HDFSã®ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ã‚¹ãƒ†ãƒ¼ãƒˆ
原文: http://blog.cloudera.com/blog/2017/05/hdfs-maintenance-state/ ã¯ã˜ã‚ã«:OSã®æ›´æ–°ã€ã‚»ã‚ュリティパッãƒã®é©ç”¨ã€ä¿®æ£ãƒ—ãƒã‚°ãƒ©ãƒ ãªã©ã®ã‚·ã‚¹ãƒ†ãƒ ã®ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ä½œæ¥ã¯ã€ã©ã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼ã§ã‚‚日常的ãªä½œæ¥ã§ã™ã€‚ ã“ã®ã‚ˆã†ãªãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ä½œæ¥ã‚’è¡Œã£ã¦ã„る最ä¸ã®DataNodeã¯ã€æ•°åˆ†ã‹ã‚‰æ•°æ™‚é–“ã®é–“オフラインã«ãªã‚‹å¯èƒ½æ€§ãŒã‚ã‚Šã¾ã™ã€‚ è¨è¨ˆä¸Šã€Apache Hadoopã®HDFSã¯DataNodeã®ãƒ€ã‚¦ãƒ³ã«å¯¾å¿œã§ãã¾ã™ãŒã€åŒæ™‚ã«è¤‡æ•°ã®DataNodeã§èª¿æ•´ã•ã‚Œã¦ã„ãªã„メンテナンスæ“作ãŒã‚ã‚Œã°ã€ä¸€æ™‚çš„ã«ãƒ‡ãƒ¼ã‚¿å¯ç”¨æ€§ã®å•é¡Œã‚’引ãèµ·ã“ã™å¯èƒ½æ€§ãŒã‚ã‚Šã¾ã™ã€‚ HDFSã¯ç¾åœ¨ã€è¨ˆç”»ã•ã‚ŒãŸä¿å®ˆä½œæ¥ã‚’実行ã™ã‚‹ãŸã‚ã«æ¬¡ã®æ©Ÿèƒ½ã‚’サãƒãƒ¼ãƒˆã—ã¦ã„ã¾ã™: ãƒãƒ¼ãƒªãƒ³ã‚°ã‚¢ãƒƒãƒ—グレードデコミッションメンテナンスステート (CDH 5.11以é™ï¼‰ãƒãƒ¼ãƒªãƒ³ã‚°ã‚¢ãƒƒãƒ—グレードã®
Apache Hadoopã®HDFS Erasure Codingã®ç´¹ä»‹
原文: https://blog.cloudera.com/blog/2015/09/introduction-to-hdfs-erasure-coding-in-apache-hadoop/ 訳注：2015/9/23ã«å…¬é–‹ã•ã‚ŒãŸãƒ–ãƒã‚°ã®ç¿»è¨³ã§ã™ã€‚ブãƒã‚°å…¬é–‹å½“時ã¨ã¯ç•°ãªã‚‹ç‚¹ã‚‚ã‚ã‚Šã¾ã™ã®ã§ã€æœ€æ–°æƒ…å ±ã¯HDFS-7285ã€HDFS-8031ãªã©ã‚’ã”確èªãã ã•ã„。 HDFSã®æ–°æ©Ÿèƒ½ã§ã‚るイレイジャーコーディング(Erasure Coding)ã¯ã€ãƒ¬ãƒ—リケーション（複製）ã¨æ¯”較ã—ã¦ã€åŒç‰ã®æ°¸ç¶šæ€§ã®ä¿è¨¼ã‚’ç¶æŒã—ãªãŒã‚‰ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã®ã‚ªãƒ¼ãƒãƒ¼ãƒ˜ãƒƒãƒ‰ã‚’ç´„50％削減ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ ã“ã®ãƒ–ãƒã‚°ã§ã¯ã€Erasure CodingãŒã©ã®ã‚ˆã†ã«å‹•ä½œã™ã‚‹ã‹ã‚’説明ã—ã¾ã™ã€‚ デフォルトã§HDFSã¯å„ブãƒãƒƒã‚¯ã‚’3回複製ã—ã¾ã™ã€‚ レプリケーションã¯ã€ã»ã¨ã‚“ã©ã®éšœå®³ã‚·ãƒŠãƒªã‚ªã‚’回é¿ã™ã‚‹ãŸã‚ã®ã€ã‚·ãƒ³ãƒ—ルã§å …牢ãªå†—長性ã®å½¢å¼ã‚’æ
[HADOOP-1687] Name-node memory size estimates and optimization proposal. - ASF JIRA
