The cost of DRAM contributes significantly to the operating costs of in-memory database management systems (IMDBMS). Persistent memory (PMEM) is an alternative type of byte-addressable memory that offers --- in addition to persistence --- higher capacities than DRAM at a lower price with the disadvantage of increased latencies and reduced bandwidth. This paper evaluates PMEM as a cheaper alternative to DRAM for storing table base data, which can make up a significant fraction of an IMDBMS' total memory footprint. Using a prototype implementation in the SAP HANA IMDBMS, we find that placing all table data in PMEM can reduce query performance in analytical benchmarks by more than a factor of two, while transactional workloads are less affected. To quantify the performance impact of placing individual data structures in PMEM, we propose a cost model based on a lightweight workload characterization. Using this model, we show how to place data pareto-optimally in the heterogeneous memory. Our evaluation demonstrates the accuracy of the model and shows that it is possible to place more than 75% of table data in PMEM while keeping performance within 10% of the DRAM baseline for two analytical benchmarks.

Non-volatile memory (NVM) offers lower costs per capacity and higher total capacities than DRAM. However, NVM cannot simply be used as a drop-in replacement for DRAM in database management systems due to its different performance characteristics. We thus investigate the placement of column-store data structures in a hybrid hierarchy of DRAM and NVM, with the goal of placing as much data as possible in NVM without compromising performance. After analyzing how different memory access patterns affect query runtimes when columns are placed in NVM, we propose a heuristic that leverages lightweight access counters to suggest which structures should be placed in DRAM and which in NVM. Our evaluation using TPC-H shows that more than 80% of the data touched by queries can be placed in NVM with almost no slowdown, while naively placing all data in NVM would increase runtime by 53%.

Re-Pair is a compression algorithm well-suited for applications that require random accesses to compressed data, but has not found widespread use in the data management community due to its prohibitively high compression times. As Re-Pair is a computationally expensive algorithm and FPGAs are becoming more and more common to accelerate such problems in data centers, we devise an FPGA system that performs Re-Pair compression. The system is implemented in OpenCL, aside from a hash table and sorting component realized in RTL for more control over the synthesized hardware. Our experiments demonstrate that an Intel Arria® 10 GX FPGA with our system compresses an order of magnitude faster than a highly-optimized CPU version of Re-Pair. We discuss further optimization opportunities and argue that our system can scale to being deployed on a more resourceful FPGA for even better performance.

Abstract String dictionaries constitute a large portion of the memory footprint of database applications. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. Therefore, lightweight algorithms such as front coding (PFC) are favored in practice. This paper endeavors to make strong string dictionary compression practical. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. To accelerate compression times, we propose block-based RPFC (BRPFC) which consists in independently compressing small blocks of the dictionary. For further accelerated compression times especially on large string dictionaries, we also propose an alternative version of BRPFC that uses sampling to speed up compression. Moreover, to accelerate access times, we devise a vectorized access method, using $$\hbox {Intel}^{\circledR }$$ Intel ® Advanced Vector Extensions 512 ( $$\hbox {Intel}^{\circledR }$$ Intel ® AVX-512). Our experimental evaluation shows that sampled BRPFC offers compression times up to 190 $$\times $$ × faster than RPFC, and random string lookups 2.3 $$\times $$ × faster than RPFC on average. These results move our modified RPFC into a practical range for use in database systems because the overhead of Re-Pair-based compression for access times can be reduced by 2 $$\times $$ × .

String dictionaries constitute a large portion of the memory foot-print of database applications. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. Therefore, lightweight algorithms such as front coding are favored in practice. This paper endeavors to make strong string dictionary compression practical. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. To accelerate compression times, we propose block-based RPFC, which consists in compressing independently small blocks of the dictionary. Moreover, to accelerate access times, we devise a vectorized access method, using Intel® Advanced Vector Extensions 512 (Intel® AVX-512), that is enabled by two specific changes we propose to RPFC. Our experimental evaluation shows that our proposed techniques accelerate compression and access times by up to 24x and 2.9x, respectively. These results move our modified RPFC into a practical range for use in database systems.