[B! impala] mogwaingã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

mogwaing id:mogwaing

impalaã«é–¢ã™ã‚‹mogwaingã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (16)

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ç¬¬19å›žã€€Impalaã®è¨è¨ˆã¨å®Ÿè£…ï¼»3ï¼½ | gihyo.jp
ã¯ã˜ã‚ã« ä»Šå›žã¯ã€Impalaã«ãŠã‘ã‚‹I/Oã®é«˜é€ŸåŒ–æŠ€æ³•ã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ã€‚ å‰å›žèª¬æ˜Žã—ãŸã‚ˆã†ã«ã€Impalaã®å®Ÿè¡Œã‚¨ãƒ³ã‚¸ãƒ³ã¯å¯èƒ½ãªé™ã‚Šãƒ¡ãƒ¢ãƒªä¸Šã§å‡¦ç†ã‚’ã™ã‚‹ã“ã¨ã§ã‚¢ãƒ‰ãƒ›ãƒƒã‚¯ã‚¯ã‚¨ãƒªã®ãƒ¬ã‚¤ãƒ†ãƒ³ã‚·ã‚’ä¸‹ã’ã€ã‚¹ãƒ«ãƒ¼ãƒ—ãƒƒãƒˆã‚’å‘ä¸Šã•ã›ã‚‹ã€ã¨ã„ã†è¨è¨ˆæ–¹é‡ã§é–‹ç™ºã•ã‚Œã¦ã„ã¾ã™ã€‚ ã—ã‹ã—ã€ãƒ‡ãƒ¼ã‚¿ã¯ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ï¼ˆäºŒæ¬¡è¨˜æ†¶è£…ç½®ï¼‰ã«æ ¼ç´ã•ã‚Œã¦ã„ã‚‹ãŸã‚ã€å½“ç„¶ã€ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã¸ã®I/Oã‚’å›žé¿ã™ã‚‹ã“ã¨ã¯ã§ãã¾ã›ã‚“ã€‚ã¾ãŸã€Impalaã¯å®Ÿè¡Œæ™‚ã«ååˆ†ãªãƒ¡ãƒ¢ãƒªã‚’ç¢ºä¿ã™ã‚‹ã¹ãã€ãƒ‡ãƒ¼ã‚¿ã‚’ãƒ¡ãƒ¢ãƒªä¸Šã«ä¿æŒï¼ˆã‚ãƒ£ãƒƒã‚·ãƒ¥ï¼‰ã—ãªã„ãŸã‚ã€ã‚¯ã‚¨ãƒªã‚’å®Ÿè¡Œã™ã‚‹ãŸã³ã«ãƒ‡ãƒ¼ã‚¿ã‚’ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã‹ã‚‰èªã¿å‡ºã™ã“ã¨ã‚’å‰æã¨ã—ã¦è¨è¨ˆã•ã‚Œã¦ã„ã¾ã™[1]â ã€‚ ä»Šå›žã¯ã€ã“ã®ã‚ˆã†ãªãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã‚’è€ƒæ…®ã—ã¤ã¤ã€é«˜é€Ÿãƒ»é«˜åŠ¹çŽ‡ãªã‚¢ãƒ‰ãƒ›ãƒƒã‚¯ã‚¯ã‚¨ãƒªã‚’å®Ÿè¡Œã™ã‚‹ãŸã‚ã®I/Oå‡¦ç†æ–¹å¼ã¨ãƒ‡ãƒ¼ã‚¿ãƒ¬ã‚¤ã‚¢ã‚¦ãƒˆã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ Short-Circ uit Local Readsã«ã‚ˆã‚‹I/Oã®é«˜åŠ¹çŽ‡åŒ– Sh
mogwaing 2016/04/13
impala

