[B! database] rawwellã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

rawwell id:rawwell

databaseã«é–¢ã™ã‚‹rawwellã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (112)

${{author_name}}$

{{{comment_expanded}}}

{{label}}

{{#is_bookmark}}ãƒªã‚¹ãƒˆ{{/is_bookmark}}{{^is_bookmark}}ãƒªãƒ³ã‚¯{{/is_bookmark}}

${{author_name}}$
{{author_name}}{{created}}
{{ #comment }}{{ comment }}{{ /comment }}
- {{ label }}

${{author_name}}$

{{{comment_expanded}}}

{{label}}

{{#is_bookmark}}ãƒªã‚¹ãƒˆ{{/is_bookmark}}{{^is_bookmark}}ãƒªãƒ³ã‚¯{{/is_bookmark}}

megakv.dvi
rawwell 2020/07/23
database

gpu
ãƒªãƒ³ã‚¯
Are ORMs really a thing of the past ?
rawwell 2012/12/13
"If you are not designing an ActiveRecord based model, it's of paramount importance that you keep your domain model decoupled from the persistent model. And ORMs offer the most pragmatic way towards this approach. I know people will say that it's indeed difficult to achieve this in a real life world

database

checkitout
ãƒªãƒ³ã‚¯
What are the best recommended research topics on databases according to edge technologies and recent research trends?
Answer (1 of 4): * Acording to the DBWorld mailing list trends the hottest topic of research today is Distributed Databases (tera-scale and peta-scale): http://www.cs.wisc.edu/dbworld/ * Also see VLDB conference program: http://www.vldb2010.org/accept.htm and CIDR: http://www.cidrdb.org/cidr20...
rawwell 2011/07/01
checkitout

database
ãƒªãƒ³ã‚¯
IBM DB2 ã‚¤ãƒ³ãƒ•ã‚©ãƒ¡ãƒ¼ã‚·ãƒ§ãƒ³ãƒ»ã‚»ãƒ³ã‚¿ãƒ¼
rawwell 2011/06/13
checkitout

database
ãƒªãƒ³ã‚¯
Transaction Processing Performance Council - Wikipedia
rawwell 2011/04/04
work

database

research
ãƒªãƒ³ã‚¯
solidDB - Wikipedia, the free encyclopedia
rawwell 2011/04/04
research

database

work
ãƒªãƒ³ã‚¯
VoltDB - Wikipedia
VoltDBã¯Ingresã‚„PostgreSQLã«æºã‚ã£ãŸãƒžã‚¤ã‚±ãƒ«ãƒ»ã‚¹ãƒˆãƒ¼ãƒ³ãƒ–ãƒ¬ãƒ¼ã‚«ãƒ¼ã«ã‚ˆã‚Šè¨è¨ˆã•ã‚ŒãŸã‚¤ãƒ³ãƒ¡ãƒ¢ãƒªãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã§ã‚ã‚‹ã€‚ACIDã«æº–æ‹ ã—ãŸé–¢ä¿‚ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚·ã‚¹ãƒ†ãƒ ã§ã‚ã‚Šã€ã‚·ã‚§ã‚¢ãƒ¼ãƒ‰ãƒ»ãƒŠãƒƒã‚·ãƒ³ã‚°ãƒ»ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã«ã‚ˆã‚‹ã‚‚ã®ã§ã‚ã‚‹ã€‚GPL ãƒãƒ¼ã‚¸ãƒ§ãƒ³3ã®ã‚‚ã¨ã§ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¦ã„ã‚‹è‡ªç”±ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã‚ã‚‹ã€‚VoltDBã¯å¦è¡“çš„ãªH-Storeãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã‚’åŸºç¤Žã¨ã—ã¦ã„ã‚‹ã€‚[2] VoltDBã¯é–¢ä¿‚ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã§ã‚ã‚Šã€ãƒ—ãƒªã‚³ãƒ³ãƒ‘ã‚¤ãƒ«ã•ã‚ŒãŸJavaã‚¹ãƒˆã‚¢ãƒ‰ãƒ—ãƒã‚·ãƒ¼ã‚¸ãƒ£ã‹ã‚‰ã®SQLã‚¢ã‚¯ã‚»ã‚¹ã‚’ã‚µãƒãƒ¼ãƒˆã™ã‚‹ã€‚ãƒˆãƒ©ãƒ³ã‚¶ã‚¯ã‚·ãƒ§ãƒ³ã®å˜ä½ã¯ã‚¹ãƒˆã‚¢ãƒ¼ãƒ‰ãƒ—ãƒã‚·ãƒ¼ã‚¸ãƒ£ã§ã‚ã‚Šã€ã“ã‚Œã¯SQLã¨JavaãŒæ··åˆã•ã‚ŒãŸã‚‚ã®ã§ã‚ã‚‹ã€‚VoltDBã¯ã‚¹ã‚±ãƒ¼ãƒ©ãƒ“ãƒªãƒ†ã‚£ã®ãŸã‚ã«å€‹ã€…ã®ãƒãƒ¼ãƒ‰ã‚¦ã‚§ã‚¢ã«ãŠã‘ã‚‹ã‚¹ãƒ¬ãƒƒãƒ‰ã§ã®æ°´å¹³ãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ‹ãƒ³ã‚°ã‚’åˆ©ç”¨ã—ã€å …ç‰¢æ€§ã®ãŸã‚ã«k-safety(åŒæœŸçš„ãƒ¬ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³)ã‚’åˆ©ç”¨ã—ã¦ã„ã‚‹ã€‚ ã‚¹ãƒˆã‚¢ãƒ‰ãƒ—ãƒã‚·
rawwell 2011/04/04
work

