Joins
Oracle Database provides several optimizations for joining row sets. This chapter contains the following topics: About Joins Join Methods Join Types Join Optimizations A join combines the output from exactly two row sources, such as tables or views, and returns one row source. The returned row source is the data set. A join is characterized by multiple tables in the WHERE (non-ANSI) or FROM ... JO
Parallel & Distributed Joins | www.systems.ethz.ch
Project Description The architectural changes introduced with multi-core processors have triggered a redesign of main-memory join algorithms for relational database systems. Join processing in database systems is a complex and a demanding operation. Traditionally, there have been sorting and hashing based approaches for implementing joins. However, in the last few years several diverging views hav
HyPer: Hybrid OLTP&OLAP High-Performance Database System
HyPer – A Hybrid OLTP&OLAP High Performance DBMS HyPer is a main-memory-based relational DBMS for mixed OLTP and OLAP workloads. It is a so-called all-in-one New-SQL database system that entirely deviates from classical disk-based DBMS architectures by introducing many innovative ideas including machine code generation for data-centric query processing and multi-version concurrency control, leadin
Peter Bailis: Highly Available, Seldom Consistent | Peter Bailis
Peter Bailis :: Highly Available, Seldom Consistent Data management, distributed systems, and beyond Index How To Make Fossils Productive Again (30 Apr 2016) You Can Do Research Too (24 Apr 2016) Lean Research (20 Feb 2016) I Loved Graduate School (01 Jan 2016) NSF Graduate Research Fellowship: N=1 Materials for Systems Research (03 Sep 2015) Worst-Case Distributed Systems Design (03 Feb 2015) Whe
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2014å¹´3月18日紙版発売 2014å¹´3月18日電å版発売 山崎泰å²ï¼Œæ¦å‰ä½‘ç¥ã€€è‘— A5判ï¼224ページ 定価2,948円（本体2,680円＋税10%） ISBN 978-4-7741-6364-2 ãŸã ã„ã¾å¼Šç¤¾åœ¨åº«ã¯ã”ã–ã„ã¾ã›ã‚“。 Amazon 楽天ブックス ä¸¸å–„ã‚¸ãƒ¥ãƒ³ã‚¯å ‚æ›¸åº— ヨドãƒã‚·.com é›»å版 Gihyo Digital Publishing Amazon Kindle 楽天kobo honto ã“ã®æœ¬ã®æ¦‚è¦ ã€ŒRDBMSã ã¨å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã‚’ã†ã¾ã扱ãˆãªã„ã€ã¨ã„ã‚れ，NoSQLã®ã‚ˆã†ãªä»£æ›¿æŠ€è¡“ãŒç”Ÿã¾ã‚Œã¦ãã¦ã„ã¾ã™ãŒï¼Œæœ¬å½“ã§ã—ょã†ã‹ï¼Ÿ ビッグデータ時代ã§ã‚‚システムã®ä¸æ ¸ã¨ã—ã¦ä¾ç„¶é‡è¦ãªRDBMSã®åŠ›ã‚’100％発æ®ã§ãã‚Œã°ï¼Œé–‹ç™ºã‚„é‹ç”¨ã¯ã‚‚ã£ã¨ãƒ©ã‚¯ã«ãªã‚Šã¾ã™ã€‚ 本書ã§ã¯ï¼Œã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ï¼ŒCPU，ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã¨ã„ã£ãŸã‚らゆる点ã‹ã‚‰ã€Œãªãœï¼ŒRDBMSã¯é…ããªã‚‹ã®ã‹ï¼Ÿã€ã¨ã€Œã©ã†ã™ã‚Œã°ï¼Œæ€§èƒ½ã‚’最
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The Traditional RDBMS Wisdom is All Wrong
