[B! DB] nsyeeã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

nsyee id:nsyee

DBã«é–¢ã™ã‚‹nsyeeã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (162)

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è¶…ä¾¿åˆ©ãªDBç”¨GUIãƒ„ãƒ¼ãƒ«ã€Ž0xDBEã€ãŒã€ŽDataGrip 1.0ã€ã¨ã—ã¦æ£å¼ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸ | DevelopersIO
Welcome DataGrip 1.0: a JetBrains IDE for DBs and SQL (formerly @0xDBE) https://t.co/n5mZkr7Dim pic.twitter.com/R6mYxcZ5t7 â€” JetBrains (@jetbrains) 2015, 12æœˆ 16 ä»Šå¹´7æœˆã«ä»¥ä¸‹ã‚¨ãƒ³ãƒˆãƒªã§ç´¹ä»‹ã—ã¦ã„ãŸDBå°‚ç”¨GUIãƒ„ãƒ¼ãƒ«ã€Ž0xDBEã€ãŒã€æ£å¼ã«ã€ŽDataGripã€ã¨ã„ã†è£½å“åã§ã“ã®åº¦ãƒªãƒªãƒ¼ã‚¹ã•ã‚ŒãŸæ¨¡æ§˜ã§ã™ã€‚ Mac/Windows/Linuxã§åˆ©ç”¨å¯èƒ½ãªDBå°‚ç”¨GUIãƒ„ãƒ¼ãƒ«ã€Ž0xDBEã€ã«ã¤ã„ã¦ ï½œ Developers.IO å½“ã‚¨ãƒ³ãƒˆãƒªã§ã¯ã€ãƒ„ãƒ¼ãƒ«ã€ŽDataGripã€ã®æ©Ÿèƒ½åŠã³è²»ç”¨ã«ã¤ã„ã¦ç°¡å˜ã«ã§ã¯ã‚ã‚Šã¾ã™ãŒã”ç´¹ä»‹ã—ã¦ã¿ãŸã„ã¨æ€ã„ã¾ã™ã€‚ ç›®æ¬¡ DataGrip: ä¸»ãªæ©Ÿèƒ½ DataGrip: ä¾¡æ ¼ ã¾ã¨ã‚ DataGrip: ä¸»ãªæ©Ÿ
nsyee 2016/04/04
DB

GUI

IntelliJ
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How does a relational database work | Coding Geek
When it comes to relational databases, I canâ€™t help thinking that something is missing. Theyâ€™re used everywhere. There are many different databases: from the small and useful SQLite to the powerful Teradata. But, there are only a few articles that explain how a database works. You can google by yourself â€œhow does a relational database workâ€ to see how few results there are. Moreover, those article
nsyee 2015/08/20
DB

architecture
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GitHub - mirage/irmin: Irmin is a distributed database that follows the same design principles as Git
Irmin is based on distributed version-control systems (DVCs), extensively used in software development to track data provenance and show modifications in the source code. Irmin applies DVC's principles to large-scale distributed data and includes similar functions to Git (clone, push, pull, branch, rebase). The Git workflow was initially designed for humans to manage changes within source code. Ir
nsyee 2015/06/26
OCaml

DB

Git

distributed
ãƒªãƒ³ã‚¯
Welcome to the Atlas Device SDK Docs - Atlas Device SDKs
Atlas Device SDKs are deprecated. Refer to the deprecation page for details. Atlas Device SDK is a suite of app development tools optimized for data access and persistence on mobile devices. Use the SDKs to build data-driven mobile, web, desktop, and IoT apps. The SDKs provide tools to read and write Atlas data from devices. Your app can sync automatically with Mongo DB Atlas and other devices usin
nsyee 2015/03/20
realm

Mobile

DB
ãƒªãƒ³ã‚¯
æˆ‘ã€…ã¯ä½•æ•…RDBMSã‚’ä½¿ã†ã®ã‹
Your browser doesn't support the features required by impress.js, so you are presented with a simplified version of this presentation. For the best experience please use the latest Chrome or Safari browser. Firefox 10 (to be released soon) will also handle it.
nsyee 2015/02/17
DB

