ã—ã„æ‰€ã§ã™ï¼Ž æ‡‡è¦ªä¼šã§ã‚‚ãŸãã•ã‚“ã®äººã¨è©±ã‚’ã—ã¾ã—ãŸãŒï¼Œã‚„ã¯ã‚Šå®‰å®šã—ãŸé‹ç”¨ã®ã—ã‚„ã™ã•ã¨ã‹ï¼Œã‚³ãƒã‚¯ã‚¿ã«ã‚ˆã‚‹è¤‡æ•°ã‚½ãƒ¼ã‚¹ã¸ã®ã‚¢ã‚¯ã‚»ã‚¹ãªã©ï¼Œè‰²ã€…ã¨ä½¿ã£ã¦ã„ã‚‹ç†ç”±å«ã‚æƒ…å ±äº¤æ›å‡ºæ¥ãŸã®ãŒè‰¯ã‹ã£ãŸã§ã™ï¼Žç™ºè¡¨ã—ã¦ãã‚Œãã†ãªäººã‚’äºŒäººãã‚‰ã„è¦‹ã¤ã‘ãŸã®ã§ï¼Œæ—¥ç¨‹ã¯æ±ºã¾ã£ã¦ã¾ã›ã‚“ãŒï¼Œã¾ãŸç¬¬äºŒå›žã§ã‚‚ã‚„ã‚ã†ã¨æ€ã£ã¦ã„ã¾ã™ï¼Ž ã“ã®ã‚¤ãƒ™ãƒ³ãƒˆã®å¾Œï¼Œç™ºè¡¨ã«é–¢ã—ã¦ã®è°è«–ãŒä»–ã§è¡Œã‚ã‚ŒãŸ
InoHiro 2015/02/02
Presto
ãƒªãƒ³ã‚¯
A year of using Presto in production | GREE Engineering
I'm Yuyang Lan @ Data Infrastructure team. Here is the 14th entry of GREE Advent Calendar 2014. Please enjoy it! Presto (distributed SQL query engine for Big Data open sourced by Facebook) has already been used in GREE's production environment for more than 1 year, and is now an indispensable part to our Big Data toolbox. In this article, I will present you how we use presto and hope that would be
InoHiro 2014/12/14
gree

Presto
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Prestoã§å®Ÿç¾ã™ã‚‹ã‚¤ãƒ³ã‚¿ãƒ©ã‚¯ãƒ†ã‚£ãƒ–ã‚¯ã‚¨ãƒª - dbtech showcase 2014 Tokyo
Treasure Dataã§ã¯Fluentdãªã©ã§åŽé›†ã—ãŸãƒ‡ãƒ¼ã‚¿ã«å¯¾ã—ã€Prestoã«ã‚ˆã‚‹ä½Žãƒ¬ã‚¤ãƒ†ãƒ³ã‚·ã‚¯ã‚¨ãƒªã‚µãƒ¼ãƒ“ã‚¹ã‚’æä¾›ã—ã¦ã„ã¾ã™ã€‚ã“ã‚Œã«ã‚ˆã‚Šãƒ¦ãƒ¼ã‚¶ãƒ¼ã¯ã™ã°ã‚„ããƒ‡ãƒ¼ã‚¿ã«é–¢ã™ã‚‹çŸ¥è¦‹ã‚’å¾—ã‚‹ã“ã¨ãŒã§ãã€ãƒ‡ãƒ¼ã‚¿åˆ†æžã®ç”Ÿç”£æ€§ã‚’å‘ä¸Šã§ãã¾ã™ã€‚ã“ã®ã‚¹ãƒ©ã‚¤ãƒ‰ã§ã¯åˆ†æ•£SQLã‚¨ãƒ³ã‚¸ãƒ³ã§ã‚ã‚‹Prestoã®ç‰¹å¾´ã¨ãã®å®Ÿè£…ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¾ã™ã€‚ ã“ã®å†…å®¹ã¯dbtech showcase 2014 Tokyo @ç§‹è‘‰åŽŸUDX ã§ç´¹ä»‹ã—ã¾ã—ãŸã€‚ http://www.insight-tec.com/dbts-tokyo-2014.htmlRead less
InoHiro 2014/12/12
TD