hadoop

parallel processing

parallel db
ãƒªãƒ³ã‚¯
ç¬¬18å›žã€€Impalaã®è¨è¨ˆã¨å®Ÿè£…ï¼»2ï¼½ | gihyo.jp
ã¯ã˜ã‚ã« ä»Šå›žã¯ã€Impalaã®SQLå‡¦ç†ã®é«˜é€ŸåŒ–ã«ãŠã„ã¦é‡è¦ãªå½¹å‰²ã‚’å ã‚ã‚‹ã‚¯ã‚¨ãƒªå‡¦ç†ã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ã€‚ Impalaã®ã‚¯ã‚¨ãƒªå‡¦ç†ã®ç‰¹å¾´ Impalaã¯ã€MapReduceã‚„Sparkã‚’ã¯ã˜ã‚ã¨ã™ã‚‹æ—¢å˜ã®æ‰‹ç¶šãåž‹ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚¨ãƒ³ã‚¸ãƒ³ã‚’ä½¿ç”¨ã›ãšã€ã‚¢ãƒ‰ãƒ›ãƒƒã‚¯ãªSQLã‚¯ã‚¨ãƒªã®å‡¦ç†ã®é«˜åŠ¹çŽ‡åŒ–ã«ç„¦ç‚¹ã‚’ç½®ã„ãŸè¨è¨ˆã¨å®Ÿè£…ãŒç‰¹å¾´ã§ã™ã€‚ãŸã¨ãˆã°ã€çµåˆæ–¹æ³•ã‚’è¦‹ã¦ã¿ã‚‹ã¨ã€Mapã‚„Reduceã‚‚ã—ãã¯MapReduceã‚¸ãƒ§ãƒ–ãªã©ã®ãƒ–ãƒãƒƒã‚ãƒ³ã‚°ã‚ªãƒšãƒ¬ãƒ¼ã‚¿ï¼ˆç¬¬16å›žï¼‰ã‚’çµ„ã¿åˆã‚ã›ã¦ã„ãå‡¦ç†ã‚¨ãƒ³ã‚¸ãƒ³ã«ãŠã„ã¦ã¯ã€Impalaã«ãŠã‘ã‚‹ãƒ‘ã‚¤ãƒ—ãƒ©ã‚¤ãƒ³çµåˆå‡¦ç†ãªã©ã‚’å®Ÿç¾ã™ã‚‹ã“ã¨ã¯å¿…ãšã—ã‚‚å®¹æ˜“ã§ã¯ã‚ã‚Šã¾ã›ã‚“ï¼ˆç¬¬8å›žã€ŒImpalaï¼Prestoã«ãŠã‘ã‚‹çµåˆå‡¦ç†ã€â ï¼‰â ã€‚ ã¾ãŸã€MapReduceã‚„Sparkã§ã¯ä¸é–“ãƒ‡ãƒ¼ã‚¿ã‚’ãƒ‡ã‚£ã‚¹ã‚¯ã«æ›¸ãè¾¼ã‚€ã“ã¨ã«ã‚ˆã‚Šé«˜ã„è€éšœå®³æ€§ã‚’å®Ÿç¾ã—ã¾ã™ãŒã€Impalaã§ã¯è€éšœå®³æ€§ã‚’å¤šå°‘çŠ ç‰²ã«ã—ã¦ãƒ¡ãƒ¢ãƒªä¸Šã§å‡¦ç†
mogwaing 2016/03/23
impala

