Effective Use of SSDs in Database Systems

With the advent of solid state drives (SSDs), the storage industry has experienced a revolutionary improvement in I/O performance. Compared to traditional hard disk drives (HDDs), SSDs benefit from shorter I/O latency, better power efficiency, and cheaper random I/Os. Because of these superior properties, SSDs are gradually replacing HDDs. For decades, database management systems have been designed, architected, and optimized based on the performance characteristics of HDDs. In order to utilize the superior performance of SSDs, new methods should be developed, some database components should be redesigned, and architectural decisions should be revisited. In this thesis, novel methods are proposed to exploit the new capabilities of modern SSDs to improve the performance of database systems. The first is a new method for using SSDs as a fully persistent second level memory buffer pool. This method uses SSDs as a supplementary storage device to improve transactional throughput and to reduce the checkpoint and recovery times. A prototype of the proposed method is compared with its closest existing competitor. The second considers the impact of the parallel I/O capability of modern SSDs on the database query optimizer. It is shown that a query optimizer that is unaware of the parallel I/O capability of SSDs can make significantly sub-optimal decisions. In addition, a practical method for making the query optimizer parallel-I/O-aware is introduced and evaluated empirically. The third technique is an SSD-friendly external merge sort. This sorting technique has better performance than other common external sorting techniques. It also improves the SSD's lifespan by reducing the number of write operations required during sorting

A3CRank: an adaptive ranking method based on connectivity, content and click-through data

Due to the proliferation and abundance of information on the web, ranking algorithms play an important role in web search. Currently, there are some ranking algorithms based on content and connectivity such as PageRank and BM25. Unfortunately, these algorithms have low precision and are not always satisfying for users. In this paper, we propose an adaptive method based on the content, connectivity and click-through data triple, called A3CRank. The aggregation idea of meta search engines has been used to aggregate ranking algorithms such as PageRank, BM25, TF-IDF. We have used reinforcement learning to incorporate user behavior and find a measure of user satisfaction for each ranking algorithm. Furthermore, OWA, an aggregation operator is used for merging the results of the various ranking algorithms. A3CRank adapts itself with user needs and makes use of user clicks to aggregate the results of ranking algorithms. A3Crank is designed to overcome some of the shortcomings of existing ranking algorithms by combining them together and producing an overall better ranking criterion. Experimental results indicate that A3CRank outperforms all other single ranking algorithms in P@n and NDCG measures. We have used 130 queries on University of California at Berkeley’s web to train and evaluate our method