アリãƒãƒã®ç‹¬è‡ªé–‹ç™ºDBã€ã‚ªãƒ©ã‚¯ãƒ«æŠœã世界一㫠20人ã®éƒ¨éšŠã‹ã‚‰
ã€10月13æ—¥ CNS】アリãƒãƒç³»ãƒãƒƒãƒˆæ±ºæ¸ˆãƒ—ラットフォーム「螞蟻金èžæœå‹™ï¼ˆã‚¢ãƒ³ãƒˆãƒ•ã‚£ãƒŠãƒ³ã‚·ãƒ£ãƒ«ã€Ant Financial）ã€ã¯2æ—¥ã€åŒç¤¾ãŒç‹¬è‡ªã«é–‹ç™ºã—ãŸãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®OceanBaseãŒã€TPC-Cデータベース基準性能テストã®ä¸–界記録を打ã¡ç ´ã‚Šã€å‰ä¸–界記録ä¿æŒè€…ã§ã‚るオラクル（Oracle）ã®2å€ã®æˆç¸¾ã‚’ç²å¾—ã—ãŸã€‚データベースã§æœ€ã‚‚権å¨æ€§ã®ã‚る国際的機構「トランザクション処ç†æ€§èƒ½è©•è°ä¼šï¼ˆTPC）ã€ãŒå…¬å¼ã‚µã‚¤ãƒˆã§æœ€æ–°çµæžœã‚’å ±ã˜ãŸã€‚ ã€é–¢é€£è¨˜äº‹ã€‘アリãƒãƒã®AIé‹ç”¨ã€æ¯Žæ—¥1兆回 世界ã®10億人ã«ã‚µãƒ¼ãƒ“ス TPC-Cã¯ä¸–ç•Œã®ä¸»æµã‚³ãƒ³ãƒ”ューターメーカーやデータベースメーカーãŒèªã‚る評価基準ã§ã€ã€Œãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹é ˜åŸŸã®ãƒ¯ãƒ¼ãƒ«ãƒ‰ã‚«ãƒƒãƒ—ã€ã¨ç§°ã•ã‚Œã‚‹ã€‚ ä¸å›½å·¥ç¨‹é™¢ï¼ˆChinese Academy of Engineering）院士ã§ã‚³ãƒ³ãƒ”ューター専門家ã§ã‚ã‚‹æŽå›½å‚‘（Li Guojie）ã•ã‚“ã¯ã€Œã‚ªãƒ©ã‚¯ãƒ«ãŒ9å¹´
A Benchmark Test on Presto, Spark Sql and Hive on Tez
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.
The History of etcd | CoreOS
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PostgreSQL and MySQL: Millions of Queries per Second
This blog compares how PostgreSQL and MySQL handle millions of queries per second. Anastasia: Can open source databases cope with millions of queries per second? Many open source advocates would answer “yes.†However, assertions aren’t enough for well-grounded proof. That’s why in this blog post, we share the benchmark testing results from Alexander Korotkov (CEO of Development, Postgres Professio
osc16tk ã§Oracleã¨PostgreSQLを比較 - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? 11/5ã«ã‚ªãƒ¼ãƒ—ンソースカンファレンス 2016 Tokyo/Fallã«è¡Œã£ã¦ã€PostgreSQLä¸å¿ƒã«è´ã„ã¦ãã¾ã—ãŸã€‚ ç·åˆãƒãƒƒã‚·ãƒ¥ã‚¿ã‚°ã¯ #osc16tk 「生誕20周年を迎ãˆãŸPostgreSQLを使ã£ã¦ã¿ã‚ˆã†ã€ JPUGä¸å›½æ”¯éƒ¨é•·ã€æ›½æ ¹å£®å¤§æ° Speaker Deckã®ã‚¹ãƒ©ã‚¤ãƒ‰ 実æ³tweet拾ã„上㒠データ分æžã‚„ã‚‹ãªã‚‰Pythonã§ã—ょ。ã§ã€Pythonã¨PostgreSQLã¯PL/Pythonã‚ã£ã¦ä½¿ã„ã‚„ã™ã„。例ãˆã°Pythonã®æ©Ÿæ¢°å¦ç¿’Libraryã‚’PL/Pythonã‹ã‚‰å‘¼ã¹ã‚‹ã€‚パラレルクエリもã‚る。 Pythonã§ã‚¹
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The Netflix member experience is offered to 83+ million global members, and delivered using thousands of microservices. These services are owned by multiple teams, each having their own build and release lifecycles, generating a variety of data that is stored in different types of data store systems. The Cloud Database Engineering (CDE) team manages those data store systems, so we run benchmarks t
TPCãŒè¨å®šã—ãŸãƒ“ッグデータå‘ã‘ベンãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆã¯ã€éžæ§‹é€ 化データやåŠæ§‹é€ 化データã®å‡¦ç†ã€æ©Ÿæ¢°å¦ç¿’ã¾ã§æ€§èƒ½æ¸¬å®šã®å¯¾è±¡ ï¼»PRï¼½