parallel processing

hadoop
ãƒªãƒ³ã‚¯
ç¬¬17å›žã€€Impalaã®è¨è¨ˆã¨å®Ÿè£…ï¼»1ï¼½ | gihyo.jp
ã¯ã˜ã‚ã« ä»Šå›žã‹ã‚‰3å›žã«æ¸¡ã£ã¦ã€Hadoopä¸Šã§å‹•ä½œã™ã‚‹ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã®1ã¤ã§ã‚ã‚‹Apache Impalaï¼ˆincubating)ï¼ˆä»¥ä¸‹ã€Impalaï¼‰ã«ã¤ã„ã¦ã€ä»¥ä¸‹ã®æµã‚Œã§èª¬æ˜Žã—ã¦ã„ãã¾ã™ã€‚ Impalaæ¦‚è¦ï¼ˆä»Šå›žï¼‰ ã‚¯ã‚¨ãƒªå®Ÿè¡Œæ™‚ã®ä¸¦åˆ—åŒ–ã®ä»•çµ„ã¿ï¼ˆç¬¬18å›žï¼‰ I/Oå‡¦ç†ã«ãŠã‘ã‚‹é«˜é€ŸåŒ–ã®ä»•çµ„ã¿ï¼ˆç¬¬19å›žï¼‰ Impalaã®ç‰¹å¾´ã®ã™ã¹ã¦ã‚’ãŠä¼ãˆã§ãã‚‹ã‚ã‘ã§ã¯ã‚ã‚Šã¾ã›ã‚“ãŒã€Impalaã®é€Ÿåº¦ã«å¯¾ã™ã‚‹å–ã‚Šçµ„ã¿ã«ã¤ã„ã¦å‚è€ƒã«ãªã‚Œã°å¹¸ã„ã§ã™ã€‚ ä»Šå›žã¯ã€ImpalaãŒé–‹ç™ºã•ã‚Œã‚‹ã«è‡³ã£ãŸèƒŒæ™¯ã‚„ç‰¹å¾´ã€ãŠã‚ˆã³å‹•ä½œã®æ¦‚è¦ã¾ã§ã‚’ç´¹ä»‹ã—ã¦ã„ãã¾ã™ã€‚ Impalaé–‹ç™ºã®èƒŒæ™¯ ã“ã‚Œã¾ã§ã®é€£è¼‰å†…ã§ã‚‚è§¦ã‚Œã‚‰ã‚Œã¦ãã¾ã—ãŸãŒã€Impalaã¯Hadoopä¸Šã§SQLï¼ˆæ£ç¢ºã«ã¯Hive Query Languageï¼HiveQLï¼‰ã‚’é«˜é€Ÿã«å‡¦ç†ã™ã‚‹ãŸã‚ã«é–‹ç™ºã•ã‚ŒãŸä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã§ã™ã€‚Impalaä»¥å‰ã‹ã‚‰ã€SQLã‚’å®Ÿè¡Œã™ã‚‹H
mogwaing 2016/03/02
hadoop

impala

parallel processing

distributed system
ãƒªãƒ³ã‚¯
ç¬¬10å›žã€€ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®æœ€é©åŒ– | gihyo.jp
ã¯ã˜ã‚ã« å‰å›žã¯ã€ã“ã‚Œã¾ã§èª¬æ˜Žã—ã¦ããŸã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã®æ€§èƒ½ã‚’å®šé‡çš„ã«è¦‹ç©ã‚Šã€æ¯”è¼ƒã—ã¾ã—ãŸã€‚ä»Šå›žã¯ã€ã“ã‚Œã‚‰ã®æ€§èƒ½è¦‹ç©ã‚Šã‚’ç”¨ã„ã¦è¡Œã†ãƒ‡ãƒ¼ã‚¿å‡¦ç†ï¼ˆå•ã„åˆã‚ã›ï¼‰ã®æœ€é©åŒ–æ–¹æ³•ã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ã€‚ ãƒ‡ãƒ¼ã‚¿å‡¦ç†ï¼ˆå•ã„åˆã‚ã›ï¼‰ã®æœ€é©åŒ– ç¬¬4å›žã§è¿°ã¹ãŸã‚ˆã†ã«ã€Hadoopã®SQLå‡¦ç†ç³»ã§ã‚ã‚‹Hiveã‚’ã¯ã˜ã‚ã¨ã—ã€Impalaã‚„Prestoãªã©ã®å®£è¨€åž‹è¨€èªžã‚’ç”¨ã„ã‚‹ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã«ãŠã„ã¦ã¯ã€åˆ©ç”¨è€…ã¯ä½•ã‚’ï¼ˆWhatï¼‰å‡¦ç†ã—ã¦ã»ã—ã„ã‹ã‚’å‡¦ç†ç³»ã«æŒ‡ç¤ºã™ã‚‹ã®ã¿ã§ã‚ã‚Šã€ã©ã®ã‚ˆã†ã«ï¼ˆHowï¼‰å‡¦ç†ã‚’ã—ã¦ã»ã—ã„ã‹ã¯æŒ‡å®šã—ã¾ã›ã‚“ã€‚ã™ãªã‚ã¡ã€å½“è©²å‡¦ç†ç³»ã«ãŠã„ã¦ã¯ã€ã©ã®ã‚ˆã†ã«å‡¦ç†ã‚’ã™ã‚‹ã‹ã¯å‡¦ç†ç³»è‡ªä½“ãŒæ±ºã‚ã‚‹å¿…è¦ãŒã‚ã‚Šã€ä¸Žãˆã‚‰ã‚ŒãŸå•ã„åˆã‚ã›ï¼ˆã‚¯ã‚¨ãƒªï¼‰ã‚’æœ€ã‚‚è‰¯ã„ã¨æ€ã‚ã‚Œã‚‹æ–¹æ³•ã§å‡¦ç†ã—ã¾ã™ã€‚ã“ã®ã‚ˆã†ã«ã€å•ã„åˆã‚ã›ã«ãŠã„ã¦æœ€è‰¯ã¨æ€ã‚ã‚Œã‚‹ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®æ–¹æ³•ã‚’è¦‹ã¤ã‘ã‚‹ã“ã¨ã‚’ã€Œâ ï¼ˆâ å•ã„åˆã‚ã›ï¼‰æœ€é©åŒ–ã€ã¨å‘¼ã³ã¾ã™ã€‚ æœ€é©åŒ–ã«ãŠã„ã¦ã¯ã€å•ã„åˆã‚ã›ã‚’å®Ÿè¡Œã™
mogwaing 2015/09/16
hadoop