The Traditional RDBMS Wisdom is All Wrong by Michael Stonebraker Traditional RDBMS Wisdom ïµData is in disk block formatting (heavily encoded) ïµWith a main memory buffer pool of blocks ïµQuery plans ïµOptimize CPU, I/O ïµFundamental operation is read a row ïµIndexing via B-trees ïµClustered or unclustered Traditional RDBMS Wisdom ïµDynamic row-level locking ïµAries-style write-ahead log ïµReplication (asyn
Schema-on-need | DBMSÂ 2 : DataBase Management System Services
Two years ago I wrote about how Zynga managed analytic data: Data is divided into two parts. One part has a pretty ordinary schema; the other is just stored as a huge list of name-value pairs. (This is much like eBay‘s approach with its Teradata-based Singularity, except that eBay puts the name-value pairs into long character strings.) … Zynga adds data into the real schema when it’s clear it will
DBMS development and other subjects | DBMSÂ 2 : DataBase Management System Services
The cardinal rules of DBMS development Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars. That’s if things go extremely well. Rule 2: You aren’t an exception to Rule 1. In particular: Concurrent workloads benchmarked in the lab are poor predictors of concurrent performance in real life. Mixed workload management is harder than you’re assuming it is. Those minor edge
Optimus: A Dynamic Rewriting Framework for Data-Parallel Execution Plans - Microsoft Research
In distributed data-parallel computing, a user program is compiled into an execution plan graph (EPG), typically a directed acyclic graph. This EPG is the core data structure used by modern distributed execution engines for task distribution, job management, and fault tolerance. Once submitted for execution, the EPG remains largely unchanged at runtime except for some limited modifications. This m
Flash-Based DBMSã®æœ€å‰ç·š
フラッシュメモリーを使ã£ãŸSolid State Drive (SSD)ã®å®¹é‡ãŒ160GBã«åˆ°é”ã—ã€å¸‚å ´ä¾¡æ ¼ã‚‚ä¸‹ãŒã£ã¦ããŸã“ã¨ã«ã‚ˆã‚Šã€ãƒãƒ¼ãƒ‰ãƒ‡ã‚£ã‚¹ã‚¯ã®ä»£æ›¿å“ã¨ã—ã¦SSDを使ã†ç”¨é€”ãŒã„よã„よç¾å®Ÿå‘³ã‚’帯ã³ã¦ãã¾ã—ãŸã€‚低容é‡ã®ã‚‚ã®ãªã‚‰æ—¢ã«iPodやデジカメ用ã®ãƒ¡ãƒ‡ã‚£ã‚¢ãªã©èº«ã®å›žã‚Šã«ã‚‚æ™®åŠã—ã¦ãŠã‚Šã€å¸‚å ´ã§ã¯ã™ã§ã«ã€Œç ´å£Šçš„イノベーション（「イノベーションã®ã‚¸ãƒ¬ãƒ³ãƒžâ€•æŠ€è¡“é©æ–°ãŒå·¨å¤§ä¼æ¥ã‚’æ»…ã¼ã™ã¨ãã€ã‚ˆã‚Šï¼‰ã€ãŒèµ·ã“ã£ã¦ã„ã‚‹ã¨ã„ãˆã¾ã™ã€‚（HDDæ載ã®Walkmanã¨ã‹æ—¢ã«æ»…ã‚“ã§ã„る例もã‚ã‚‹ã—。。。）
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ã¾ãŸã€ãƒ‡ãƒ¼ã‚¿ã®å…¨ä»¶å‡¦ç†ã€ç‰¹ã«I/Oã®æ€§èƒ½ãŒæ±‚ã‚られる多é‡ã®ãƒãƒƒãƒå‡¦ç†ã«ã¯MapReduceãŒæ–°ãŸãªé¸æŠžè‚¢ã¨ã—ã¦åŠ ã‚ã‚Šã¾ã™ã€‚MapReduceã¯ãƒ‡ãƒ¼ã‚¿ã‚¤ãƒ³ãƒ‡ãƒƒã‚¯ã‚·ãƒ³ã‚°ã‚„éžæ§‹é€ 化データã®å‰å‡¦ç†ï¼ˆETLã€ã‚¯ãƒ¬ãƒ³ã‚¸ãƒ³ã‚°ï¼‰ã¸ã®é©ç”¨ãŒæœ‰åã§ã™ãŒã€ã‚ˆã‚Šæ±Žç”¨çš„ãªãƒãƒƒãƒå‡¦ç†ã®ã‚¤ãƒ³ãƒ•ãƒ©æŠ€è¡“[3]ã¨ã—ã¦é©ç”¨ç¯„囲ãŒåºƒãŒã£ã¦ã„ã¾ã™ã€‚ ã“ã†ã—ãŸæŠ€è¡“ã®é¸æŠžè‚¢ã®ä¸ã§ã€ç‰¹ã«ã‚¹ã‚±ãƒ¼ãƒ«ã•ã›ã‚‹ãŸã‚ã«ã¯ãƒ‡ãƒ¼ã‚¿ã‚’分割ã—ã€åˆ†æ•£é…ç½®ã•ã›ã‚‹ã“ã¨ã¨ã€ãƒ‡ãƒ¼ã‚¿ã®é…ç½®å ´æ‰€ã«å‡¦ç†ã‚’æŒã£ã¦ã„ãã“ã¨ã€ã¤ã¾ã‚Šã€ã‚·ã‚§ã‚¢ãƒ¼ãƒ‰ãƒŠãƒƒã‚·ãƒ³ã‚°ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹æŠ€è¡“ãŒã‚¢ãƒ—リケーションアーã‚テクãƒãƒ£ãƒ¼ã«å¤§ããªå½±éŸ¿ã‚’åŠã¼ã—ã¦ã„ã¾ã™ã€‚ éžæ§‹é€ 化データをå«ã‚ãŸå¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿åˆ†æžã¯MapReduceã§å®Ÿè¡Œã—ã€ãã®çµæžœã®é›†è¨ˆã‚„レãƒãƒ¼ãƒˆæ©Ÿèƒ½ã«RDBを利用ã—ã¦åˆ†æžçµæžœã®å¯è¦–化をã™ã‚‹ã®ãŒç¾åœ¨ã¯ä¸»æµã¨ãªã£ã¦ã„ã¾ã™ã€‚ã—ã‹ã—ã€ã“ã®æ–¹æ³•ã¯å分ã«å…¨ä½“最é©åŒ–ãŒã•ã‚Œã¦ãªãã€ãƒ‡ãƒ¼ã‚¿è»¢é€ã‚³ã‚¹ãƒˆã®ç„¡é§„ãŒç™ºç”Ÿã—ã¦
Apache Derby
Apache Derby, an Apache DB subproject, is an open source relational database implemented entirely in Java and available under the Apache License, Version 2.0. Some key features include: Derby has a small footprint -- about 3.5 megabytes for the base engine and embedded JDBC driver. Derby is based on the Java, JDBC, and SQL standards. Derby provides an embedded JDBC driver that lets you embed Derby
Paper: Hyder - Scaling Out without Partitioning - High Scalability -
Partitioning is what differentiates scaling-out from scaling-up, isn't it? I thought so too until I read Pat Helland's blog post on Hyder, a research database at Microsoft, in which the database is the log, no partitioning is required, and the database is multi-versioned. Not much is available on Hyder. There's the excellent summary post from Mr. Helland and these documents: Scaling Out without Pa
Top 5 Database Research Topics in 2008
岡野原å›ãŒè‡ªç„¶è¨€èªžå‡¦ç†é–¢é€£ã§2008å¹´ã«èªã‚“ã è«–æ–‡ã®ãƒ™ã‚¹ãƒˆ5を紹介ã—ã¦ã„ã¾ã™ã€‚ãã‚Œã«å€£ã£ã¦ã€åƒ•ã‚‚個人的ã«ã‚¤ãƒ³ãƒ‘クトã®ã‚ã£ãŸ2008å¹´ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ç ”究ã®ãƒ™ã‚¹ãƒˆï¼•ã‚’集ã‚ã¦ã¿ã¾ã—ãŸã€‚ Michael J. Cahill, Uwe Röhm and Alan D. Fekete. Serializable Isolation for Snapshot Databases. SIGMOD 2008. (ACM DOI) 真ã£å…ˆã«æ€ã„æµ®ã‹ã‚“ã§ããŸã®ãŒã“ã®è«–文。SIGMOD2008ã®ãƒ™ã‚¹ãƒˆãƒšãƒ¼ãƒ‘ーã§ã‚‚ã‚ã‚Šã¾ã™ã€‚僕自身ã€ãƒˆãƒ©ãƒ³ã‚¶ã‚¯ã‚·ãƒ§ãƒ³å‡¦ç†ã‚’é•·ãç ”ç©¶ã—ã¦ã„ãŸçµŒé¨“ã‹ã‚‰ã€Serializability（ディスクã®read/writeã®é †ç•ªã‚’ã‚るプãƒãƒˆã‚³ãƒ«ã«å¾“ã£ã¦å…¥ã‚Œæ›¿ãˆã¦ã‚‚ã€ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®æ¤œç´¢ãƒ»æ›´æ–°çµæžœã«å½±éŸ¿ã‚’与ãˆãªã„）をä¿éšœã—ã¤ã¤ã€ä¸€ç§’é–“ã‚ãŸã‚Šã«å‡¦ç†ã§ãるトランザクションã®æ•°ï¼ˆã¤ã¾ã‚Šã‚¹ãƒ«ãƒ¼ãƒ—ット）を上ã’ã‚‹
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https://dl.acm.org/citation.cfm?id=2996454
Availability, Consistency, and Horizontally Scalable Data Management (SF Bay Area ACM)
Just-in-time Compilation in Vectorized Query Execution (PDF)
The Desgin and Implementation of Modern Column-Oriented Database Systems
開発メモ: ãƒãƒƒã‚·ãƒ¥DBã®ãƒ¡ã‚¿ãƒ‡ãƒ¼ã‚¿ã®ãƒ•ã‚©ãƒ¼ãƒžãƒƒãƒˆ
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