[B! HDFS] Oooã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

Ooo id:Ooo

HDFSã«é–¢ã™ã‚‹Oooã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (12)

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Kuduãƒªãƒªãƒ¼ã‚¹ï¼
ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®æ–°ã—ã„ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã‚¨ãƒ³ã‚¸ãƒ³ã€Kudu ã¤ã„ã«æœ¬æ—¥ã‚¢ãƒŠã‚¦ãƒ³ã‚¹ã•ã‚ŒãŸ Kudu (ã‚¯ãƒ‰ã‚¥ï¼‰ã€æ¬¡ä¸–ä»£ãƒãƒ¼ãƒ‰ã‚¦ã‚§ã‚¢ã«ã‚‚å¯¾å¿œã™ã‚‹ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã‚¨ãƒ³ã‚¸ãƒ³ã§ã™ã€‚ã¾ã ãƒ™ãƒ¼ã‚¿ç‰ˆã§ã™ãŒã€3å¹´ã‚‚ã®é–‹ç™ºæœŸé–“ã‚’çµŒã¦ã¤ã„ã«å…¬é–‹ã•ã‚Œã¾ã—ãŸï¼ Super excited to finally talk about what I've been working on the last 3 years: Kudu! http://t.co/1W4sqFBcyH http://t.co/1mZCwgdOO5 â€” Todd Lipcon (@tlipcon) September 28, 2015 æ•°æ—¥å‰ã«ãƒªãƒ¼ã‚¯ã•ã‚ŒãŸè¨˜äº‹ã«ã€ŒKuduã¯HDFSã‚„HBaseã‚’ç½®ãæ›ãˆã‚‹ã‚‚ã®ã ã€ã¨æ›¸ã‹ã‚Œã¦ã„ã¾ã—ãŸãŒã€ãã‚Œã¯é–“é•ã„ã§ã™ã€‚è©³ç´°ã¯FAQãªã©ã«ã‚‚æ›¸ã‹ã‚Œã¦ã„ã¾ã™ãŒã€HDFSã¨HBaseã‚’è£œã†æ–°ã—ã„ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã‚¨ãƒ³ã‚¸ãƒ³ã§ã™
Ooo 2015/09/29
Kudu

HDFS

hbase

analytics
ãƒªãƒ³ã‚¯
HDFSã¯ãƒ•ã‚¡ã‚¤ãƒ«ã‚µãƒ¼ãƒãƒ¼ã«ä½¿ã†ã‚‚ã®ã˜ã‚ƒãªã„ã‚ˆ
Hadoopï¼ˆ0.22ä»¥å‰ã‚„1.0.0ï¼‰ã®HDFSã¯NameNodeãŒSPOFï¼ˆå˜ä¸€éšœå®³ç‚¹ï¼‰ãªã®ã§ã€ãƒ•ã‚¡ã‚¤ãƒ«ã‚µãƒ¼ãƒãƒ¼ã‚„ãƒ‡ãƒ¼ã‚¿ã®â€œãƒãƒƒã‚¯ã‚¢ãƒƒãƒ—â€ã«ã¯å‘ã‹ãªã„ã¨ã„ã†è©±ã§ã™ã€‚ HDFSä¸Šã«æ§‹ç¯‰ã•ã‚Œã¦ã„ã‚‹HBaseã®å ´åˆã€ï¼ˆHBaseè‡ªèº«ã«ã¯SPOFã¯ç„¡ã„ã‘ã‚Œã©ã‚‚ï¼‰HDFSã«SPOFãŒã‚ã‚‹ã®ã§ã€NameNodeã¯å†—é•·åŒ–ã—ãŸæ–¹ãŒè‰¯ã„ã‚ˆã†ã§ã™ã€‚ å†—é•·åŒ–ã«ã¯ä»¥ä¸‹ã®ã‚ˆã†ãªä»•çµ„ã¿ãŒè€ƒãˆã‚‰ã‚Œã¦ã„ã‚‹ã‚ˆã†ã§ã™ã€‚ ç¶šãã‚’èªã‚€
Ooo 2010/12/08
hadoop