I've done some estimates on how much space our data structures take on the name-node per block, file and directory. Brief overview of the data structures: Directory tree (FSDirectory) is built of inodes. Each INode points either to an array of blocks if it corresponds to a file or to a TreeMap<String, INode> of children INodes if it is a directory. [Note: this estimates were made before Dhruba rep
Using CombineInputFormat to Combat Hadoop’s Small Files Problem | Amazon Web Services
AWS Big Data Blog Using CombineInputFormat to Combat Hadoop’s Small Files Problem James Norvell is a Big Data Cloud Support Engineer for AWS Many Amazon EMR customers have architectures that track events and streams and store data in S3. This frequently leads to many small files. It’s now well known that Hadoop doesn’t deal well with small files. This issue can be amplified when migrating from Had
第19回　Impalaã®è¨è¨ˆã¨å®Ÿè£…ï¼»3ï¼½ | gihyo.jp
ã¯ã˜ã‚㫠今回ã¯ã€Impalaã«ãŠã‘ã‚‹I/Oã®é«˜é€ŸåŒ–技法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‚’下ã’ã€ã‚¹ãƒ«ãƒ¼ãƒ—ットをå‘上ã•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£…置）ã«æ ¼ç´ã•ã‚Œã¦ã„ã‚‹ãŸã‚ã€å½“然ã€ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã¸ã®I/Oを回é¿ã™ã‚‹ã“ã¨ã¯ã§ãã¾ã›ã‚“。ã¾ãŸã€Impalaã¯å®Ÿè¡Œæ™‚ã«å分ãªãƒ¡ãƒ¢ãƒªã‚’確ä¿ã™ã‚‹ã¹ãã€ãƒ‡ãƒ¼ã‚¿ã‚’メモリ上ã«ä¿æŒï¼ˆã‚ャッシュ）ã—ãªã„ãŸã‚ã€ã‚¯ã‚¨ãƒªã‚’実行ã™ã‚‹ãŸã³ã«ãƒ‡ãƒ¼ã‚¿ã‚’ストレージã‹ã‚‰èªã¿å‡ºã™ã“ã¨ã‚’å‰æã¨ã—ã¦è¨è¨ˆã•ã‚Œã¦ã„ã¾ã™[1]â 。 今回ã¯ã€ã“ã®ã‚ˆã†ãªãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã‚’考慮ã—ã¤ã¤ã€é«˜é€Ÿãƒ»é«˜åŠ¹çŽ‡ãªã‚¢ãƒ‰ãƒ›ãƒƒã‚¯ã‚¯ã‚¨ãƒªã‚’実行ã™ã‚‹ãŸã‚ã®I/O処ç†æ–¹å¼ã¨ãƒ‡ãƒ¼ã‚¿ãƒ¬ã‚¤ã‚¢ã‚¦ãƒˆã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ Short-Circuit Local Readsã«ã‚ˆã‚‹I/Oã®é«˜åŠ¹çŽ‡åŒ– Sh
Asakusa 0.8 with M3BP - 急ãŒã°å›žã‚Œã€é¸ã¶ãªã‚‰è¿‘é“
