Flash SSDs are being incorporated in many enterprise storage platforms recently and expected to play a notable role for IO-intensive applications. However, the IO characteristics of flash SSDs are very different from those of hard disks. Since existent storage subsystems are designed on the basis of characteristics of hard disks, the IO performance of flash SSDs may not be obtained as expected. This paper provides an evaluation of flash SSDs in transaction processing systems with TPC-C benchmark. We present performance results with various configurations and describe our observations of the IO behaviors at different levels along the IO path, which helps to understand the performance of flash-based transaction processing systems and provides certain references to build flash-based systems for IO-intensive applications.

Parallel processing is a typical approach to answer analytical queries on large database. As the size of the database increases, we often try to increase the parallelism by incorporating more processing nodes. However, this approach increases the possibility of node failure as well. According to the conventional practice, if a failure occurs during query processing, the database system restarts the query processing from the beginning. Such temporal cost may be unacceptable to the user. This paper proposes a fault-tolerant query processing mechanism, named PhoeniQ, for analytical parallel database systems. PhoeniQ continuously takes a checkpoint for every operator pipeline and replicates the output of each stateful operator among different processing nodes. If a single processing node fails during query processing, another can promptly take over the processing. Hence, PhoneniQ allows the database system to efficiently resume query processing after a partial failure event. This paper presents a key design of PhoeniQ and prototype-based experiments to demonstrate that PhoeniQ imposes negligible performance overhead and efficiently continues query processing in the face of node failure.