STEP2. ã¢ã¦ãããããå®ç¾ããããã«å¿ è¦ãªãã¼ã¿ã½ã¼ã¹ãæ¸ãåºã ã¢ã¦ããããã®æ´çãã§ããããä»åº¦ã¯ã¤ã³ãããã¨ãªããã¼ã¿ã½ã¼ã¹ã®æ´çãè¡ãã¾ãããã å¿ è¦ãªãã¼ã¿ã½ã¼ã¹ã¯è¦ä»¶ããèªã¿è§£ããã¨ãã§ãã¾ãã ä»åã¯ã10代ã®ã¦ã¼ã¶ã¼ã®æéè¦è´æ°ï¼æ§å¥ / åç»ã«ãã´ãªãã¨ï¼ã®æ¨ç§»ãã°ã©ãã§è¦ãããã¨ããè¦ä»¶ã§ãã ããããããã®åæã«å¿ è¦ãªã¨ã³ãã£ãã£ï¼å®ä½ï¼ã¨ãã®å±æ§ãéè¨å¤ãæ½åºãã¾ãããã ã¨ã³ãã£ãã£ã¨å±æ§ ã¦ã¼ã¶ã¼ æ§å¥ 年代 åç» ã«ãã´ãª éè¨å¤ è¦è´æ° ãããã®ãã¼ã¿ã管çãããã¼ãã«ãã調æ»ããã¢ãªã³ã°ã宿½ãã¦æ¢ãã¾ãã ä»åã¯ä»¥ä¸ã®ãã¼ãã«ã使ç¨ãããã¨ã¨ãã¾ãã userï¼ã¦ã¼ã¶ã¼ç»é²ã«å¿ é ãªå ¥åé ç®ã管çãããã¼ãã« user_profileï¼ã¦ã¼ã¶ã¼ãç»é²å¾ã«è¨å®ã§ããä»»æã®å ¥åé ç®ã管çãããã¼ãã« videoï¼ã¦ã¼ã¶ã¼ãæç¨¿ããåç»ã管çãããã¼ãã«
AWSã®ALB(Application Load Balancer)ã®ãã°ã¯S3ã«ç½®ããããããã®ä¸èº«ããµã¯ãã¨èª¿ã¹ããã¨ããAthenaãä½¿ãæ¹æ³ãæ¨æºçã§ãä¸è¨ã§æ¡å ããã¦ããããã«ãã¼ãã£ã·ã§ã³å°å½±(Partition Projection)ã§ãã¼ãã«ãä½ã£ã¦Athenaããã¯ã¨ãªããã ãã¼ãã£ã·ã§ã³å°å½±ã使ç¨ã㦠Athena ã§ ALB ã¢ã¯ã»ã¹ãã°ç¨ãã¼ãã«ã使ãã - Amazon Athena ç§ã徿¥ã¯ãã®æ¹æ³ã使ã£ã¦ããããAthenaã¯ãã©ã¦ã¶ãã使ãã¨åä½ããã£ãããã¦ããããæ±ºã¾ã£ãã¯ã¨ãªã1åããå®è¡ãã¦çµæãåå¾ãããã ãã®ã¨ããªãã¾ã ãããæ¢ç´¢çã«ã¯ã¨ãªãä½çºãå®è¡ãããã¨ãã«ã¯ä½¿ãåæãæªãã æè¿ä»ã®ããã¸ã§ã¯ãã§DuckDBã使ãããã«ãªã£ã¦ã使ãåæã®è¯ãã«æåãã¦ããããDuckDBã¯ALBã®ãã°ãæ¢ç´¢çã«èª¿ã¹ããã¨ãã«ããã£ã¡ã使ããã¨æã£ã
ç¿»è¨³ã¯æ©æ¢°ç¿»è¨³ã«ããæä¾ããã¦ãã¾ããæä¾ããã翻訳å 容ã¨è±èªçã®éã§é½é½¬ãä¸ä¸è´ã¾ãã¯çç¾ãããå ´åãè±èªçãåªå ãã¾ãã JSON ããã®ãã¼ã¿ã®æ½åº Athena ã®ãã¼ãã«ã«ãã·ãªã¢ã©ã¤ãºããããªããJSON ã§ã¨ã³ã³ã¼ããããæååãå«ã¾ãããã¼ã¿ã½ã¼ã¹ãããå ´åãããã¾ãããã®ãããªå ´åã§ããPresto ã® JSON 颿°ã使ç¨ãããã®ãã¼ã¿ã«å¯¾ã㦠SQL ãªãã¬ã¼ã·ã§ã³ãå®è¡ã§ãã¾ãã æ¬¡ã® JSON æååããã¼ã¿ã»ããä¾ã¨ãã¦èãã¾ãã {"name": "Susan Smith", "org": "engineering", "projects": [ {"name":"project1", "completed":false}, {"name":"project2", "completed":true} ] } ä¾: ããããã£ã®æ½åº ãã® JSON æååãã