AsakusaãŒæ–°è¦ã«é«˜é€Ÿå®Ÿè¡Œã‚¨ãƒ³ã‚¸ãƒ³ï¼ˆM3BP）をサãƒãƒ¼ãƒˆã—ãŸã€‚M3BPã¯ãƒ¡ãƒ‹ãƒ¼ã‚³ã‚¢ç‰¹åŒ–åž‹ã®C++ã§å®Ÿè£…ã•ã‚ŒãŸDAGã®å®Ÿè¡Œã‚¨ãƒ³ã‚¸ãƒ³ã«ãªã‚‹ã€‚ノーãƒãƒ©ã‚¹ã¨Fixstarsã®å…±åŒé–‹ç™ºã®OSSã§ã€å˜ãƒŽãƒ¼ãƒ‰ãƒ»ãƒ¡ãƒ‹ãƒ¼ã‚³ã‚¢ã§ã®ã€Œå‡¦ç†ã®é«˜é€ŸåŒ–ã€ã«æŒ¯ã£ã¦ã„る。ã„ã‚ゆるIn-memoryã®å®Ÿè¡Œã‚¨ãƒ³ã‚¸ãƒ³ã§ã€ãƒŽãƒ¼ãƒ‰ã®CPUコアを使ã„切るã“ã¨ã‚’目標ã—ã¦ãŠã‚Šã€ä½™è¨ˆãªæ©Ÿèƒ½ã¯ã™ã¹ã¦å‰Šã£ãŸã€‚データãŒã‚µãƒ¼ãƒãƒ»ãƒ¡ãƒ¢ãƒªãƒ¼ã«ä¹—るクラスã®ãƒãƒƒãƒå‡¦ç†ã§ã‚ã‚Œã°ã€ã»ã¼ç‰©ç†é™ç•Œã¾ã§ãƒ‘フォーマンスをãŸãŸã出ã™ã€‚ http://www.asakusafw.com/release/20160412.html 実際ã®ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ã¯ä»¥ä¸‹ã®white paperã«ã‚る。 http://www.asakusafw.com/wp/wp-content/uploads/2016/04/M3forBP_WP_JA_2016Apr12.pdf ベンãƒãƒžãƒ¼ã‚¯å¯¾è±¡
HDFS Snapshot + distcp 㨠MapR-FS Volume Mirroring ã®é•ã„ - nagix
MapR 㯠HDFS ã®ä»£ã‚ã‚Šã« MapR-FS を使用ã—ã¦ã„ã‚‹ Hadoop ディストリビューションã§ã™ã€‚性能ã®å‘上ã€ä¿¡é ¼æ€§ã®å‘上ã€ãƒ©ãƒ³ãƒ€ãƒ リードライトå¯èƒ½ãªNFSã€NoSQL データベースã¨ã®çµ±åˆã€ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ãƒ³ã‚°ã‚ューã¨ã®çµ±åˆã€ãƒ»ãƒ»ãƒ»ã¨ MapR-FS ã®ãƒ¡ãƒªãƒƒãƒˆã¯æŒ™ã’ã‚Œã°ãã‚ŠãŒãªã„ã®ã§ã™ãŒã€HDFS API ã¯ãã®ã¾ã¾åˆ©ç”¨ã§ãã‚‹ãŸã‚ã€ã™ã¹ã¦ã® Hadoop アプリケーションやライブラリã¯é•ã„ã‚’æ„è˜ã™ã‚‹ã“ã¨ãªã動作ã—ã¾ã™ã€‚ ã•ã¦ã€Hadoop クラスタをé‹ç”¨ã™ã‚‹éš›ã«ã€ãƒ‡ãƒ¼ã‚¿æ›´æ–°ã‚’è¡Œã†æ¥å‹™ã‚¢ãƒ—リケーションã¨ã€å‚ç…§ãŒãƒ¡ã‚¤ãƒ³ã®åˆ†æžã‚¢ãƒ—リケーション間ã§åŒã˜ãƒ‡ãƒ¼ã‚¿ã‚’共有ã—ãŸã„ã€ã¨ã„ã†ã‚±ãƒ¼ã‚¹ã¯ã‚ˆãã‚ã‚‹ã¨æ€ã„ã¾ã™ã€‚ãŸã ã—ã€åˆ†æžã‚¢ãƒ—リã¯ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã®ç‰¹å®šã®æ™‚点ã®ä¸€è²«æ€§ã®ã‚るスナップショットã«å¯¾ã—ã¦å‡¦ç†ã‚’è¡Œã†ã¹ãã§ã‚ã‚‹ãŸã‚ã€ä»»æ„ã®æ™‚点ã§æ›´æ–°ãŒç™ºç”Ÿã™ã‚‹æ¥å‹™ã‚¢ãƒ—リã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã«ãã®ã¾ã¾ã‚¢ã‚¯ã‚»ã‚¹ã™ã‚‹ã‚ã‘ã«
A Guide to Checkpointing in Hadoop | Cloudera Blog
Understanding how checkpointing works in HDFS can make the difference between a healthy cluster or a failing one. Checkpointing is an essential part of maintaining and persisting filesystem metadata in HDFS. It’s crucial for efficient NameNode recovery and restart, and is an important indicator of overall cluster health. However, checkpointing can also be a source of confusion for operators of Apa