parallel db

parallel processing

distributed system

query optimizer

impala

hive
ãƒªãƒ³ã‚¯
ç¬¬8å›žã€€ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã«ãŠã‘ã‚‹ä¸¦åˆ—ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ï¼»3ï¼½ | gihyo.jp
ã¯ã˜ã‚ã« å‰å›žã¯ã€çµåˆå‡¦ç†ã®ä¸¦åˆ—åŒ–ã«ãŠã‘ã‚‹åŸºæœ¬æˆ¦ç•¥ã«ã¤ã„ã¦èª¬æ˜Žã—ã€ã‚½ãƒ¼ãƒˆãƒžãƒ¼ã‚¸çµåˆã«ãŠã‘ã‚‹å…·ä½“çš„ãªä¸¦åˆ—ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’èª¬æ˜Žã—ã¾ã—ãŸã€‚ä»Šå›žã¯ã€Impalaã‚„Prestoã«åŠ ãˆã¦ã€Apache Sparkã‚„Hadoop MapReduceã®Map Joinã«ãŠã„ã¦ã‚‚ç”¨ã„ã‚‰ã‚Œã¦ã„ã‚‹ãƒãƒƒã‚·ãƒ¥çµåˆã«ãŠã‘ã‚‹å…·ä½“çš„ãªä¸¦åˆ—ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’èª¬æ˜Žã—ã¾ã™ã€‚ ãƒãƒƒã‚·ãƒ¥çµåˆã«ãŠã‘ã‚‹ä¸¦åˆ—ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ãƒãƒƒã‚·ãƒ¥çµåˆã¯ã€2ã¤ã®ãƒ‡ãƒ¼ã‚¿ã«ãŠã„ã¦åŒä¸€ã®å±žæ€§å€¤ã‚’ã‚‚ã¤ãƒ¬ã‚³ãƒ¼ãƒ‰ã‚’è¦‹ã¤ã‘ã‚‹æ–¹æ³•ã¨ã—ã¦ã€ãƒ¬ã‚³ãƒ¼ãƒ‰ã®ãƒãƒƒã‚·ãƒ¥å€¤ã‚’ç”¨ã„ã‚‹ã‚‚ã®ã§ã™[1]â ã€‚ã™ãªã‚ã¡ã€å½“è©²æ–¹æ³•ã«ãŠã„ã¦ã¯ã€ä¸€æ–¹ã®ãƒ‡ãƒ¼ã‚¿ã®ã™ã¹ã¦ã®ãƒ¬ã‚³ãƒ¼ãƒ‰ã®çµåˆã‚ãƒ¼ã«å¯¾ã—ã¦ãƒãƒƒã‚·ãƒ¥é–¢æ•°ã‚’ç”¨ã„ã¦ãƒãƒƒã‚·ãƒ¥å€¤ã‚’è¨ˆç®—ã—ã€å½“è©²ãƒãƒƒã‚·ãƒ¥å€¤ã‹ã‚‰ãªã‚‹ãƒãƒƒã‚·ãƒ¥è¡¨ã‚’äº‹å‰ã«æ§‹ç¯‰ã—ã¦ãŠãã€ä»–æ–¹ã®ãƒ‡ãƒ¼ã‚¿ã®ãƒ¬ã‚³ãƒ¼ãƒ‰ã®çµåˆã‚ãƒ¼ã«å¯¾ã—ã¦åŒä¸€ã®ãƒãƒƒã‚·ãƒ¥é–¢æ•°ã‹ã‚‰å¾—ã‚‰ã‚ŒãŸãƒãƒƒã‚·ãƒ¥å€¤ã‚’ç”¨ã„ã¦ãƒãƒƒã‚·ãƒ¥è¡¨ã‚’å‚ç…§ã™ã‚‹ã“ã¨ã«ã‚ˆã‚Šã€åŒä¸€ã®
mogwaing 2015/08/05
hadoop