TO_TIMESTAMP 㯠TIMESTAMP æååã TIMESTAMPTZ ã«è¿ãã¾ããAmazon Redshift ã®ãã®ä»ã®æ¥ä»ããã³æå»é¢æ°ã®ãªã¹ãã«ã¤ãã¦ã¯ããæ¥ä»ããã³æå»é¢æ°ããåç §ãã¦ãã ããã æ§æ timestamp format ã«ããæå®ãããå½¢å¼ã§ã¿ã¤ã ã¹ã¿ã³ãå¤ã表ãæååããã®å¼æ°ã空ã®ã¾ã¾ã«ããã¨ãã¿ã¤ã ã¹ã¿ã³ãå¤ã¯ããã©ã«ãã§ 0001-01-01 00:00:00 ã«è¨å®ããã¾ãã format timestamp å¤ã®å½¢å¼ãå®ç¾©ããæååãªãã©ã«ãã¿ã¤ã ã¾ã¼ã³ (TZãtzãã¾ã㯠OF) ãå«ãå½¢å¼ã¯ãå ¥åã¨ãã¦ãµãã¼ãããã¦ãã¾ãããæå¹ãªã¿ã¤ã ã¹ã¿ã³ãå½¢å¼ã«ã¤ãã¦ã¯ããæ¥æå½¢å¼ã®æååããåç §ãã¦ãã ããã is_strict å ¥åã¿ã¤ã ã¹ã¿ã³ãå¤ãç¯å²å¤ã§ããå ´åã«ã¨ã©ã¼ãè¿ããã©ãããæå®ãããªãã·ã§ã³ã®ãã¼ã«å¤ãis_strict
GoogleSQL for BigQuery ã§ã¯ãã¯ã¨ãªå¼ã§åç §ã§ãã䏿ãã¼ãã«ã¨ 1 ã¤ä»¥ä¸ã®å ±éãã¼ãã«å¼ï¼CTEï¼ã WITH å¥ã«å«ã¾ãã¦ãã¾ããCTE ã¯ãéå帰ãå帰ãã¾ãã¯ãã®ä¸¡æ¹ã«ãªãå¾ã¾ããWITH å¥ã§ RECURSIVE ãã¼ã¯ã¼ããæå®ããã¨ï¼WITH RECURSIVEï¼ãå帰ã«ãªãã¾ãã å帰 CTE ã¯ããã® CTE èªä½ãå è¡ãã CTEãã¾ãã¯å¾ç¶ã® CTE ãåç §ã§ãã¾ããéå帰 CTE ã¯å è¡ãã CTE ã®ã¿ãåç §ã§ãããã® CTE èªä½ã¯åç §ã§ãã¾ãããå帰 CTE ã¯ãæ°ããçµæãè¦ã¤ããã¾ã§ç¶ç¶çã«å®è¡ããã¾ãããéå帰 CTE 㯠1 åã ãå®è¡ããã¾ãããã®ãããªçç±ãããå帰 CTE ã¯é層ãã¼ã¿ãã°ã©ããã¼ã¿ã®ã¯ã¨ãªã«ãã使ç¨ããã¾ãã ãã¨ãã°ãåè¡ã 1 ã¤ã®ãã¼ãã表ãããã®ãã¼ãã¯ä»ã®ãã¼ãã«ãªã³ã¯ã§ããã°ã©ããèãã¦ã¿ã¾ã
dbtã§ã®SQLã¢ãã«è¨è¿°æã«å©ç¨æ¨å¥¨ããã¦ãããå ±éãã¼ãã«å¼(CTE/Common Table Expression)ãã«ã¤ã㦠ã¢ã©ã¤ã¢ã³ã¹äºæ¥é¨ ã¨ã³ã¸ãã¢ã°ã«ã¼ã ã¢ãã³ãã¼ã¿ã¹ã¿ãã¯(MDS)ãã¼ã ã®ãããã§ãã dbtã§ã¯ãSQLã¢ãã«ãè¨è¿°ããéã«ãå ±éãã¼ãã«å¼(CTE/Common Table Expression)ãã®å©ç¨ãæ¨å¥¨ããã¦ãã¾ãã ãã®å ±éãã¼ãã«å¼=CTE/Common Table