Write Optimized Tree Indexing

Due to the poor random write performance of flash SSDs (Solid State Drives), write optimized tree indexes have been proposed to improve the update performance. BFTL was proposed to balance the inferior random write performance and fast random read performance for flash memory based sensor nodes and embedded systems. It allows the index entries in one logical B-tree node to span over multiple physical pages, and maintains an in-memory table to map each B-tree node to multiple physical pages. Newly inserted entries are packed and then written together to some new blocks. The table entries of corresponding B-tree nodes are updated, thus reducing the number of random writes. However, BFTL entails a high search cost since it accesses multiple disk pages to search a single tree node. Furthermore, even though the in-memory mapping table is compact, the memory consumption is still high. FlashDB was proposed to implement a self-tuning scheme between standard B+-tree and BFTL, depending on the workloads and the types of flash devices. Since our proposed index mostly outperforms both B+-tree and BFTL under various workloads on different flash SSDs, we do not compare our index with this self-tuning index in this paper. More recently, LA-tree was proposed for flash memory devices by adding adaptive buffers between tree nodes. LA-tree focuses on raw, small-capacity and byte addressable flash memory devices, such as sensor nodes, whereas our work is targeted for off theshelf large flash SSDs, which provide only a block-based access interface. Different target devices of these two indexes result in their differences in design.

On the hard disk, many disk-based indexes optimized for write operations have also been proposed.
Graefe proposed a write-optimized B-tree by applying the idea of the log file system to the B-tree
index. Y-tree supports high volume insertions for data warehouses following the idea of buffer tree.
The logarithmic structures have been widely applied to optimize the write performance. O’Neil et al.
proposed LSM-tree and its variant LHAM for multi-version databases. Jagadish et al. used a similar
idea to design a stepped tree index and the hash index for data warehouses. Our FD-tree follows the
idea of logarithmic method. The major difference is that we propose a novel method based on the
fractional cascading technique to improve the search performance on the logarithmic structure.


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