hdfs

apache

filesystem

file
ãƒªãƒ³ã‚¯
Hadoop Archive: File Compaction for HDFS (Yahoo! Hadoop Blog)
Thanks to Tsz-Wo Nicholas Sze and Mahadev Konar for this article. The Probl em of Many Small Files The Hadoop Distributed File System (HDFS) is designed to store and process large (terabytes) data sets. At Yahoo!, for example, a large production cluster may have 14 PB disk spaces and store 60 millions of files. However, storing a large number of small files in HDFS is inefficient. We call a file sm
Ooo 2010/08/12
HDFS

hadoop
ãƒªãƒ³ã‚¯
Hadoop èª¿æŸ»å ±å‘Šæ›¸ ã‚¨ãƒŒãƒ»ãƒ†ã‚£ãƒ»ãƒ†ã‚£ ãƒ¬ã‚¾ãƒŠãƒ³ãƒˆæ ªå¼ä¼šç¤¾ æ ªå¼ä¼šç¤¾ Preferred Infrastructure å¹³æˆ 20 å¹´ 8 æœˆ 25 æ—¥ â€¢ å…è²¬æ¡é … æœ¬å ±å‘Šæ›¸ã¯ã‚¨ãƒŒãƒ»ãƒ†ã‚£ãƒ»ãƒ†ã‚£ ãƒ¬ã‚¾ãƒŠãƒ³ãƒˆæ ªå¼ä¼šç¤¾ (ä»¥ä¸‹ã€ŒNTT ãƒ¬ã‚¾ãƒŠãƒ³ï¿½ï¿½
Hadoop èª¿æŸ»å ±å‘Šæ›¸ ã‚¨ãƒŒãƒ»ãƒ†ã‚£ãƒ»ãƒ†ã‚£ ãƒ¬ã‚¾ãƒŠãƒ³ãƒˆæ ªå¼ä¼šç¤¾ æ ªå¼ä¼šç¤¾ Preferred Infrastructure å¹³æˆ 20 å¹´ 8 æœˆ 25 æ—¥ â€¢ å…è²¬æ¡é … æœ¬å ±å‘Šæ›¸ã¯ã‚¨ãƒŒãƒ»ãƒ†ã‚£ãƒ»ãƒ†ã‚£ ãƒ¬ã‚¾ãƒŠãƒ³ãƒˆæ ªå¼ä¼šç¤¾ (ä»¥ä¸‹ã€ŒNTT ãƒ¬ã‚¾ãƒŠãƒ³ãƒˆã€ï¼‰ã¨æ ªå¼ä¼šç¤¾ Preferred Infras- tructure(ä»¥ä¸‹ã€ŒPreferred Infrastructureã€) ãŒä½œæˆã—ãŸã‚‚ã®ã§ã™ãŒã€å ±å‘Šæ›¸ã®å†…å®¹åŠã³æƒ…å ±ã®æ£ç¢ºæ€§ã€å®Œå…¨æ€§ã€ æœ‰ç”¨æ€§ã«ã¤ã„ã¦ã€NTT ãƒ¬ã‚¾ãƒŠãƒ³ãƒˆåŠã³ Preferred Infrastructure ã¯ä¿è¨¼ã‚’è¡Œãªã£ã¦ãŠã‚‰ãšã€ã¾ãŸã€ã„ã‹ãªã‚‹è²¬ ä»»ã‚’æŒã¤ã‚‚ã®ã§ã‚‚ã‚ã‚Šã¾ã›ã‚“ã€‚ æœ¬å ±å‘Šæ›¸ã®è‘—ä½œæ¨©ã¯ NTT ãƒ¬ã‚¾ãƒŠãƒ³ãƒˆã«å¸°å±žã—ã¾ã™ã€‚ æœ¬å ±å‘Šæ›¸ã®ã€Œãƒ—ãƒªãƒ³ãƒˆã‚¢ã‚¦ãƒˆã€ã€Œã‚³ãƒ”ãƒ¼ã€ã€Œç„¡æ–™é…å¸ƒã€ã¯å¯èƒ½ã§ã™ãŒã€å¤‰æ›´ã€æ”¹å¤‰ã€åŠ å·¥ã€åˆ‡é™¤ã€éƒ¨åˆ†åˆ©ç”¨ã€è¦ ç´„ã€ç¿»è¨³ã€å¤‰å½¢ã€è„šè‰²ã€ç¿»æ¡ˆãªã©ã¯ç¦æ¢ã—ã¾ã™
Ooo 2010/07/18
HDFS