parallel db

parallel processing

distributed system

spark

impala

presto
ãƒªãƒ³ã‚¯
ç¬¬5å›žã€€ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®ä¸¦åˆ—åŒ– | gihyo.jp
ã¯ã˜ã‚ã« å‰å›žã¯ã€ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®æ–¹æ³•ã‚’æ•´ç†ã—ã€ã¾ãŸã€å®£è¨€åž‹è¨€èªžã‚’ã‚¤ãƒ³ã‚¿ãƒ¼ãƒ•ã‚§ãƒ¼ã‚¹ã¨ã—ã¦ç”¨ã„ã‚‹ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ãªã©ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã‚’è©³ç´°ã«è¦‹ã¦ã„ãæº–å‚™ã¨ã—ã¦ã€å½“è©²ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã«ãŠã‘ã‚‹å®Ÿè¡Œãƒ—ãƒ©ãƒ³ã®ä½œæˆã®æµã‚Œã‚’ã‹ã‚“ãŸã‚“ã«èª¬æ˜Žã—ã¾ã—ãŸã€‚ä»Šå›žã¯ã€å½“è©²ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã«ãŠã„ã¦ã€ã©ã®ã‚ˆã†ã«å®Ÿè¡Œãƒ—ãƒ©ãƒ³ã‚’ä¸¦åˆ—åŒ–ã™ã‚‹ã‹ã«ã¤ã„ã¦ã€ãã®æ¦‚è¦ã‚’èª¬æ˜Žã—ã¾ã™ã€‚ ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã«ãŠã‘ã‚‹ä¸¦åˆ—æ€§ã«ã¤ã„ã¦ ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã‚’ã¯ã˜ã‚ã¨ã™ã‚‹ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã¯ã€SQLæ–‡ãªã©ã®å•ã„åˆã‚ã›ï¼ˆã‚¯ã‚¨ãƒªï¼‰ã®å†…å®¹ã«å¿œã˜ã¦ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚’è¡Œã†ã‚‚ã®ã§ã‚ã‚Šã€å•ã„åˆã‚ã›ã®è¦³ç‚¹ã«ãŠã„ã¦ã¯ã€å½“è©²å‡¦ç†ç³»ã«ãŠã„ã¦ç”¨ã„ã‚‰ã‚Œã‚‹ä¸¦åˆ—æ€§ï¼ˆParallelismï¼‰ã¯ã€æ¬¡ã®2ã¤ã«åˆ†é¡žã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ å•ã„åˆã‚ã›é–“ã®ä¸¦åˆ—æ€§ï¼ˆInter-Query Parallelismï¼‰ å•ã„åˆã‚ã›å†…ã®ä¸¦åˆ—æ€§ï¼ˆIntra-Query Parallelismï¼‰ å•ã„åˆã‚ã›é–“ã®ä¸¦åˆ—æ€§ã¯ã€è¤‡æ•°ã®ç•°ãªã‚‹å•ã„åˆã‚ã›
mogwaing 2015/05/27
hadoop