Expression(以éCTEã¨å¼ã³ã¾ã)ã馴æã¿ã®ç¡ã人ã«ã¯ãCTEï¼ä½ããï¼ãã¨ããæãã§ã¯ããããªã¨æãã¾ãã(å®éèªåãããã§ãã) ã¨ãããã¨ã§å½ã¨ã³ããªã§ã¯ããããCTEã£ã¦ã©ããããã®ãªã®ããdbtã§ãã®CTEãã©ãããé¢¨ã«æ±ã£ã¦ããã®ãè¯ãã®ããçã«ã¤ãã¦è²ã ã¨è¦ã¦ããããã¨æãã¾ãã ç®æ¬¡ ããããå ±éãã¼ãã«å¼(CTE)ã£ã¦ä½ï¼ CTEã«é¢å¿ãæã¤ã¹ãç
sqlfmt formats your dbt SQL files so you don't have to. It is similar in nature to black, gofmt, and rustfmt (but for SQL). sqlfmt promotes collaboration. An auto-formatter makes it easier to collaborate with your team and solicit contributions from new people. You will never have to mention (or argue about) code style in code reviews again. sqlfmt is fast. Forget about formatting your code, and s
Athena ã§ã¯ã¨ãªãåå®è¡ããå ´åããªãã·ã§ã³ã§æå¾ã«ä¿åãããã¯ã¨ãªçµæãåå©ç¨ãããã¨ã鏿ã§ãã¾ãããã®ãªãã·ã§ã³ã«ãããããã©ã¼ãã³ã¹ãåä¸ããã¹ãã£ã³ããããã¤ãæ°ã«ããã³ã¹ãã忏ããã¾ããã¯ã¨ãªã®çµæãåå©ç¨ãããã¨ã¯ããã¨ãã°ãç¹å®ã®æéæ å ã§çµæã«å¤åããªããã¨ãããã£ã¦ããå ´åã«å½¹ç«ã¡ã¾ããã¯ã¨ãªã®çµæãåå©ç¨ã§ããæå¤§æå¹æéãæå®ã§ãã¾ããAthena ã§ã¯ãæå®ããçµéæ¥æ°ãè¶ ããªãéããä¿åãããçµæã使ç¨ãã¾ãã詳細ã«ã¤ãã¦ã¯ãAWS Big Data Blog ã®ãReduce cost and improve query performance with Amazon Athenaããåç §ãã¦ãã ããã
ã¦ã¼ã¶ã¼ã°ã«ã¼ãã夿´ãã¾ããã¦ã¼ã¶ã¼ãã°ã«ã¼ãã«è¿½å ããããã°ã«ã¼ãããã¦ã¼ã¶ã¼ãåé¤ããããã°ã«ã¼ãåã夿´ããã«ã¯ããã®ã³ãã³ãã使ç¨ãã¾ãã æ§æ ALTER GROUP group_name { ADD USER username [, ... ] | DROP USER username [, ... ] | RENAME TO new_name } ãã©ã¡ã¼ã¿ group_name 夿´ããã¦ã¼ã¶ã¼ã°ã«ã¼ãã®ååã ADD ã¦ã¼ã¶ã¼ãã¦ã¼ã¶ã¼ã°ã«ã¼ãã«è¿½å ãã¾ãã DROP ã¦ã¼ã¶ã¼ã°ã«ã¼ãããã¦ã¼ã¶ã¼ãåé¤ãã¾ãã username ã°ã«ã¼ãã«è¿½å ãããã°ã«ã¼ãããåé¤ããã¦ã¼ã¶ã¼ã®ååã RENAME TO ã¦ã¼ã¶ã¼ã°ã«ã¼ãã®ååã夿´ãã¾ãã2 åã®ã¢ã³ãã¼ã¹ã³ã¢ã§å§ã¾ãã°ã«ã¼ãå㯠Amazon Redshift å é¨ã§ä½¿ç¨ããããã«äºç´ããã¦ãã¾ããæå¹ãªååã®è©³ç´°
