December 2012
Intermediate to advanced
572 pages
13h 8m
Chinese
Content preview from HBASE技術手冊
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與 MapReduce 整合
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305
被送到在相同的機架上的另一台伺服器上，其他的則會被送到一個遠端機架上－假設你
的 HDFS 是架在多機架環境上。如果不是，那額外的複本會被放在叢集上負載最低的一
個資料節點上。
假如複本係數（replication factor）配置得較高，那麼將會有更多的複本會被配置到不
同的主機上。不過，這裡很重要的是：現在你有一個區塊的本地端複本可以使用。對於
HBase 而言，這意謂著如果 region 伺服器運行時間夠長（這是你想要的），在對所有的
資料表做完整壓實後－這可以被手動地執行或者依據組態設定所觸發－檔案會被本地化
地儲存在相同的主機上。各個共享相同實體主機的資料節點，會保有區域伺服器需要的
所有資料的複本。如果你正在執行 scan 或 get 或任何的操作，就可以確保獲得最佳的效
能。
有一個問題必須要注意，就是在負載平衡或伺服器錯誤期間的區域搬移問題。在這些情
況下，資料已經不再是本地化，但隨著時間的移動，它會再次變成本地化。當叢集被重
新啟動時，主節點也將此列入考量：它會指派所有的區域到原始的區域伺服器上，如果
其中有一個無法對應，它會使用隨機配置區域的策略。
資料表分割
當 MapReduce 作業運作時，一般而言會使用
TableInputFormat
從資料表中讀取資料，
透過覆寫必要的
getSplits()
和
createRecordReader()
公用方法，來融入到框架當中。
在作業被執行之前，框架會呼叫
getSplit()
以決定資料如何被分割成區塊，它會設定作 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.