impala

presto

spark

parallel processing

parallel db

distributed system
ãƒªãƒ³ã‚¯
Impala 2.0 Update #impalajp
2. 2 Todayâ€™s Topic â€¢ What is Cloudera Impala? â€¢ Impala 1.4 / 2.0 update â€¢ Performance Improvement â€¢ Query Language â€¢ Resource Management and Security â€¢ Others 3. 3 Who am I ? â€¢ Pre-Ââ€sales SoluLons Architect â€¢ joined Cloudera in 2011, the first Japanese employee at Cloudera â€¢ em ail: [email protected] â€¢ twiTer: @shiumachi 5. 5 What is Impala? â€¢ MPP SQL query engine for Hadoop environment â€¢ wriTen in
mogwaing 2014/10/31
impala 2.0 update

impala
ãƒªãƒ³ã‚¯
Cloudera Blog
We are thrilled to announce the general availability of the Cloudera AI Inference service, powered by NVIDIA NIM microservices, part of the NVIDIA AI Enterprise platform, to accelerate generative AI deployments for enterprises. This service supports a range of optimized AI models, enabling seamless and scala ble AI inference. Background The generative AI landscape is evolving [â€¦] Read blog post
mogwaing 2014/05/30
impala

parquet

hadoop

benchmark
ãƒªãƒ³ã‚¯
Impala Performance Update: Now Reaching DBMS-Class Speed - Cloudera Blog
Impalaâ€™s speed now beats the fastest SQL-on-Hadoop alternatives. Test for yourself! Since the initial beta release of Cloudera Impala more than one year ago (October 2012), weâ€™ve been committed to regularly updating you about its evolution into the standard for running interactive SQL queries across data in Apache Hadoop and Hadoop-based enterprise data hubs. To briefly recap where we are today: I
mogwaing 2014/01/14
impala

parallel db

database
ãƒªãƒ³ã‚¯
Cloudera Impalaã®ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£
ï¼ˆæœ¬ãƒ–ãƒã‚°ã¯è‹¥å¹²å¤ããªã£ã¦ã„ã‚‹ã®ã§ã€Impalaæƒ…å ±ãƒšãƒ¼ã‚¸ã‚’ã”è¦§ä¸‹ã•ã„ã€‚æ¯”è¼ƒçš„æ–°ã—ã„æƒ…å ±ã‚’ã¾ã¨ã‚ã¦ã„ã¾ã™ï¼‰ ä¸€äººã‚¢ãƒ‰ãƒ™ãƒ³ãƒˆã‚«ãƒ¬ãƒ³ãƒ€ãƒ¼ï¼’ï¼•æ—¥ç›®ã€æœ€çµ‚æ—¥ã§ã™ã€‚ æœ€çµ‚æ—¥ã¯Cloudera Impalaï¼ˆä»¥ä¸‹Impalaï¼‰ã«ã¤ã„ã¦ã€‚Impalaã¯åˆ†æ•£ã‚¯ã‚¨ãƒªã‚¨ãƒ³ã‚¸ãƒ³ã§ã™ã€‚æœ€è¿‘EMRã§ã‚‚åˆ©ç”¨ã§ãã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã—ãŸã€‚ Hiveã¨ã¯ä½•ãŒé•ã†ã®ã‹ã€ãªãœHiveã‚’é«˜é€ŸåŒ–ã—ãªã‹ã£ãŸã®ã‹ã¨ã„ã†ã‚ˆã†ãªæ„è¦‹ã‚‚ã‚ã‚‹ã‚ˆã†ã§ã™ãŒã€ãã®ç”ãˆã¯Clouderaã®å‰µæ¥è€…ã§ã‚‚ã‚ã‚‹Mike OlsonãŒä»Šé€±å…¬é–‹ã—ãŸãƒ–ãƒã‚°ï¼ˆImpala v Hiveï¼‰ã«è©³ã—ãæ›¸ã‹ã‚Œã¦ã„ã¾ã™ã€‚ã‹ãªã‚Šèˆˆå‘³æ·±ã„å†…å®¹ã§ã™ãŒã€ä»Šã®ã¨ã“ã‚è‹±èªžã®ã¿ã§ã™ã€‚ãã£ã¨æ—¥æœ¬èªžã®è¨˜äº‹ã‚‚ã„ãšã‚Œèªã‚ã‚‹ã‚ˆã†ã«ãªã‚‹ã¯ãšã€‚ã€‚ã€‚ ã•ã¦ã€æœ€çµ‚æ—¥ã¯Cloudera Impalaã®ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã«ã¤ã„ã¦æ›¸ã„ã¦ã¿ã¾ã™ã€‚å¼•ç”¨ã—ã¦ã„ã‚‹è³‡æ–™ã¯Slideshareã§ClouderaãŒå…¬é–‹ã—ã¦ã„ã‚‹ã‚‚ã®ã§ã™
mogwaing 2013/12/26
Left-deepã¨è¨€ã„ã¤ã¤ã€Right-deepã§ã¯ã€‚ã€‚ã€‚

