Sysbench, a large server and fast SSD vs MyRocks, InnoDB and TokuDB
Sysbench, a large server and fast SSD vs MyRocks, InnoDB and TokuDB I have new performance results to share starting with modern sysbench, a large server, fast SSD and a database that is larger than RAM. Competitive read performance with much better space and write efficiency is why I like MyRocks and this is an example of that. However, we have work to do on read efficiency that will be apparent
HPC サーãƒãƒ¼ AMD 姫野ベンムデーターセンター AS-1023US-TR4(VT64 Server AE-1S) | ビジュアルテクノãƒã‚¸ãƒ¼æ ªå¼ä¼šç¤¾
VT64 Server AE-1S (AS-1023US-TR4) 性能事例 1ノード（EPYC 7601 2CPU構æˆï¼‰ã§å§«é‡Žãƒ™ãƒ³ãƒå„ªã«100GFLOPS超ãˆ! 2017å¹´6月20æ—¥ã«AMDã‹ã‚‰ç™ºè¡¨ã•ã‚ŒãŸæ–°CPU「EPYCã€ã§ã€æ—©é€Ÿå§«é‡Žãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆã‚’実施ã—ã¾ã—ãŸã€‚ 比較対象ã®CPUã¯ä»¥ä¸‹ã®é€šã‚Šã§ã€AMDã®ã€ŒEPYCã€ã¯ã€EPYC 7601 (2.2GHz 32c x 2) DDR4-2666ã§ã™ã€‚ E5 2695v2 (2.4GHz 12c) x2 DDR3-1866 E5 2695v3 (2.3GHz 14c) x2 DDR4-2133 E5-2697v4 (2.3GHz 18c x 2) DDR4-2400 AMDã®EPYC 7601ã¯ã€1CPUã«32コアæ載ã•ã‚Œã€æœ€å¤§64並列ã¾ã§æ¸¬å®šã—ã¾ã—ãŸã€‚Xeonã‚‚Dual CPUã§è¨ˆæ¸¬å¯èƒ½ãªæœ€å¤§ä¸¦åˆ—æ•°ã§ã®æ¯”較ã§ã™ã€‚姫野ベンãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆ
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æ–°ã—ã„ストレージ最é©åŒ–インスタンス I3 ã§NVMeをベンãƒãƒžãƒ¼ã‚¯ã—ã¦ã¿ã¾ã—㟠| DevelopersIO
å¤§æ —ã§ã™ã€‚ 先日新ã—ã„ストレージ最é©åŒ–インスタンスã¨ã—㦠I3 ファミリーãŒåˆ©ç”¨å¯èƒ½ã«ãªã‚Šã¾ã—ãŸã€‚旧世代㮠I2ã§ã¯SATA接続ã®SSDã§ã—ãŸãŒã€I3ã§ã¯NVMeã¨ãªã£ã¦ã„ã¾ã™ã€‚SATAã¨æ¯”較ã—ãŸNVMeã®é•ã„ã¨ã—ã¦ä¸¦åˆ—アクセスã«å¼·ã„ã¨ã„ã†éƒ¨åˆ†ãŒã‚ã‚‹ã®ã§ä¸¦åˆ—度を変ãˆã¦ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã‚’å–å¾—ã—ã¦ã¿ã¾ã—ãŸã€‚ 「i3ã€æ–°ã—ã„ストレージ最é©åŒ–インスタンスãŒåˆ©ç”¨å¯èƒ½ã«ãªã‚Šã¾ã—㟠ベンãƒãƒžãƒ¼ã‚¯ã®ç’°å¢ƒ EC2環境 リージョン: æ±äº¬ï¼ˆap-northeast-1） OS: Amazon Linux AMI 2016.09.1.20170119 x86_64 HVM GP2 (ami-56d4ad31) インスタンスタイプ i3.xlarge i2.xlarge ファイルシステム: ext4 ベンãƒãƒžãƒ¼ã‚¯ãƒ„ール 使用ソフト: fio アーã‚テクãƒãƒ£: x86_64 ãƒãƒ¼ã‚¸ãƒ§ãƒ³: 2.1.5-1.5.amzn1
Intel® Memory Latency Checker v3.11
Contributors Vish Viswanathan, Karthik Kumar, Thomas Willhalm, Sri Sakthivelu, Sharanyan Srikanthan Introduction An important factor in determining application performance is the time required for the application to fetch data from the processor’s cache hierarchy and from the memory subsystem. In a multi-socket system where Non-Uniform Memory Access (NUMA) is enabled, local memory latencies and cr
A Benchmark Test on Presto, Spark Sql and Hive on Tez