hadoop

mapreduce
ãƒªãƒ³ã‚¯
HDFSã®ã‚¹ã‚±ãƒ¼ãƒ©ãƒ“ãƒªãƒ†ã‚£ã¨ãƒžãƒ«ãƒãƒžã‚¹ã‚¿ã¸ã®å–ã‚Šçµ„ã¿
6. The Internal Load3 block reports per second, each reporting 60,000 replicas10,000 heartbeats per secondNNThroughputãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã®çµæžœBlock reportsã«ã‚ˆã£ã¦å¸¸ã«CPUã®30%ãŒæ¶ˆè²»ã•ã‚Œã¦ã„ã‚‹ 7. Reasonable Load ExpectationsHDFS read and write throughputOpen and create throughputInternal loadãŒã‚ã‚‹ã®ã§Throughputã®ã†ã¡70%ã®ã¿å®Ÿéš›ã«ä½¿ç”¨å¯èƒ½100,000ã‚¯ãƒ©ã‚¤ã‚¢ãƒ³ãƒˆã§å„ã‚¯ãƒ©ã‚¤ã‚¢ãƒ³ãƒˆãŒ96MBæ›¸ãè¾¼ã‚€ã¨ã™ã‚‹ã¨ã€2.4ç§’ãªã®ã§ã€41,667 create block / sec3920 ops/sã—ã‹æ›¸ãè¾¼ã‚ãªã„
Ooo 2010/05/30
HDFS

ceph

hadoop

storage

system

scalability

apache

yahoo
ãƒªãƒ³ã‚¯
Hadoopå…¥é–€ã¨ã‚¯ãƒ©ã‚¦ãƒ‰åˆ©ç”¨
This document discusses Hadoop and related techno logies. It introduces Hadoop, its components MapReduce and HDFS, and how they work together. It also briefly mentions related Apache projects like Mahout and how companies like Amazon, Yahoo and Facebook use Hadoop in their systems. Finally, it covers Amazon's Elastic MapReduce service, which allows running Hadoop jobs in the cloud.Read less
Ooo 2010/05/17
hadoop

HDFS

mapreduce
ãƒªãƒ³ã‚¯
The Curse of the Singletons! The Vertical Scalability of Hadoop NameNode
Introduction HDFS is designed to be a highly scala ble storage system and sites at Facebook and Yahoo have 20PB size file systems in production deployments. The HDFS NameNode is the master of the Hadoop Distributed File System (HDFS). It maintains the critical data structures of the entire file system. Most of HDFS design has focussed on scalability of the system, i.e. the ability to support a larg
Ooo 2010/05/16
HDFS

hadoop
ãƒªãƒ³ã‚¯
Scalability of the Hadoop Distributed File System (Hadoop and Distributed Computing at Yahoo!)
The bottom line is that we achieved the target in Petabytes and got close to the target in the number of files. But this is done with a smaller number of nodes and the need to support a workload close to 100,000 clients has not yet materialized. The question now is whether the goals are feasible with the current system architecture. Namespace Limitations HDFS is based on an architecture where the
Ooo 2010/05/12
hadoop