impala
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Big Data Benchmark
Click Here for the previous version of the benchmark Introduction Several analytic frameworks have been announced in the last year. Among them are inexpensive data-warehousing solutions based on traditional Massively Parallel Processor (MPP) architectures (Redshift), systems which impose MPP-like execution engines on top of Hadoop (Impala, HAWQ), and systems which optimize MapReduce to improve per
mogwaing 2013/06/28
database

benchmark

parallel processing

impala

shark

spark

redshift

hawq
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Cloudera Blog
Enterprise IT leaders across industries are tasked with preparing their organizations for the techno logies of the future â€“ which is no simple task. With the use of AI exploding, Cloudera, in partnership with Researchscape, surveyed 600 IT leaders who work at companies with over 1,000 employees in the U.S., EMEA and APAC regions. The survey, [â€¦] Read blog post
mogwaing 2013/02/24
impala

cloudera

llvm
ãƒªãƒ³ã‚¯
Impala Q&A - still deeper
2012/11/7ã«é–‹å‚¬ã•ã‚ŒãŸCloudera World Tokyoã«å‚åŠ ã—ã¦ãã¾ã—ãŸã€‚ æœ¬ç·¨ã«ã¤ã„ã¦ã¯ä»–ã®äººãŒã¾ã¨ã‚ã¦ãã‚Œã‚‹ã¯ãšã§ã™ã®ã§çœç•¥ã€‚ æ‡‡è¦ªä¼šã§ã¯ç±³å›½Clouderaç¤¾ã®CTOã€Dr. Amr Awadallahæ°ã«ç›´æŽ¥Impalaã®ç–‘å•ã«ç”ãˆã¦ã„ãŸã ãã¾ã—ãŸã€‚éžå¸¸ã«è²´é‡ãªè©±ã‚’èžã‘ãŸã®ã§ã¾ã¨ã‚ã¦ãŠãã¾ã™ã€‚ï¼ˆå…¬é–‹è¨±å¯æ¸ˆã¿ï¼‰ ãã®å ´ã§ãƒ¡ãƒ¢ã‚’å–ã£ã¦ã„ãŸã‚ã‘ã§ã¯ãªãæ€ã„å‡ºã—ãªãŒã‚‰ã®ã¾ã¨ã‚ãªã®ã§ã€ä¸€ç·’ã«èžã„ã¦ã„ãŸæ–¹ã€clouderaã®æ–¹ã¯è£œè¶³ã‚’ãŠé¡˜ã„ã—ã¾ã™ã€‚ Q&A Q. ãªãœJavaã§ãªãã¦C++ã§å®Ÿè£…ã—ãŸã‹ï¼Ÿ A. Impalaã®ãƒ¡ã‚¤ãƒ³ãƒ‡ã‚¶ã‚¤ãƒŠãƒ¼ãŒGoogleã§C++ã‚’ä½¿ã£ã¦åˆ†æ•£å‡¦ç†(Dremelã®ã“ã¨ï¼Ÿ)ã‚’å®Ÿè£…ã—ãŸäººç‰©ã§ã‚ã‚‹ã®ã¨ã€JVMã®èµ·å‹•ã‚³ã‚¹ãƒˆãŒãƒ¬ã‚¤ãƒ†ãƒ³ã‚·ãƒ¼ã®å¢—åŠ ã«ã¤ãªãŒã‚‹ãŸã‚ è£œè¶³: ã“ã®äººã§ã—ã‚‡ã†ã‹ Q. 1ãƒŽãƒ¼ãƒ‰ã«åã£ãŸãƒ‡ãƒ¼ã‚¿ã‚’èªã‚€å¿…è¦ãŒã‚ã‚‹ã‚¯ã‚¨ãƒªãŒãã‚‹ã¨ä½Žãƒ¬ã‚¤ãƒ†ãƒ³ã‚·ãƒ¼ã‚’
mogwaing 2012/11/09
hadoop