Presto
ãƒªãƒ³ã‚¯
ã€ŽPrestoã¨ã¯ä½•ã‹ï¼ŒPrestoã§ä½•ãŒã§ãã‚‹ã‹ã€ - ãƒˆãƒ¬ã‚¸ãƒ£ãƒ¼ãƒ‡ãƒ¼ã‚¿ï¼ˆTreasure Dataï¼‰ãƒ–ãƒã‚°
ãƒˆãƒ¬ã‚¸ãƒ£ãƒ¼ãƒ‡ãƒ¼ã‚¿ã¯ã‚¯ãƒ©ã‚¦ãƒ‰ã§ãƒ‡ãƒ¼ã‚¿ãƒžãƒãƒ¼ã‚¸ãƒ¡ãƒ³ãƒˆã‚µãƒ¼ãƒ“ã‚¹ã‚’æä¾›ã—ã¦ã„ã¾ã™ã€‚ Hadoop Conference Japan 2014 ä»¥å‰ã«å‘ŠçŸ¥ã—ãŸHadoop Conference Japan 2014ã§ï¼Œå¼Šç¤¾Software Architectã®å¤æ©‹ãŒç™ºè¡¨ã—ã¾ã—ãŸã€‚ ãƒ†ãƒ¼ãƒžã¯ï¼ŒFacebookãŒå…¬é–‹ã—ãŸæ–°ã—ã„åˆ†æ•£å‡¦ç†åŸºç›¤ï¼ŒPrestoã€‚å®Ÿã¯FacebookãŒå½¼ã‚‰ã®è¶…å¤§è¦æ¨¡ãªãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã«å¯¾ã—ã¦ã‚¤ãƒ³ã‚¿ãƒ©ã‚¯ãƒ†ã‚£ãƒ–ã«çµæžœã‚’è¿”ã›ã‚‹ã‚ˆã†ã«ã¨é–‹ç™ºã•ã‚ŒãŸã‚‚ã®ã§ã™ã€‚é–‹ç™ºãŒå§‹ã¾ã£ã¦ã¾ã 2å¹´ã‚‚çµŒã£ã¦ãŠã‚Šã¾ã›ã‚“ãŒï¼Œä»Šã§ã¯ãƒˆãƒ¬ã‚¸ãƒ£ãƒ¼ãƒ‡ãƒ¼ã‚¿ã‚’åˆã‚ã¨ã—ã¦å¤šãã®ãƒãƒƒã‚«ãƒ¼é”ãŒã‚³ãƒŸãƒƒã‚¿ãƒ¼ã¨ã—ã¦å‚åŠ ã™ã‚‹æ´»ç™ºçš„ãªãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã«æˆé•·ã—ã¦ã„ã¾ã™ã€‚ Prestoã¯Hiveã‚„Impalaã¨åŒã˜ã€ŒSQL Query Engineã€ã§ã‚ã‚Šï¼Œç‰¹ã«æ•°ç™¾GBã‚’è¶…ãˆã‚‹å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã«å¯¾ã—ã¦ã‚‚ã‚¤ãƒ³ã‚¿ãƒ©ã‚¯ãƒ†ã‚£ãƒ–ãªãƒ¬ã‚¹ãƒãƒ³ã‚¹ã‚’ï¼ˆã‚³ãƒ³ãƒž0ç§’ä»¥ä¸‹ï¼Œé…ãã¦
InoHiro 2014/08/05
Presto

TD
ãƒªãƒ³ã‚¯
Presto @ Facebook: Past, Present and Future
InoHiro 2014/07/09
Presto
ãƒªãƒ³ã‚¯
Presto - Hadoop Conference Japan 2014
Presto is a distributed SQL query engine that allows for interactive analysis of large datasets across various data sources. It was created at Facebook to enable interactive querying of data in HDFS and Hive, which were too slow for interactive use. Presto addresses probl ems with existing solutions like Hive being too slow, the need to copy data for analysis, and high costs of commercial databases
InoHiro 2014/07/09
Presto
ãƒªãƒ³ã‚¯
Hive (SQL-style) Query Language | Treasure Data
{"serverDuration": 35, "requestCorrelationId": "b45a8231fbaabbfb"}
InoHiro 2012/11/07
TD
ãƒªãƒ³ã‚¯
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