è¨è¨ˆ

mysql
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ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®ãƒãƒ«ã‚¯ãƒ‡ãƒ¼ã‚¿ãƒãƒ¼ãƒ€ãƒ¼ã€ŒEmbulkã€ç™»å ´ã€‚fluentdã®ãƒãƒƒãƒç‰ˆã€ãƒˆãƒ¬ã‚¸ãƒ£ãƒ¼ãƒ‡ãƒ¼ã‚¿ãŒæ”¯æ´
ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®ãƒãƒ«ã‚¯ãƒ‡ãƒ¼ã‚¿ãƒãƒ¼ãƒ€ãƒ¼ã€ŒEmbulkã€ç™»å ´ã€‚fluentdã®ãƒãƒƒãƒç‰ˆã€ãƒˆãƒ¬ã‚¸ãƒ£ãƒ¼ãƒ‡ãƒ¼ã‚¿ãŒæ”¯æ´ ä½•ã‚®ã‚¬ãƒã‚¤ãƒˆã‚‚ã‚ã‚‹CSVã‚’ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã«èªã¿è¾¼ã¾ã›ã‚‹ã‚ˆã†ãªãƒãƒ«ã‚¯ãƒ‡ãƒ¼ã‚¿ã‚’ãƒãƒƒãƒå‡¦ç†ã™ã‚‹ãŸã‚ã®ãƒ„ãƒ¼ãƒ«ã€ŒEmbulkã€ãŒã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã§å…¬é–‹ã•ã‚Œã¾ã—ãŸã€‚ ã‚³ãƒŸãƒƒã‚¿ãƒ¼ã¨ã—ã¦é–‹ç™ºã—ã¦ã„ã‚‹ã®ã¯ã€ãƒã‚°åŽé›†ãƒ„ãƒ¼ãƒ«ã¨ã—ã¦çŸ¥ã‚‰ã‚Œã‚‹fluentdãªã©ã®é–‹ç™ºè€…ã¨ã—ã¦çŸ¥ã‚‰ã‚Œã‚‹å¤æ©‹è²žä¹‹æ°ã€è¥¿æ¾¤ç„¡æˆ‘æ°ã€ä¸æ‘æµ©å£«æ°ã‚‰ã§ã€3äººãŒæ‰€å±žã™ã‚‹Treasure Dataã‚‚é–‹ç™ºã‚’æ”¯æ´ã—ã¦ã„ã¾ã™ã€‚ å¤æ©‹æ°ã¯Embulkã«ã¤ã„ã¦ã€Œç°¡å˜ã«è¨€ã†ã¨fluentdã®ãƒãƒƒãƒç‰ˆã§ã™ã€ã¨èª¬æ˜Žã€‚1æœˆ27æ—¥ã«è¡Œã‚ã‚ŒãŸã€Œãƒ‡ãƒ¼ã‚¿è»¢é€ãƒŸãƒ‰ãƒ«ã‚¦ã‚§ã‚¢å‹‰å¼·ä¼šã€ã§ã€Embulkã®ç´¹ä»‹ã‚’è¡Œã„ã¾ã—ãŸã€‚ Embulkã¯ãƒ—ãƒ©ã‚°ã‚¤ãƒ³ãƒ™ãƒ¼ã‚¹ã®ãƒãƒ«ã‚¯ãƒ‡ãƒ¼ã‚¿ãƒãƒ¼ãƒ€ãƒ¼ å¤æ©‹æ°ã¯ã¾ãšã€ä¾‹ãˆã°CSVãƒ•ã‚¡ã‚¤ãƒ«ã‚’PostgreSQLã«èªã¿è¾¼ã¾ã›ã‚ˆã†ã¨ã™ã‚‹ã¨ã€CSVã®æ—¥ä»˜ã®ãƒ•ã‚©ãƒ¼ãƒžãƒƒãƒˆãŒ
nsyee 2015/01/28
Embulk

treasuredata

csv

DB
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Hasql benchmarks
This post is all about the performance of the â€œhasqlâ€ library and particularly its PostgreSQL back end in comparison to its popular direct competitors: â€œHDBCâ€ and â€œpostgresql-simpleâ€. Benchmarks What makes Hasql so fast? Benchmarks The source of the PostgreSQL back end comes with a suite of 3 benchmarks. Iâ€™ll analyze them one by one. Results parsing In this benchmark a simple SELECT query without
nsyee 2014/11/21
haskell