research

database
ãƒªãƒ³ã‚¯
https://docs.google.com/viewer?a=v&q=cache:8DTllz1c5A0J:pages.cs.wisc.edu/~jignesh/publ/hadoopjoin.pdf+filetype:pdf+%22A+comparison+of+join+algorithms+for+log+processing+in+MaPreduce%22&hl=en&pid=bl&srcid=ADGEESgXjmGdMCDX5mmKDDVF7tr7rNbOzD_VvDBYko1yK9LMCcC
rawwell 2011/03/05
checkitout

database

mapreduce
ãƒªãƒ³ã‚¯
https://docs.google.com/viewer?a=v&q=cache:oDLjTp6MWvMJ:db.apache.org/derby/docs/10.3/getstart/getstartderby.pdf+filetype:pdf+%22Introduction+to+Derby%22&hl=en&pid=bl&srcid=ADGEESgaNPzVazjRW_yP1qHnnX8t4p7ETGrxHmO-RxOAJ4fyEJcvJmUXTUBg40dARSGbQ8WfQLVckCaHq0s
rawwell 2010/10/07
checkitout

database
ãƒªãƒ³ã‚¯
https://docs.google.com/viewer?url=http://mesos.berkeley.edu/mesos_tech_report.pdf
rawwell 2010/10/07
checkitout

database
ãƒªãƒ³ã‚¯
TwitterãŒåˆ†æ•£ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã€ŒGizzardã€å…¬é–‹ï¼ Scalaã§æ›¸ã‹ã‚ŒãŸShardingã‚’å®Ÿç¾ã™ã‚‹ãƒŸãƒ‰ãƒ«ã‚¦ã‚§ã‚¢
TwitterãŒåˆ†æ•£ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã€ŒGizzardã€å…¬é–‹ï¼ Scalaã§æ›¸ã‹ã‚ŒãŸShardingã‚’å®Ÿç¾ã™ã‚‹ãƒŸãƒ‰ãƒ«ã‚¦ã‚§ã‚¢ Twitterã¯ç‹¬è‡ªã«é–‹ç™ºã—ãŸåˆ†æ•£ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã®ã€ŒGizzardã€ã‚’ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã¨ã—ã¦å…¬é–‹ã—ã¾ã—ãŸã€‚Gizzardã¯Scalaã§æ›¸ã‹ã‚ŒãŸJavaVMä¸Šã§å‹•ä½œã™ã‚‹ãƒŸãƒ‰ãƒ«ã‚¦ã‚§ã‚¢ã§ã€PHPã‚„Rubyã¨ã„ã£ãŸWebã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã‹ã‚‰ã®è¦æ±‚ã‚’è‡ªå‹•çš„ã«ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã«åˆ†æ•£ã™ã‚‹ã“ã¨ã§ã€å¤§è¦æ¨¡ã§å¯ç”¨æ€§ã®é«˜ã„åˆ†æ•£ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚’å®¹æ˜“ã«å®Ÿç¾ã™ã‚‹ãŸã‚ã®ã‚‚ã®ã§ã™ã€‚ Gizzardï¼šãƒ•ã‚©ãƒ«ãƒˆãƒˆãƒ¬ãƒ©ãƒ³ãƒˆãªåˆ†æ•£ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚’å®Ÿç¾ The Twitter Engineering Blog: Introducing Gizzard, a framework for creating distributed datastores Twitterã®ãƒ–ãƒã‚°ã«ãƒã‚¹ãƒˆã•ã‚ŒãŸã€ŒIntroducing Gizzard
rawwell 2010/04/08
"ã“ã®2ã¤ã®æŠ€è¡“ã‹ã‚‰ãªã‚‹ã€ŒShardingã€ã¯ã€ã—ã‹ã—é›£ã—ã„æŠ€è¡“ã ã¨ã„ã„ã¾ã™ã€‚ The problem is: sharding is difficult. Determining smart partitioning schemes for particular kinds of data requires a lot of thought. And even more difficult is ensuring that all of the copies of th