HDFS新機能ç·ã¾ã¨ã‚in 2015 （日本Hadoopユーザー会 ãƒ©ã‚¤ãƒˆãƒ‹ãƒ³ã‚°ãƒˆãƒ¼ã‚¯ï¼ Cloudera World Tokyo 2015　講演資料）
HDFS新機能ç·ã¾ã¨ã‚in 2015 （日本Hadoopユーザー会 ãƒ©ã‚¤ãƒˆãƒ‹ãƒ³ã‚°ãƒˆãƒ¼ã‚¯ï¼ Cloudera World Tokyo 2015　講演資料）
Hadoop filesystem at Twitter
Twitter runs multiple large Hadoop clusters that are among the biggest in the world. Hadoop is at the core of our data platform and provides vast storage for analytics of user actions on Twitter. In this post, we will highlight our contributions to ViewFs, the client-side Hadoop filesystem view, and its versatile usage here. ViewFs makes the interaction with our HDFS infrastructure as simple as a
Kuduリリースï¼
オープンソースã®æ–°ã—ã„ストレージエンジンã€Kudu ã¤ã„ã«æœ¬æ—¥ã‚¢ãƒŠã‚¦ãƒ³ã‚¹ã•ã‚ŒãŸ Kudu (クドゥ）ã€æ¬¡ä¸–代ãƒãƒ¼ãƒ‰ã‚¦ã‚§ã‚¢ã«ã‚‚対応ã™ã‚‹ã‚ªãƒ¼ãƒ—ンソースã®ã‚¹ãƒˆãƒ¬ãƒ¼ã‚¸ã‚¨ãƒ³ã‚¸ãƒ³ã§ã™ã€‚ã¾ã ベータ版ã§ã™ãŒã€3å¹´ã‚‚ã®é–‹ç™ºæœŸé–“を経ã¦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 — 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€‚詳細ã¯FAQãªã©ã«ã‚‚書ã‹ã‚Œã¦ã„ã¾ã™ãŒã€HDFSã¨HBaseを補ã†æ–°ã—ã„ストレージエンジンã§ã™
ã€ã‚»ãƒƒã‚·ãƒ§ãƒ³ãƒ¬ãƒãƒ¼ãƒˆã€‘『Best Practices of Running Hadoop on EC2 vol.2〠#cmdevio2015E | DevelopersIO
Eトラック最後ã®ã‚»ãƒƒã‚·ãƒ§ãƒ³ã¯ã€Hortonworksã®è’‹é€¸å³°æ§˜ã«ã‚ˆã‚‹ã€ŒBest Practices of Running Hadoop on EC2 vol.2ã€ã§ã™ã€‚ ä¸å›½å‡ºèº«ã®è’‹ï¼ˆã‚·ãƒ§ã‚¦ï¼‰ã•ã‚“ã¯ã€æ—¥æœ¬ã«æ¥ã¦11å¹´ç›®ã¨ã®ã“ã¨ã€‚ 日本語ã§è‡ªç„¶ã«è©±ã™ã“ã¨ã¯å‹¿è«–ã§ã™ãŒã€èŠ±ç²‰ç—‡ã«æ‚©ã¾ã•ã‚Œã‚‹ãらã„日本ã«é †å¿œã•ã‚Œã¦ã„るよã†ã§ã™ã€‚ 今回ã®ã‚»ãƒƒã‚·ãƒ§ãƒ³ã§ã¯ã€Hadoop関連ã¨è¨€ã†ã“ã¨ã§ã€å…ˆæ—¥ã®Jaws Days 2015ã§ã®ã‚»ãƒƒã‚·ãƒ§ãƒ³ï¼ˆHadoop Trends and Best Practices of Running Hadoop on EC2）ã§è©±ã•ãªã‹ã£ãŸã‚³ã‚¢ãªéƒ¨åˆ†ã‚’ãŠè©±ã„ãŸã ãã¾ã—ãŸã€‚ Hadoopã®æœ€æ–°çŠ¶æ³ ã¯ã˜ã‚ã«ã€Jaws Days 2015ã§ã®ã‚»ãƒƒã‚·ãƒ§ãƒ³ã¨åŒæ§˜ã«ã€Hadoop界隈ã§ã®å‹•å‘ãªã©ã‚’軽ã話ã—ã¦ã„ãŸã ãã¾ã—ãŸã€‚ 最近ã®Hadoop界隈ã®å‹•å‘を一言ã§è¡¨ã™ãªã‚‰ã°ã€ã€Žã“ã“1−2å¹´ã¯ã‚³ãƒŸ
