Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID

Evaluating the performance of scientific data processing systems is a difficult task considering the plethora of application-specific solutions available in this landscape and the lack of a generally-accepted benchmark. The dual structure of scientific data coupled with the complex nature of processing complicate the evaluation procedure further. SS-DB is the first attempt to define a general benchmark for complex scientific processing over raw and derived data. It fails to draw sufficient attention though because of the ambiguous plain language specification and the extraordinary SciDB results. In this paper, we remedy the shortcomings of the original SS-DB specification by providing a formal representation in terms of ArrayQL algebra operators and ArrayQL/SciQL constructs. These are the first formal representations of the SS-DB benchmark. Starting from the formal representation, we give a reference implementation and present benchmark results in EXTASCID, a novel system for scientific data processing. EXTASCID is complete in providing native support both for array and relational data and extensible in executing any user code inside the system by the means of a configurable metaoperator. These features result in an order of magnitude improvement over SciDB at data loading, extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure

High Throughput Push Based Storage Manager

The storage manager, as a key component of the database system, is responsible for organizing, reading, and delivering data to the execution engine for processing. According to the data serving mechanism, existing storage managers are either pull-based, incurring high latency, or push-based, leading to a high number of I/O requests when the CPU is busy. To improve these shortcomings, this thesis proposes a push-based prefetching strategy in a column-wise storage manager. The proposed strategy implements an efficient cache layer to store shared data among queries to reduce the number of I/O requests. The capacity of the cache is maintained by a time access-aware eviction mechanism. Our strategy enables the storage manager to coordinate multiple queries by merging their requests and dynamically generate an optimal read order that maximizes the overall I/O throughput. We evaluated our storage manager both over a disk-based redundant array of independent disks (RAID) and an NVM Express (NVMe) solid-state drive (SSD). With the high read performance of the SSD, we successfully minimized the total read time and number of I/O accesses