database

checkitout
ãƒªãƒ³ã‚¯
English (US)
Did someone say â€¦ cookies? X and its partners use cookies to provide you with a better, safer and faster service and to support our business. Some cookies are necessary to use our services, improve our services, and make sure they work properly. Show more about your choices.
rawwell 2010/04/08
checkitout

database
ãƒªãƒ³ã‚¯
Google File System v2, part 2 | StorageMojo
Google File System v2, part 2 by Robin Harris | Tuesday, August 18, 2009 | Architecture, Cloud computing & storage, Clusters, Information Management | 1 comment Bigtable to the rescue (sort of) In Part 1, Sean Quinlan, a Google engineer, related how the original GFS single master architecture became a bottleneck. But since Google controls its entire software stack from OS to apps, it could compens
rawwell 2010/04/07
"Google engineers have been hard at work for the last 2 years building a distributed master system that will work better with Bigtable to fix many of the current problems. Still, it is amazing that in 1 year 4 or 5 people could put together a file system critical to Googleâ€™s success for almost 10

checkitout

database

scalability
ãƒªãƒ³ã‚¯
TPC-C: comparing SSD & disk | StorageMojo
rawwell 2010/04/07
checkitout

database
ãƒªãƒ³ã‚¯
NoSQL Job Trends â€“ March 2010
rawwell 2010/03/15
"HBase is clearly growing quicker than the others, so we can assume that HBase and its Hadoop siblings will continue to be in demand. SimpleDB shows the same growth spike as it does in the raw job data. MongoDB is one growth line that does stand out as it does not directly correlate to its current p

checkitout

database

nosql
ãƒªãƒ³ã‚¯
kumofsã‹ã‚‰å¦ã¶Not only SQLã®æŠ€è¡“ - Blog by Sadayuki Furuhashi
NoSQLã‚’çŸ¥ã‚‹ã€œkumofsã‹ã‚‰å¦ã¶Not only SQLã®æŠ€è¡“ ã¨é¡Œã—ã¦ã€Developers Summit 2010ã§ç™ºè¡¨ã—ã¾ã—ãŸã€‚ twitterã®#devsumi2010 kumofsã‚’è¦‹ã‚‹é™ã‚Šã§ã¯å¤§å¤‰ã”å¥½è©•ã‚’ã„ãŸã ã„ãŸã‚ˆã†ã§ã€ã²ã¨ã¾ãšã»ã£ã¨ã—ã¦ã„ã¾ã™ã€‚ ãƒ—ãƒ¬ã‚¼ãƒ³ãƒ†ãƒ¼ã‚·ãƒ§ãƒ³ã®è³‡æ–™ã‚’å…¬é–‹ã—ã¾ã—ãŸã€‚å†…å®¹ã¯ã©ã‚Œã‚‚åŒã˜ã§ã™ãŒã€ã‚¯ãƒªãƒƒã‚¯ã§é€²ã‚€ãƒ ãƒ¼ãƒ“ãƒ¼ç‰ˆãŒã‚ªã‚¹ã‚¹ãƒ¡ã§ã™ã€‚ ã‚¯ãƒªãƒƒã‚¯ã§é€²ã‚€ãƒ ãƒ¼ãƒ“ãƒ¼ï¼ˆã‚¯ãƒªãƒƒã‚¯/çŸ¢å°ã‚ãƒ¼ã§é€²ã‚€ï¼‰ PDF Keynoteãƒ•ã‚¡ã‚¤ãƒ«ï¼ˆKeynote '09ãŒå¿…è¦ï¼‰ NoSQLã‚’çŸ¥ã‚‹View more presentations from frsyuki. Consistent Hashingã¨double-hash-spaceã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã®ç´¹ä»‹ã¯ã€68ãƒšãƒ¼ã‚¸ä»¥é™ã«ã‚ã‚Šã¾ã™ã€‚ ç¬¬101å›ž ã‚«ãƒ¼ãƒãƒ«èªæ›¸ä¼š 2æœˆ25æ—¥ã«æ¥½å¤©ã‚¿ãƒ¯ãƒ¼ã§è¡Œã‚ã‚Œã‚‹ã‚«ãƒ¼ãƒãƒ«èªæ›¸ä¼šã§ã‚‚ã€kumofsé–¢é€£
rawwell 2010/03/04
database