ä»åã¯ãSQLãæ¸ãä¸ã§ç¹ã«ããã©ã¼ãã³ã¹ã«å½±é¿ã®ããSQLã®å®è¡è¨ç»ã®èªã¿æ¹ã«ã¤ãã¦è§£èª¬ãã¾ããå®è¡è¨ç»ã¯ãã¼ã¿ãã¼ã¹è£½åã«ãã£ã¦ãã¾ãã¾ã«å·®ç°ãããã¾ãããããã§ã¯æ¯è¼çã©ã®ãã¼ã¿ãã¼ã¹è£½åã§ãå ±éããå 容ã«ã¤ãã¦è§£èª¬ãã¾ãã å®è¡è¨ç»ã¨ã¯è¨è¿°ããSQLãå®éã«ãã¼ã¿ãã¼ã¹ã®å é¨ã§ã©ã®ããã«å¦çããã¦çµæãè¿ããããã®å¦çæ¹æ³ãè¨è¿°ããæ å ±ã§ãã A5:SQL Mk-2ã§ã¯ãSQLã¨ãã£ã¿ã§å®è¡è¨ç»ãè¦ãã SQL ã®ä¸ã«ãã£ã¬ãããããç¶æ ã§ã¡ãã¥ã¼ãã [SQL(S)] â [SQLã®å®è¡è¨ç»(J)] ã¾ãã¯ãCtrl+E ã§è¡¨ç¤ºã§ãã¾ãã 表示ã®ä»æ¹ã¯ãã¼ã¿ãã¼ã¹è£½åãã¨ã«ç°ãªãã¾ãããå¤ãã®ãã¼ã¿ãã¼ã¹è£½åã§ã¯ããªã¼ç¶ã®æ å ±ã¨ãã¦è¡¨ç¾ããã¾ããï¼ãã®ãã A5:SQL Mk-2ã§ãããªã¼ãã¥ã¼ã§å®è¡è¨ç»ã表示ãã¾ããï¼ ããªã¼ã®ãªã¼ãï¼ç«¯ï¼ããå¦çãè¡ãããã«ã¼ãï¼æ ¹ï¼ã«åãã£
LMQL Playgroundã§ã¯ã¨ãªã試ãLMQLã«ã¯åä½ãç°¡åã«æ¤è¨¼ã§ããPlaygroundãç¨æããã¦ãã¾ãããã¼ã«ã«ã§Playgroundãèµ·åãããã¨ãã§ãã¾ãã ã¾ãã¯Getting Startedã§ç´¹ä»ããã¦ãã以ä¸ã®ã¯ã¨ãªãå®è¡ãã¾ãã argmax "Hello[WHO]" from "openai/text-ada-001" where len(WHO) < 10ãRunããã¿ã³ãã¯ãªãã¯ããã¨OpenAIã®API KEYãæ±ããããã®ã§ãå ¥åãã¾ãã å®è¡ããã¨Model Responseã®æ ã«çµæã表示ããã¾ãã LMQLã®åºæ¬æ§é LMQLã¯è¨æ³çã«ã¯SQLã¨ä¼¼ã¦ãã¦ã以ä¸ã®ãããªæ§é ãæã£ã¦ãã¾ãã ãã³ã¼ãç¯ï¼Decoder Clauseï¼ï¼ ããã¹ãçæã«ä½¿ç¨ãããã³ã¼ãã»ã¢ã«ã´ãªãºã ãæå®ãã¾ããLMQLã§ã¯æ§ã ãªãã³ã¼ãã»ã¢ã«ã´ãªãºã ã鏿ãããã¨ãã§ã
LMQL is a programming language for LLMs. Robust and modular LLM prompting using types, templates, constraints and an optimizing runtime. @lmql.query def meaning_of_life(): '''lmql # top-level strings are prompts "Q: What is the answer to life, the \ universe and everything?" # generation via (constrained) variables "A: [ANSWER]" where \ len(ANSWER) < 120 and STOPS_AT(ANSWER, ".") # results are dir