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今日ã‹ã‚‰ã¯ã˜ã‚ã‚‹ãŠæ‰‹è»½ Hive データ移行 - mixi engineer blog
ã“ã‚“ã«ã¡ã¯ï¼Ž 昨年知人ã®ã‚ªãƒ¼ã‚±ã‚¹ãƒˆãƒ©æ¼”å¥ä¼šã§è´ã„ãŸãƒ¡ãƒ³ãƒ‡ãƒ«ã‚¹ã‚¾ãƒ¼ãƒ³äº¤éŸ¿æ›²ç¬¬ï¼”番「イタリアã€ãŒå¤§å¥½ããªï¼ŒæŠ€è¡“部ã®çŸ³å·æœ‰ã§ã™ï¼Ž ãã‚“ãªã€Œã‚¤ã‚¿ãƒªã‚¢ã€å¤§å¥½ããªç§ã§ã™ãŒ Hive ã®ãƒ‡ãƒ¼ã‚¿ç§»è¡Œã‚‚大好ãã¨ã„ã†ä½“ã§ï¼Œ 今回ã®è¨˜äº‹ã§ã¯ã‚ªãƒ³ãƒ—レミス㧠Hive ã‚’é ‘å¼µã£ã¦é‹ç”¨ã—ã¦ã„ã‚‹æ–¹ãŸã¡ã«å‘ã‘ã¦ï¼Œã©ã†ã—ã¦ã‚‚ã¨ã„ã†ã¨ãã® Hive ã®ãƒ‡ãƒ¼ã‚¿ç§»è¡Œæ–¹æ³•ã‚’紹介ã—ãŸã„ã¨æ€ã„ã¾ã™ï¼Ž 弊社ã§ã¯æœ€è¿‘，Cloudera's Distribution including Apache Hadoop 3 update 2 (CDH3u2) ã‹ã‚‰ CDH 4.1.2 ã«ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã‚¢ãƒƒãƒ—ã‚’è¡Œãªã£ã¦ã„ã¾ã™ï¼Ž ã“ã®ã‚ˆã†ã«ã‚ªãƒ³ãƒ—レミス㧠Hive ã‚’é‹ç”¨ã—ã¦ã„ã‚‹æ–¹ãŸã¡ã®ä¸ã«ã¯ï¼Œã™ã§ã«æŒã£ã¦ã„るデータをã†ã¾ã移行ã—ãŸã„ã¨ã„ã†æ–¹ã‚‚多ã„ã‹ã¨æ€ã„ã¾ã™ï¼Ž Hive ã«ã¯ç¾çŠ¶ MySQL ã® mysqldump ã®ã‚ˆã†ãªæ©Ÿèƒ½ãŒæä¾›ã•ã‚Œã¦ã„ãªã„ã®ã§ãƒ
Hadoop ã®ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã«é–¢ã—ã¦ã¾ã¨ã‚ - 毛無ã—ã•ã‚“@ã‚レートレモン
1効率ã®è‰¯ã„ HiveQL を書ãã¨ãã«æ°—ã‚’ã¤ã‘ã‚‹ã¹ãã“ã¨ã‚’ã¾ã¨ã‚ã¦ã¿ã‚‹ã€‚ 以下ã«æ›¸ã„ãŸã“ã¨ã¯ã€ã»ã¨ã‚“ã©å…¨éƒ¨è‡ªåˆ†ãªã‚Šã®è§£é‡ˆãªã®ã§ã€é–“é•ã„ãŒå¤šã€…ã‚ã‚‹ã¨æ€ã„ã¾ã™ Hadoop一般ã®ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã«é–¢ã—ã¦ã¯ã€ã“ã“ã‚’å‚考ã«ã—ãŸã€‚ http://www.cloudera.co.jp/jpevents/cloudera-world-tokyo/pdf/A3_Hadoop%E3%81%AE%E3%82%B7%E3%82%B9%E3%83%86%E3%83%A0%E8%A8%AD%E8%A8%88_%E9%81%8B%E7%94%A8%E3%81%AE%E3%83%9D%E3%82%A4%E3%83%B3%E3%83%88.pdf Hadoop本 ã‚’å‚考ã«ã€ã“ã®è³‡æ–™ã®è£œè¶³ã—ã¦ã¿ãŸâ†“ ラック内ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã¨ãƒ©ãƒƒã‚¯é–“ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã¯ä½•ãŒé•ã†ã®ã‹ï¼Ÿ (p.69 「ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãƒˆãƒãƒã‚¸ã¨Hadoop〠/ p.267
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