checkitout
ãƒªãƒ³ã‚¯
ã¯ã¦ãªãƒ–ãƒã‚° | ç„¡æ–™ãƒ–ãƒã‚°ã‚’ä½œæˆã—ã‚ˆã†
ã€çŒ®è¡€ãƒ‡ãƒ“ãƒ¥ãƒ¼ã€‘ä½“é‡ãŒå°‘ã—è¶³ã‚Šãš400mlçŒ®è¡€ã¯ã§ããªãã¨ã‚‚ã€çŒ®è¡€ãƒ«ãƒ¼ãƒ ã§ã®æˆåˆ†çŒ®è¡€ãªã‚‰ã§ããŸãžã€ã¨ã„ã†è©± ã„ãã•ã¤ 2025å¹´ã®æŠ±è² ã¨ã—ã¦ã€Œ400mlçŒ®è¡€ã‚’ã§ãã‚‹ã‚ˆã†ã«ãªã‚‹ã€ã‚’æŽ²ã’ã¦ã‹ã‚‰ã€å†¬ã‚’è¶Šã—æ˜¥ãŒéŽãŽå¤ãŒçµ‚ã‚â€¦â€¦â€¦ãªã‹ãªã‹çµ‚ã‚ã‚‰ãªã„ãªâ€¦â€¦8æœˆã‚‚çµ‚ã‚ã‚ã†ã¨ã—ã¦ã„ã‚‹ã€‚è¨˜äº‹ã‚’æ›¸ã„ãŸé ƒã®ä½“é‡ã‹ã‚‰ã¯1kgãã‚‰ã„å¢—ãˆãŸã¨ã“ã‚ã ã€‚ å¤ãƒãƒ†ãªã‚“ã¦ã©ã“å¹ãé¢¨ã¨ã°ã‹ã‚Šã«ã€ã“ã“æ•°é€±é–“ã¯ç§ã®â€¦
rawwell 2010/03/04
"Coherence â‰’ memcached + è€éšœå®³æ€§ + ä¸¦åˆ—å‡¦ç† + ã‚¤ãƒ™ãƒ³ãƒˆé€šçŸ¥ã€ã‹ãª? è€éšœå®³æ€§ã¨ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯å‡¦ç†ã‚’æ°—ã«ã—ãªãã¦ã‚ˆã„ ãƒã‚¤ãƒ³ãƒˆã¯ã€å„ãƒŽãƒ¼ãƒ‰ã§ç®¡ç†ã•ã‚Œã¦ã„ã‚‹ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã«ã‚ã‚Šã¾ã™ã€‚åˆ†æ•£å‰æã®ãƒ‡ãƒ¼ã‚¿æ§‹é€ ï¼ˆã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆ: Coheren

checkitout

database
ãƒªãƒ³ã‚¯
pnuts.pdf
rawwell 2010/02/08
"We describe PNUTS, a massively parallel and geographi- cally distributed database system for Yahoo!â€™s web applica- tions. PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of con- current requests including updates and queries, and novel per-record c

checkitout

database
ãƒªãƒ³ã‚¯
Data Miners Blog: MapReduce versus Relational Databases?
rawwell 2010/02/08
"There are a few statements that I basically disagree with. "Lastly, the reshuffle that occurs between the Map and Reduce tasks in MR is equivalent to a GROUP BY operation in SQL." The issue here at first seems like a technicality. In a relational database, an input row can only into one group. MR c

checkitout

database
ãƒªãƒ³ã‚¯
Good Math, Bad Math : Databases are hammers; MapReduce is a screwdriver.
Search this blog Profile Mark Chu-Carroll (aka MarkCC) is a PhD Computer Scientist, who works for Google as a Software Engineer. My professional interests center on programming languages and tools, and how to improve the languages and tools that are used for building complex software systems. Other Information A bunch of people have sent me links to an article about MapReduce. I've hesitated to wr
rawwell 2010/02/08
"To be specific, the criticisms from the DB guys were that MapReduce is: 1. A giant step backward in the programming paradigm for large-scale data intensive applications 2. A sub-optimal implementation, in that it uses brute force instead of indexing 3. Not novel at all -- it represents a s

database

checkitout
ãƒªãƒ³ã‚¯
1 2 3 4 5 6 æ¬¡ã®ãƒšãƒ¼ã‚¸