DB

postgresql
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ã‚ãªãŸãŒçŸ¥ã‚‰ãªã„ ãƒªãƒ¬ãƒ¼ã‚·ãƒ§ãƒŠãƒ«ãƒ¢ãƒ‡ãƒ«
1. ã‚ãªãŸãŒçŸ¥ã‚‰ãªã„ ãƒªãƒ¬ãƒ¼ã‚·ãƒ§ãƒŠãƒ«ãƒ¢ãƒ‡ãƒ« @dbtech showcase tokoy 2014 å¥¥é‡Ž å¹¹ä¹Ÿ Twitter: @nippondanji mikiya (dot) okuno (at) gmail (dot) com 3. è‡ªå·±ç´¹ä»‹ â— MySQL ã‚µãƒãƒ¼ãƒˆã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ â€“ æ—¥ã€…ã®ã—ã”ã¨ â— ãƒˆãƒ©ãƒ–ãƒ«ã‚·ãƒ¥ãƒ¼ãƒ†ã‚£ãƒ³ã‚°å…¨èˆ¬ â— Q&Aå›žç” â— ãƒ‘ãƒ•ã‚©ãƒ¼ãƒžãƒ³ã‚¹ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚° ãªã© â— ãƒ©ã‚¤ãƒ•ãƒ¯ãƒ¼ã‚¯ â€“ è‡ªç”±ãªã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã®æ™®åŠ â— ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã§ã¯ãªã„ â— ãƒ–ãƒã‚° ä»Šæ—¥ã¯å€‹äººã¨ã—ã¦ å‚åŠ ã—ã¦ã„ã¾ã™ã€‚ â€“ æ¼¢ã®ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿é“ â€“ http://nippondanji.blogspot.com/
nsyee 2014/11/19
SQL

DB
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6. Step1 ã‚¨ãƒ³ãƒ†ã‚£ãƒ†ã‚£ã®æŠ½å‡º ç™ºé€æ‹…å½“è€…ãŒå—æ³¨ãƒªã‚¹ãƒˆã‚’ã‚‚ã¨ã«ã€å•†å“ã®åœ¨åº«ã‚’ç¢ºèªã—ã€åœ¨åº«ãŒ ã‚ã‚Œã°å•†å“ã‚’ç™ºé€ã™ã‚‹ã€‚ â‘ è¦æ±‚ä»•æ§˜ã®ã€Œå‹•è©žã€ã‚’æŠœãå‡ºã—ã‚¨ãƒ³ãƒ†ã‚£ãƒ†ã‚£ã¨ã™ã‚‹ã€‚ â‘¡ â‘ ã«é–¢ã‚ã‚‹ã€Œåè©žã€ã‚’æŠœãå‡ºã—ã‚¨ãƒ³ãƒ†ã‚£ãƒ†ã‚£ã¨ã™ã‚‹ã€‚ â‘¢ ã‚¨ãƒ³ãƒ†ã‚£ãƒ†ã‚£é–“ã®é–¢é€£ã«ç·šã‚’å¼•ã â‘£ å±žæ€§ã‚„å€™è£œã‚ãƒ¼ã‚‚åˆ†ã‹ã‚‹ç¯„å›²ã§æ›¸ã„ã¦ãŠãã¾ã™ã€‚ é–“é•ã„ï¼ ã“ã®æ®µéšŽã§å®Ÿè£…ã‚’ãƒ—ãƒãƒ‘ãƒ†ã‚£ãƒ•ã‚¡ã‚¤ãƒ«ã«ã™ã‚‹ã¨ã‹ã€Enum ã«ã™ã‚‹ã¨ã‹æ±ºã‚æ‰“ã¡ã§ã‚¨ãƒ³ãƒ†ã‚£ãƒ†ã‚£ã¨ã—ã¦è¡¨ã•ãªã„ã®ã¯ã‚„ ã‚ã¾ã—ã‚‡ã†ã€‚ ã¾ãšã€ã¯ã˜ã‚ã«ã‚¨ãƒ³ãƒ†ã‚£ãƒ†ã‚£ã‚’æŠ½å‡ºã—ã¾ã™ã€‚
nsyee 2014/10/25
DB

