SQLite has been deployed in millions of mobile devices from Web to smartphone applications on various mobile operating systems. However, due to the uncoordinated IO interactions with the underlying file system (e.g., ext4), SQLite is not efficient with low transactions per second. In this paper, we for the first time propose a new SQLite-like database engine, called SQLiteKV, which adopts the LSM-tree-based data structure but retains the SQLite operation interfaces. With its SQLite interface, SQLiteKV can be utilized by existing applications without any modification, while providing high performance with its LSM-tree-based data structure. We separate SQLiteKV into the front-end and back-end. In the front-end, we develop a light-weight SQLite-to-KV compiler to solve the semantic mismatch, so SQL statements can be efficiently translated into KV operations. We also design a novel coordination caching mechanism with memory defragmentation so query results can be effectively cached inside SQLiteKV by alleviating the discrepancy of data management between front-end SQLite statements and back-end data organization. In the back-end, we adopt an LSM-tree-based key-value database engine, and propose a lightweight metadata management scheme to mitigate the memory requirement. We have implemented and deployed SQLiteKV on a Google Nexus 6P smartphone. Experimental results with various workloads show that SQLiteKV outperforms SQLite up to six times.

SQLite has been deployed in millions of mobile devices from web to smartphone applications on various mobile operating systems. However, SQLite is not efficient with low transactions per second. In this paper, we for the first time propose a new SQLite-like database engine, called SQLiteKV, which adopts the LSM-tree-based data structure but retains the SQLite operation interfaces. With its SQLite interface, SQLiteKV can be utilized by existing applications without any modification, while providing high performance with its LSM-tree-based data structure. In SQLiteKV, we develop a light-weight SQLite to key-value compiler to solve the semantic mismatch, so SQL statements can be efficiently translated into KV operations. We also design a novel coordination caching mechanism so query results can be effectively cached inside SQLiteKV by alleviating the discrepancy of data management between front-end SQLite statements and back-end key-value data organization. We have implemented and deployed SQLiteKV on a Google Nexus 6P smartphone. Experiments results show that SQLiteKV outperforms SQLite up to 6 times.

Bit Patterned Media (BPM) are a possible technology for use in future hard disk drives where a single nanoscale island is used to store each bit of information. However, the imperfect island fabrication process results in unavoidable variations in the geometry of the islands, which introduces deterministic changes in the replay signal and leads to errors in the conventional Maximum Likelihood (ML) detector. If the detector can be extended to include estimates of island size, then detector decisions can be improved and error rates reduced. A read channel is proposed that reduces the impact of island size variations by modifying the detector trellis to include two extra branches per state transition to improve the data error rate at the output of the detector. An analytical model of the modified read channel is developed that is capable of rapidly estimating the bit-error-rate (BER) performance so that the modified read channel trellis parameters can be optimized to maximize the BER improvement offered by the proposed detector. In order to verify the performance of the analytical model its output is compared with that produced from a numerical model, which takes full account of multi-bit inter-symbol interference.