Prestoã€Spark SQLã¨Hive on Tezã®æ€§èƒ½ã«é–¢ã—ã¦ã€æ•°ä¸‡ä»¶ã‹ã‚‰æ•°å億件ã¾ã§ã®ãƒ‡ãƒ¼ã‚¿ä¸Šã«ã€å¸¸ç”¨ã‚¯ã‚¨ãƒªãƒ‘ターンã®å®Ÿè¡Œã‚¹ãƒ”ードãªã©ã‚’検証ã—ã¦ã¿ãŸã€‚ We conducted a benchmark test on mainstream big data sql engines including Presto, Spark SQL, Hive on Tez. We focused on the performance over medium data (from tens of GB to 1 TB) which is the major case used in most services. Read less
IaaSクラウドサーãƒãƒ¼ã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ï¼ˆ2016å¹´9月版） | 稲葉サーãƒãƒ¼ãƒ‡ã‚¶ã‚¤ãƒ³
（2016.11.1追記） ã•ãらã®ã‚¯ãƒ©ã‚¦ãƒ‰ã¨AWSã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆã¯SSDディスクを使用ã—ãŸã®ã§ã™ãŒã€ã‚ã‹ã‚Šã«ãã„よã†ã§ã—ãŸã®ã§è¡¨ã«è¿½è¨˜ã—ã¾ã—ãŸã€‚ 2014å¹´4月ã«JAWS-UGæœå¹Œã§ã€ŒIaaSクラウド比較’14ã€ã¨é¡Œã—ã¦ç™ºè¡¨ã—ã¦ã‹ã‚‰2å¹´åŠè¿‘ã経ã¡ã¾ã—ãŸã€‚ ã“ã®ã¨ãã®ç™ºè¡¨ã§ã€ã„ãã¤ã‹ã®IaaSクラウドサーãƒãƒ¼ã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆçµæžœã‚’公開ã—ãŸã®ã§ã™ãŒã€ãã®å¾Œã€AWSã¯SSDã«å¯¾å¿œã—ãŸEBSをリリースã—ディスク性能ãŒå¤§ããå‘上ã—ã¾ã—ãŸã€‚ ã¾ãŸã€Google Cloud Platformã‚„Microsoft Azureもユーザー数を増やã—ã¦ã„ã¾ã™ã€‚ 今回ã¯ã€ã‚ˆã‚Šå¤šãã®IaaSクラウドサービスã§ã€åƒ•ãŒã‚ˆã使ã†ãƒãƒ¼ã‚¨ãƒ³ãƒ‰ã®Linuxサーãƒãƒ¼ã«å¯¾ã—ã¦ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆã‚’è¡Œã£ã¦ã¿ãŸã®ã§ã€ãã®çµæžœã‚’ã“ã“ã«ã¾ã¨ã‚ã¾ã™ã€‚ 2016å¹´9月時点ã®ã€æœ€æ–°ç‰ˆãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆçµæžœã§ã™ã€‚ ãªãŠã€ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹
Getting the most out of your NVMe SSD
Getting the most out of your NVMe SSD Charles Rose Lakshmi Narayanan Durairajan Red Hat Summit June 2015 2 Agenda • NVMe – A Brief Introduction – What is NVMe – Why NVMe • Overview of NVMe Components • NVMe SSD vs. SAS SSD • NVMe Usage Scenarios and Performance – File Systems on NVMe – dm-cache – Mariadb NVMe – A Brief Introduction 4 NVMe (Non-Volatile Memory Express) • A specification for accessi
Benchmarking Apache Parquet: The Allstate Experience - Cloudera Blog
Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working at Allstate Insurance, for the guest post below about his experiences comparing use of the Apache Avro and Apache Parquet file formats with Apache Spark. Over the last few months, numerous hallway conversations, informal discussions, and meetings have occurred at Allstate about the relative merits of
The Performance Impact Of Turbo Boost On Linux - Phoronix
The Performance Impact Of Turbo Boost On Linux Written by Michael Larabel in Intel on 28 February 2016 at 10:25 AM EST. 21 Comments After the tests this weekend of Intel Hyper Threading and core scaling with the new Intel Xeon E3 "Skylake" processor, I proceeded to do some Turbo Boost benchmarking. Namely, I ran a set of open-source Linux benchmarks when the Xeon E3 1280 v5 was running in its defa
Hyper Threading Performance & CPU Core Scaling With Intel's Skylake Xeon - Phoronix
