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                最近,TokuDB的創始人Dr. Bradley Kuzmaul發表了一篇文章:?[A Comparison of Log-Structured Merge (LSM) and Fractal Tree Indexing](http://forms.tokutek.com/acton/attachment/6118/f-0039/1/-/-/-/-/lsm-vs-fractal.pdf),從write amplification(WAMP), read amplification(RAMP), and space amplification三個方面對B-Trees,LSM-Trees(LSM)以及Fractal-Trees(FT)進行了詳細的分析和對比。 Dr. Bradley Kuzmaul的結果是(頁13):? ![Lsmft.png](https://box.kancloud.cn/2015-09-24_56038f4bd11f6.png) 從結果來看: ~~~ 在WAMP上,FT跟LSM(leveled)是相同的 在RAMP(range)上,LSM(leveled)的復雜度明顯要高不少(FT的O(logN/B)倍) ~~~ 不過,RAMP這塊的分析有個小問題:? LSM(leveled)在實現上(比如LevelDB),可以通過meta-info打"錨點"的方式,把RAMP(range)降低甚至做到跟FT一樣,如果是point queries的RAMP,則可以通過Bloom filter來降低。 具體的推導過程請閱讀原作,下面簡單分析下FT的RAMP為啥比LSM的要低。? FT的讀方式比較"特殊",由于每個節點都有個message buffer,當有讀請求時,需要把inner node的message buffer數據(部分)推(apply)到leaf node,最后只在leaf node上做二分查找,所以RAMP基本就是樹的高度。 另外,在數據流向上(compaction過程中數據走向),LSM強調"level"(橫向),從level-L根據規則選取部分數據merge到level-(L+1),如果選取數據的策略不好,會搶占磁盤帶寬,容易引起性能抖動,而FT強調"root-to-leaf"(縱向),數據從root有序的逐層merge到leaf節點,每條數據的merge路徑是很明確的。
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