TPCãŒè¨å®šã—ãŸãƒ“ッグデータå‘ã‘ベンãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆã¯ã€éžæ§‹é€ 化データやåŠæ§‹é€ 化データã®å‡¦ç†ã€æ©Ÿæ¢°å¦ç¿’ã¾ã§æ€§èƒ½æ¸¬å®šã®å¯¾è±¡ ï¼»PRï¼½ 「TPCã€ï¼ˆTransaction Processing Performance Council：トランザクション処ç†æ€§èƒ½è©•è°ä¼šï¼‰ã¨ã„ãˆã°ã€ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®æ€§èƒ½ã‚’計測ã™ã‚‹ã•ã¾ã–ã¾ãªãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆã‚’ç–定ã—発表ã—ã¦ã„るベンダニュートラルãªå›£ä½“ã¨ã—ã¦ã€å¤šãã®ITエンジニアã«çŸ¥ã‚‰ã‚Œã¦ã„ã‚‹ã§ã—ょã†ã€‚ ãã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ãƒ†ã‚¹ãƒˆã¯äº‹å®Ÿä¸Šã®æ¨™æº–ã¨ã—ã¦å—ã‘入れられã€å¤šãã®ãƒ™ãƒ³ãƒ€ã‚„ユーザーãŒè£½å“ã®å‘上やé¸æŠžã®ãŸã‚ã«å‚ç…§ã—活用ã—ã¦ã„ã¾ã™ã€‚ TPCãŒå…¬é–‹ã—ã¦ã„るベンãƒãƒžãƒ¼ã‚¯ã«ã¯ã€ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ãƒˆãƒ©ãƒ³ã‚¶ã‚¯ã‚·ãƒ§ãƒ³å‡¦ç†ï¼ˆOLTP）性能を計測ã™ã‚‹ã€ŒTPC-Cã€ã‚„「TPC-Eã€ã ã‘ã§ãªãã€å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿åˆ†æžã«ã‚ˆã‚‹ãƒ‡ã‚·ã‚¸ãƒ§ãƒ³ã‚µãƒãƒ¼ãƒˆã®ãŸã‚ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹æ€§èƒ½ã‚’計測ã™ã‚‹ã€ŒTPC-Hã€ã€ŒTPC-DSã€ã€ŒTPC
Spark Takes On Dataflow in Benchmark Test
Google Cloud Dataflow crunched data two to five times faster than Apache Spark in a benchmark test of batch analytics performed by Mammoth Data. While Dataflow’s raw power is impressive, don’t throw in the towel on Spark just yet. If you’re looking to choose a framework to analyze your big data, good luck. With so many options out there, you’ve got your work cut out for you. This embarrassment of
ビッグデータå¯è¦–化ã®æ€§èƒ½ã‚’徹底検証 〜SparkSQLã€Hive on Tezã€Hive LLAPを用ã„ãŸæ—¢å˜RDBデータ 処ç†ã®ç‰¹å¾´ã€œ
Apache Hadoop ã®ç¾åœ¨ã¨å°†æ¥ï¼ˆHadoop / Spark Conference Japan 2016 ã‚ーノート講演資料）
TRex
Realistic Traffic GeneratorTRex is an open source, low cost, stateful and stateless traffic generator fuelled by DPDK. It generates L3-7 traffic and provides in one tool capabilities provided by commercial tools. TRex Stateless functionality includes support for multiple streams, the ability to change any packet field and provides per stream/group statistics, latency and jitter. Advanced Stateful
HSQLDB example - JDBCBench.java - exception, exception, query, query, string, string
HSQLDB example source code file (JDBCBench.java) This example HSQLDB source code file (JDBCBench.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM. Java - HSQLDB tags/keywords aid, client, e, e, exception, exception, integer, io, jdbc, query, query, sql, string, string, tabfile, table, util, where The HS
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NoSQL benchmarks and performance evaluations
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