With the exponential growth of data size, data storage and analysis have been exposed to more challenges due to the lack of disk capacity and the limited network bandwidth. Data compression technique provides a good solution to mitigate these effects. In this paper, we propose a self-aware data compression system on FPGA for typical data warehousing, such as Hive, with column stored data and multi-threading requirements. The hardware accelerators can change the degree and hierarchy of parallelism depending on the data to be compressed (during the runtime). We test the system performance on a Xilinx VC707 FPGA board and the experimental results show that, up to 16 3-parallelism accelerators can be implemented and the throughput could be improved up to 432 MB/s. It is 6.25X speedup compared with the software solution under the same number of threads.

Phase change memory (PCM) has been considered an attractive alternative to flash memory and DRAM. It has promising features, including non-volatile storage, byte addressability, fast read and write operations, and supports random accesses. However, there are challenges in designing algorithms for PCM-based memory systems, such as longer write latency and higher energy consumption compared to DRAM. In this paper, we propose a new predictive B+-tree index, called the Bp-tree, which is tailored for database systems that make use of PCM. Our Bp-tree reduces data movements caused by tree node splits and merges that arise from insertions and deletions. This is achieved by pre-allocating space on PCM for near future data. To ensure the space are allocated where they are needed, we propose a novel predictive model to ascertain future data distribution based on the current data. In addition, as in [4], when keys are inserted into a leaf node, they are packed but need not be in sorted order. We have implemented the Bp-tree in PostgreSQL and evaluated it in an emulated environment. Our experimental results show that the Bp-tree significantly reduces the number of writes, therefore making it write and energy efficient and suitable for a PCM-like hardware environment.

Multi-tenant data management is a major application of Software as a Service (SaaS). For example, many companies want to outsource their data to a third party that hosts a multi-tenant database system to provide data management services. The multi-tenant database system needs to have high performance, low space requirement, and excellent scalability. One big challenge is devising a high-quality database schema. Independent Tables Shared Instances (ITSI) and Shared Tables Shared instances (STSI) are two state-of-the-art approaches to designing the schema. However, they suffer from some limitations. ITSI has poor scalability since it needs to maintain large numbers of tables. STSI achieves good scalability at the expense of poor performance and high space overhead. Thus, an effective schema design method that addresses these problems is needed. In this paper, we propose an adaptive database schema design method for multi-tenant applications. We trade-off ITSI and STSI and find a balance between them to achieve good scalability and high performance with low space requirement. To this end, we identify the important attributes and use them to generate an appropriate number of base tables. For the remaining attributes, we construct supplementary tables. We discuss how to use the kernel matrix to determine the number of the base tables, apply graph-partitioning algorithms to construct the base tables, and evaluate the importance of attributes using the well-known PageRank algorithm. We propose a cost-based model to adaptively generate the base tables and supplementary tables. Our method has the following advantages. First, our method achieves high scalability. Second, our method achieves high performance and can trade-off the performance and space requirement. Third, our method can be easily applied to existing databases (e.g., MySQL) with minor revisions. Fourth, our method can adapt to any schemas and query workloads including both OLAP and OLTP applications. Experimental results on both real and synthetic datasets show that our method achieves high performance and good scalability with low space requirement and outperforms state-of-the-art methods.

Multi-tenant data management is a major application of software as a Service (SaaS). Many companies outsource their data to a third party which hosts a multi-tenant database system to provide data management service. The system should have high performance, low space and excellent scalability. One big challenge is to devise a high-quality database schema. Independent Tables Shared Instances and Shared Tables Shared Instances are two state-of-the-art methods. However, the former has poor scalability, while the latter achieves good scalability at the expense of poor performance and high space overhead. In this paper, we trade-off between the two methods and propose an adaptive database schema design approach to achieve good scalability and high performance with low space. To this end, we identify the important attributes and use them to generate a base table. For other attributes, we construct supplementary tables. We propose a cost-based model to adaptively generate the tables above. Our method has the following advantages. First, our method achieves high scalability. Second, our method can trade-off performance and space requirement. Third, our method can be easily applied to existing databases (e.g., MySQL) with minor revisions. Fourth, our method can adapt to any schemas and query workloads. Experimental results show our method achieves high performance and good scalability with low space and outperforms state-of-the-art method.