impala

cloudera
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Man Busts Out of Google, Rebuilds Top-Secret Query Machine
You can think of Google as the research lab for the rest of the internet. Every so often, the company releases a research paper describing one of the sweeping software platforms that help drive its online empire, and a few years later, this paper will spawn an open source software project that seeks to share Google's creation with the rest of the world. On Wednesday, it happened again. You can thi
mogwaing 2012/10/31
impala

cloudera

hadoop

google

f1
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Cloudera Impala: Real-Time Queries in Apache Hadoop, For Real | Cloudera Blog
Cloudera Impala: Real-Time Queries in Apache Hadoop, For Real After a long period of intense engineering effort and user feedback, we are very pleased, and proud, to announce the Cloudera Impala project. This techno logy is a revolutionary one for Hadoop users, and we do not take that claim lightly. When Google published its Dremel paper in 2010, we were as inspired as the rest of the community by
mogwaing 2012/10/26
software stack

cloudera

impala

dremel

hadoop
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Cloudera Impala ãŒãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸ - ç§‘å¦ã¨éžç§‘å¦ã®è¿·å®®
(2012/10/25 15:48 è¿½è¨˜) Cloudera å…¬å¼ãƒ–ãƒã‚°ã§ Impala ã«ã¤ã„ã¦ã®ç´¹ä»‹è¨˜äº‹ã‚’æŽ²è¼‰ã—ã¾ã—ãŸã€‚ã“ã®ãƒ–ãƒã‚°è¨˜äº‹ã®å®Œå…¨ä¸Šä½äº’æ›ãªã®ã§ãã¡ã‚‰ã®è¨˜äº‹ã‚’ã”å‚ç…§ãã ã•ã„ã€‚ Cloudera Impalaï¼šApache Hadoopã§å®Ÿç¾ã™ã‚‹ã€çœŸã®ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã‚¯ã‚¨ãƒª | Hadoopã¨ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿ã‚½ãƒªãƒ¥ãƒ¼ã‚·ãƒ§ãƒ³ã®ãƒªãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã‚«ãƒ³ãƒ‘ãƒ‹ãƒ¼ | Cloudera Japan Cloudera ã‹ã‚‰ã€ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆã®ãŸã‚ã®ãƒªã‚¢ãƒ«ã‚¿ã‚¤ãƒ ã‚¯ã‚¨ãƒªã‚¨ãƒ³ã‚¸ãƒ³ã€ŒImpalaã€ãŒãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸã€‚Hive ã¨å®Œå…¨äº’æ›ã®ã‚¯ã‚¨ãƒªè¨€èªžã§ã€Hive ã‚ˆã‚Š10å€ä»¥ä¸Šé€Ÿãã‚¯ã‚¨ãƒªã‚’å‡¦ç†ã§ãã¾ã™ã€‚ æ¦‚è¦åŠã³ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ã¯ã“ã¡ã‚‰ã‹ã‚‰! http://www.cloudera.com/content/cloudera/en/products/cloudera-enterprise-core/clouder
mogwaing 2012/10/25
cloudera

impala
ãƒªãƒ³ã‚¯
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