HDFS
ãƒªãƒ³ã‚¯
JavaWhat - Hadoop Sample hdfs-site.xml
Ooo 2010/05/10
hadoop

hdfs
ãƒªãƒ³ã‚¯
Hadoopãƒªãƒ³ã‚¯ã¾ã¨ã‚(1) - ç§‘å¦ã¨éžç§‘å¦ã®è¿·å®®
Part1 / Part2 æ›´æ–°å±¥æ´ 2010/06/20 ãƒªãƒ³ã‚¯è¿½åŠ å…¥é–€ã€äº‹ä¾‹ç´¹ä»‹ã€ãƒ‹ãƒ¥ãƒ¼ã‚¹ Part2ã¸ç§»å‹• EC2ã€Pigã€MapReduceã€HDFS æ–°è¦è¿½åŠ æ€§èƒ½æ¸¬å®š å…¬å¼ Welcome to Apache Hadoop! æ—¥æœ¬èªžè¨³ Hadoopãƒ¦ãƒ¼ã‚¶ãƒ¼ä¼š Welcome to Hadoop MapReduce! "å¤§è¦æ¨¡ãªè¨ˆç®—ãƒŽãƒ¼ãƒ‰ãƒ»ã‚¯ãƒ©ã‚¹ã‚¿ä¸Šã«ãŠã„ã¦è†¨å¤§ãªãƒ‡ãƒ¼ã‚¿ã‚’é«˜é€Ÿã§ä¸¦åˆ—å‡¦ç†ã™ã‚‹ã‚¢ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã‚’ä½œæˆã™ã‚‹ãŸã‚ã®ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ãƒ¢ãƒ‡ãƒ«ãŠã‚ˆã³ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯" Welcome to Pig! "å¤§è¦æ¨¡ãªãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’åˆ†æžã™ã‚‹ãŸã‚ã®ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ""Pig ã®è¨€èªžãƒ¬ã‚¤ãƒ¤ã‚’æ§‹æˆã—ã¦ã„ã‚‹ã®ã¯ã€Pig Latin ã¨å‘¼ã°ã‚Œã‚‹ãƒ†ã‚ã‚¹ãƒˆãƒ™ãƒ¼ã‚¹ã®è¨€èªž" wikipedia Apache Hadoop - Wikipedia, the free encyclopedia Apa
Ooo 2010/04/26
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hadoop

hdfs

hbase
ãƒªãƒ³ã‚¯
HDFSã¸ã®ã‚¢ã‚¯ã‚»ã‚¹ãƒ¦ãƒ¼ã‚¶ - kikumotoã®ãƒ¡ãƒ¢å¸³
HDFS Permissions Guide ã‚’è¦‹ã‚‹ã¨ã€HDFS ã§ã¯ Linux ã¨ã‹ã§è¦‹ã‚‰ã‚Œã‚‹ã‚ˆã†ãªãƒ‘ãƒ¼ãƒŸãƒƒã‚·ãƒ§ãƒ³ãŒè¨å®šã•ã‚Œã‚‹ã‚ˆã†ã«ãªã£ã¦ã„ã‚‹ã‚‰ã—ã„ã€‚ ãã—ã¦ã‚¹ãƒ¼ãƒ‘ãƒ¼ãƒ¦ãƒ¼ã‚¶ã¯ NameNode ã®å®Ÿè¡Œãƒ¦ãƒ¼ã‚¶ã«ãªã‚‹ã‚‰ã—ã„ã€‚ ç¢ºã‹ã«ä»Šã¾ã§ã¯ HDFS ã«å¯¾ã™ã‚‹æ“ä½œã‚’ hadoop ãƒ¦ãƒ¼ã‚¶ã§è¡Œã£ã¦ããŸã®ã ã‘ã©ã€ãŸã¨ãˆã° root ãƒ¦ãƒ¼ã‚¶ãªã‚“ã‹ã§ãƒ•ã‚¡ã‚¤ãƒ«ã‚’ HDFS ã« put ã—ã‚ˆã†ã¨ã™ã‚‹ã¨ä»¥ä¸‹ã®ã‚ˆã†ã«æ€’ã‚‰ã‚Œã‚‹ã€‚ # ./bin/hadoop fs -put hoge.txt hoge.txt put: org.apache.hadoop.security.AccessControlException: Permission denied: user=root, access=WRITE, inode="user":hadoop:supergroup:rwxr-xr-x ãªã®ã§ã€ãŸã¨ãˆã° r
Ooo 2010/04/23
hadoop

hdfs
ãƒªãƒ³ã‚¯
HDFS ã®ä¿¡é ¼æ€§ï¼ˆåŽŸé¡Œï¼šHDFS Reliabilityï¼‰ â€Ž(Pac Learner)â€Ž
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Ooo 2010/04/14
HDFS

hadoop

storage
ãƒªãƒ³ã‚¯
1