Hyper Threading Performance & CPU Core Scaling With Intel's Skylake Xeon Written by Michael Larabel in Intel on 27 February 2016 at 12:34 PM EST. 10 Comments As some extra benchmarks following this week's 9-Way Intel Xeon E3 v5 Skylake Benchmarks On Ubuntu Linux, some Phoronix Premium readers wanted to see how well Hyper Threading worked for these latest-generation Xeon E3 processors and the core
Google BigQuery Benchmark — DoIT International :: Helping businesses adopt, manage and enhance cloud solutions from Google and Zendesk
By Vadim Solovey, Google Developer Expert.Recently, I came across an interesting benchmark of BigData systems based on "A Comparison of Approaches to Large-Scale Data Analysis" by Pavlo et al. (SIGMOD 2009). Based on the benchmark methodology, the APMLab guys from Berkeley University developed an open-source software that allows anyone to run this benchmark using public cloud (AWS in this case).Th
Java用JSONパーザã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã‚’ã—ãŸè©± | ã•ã«ã‚らãš
ã“ã®ãŸã³ JSON パーザã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã‚’書ãã¾ã—ãŸã®ã§ã€ã©ã†ãžã”査åŽä¸‹ã•ã„。 taichi/json-benchmarks Java8 ãŒã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«æ¸ˆã¿ã®ç’°å¢ƒã§ã€å½“該リãƒã‚¸ãƒˆãƒªã‚’ clone ã—㦠ã¨å®Ÿè¡Œã™ã‚‹äº‹ã§è¿½è©¦ã§ãã¾ã™ã€‚ çµæžœã«ã¤ã„ã¦#jackson-databind ãŒæœ€é€Ÿãªã®ã§æ®†ã©ã®çš†æ§˜ã«ãŠã‹ã‚Œã¾ã—ã¦ã¯å®‰å¿ƒã—ã¦ä¸‹ã•ã„。 特ã«é©šãã«å€¤ã™ã‚‹ã‚ˆã†ãªäº‹ã¯ä½•ã‚‚èµ·ãã¦ã„ã¾ã›ã‚“。 僕ã®ãƒžã‚·ãƒ³ã§å®Ÿè¡Œã—ãŸçµæžœã¯ã“ã¡ã‚‰ã§ã™ã€‚（エビデンスãŒç„¡ã„ã¨ã®æŒ‡æ‘˜ã‚’å—ã‘追記ã—ã¾ã—ãŸã€‚） Json benchmark resultsçµæ§‹ãªé•·æ–‡ã«ãªã£ã¦ã—ã¾ã„ã¾ã—ãŸã®ã§ã€ãƒžãƒ‹ã‚¢ãªçš†æ§˜ã ã‘ãŒç¶šãã‚’èªã‚“ã§ä¸‹ã•ã„。 課題è¨å®šã«ã¤ã„ã¦#ã¾ãšã€ãŠãŠã¾ã‹ãªèª²é¡Œè¨å®šã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ã€‚ JMH を使ã£ãŸæœ¬æ ¼çš„ãªãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã‚’書ãJava 用 JSON パーザã®ã‚·ãƒ§ãƒ¼ã‚±ãƒ¼ã‚¹ã‚’作る事ã§ä½¿ã„易ã„ã‚‚ã®ã‚’明らã‹ã«ã™ã‚‹çµå±€ã©ã®ãƒ‘ーザãŒé€Ÿã„ã®
Evaluating persistent, replicated message queues
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Webフレームワークベンãƒãƒžãƒ¼ã‚¯2015
Spring Bootã«ã‚ˆã‚‹APIãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰æ§‹ç¯‰å®Ÿè·µã‚¬ã‚¤ãƒ‰ 第2版 何åƒäººã‚‚ã®é–‹ç™ºè€…ãŒã€InfoQã®ãƒŸãƒ‹ãƒ–ック「Practical Guide to Building an API Back End with Spring Bootã€ã‹ã‚‰ã€Spring Bootを使ã£ãŸREST API構築ã®åŸºç¤Žã‚’å¦ã‚“ã 。ã“ã®æœ¬ã§ã¯ã€å‡ºç‰ˆæ™‚ã«æ–°ã—ãリリースã•ã‚ŒãŸãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹ Spring Boot 2 を使用ã—ã¦ã„る。ã—ã‹ã—ã€Spring Boot3ãŒæœ€è¿‘リリースã•ã‚Œã€é‡è¦ãªå¤‰...
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