è¨è¨ˆ
ãƒªãƒ³ã‚¯
ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®ã‚·ãƒ£ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã‚’è‡ªå‹•é‹ç”¨ã—ã¦ãã‚Œã‚‹ã€ŒAzure SQL Database Elastic Scaleã€ãŒå…¬é–‹
ãƒ‡ãƒ¼ã‚¿ã‚’åˆ†å‰²ã—ã¦è¤‡æ•°ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚µãƒ¼ãƒã§åˆ†æ•£å‡¦ç†ã™ã‚‹ã“ã¨ã§é«˜ã„ã‚¹ã‚±ãƒ¼ãƒ©ãƒ“ãƒªãƒ†ã‚£ã‚’å®Ÿç¾ã™ã‚‹æ‰‹æ³•ã€Œã‚·ãƒ£ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã€ãŒã€Microsoft Azureã®æ–°æ©Ÿèƒ½ã€ŒAzure SQL Database Elastic Scaleã€ã¨ã—ã¦ãƒ—ãƒ¬ãƒ“ãƒ¥ãƒ¼å…¬é–‹ã•ã‚Œã¾ã—ãŸã€‚ ã‚·ãƒ£ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã®å‡¦ç†ã¯ä¸€èˆ¬ã«ã€ãƒ‡ãƒ¼ã‚¿ã®åˆ†å‰²æ–¹æ³•ã‚„åˆ†å‰²ã—ãŸã‚·ãƒ£ãƒ¼ãƒ‰ã”ã¨ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚µãƒ¼ãƒã®å‰²ã‚Šå½“ã¦ã€ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚µãƒ¼ãƒã«å‰²ã‚Šå½“ã¦ãŸå¾Œã§è² è·ã«åã‚ŠãŒå‡ºãŸå ´åˆã®ã‚·ãƒ£ãƒ¼ãƒ‰ã®å†åˆ†å‰²ã‚„ãƒžãƒ¼ã‚¸ãªã©ã€ã•ã¾ã–ã¾ãªè¤‡é›‘ãªå‡¦ç†ã¨é‹ç”¨ã‚’è‡ªå‰ã§è¡Œã‚ãªã‘ã‚Œã°ãªã‚‰ãšã€æ‰‹é–“ã®æŽ›ã‹ã‚‹æ‰‹æ³•ã®1ã¤ã§ã—ãŸã€‚ Azure SQL Database Elastic Scaleã¯ã€ã“ã†ã—ãŸã‚·ãƒ£ãƒ¼ãƒ‰ã®ç®¡ç†ã‚„è² è·ã«å¿œã˜ãŸåˆ†å‰²ã‚„ãƒžãƒ¼ã‚¸ã¨ã„ã£ãŸé‹ç”¨ã‚’è‡ªå‹•çš„ã«è¡Œã£ã¦ãã‚Œã‚‹ã¨åŒæ™‚ã«ã€ã‚·ãƒ£ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã‚’åˆ©ç”¨ã™ã‚‹ãŸã‚ã®ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚‚æä¾›ã€‚Azure SQL Database Elastic Scale
nsyee 2014/10/06
Azure

DB

sharding
ãƒªãƒ³ã‚¯
Sqitch: Sensible database change management
Sensible database-native change management for framework-free development and dependable deployment. Download New to Sqitch? Organizations and Projects Using Sqitch
nsyee 2014/07/01
DB

SQL

migration
ãƒªãƒ³ã‚¯
Roshi: a CRDT system for timestamped events
Roshi: a CRDT system for timestamped eventsMay 9th, 2014 by Peter Bourgon Letâ€™s talk about the stream. The SoundCloud stream represents stuff thatâ€™s relevant to you primarily via your social graph, arranged in time order, newest-first. The atom of that data model, an event, is a simple enough thing. Timestamp User who did the thing Identifier of the thing that was done For example, At 2014-04-01T1
nsyee 2014/05/21
SoundCloud