GoogleVisualization API ã®ã¯ã¨ãªè¨èªã使ç¨ããã¨ããã¼ã¿ã½ã¼ã¹ã«å¯¾ããã¯ã¨ãªã§ãã¾ãã¾ãªãã¼ã¿æä½ãè¡ããã¨ãã§ãã¾ãã ç®æ¬¡ ã¯ããã« é常ããã¸ã¥ã¢ãªã¼ã¼ã·ã§ã³ã«ã¯ç¹å®ã®å½¢å¼ã®ãã¼ã¿ãå¿ è¦ã§ãããã¨ãã°ãåã°ã©ãã§ã¯ãã¼ã¿ãããã¹ãã©ãã«ã¨æ°å¤ã® 2 ã¤ã®åã§ããå ´åãããã¾ãããã¼ã¿ã½ã¼ã¹å ã®ãã¼ã¿ã¯ããã®æ§é ã¨å®å ¨ã«ã¯ä¸è´ããªãå ´åãããã¾ãã ãã¨ãã°ããã¼ã¿ã½ã¼ã¹ã« 3 ã¤ä»¥ä¸ã®åãããå ´åããåã®é åºãåã°ã©ãã§æ³å®ããã¦ããé åºã¨ç°ãªãå ´åãããã¾ãã ã¯ã¨ãªè¨èªã使ç¨ããã¨ããã¼ã¿æä½ã¨ãã©ã¼ãããã®ãªã¯ã¨ã¹ãããã¼ã¿ã½ã¼ã¹ã«éä¿¡ããè¿ããããã¼ã¿æ§é ã¨ã³ã³ãã³ããæ³å®ãããæ§é ã¨ä¸è´ããããã«ã§ãã¾ãã ã¯ã¨ãªè¨èªã®æ§æã¯ SQL ã«ä¼¼ã¦ãã¾ããSQL ã«ç²¾éãã¦ãããããããã¼ã§ããã°ããã®ã¯ã¨ãªè¨èªãããã«ç¿å¾ãã¦ä½¿ç¨ã§ãã¾ããã¦ã§ãä¸ã«å¤ã
ã¯ããã« ChatGPTã¯ãOpenAIãéçºãã髿§è½ãªèªç¶è¨èªå¦çã¢ãã«ã§ãä¼è©±ã質åå¿çãªã©å¤ãã®åéã§å¿ç¨ãå¯è½ã§ãããã®è¨äºã§ã¯ãLayerXã®ãã¯ã©ã¯è«æ±æ¸ãã¼ã ã§éå¬ããChatGPTã顿ã¨ããããã«ã½ã³ã®æ¦è¦ããææç©ã«ã¤ãã¦ãä¼ããã¾ãã (â»ä»åã®ããã«ã½ã³ã§æ¥åä¸ãã¼ã¿ã¯å©ç¨ãã¦ããã¾ãã) ããã«ã½ã³æ¦è¦ æè¿è©±é¡ãéãã¦ããChatGPTã§ãããLayerXã§ãæ°åæ¡ç¨ãç ä¿®ã«åãå ¥ãããã¨ãæ¤è¨ãã¦ãã¾ãã layerx.co.jp ChatGPTãNotion AIãã¯ããã¨ããLLMã¯ä»å¾ã®è£½åéçºã§ãããªã価å¤åµåºãæå¾ ãããããããã®æ©ä¼ã«ç¥è¦ãæ·±ãããã¨ãç®æãããã¯ã©ã¯è«æ±æ¸ãã¼ã ã§ããã«ã½ã³ã宿½ãã¾ããã ããã«ã½ã³ã®æ§åï¼è¨äºãæ¸ããæ¥ã«ä»ãã¼ã ã§ãããã«ã½ã³ãè¡ããã¦ããã®ã§ãã®åçï¼ ããã«ã½ã³ã§çã¾ããã¢ã¤ã㢠ããã«ã½ã³ã§éçºããã主
ã©ã³ãã³ã°
ã©ã³ãã³ã°
é害
ãªãªã¼ã¹ãé害æ å ±ãªã©ã®ãµã¼ãã¹ã®ãç¥ãã
ææ°ã®äººæ°ã¨ã³ããªã¼ã®é ä¿¡
å¦çãå®è¡ä¸ã§ã
j次ã®ããã¯ãã¼ã¯
kåã®ããã¯ãã¼ã¯
lãã¨ã§èªã
eã³ã¡ã³ãä¸è¦§ãéã
oãã¼ã¸ãéã
{{#tags}}- {{label}}
{{/tags}}