In this and the following posts I would like to take the opportunity to go into detail about the MapReduce process as provided by Hadoop but more importantly how it applies to HBase. MapReduce MapReduce as a process was designed to solve the problem of processing in excess of terabytes of data in a scalable way. There should be a way to design such a system that increases in performance linearly w
HBase coprocessors allow arbitrary code to run on region servers. Coprocessors are inspired by Google Bigtable and provide a flexible way to build distributed services that scale automatically. The HBase coprocessor framework is currently in development and will be released in version 0.92. It defines RegionObserver and CommandTarget interfaces that coprocessor code can implement to intercept regi
One of the more hidden aspects of HBase is how data is actually stored. While the majority of users may never have to bother about it you may have to get up to speed when you want to learn what the various advanced configuration options you have at your disposal mean. "How can I tune HBase to my needs?", and other similar questions are certainly interesting once you get over the (at times steep) l
概要 ここではHbaseで使われるHBase Shellに関しての説明を行います。従来のSQLの処理と、それに相当するHbase Shellの書き方を並べて記述しています。 基本的にこのSQLをHBase Shellで書いたら、を解説します。 HBase Shell独自の機能はHBase独自のTable/Data操作を参照してください。 RDBが二次元構造だったのに対してHBaseは三次元構造になっている為、最初はちょっと解りにくいかも知れません。 参考:Hadoop Wiki Hbase/Shell HBase0.2のhelpの取得結果:Hbase:0.2Help RDBとHBaseの差異 全て主語は「HBase」です。 IndexはCreate文ではなくInsert文で作る Indexに相当するKeyのみが検索条件の対象と成ります。 Tableの有効無効概念があり、無効状態のT
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く