Go

Redis

DB

CRDT

event-stream
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soundcloud/roshi Â· GitHub - Roshi is a large-scale CRDT set implementation for timestamped events.
CRDTs (conflict-free replicated data types) are data types on which the same set of operations yields the same outcome, regardless of order of execution and duplication of operations. This allows data convergence without the need for consensus between replicas. In turn, this allows for easier implementation (no consensus protocol implementation) as well as lower latency (no wait-time for consensus
nsyee 2014/05/21
SoundCloud

Go

Redis

DB

CRDT

event-stream
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3. è¬›ç¾©å†…å®¹ ï® åºè«– - ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®å‰ã« ï® ä¸¦åˆ—å‡¦ç†ã®åŸºç¤Ž ï® ï® ä¸¦åˆ—å‡¦ç†ã®Terminology ä¸¦åˆ—è¨ˆç®—æ©Ÿã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ ï® ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ ï® ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹å‡¦ç†ã®ä¸¦åˆ—åŒ– ï® çµåˆå‡¦ç†ã®é«˜é€ŸåŒ– ï® ï® ï® ï® ä¸¦åˆ—ãƒãƒƒã‚·ãƒ¥çµåˆ ä¸¦åˆ—ã‚½ãƒ¼ãƒˆ ãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ‹ãƒ³ã‚°æ‰‹æ³• å¤šé‡çµåˆã‚„è¨ˆç®—æ©Ÿé–“ã®ãƒ‡ãƒ¼ã‚¿äº¤æ›ã§ç™ºç”Ÿã™ã‚‹å•é¡Œ ï® MapReduceã«ã‚ˆã‚‹é–¢ä¿‚æ¼”ç®—ã®ä¸¦åˆ—å‡¦ç† 3 4. ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹é–‹ç™ºã®æµã‚Œ ï® Coddã®è«–æ–‡: 1970å¹´ ï® ï® ï® ï® System Rã‚„Ingres: 70å¹´ä»£ä¸ç›¤ Oracle, IBM DB2, Ingres: 80å¹´ä»£åºç›¤ ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®éš†ç››: 80å¹´ä»£å¾ŒåŠ ï® ï® A Relational Model of Data for Large Shared Data Banks, Communications of ACM å•†ç”¨
nsyee 2014/03/13
parallel

DB

MapReduce
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PPDB:Japanese - æ—¥æœ¬èªžè¨€ã„æ›ãˆãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹
The Paraphrase Database : Japaneseã¯æ—¥è‹±å¯¾è¨³ã‚³ãƒ¼ãƒ‘ã‚¹ã‹ã‚‰å¦ç¿’ã•ã‚ŒãŸæ—¥æœ¬èªžã®è¨€ã„æ›ãˆãƒ‡ãƒ¼ã‚¿é›†ã§ã™ã€‚ ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ æœ€æ–°ç‰ˆï¼šPPDB:Japanese 0.0.1 ä»•æ§˜æƒ…å ± ä¸€è¡Œã«ã¤ãä¸€ã¤ã®è¨€ã„æ›ãˆã‚’ç¤ºã™ã€‚ SOURCE:j ||| TARGET:j' ||| (FEATURE=VALUE)* ||| ALIGNMENT ãƒ•ãƒ¬ãƒ¼ã‚ºã¯å½¢æ…‹ç´ ã”ã¨ã«åŠè§’ã‚¹ãƒšãƒ¼ã‚¹ã§åŒºåˆ‡ã‚‰ã‚Œã€ã¾ãŸè¨€ã„æ›ãˆç¢ºçŽ‡ã‚‚ãã‚Œãžã‚ŒP(j'|j)ã¨P(j|j')ãŒåŠè§’ã‚¹ãƒšãƒ¼ã‚¹åŒºåˆ‡ã‚Šã§ä¸Žãˆã‚‰ã‚Œã¾ã™ã€‚ ç¿»è¨³ ã• ã‚Œ ãŸ ||| ç¿»è¨³ ||| 0.0125435775455 0.00034585476357 ||| 37435 2 56 è«–æ–‡æƒ…å ± ä»¥ä¸‹ã®æƒ…å ±ã‚’ã”åˆ©ç”¨ãã ã•ã„ã€‚ æ°´ä¸Š é›…åšï¼ŒGraham Neubigï¼ŒSakriani Saktiï¼Œæˆ¸ç”° æ™ºåŸºï¼Œä¸æ‘ å“². æ—¥æœ¬èªžè¨€ã„æ›ãˆãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®æ§‹ç¯‰ã¨
nsyee 2014/02/04
è‡ªç„¶è¨€èªžå‡¦ç†

