【摘要】：互聯(lián)網(wǎng)行業(yè)的蓬勃發(fā)展帶動(dòng)了數(shù)據(jù)量的爆炸式增長,傳統(tǒng)的關(guān)系型數(shù)據(jù)庫并不能滿足海量數(shù)據(jù)規(guī)模的應(yīng)用,所以Key-value存儲(chǔ)機(jī)制應(yīng)運(yùn)而生。伴隨該機(jī)制,NoSQL型數(shù)據(jù)庫得到了大量推廣,NoSQL數(shù)據(jù)庫由于其處理海量數(shù)據(jù)之快而一般用作于傳統(tǒng)關(guān)系型數(shù)據(jù)庫的前置數(shù)據(jù)庫,但這種基于內(nèi)存存儲(chǔ)的數(shù)據(jù)庫在數(shù)據(jù)持久化方面并不完善,并且存在系統(tǒng)宕機(jī)時(shí)導(dǎo)致數(shù)據(jù)丟失的風(fēng)險(xiǎn)。然而隨著互聯(lián)網(wǎng)技術(shù)的成熟,該類型數(shù)據(jù)庫開始摸索將其緩存數(shù)據(jù)持久化的方法。本文主要著眼于KV存儲(chǔ)機(jī)制的NoSQL數(shù)據(jù)庫的持久化研究,針對(duì)Google開源的LevelDB數(shù)據(jù)庫所采用的LSM-Tree持久化機(jī)制,進(jìn)行深入研究。文章首先從LSM-Tree持久化緩存模型入手,深入分析該模型的緩存機(jī)制;然后找出LSM-Tree緩存機(jī)制持續(xù)寫入快速但隨機(jī)讀取較慢的不足之處,引入改進(jìn)的LSM-Tree模型;對(duì)應(yīng)用該緩存機(jī)制的Google單機(jī)持久化存儲(chǔ)系統(tǒng)LevelDB進(jìn)行詳細(xì)研究,著重分析其關(guān)鍵技術(shù)和運(yùn)行機(jī)理,用C++語言在LevelDB的源碼上進(jìn)行改動(dòng)。文章最后使用雅虎YCSB負(fù)載生成器框架對(duì)改進(jìn)前后的LevelDB性能做比較,結(jié)果顯示在保證寫入吞吐量的同時(shí)隨機(jī)讀取吞吐量提高了一倍,隨機(jī)讀取延時(shí)有明顯降低,優(yōu)化效果較為明顯。
[Abstract]:The booming development of the Internet industry has driven the explosive growth of the data volume. The traditional relational database can not meet the application of massive data scale, so the Key-value storage mechanism came into being. Along with this mechanism, NoSQL database has been widely popularized. Because of its fast processing of massive data, NoSQL database is generally used as the predatabase of traditional relational database. However, the database based on memory storage is not perfect in data persistence, and there is the risk of data loss when the system is down. However, with the maturity of Internet technology, this type of database began to explore its cache data persistence method. This paper focuses on the persistence of NoSQL database based on KV storage mechanism and the persistence mechanism of LSM-Tree in Google open source LevelDB database. This paper first analyzes the cache mechanism of LSM-Tree persistence cache model, and then finds out the shortcomings of LSM-Tree cache mechanism that writes fast but reads slowly, and then introduces an improved LSM-Tree model. The Google single persistent storage system (LevelDB) which applies the cache mechanism is studied in detail. The key technology and running mechanism are analyzed emphatically. The source code of LevelDB is modified with C language. Finally, the LevelDB performance before and after the improvement is compared with Yahoo YCSB load generator framework. The results show that the random read throughput is doubled while the write throughput is guaranteed, and the random read delay is obviously reduced. The effect of optimization is obvious.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13

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