è¾žæ›¸

DB

æ—¥æœ¬èªž
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Cache-Oblivious ãƒ‡ãƒ¼ã‚¿æ§‹é€ å…¥é–€ @DSIRNLP#5
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nsyee 2014/01/14
DB

algorithm
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http://dat-data.com/
nsyee 2014/01/05
Git

DB

LevelDB

Node.js
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Mongo DBãªã©ã¯ã‚¹ã‚ãƒ¼ãƒžãƒ¬ã‚¹ãªãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã§ã‚ã‚Šï¼Œå…ˆã«ã‚¹ã‚ãƒ¼ãƒžæ±ºã‚ãªãã¦ã‚‚ï¼Œä½•ã§ã‚‚ã¤ã£ã“ã‚ã‚‹ã“ã¨ã«ãªã£ã¦ã‚‹ï¼Ž ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‹ã‚‰è¦‹ã‚Œã°ã‚¹ã‚ãƒ¼ãƒžãƒ¬ã‚¹ã¨ã„ã†ã“ã¨ã§ã„ã„ã‘ã©ï¼Œã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³çš„ã«ã¯ï¼Œä½•ãŒå…¥ã£ã¦ã‚‹ã‹ã¡ã‚ƒã‚“ã¨ç®¡ç†ã—ãŸã„ï¼Ž ä¸‹ã®ä¾‹ã§ã¯ï¼ŒAliceã¨Bobã§ã¯æŒã£ã¦ã‚‹ãƒ•ã‚£ãƒ¼ãƒ«ãƒ‰ãŒã¡ãŒã£ã¦ï¼ŒBobã¯hobbyã‚’æŒã£ã¦ã‚‹ã‘ã©ï¼ŒAliceã¯æŒã£ã¦ãªã„ï¼Ž { name => 'Alice', age => 20, } { name => 'Bob', age => 21, hobbies => ['tennis', 'soccer'], } ã“ã‚Œãã‚‰ã„ãªã‚‰è¦‹ã‚Œã°åˆ†ã‹ã‚‹ã‘ã©ï¼Œé•·æœŸé–“é‹ç”¨ã—ã¦ã¦ï¼Œçµå±€ä½•ãŒå…¥ã£ã¦ã‚‹ã®ã‹åˆ†ã‹ã‚‰ãªã„ï¼Œã¿ãŸã„ãªã“ã¨ãŒã‚ã£ã¦ï¼Œé›£ã—ã‹ã£ãŸï¼Ž ã‚ªãƒ–ã‚¸ã‚§ã‚¯ãƒˆã‚’ã©ã‚“ã©ã‚“æ¸¡ã—ã¦ã„ãã¨æ§‹é€ ã‚’æ•™ãˆã¦ãã‚Œã‚‹ã®ã‚’ä½œã£ãŸï¼Ž hitode909/perl-object-classifier Â· G
nsyee 2013/12/26
Perl

DB

debug

JSON

MongoDB
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BayesDB â€“ MIT Probabilistic Computing Project
BayesDB is a probabilistic programming platform that provides built-in non-parametric Bayesian model discovery. BayesDB makes it easy for users without statistics training to search, clean, and model multivariate databases using an SQL-like language. BayesDB is based on probabilistic programming, an emerging field based on the insight that probabilistic models and inference algorithms are a new ki
nsyee 2013/12/09
Bayesian Query Languageâ€¦ã ã¨ï¼Ÿ

bayes

DB

SQL
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nsyee 2013/11/20
mysql

postgresql

DB

NoSQL
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