TY - GEN
T1 - Adaptive Compression For Databases
AU - Windheuser, Leon
AU - Anneser, Christoph
AU - Zhang, Huanchen
AU - Neumann, Thomas
AU - Kemper, Alfons
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2023/11/22
Y1 - 2023/11/22
N2 - Efficient utilization of dynamic random access memory (DRAM) is crucial for achieving high-performance query processing in database systems, especially as data volumes continue to grow. Unfortunately, the cost of DRAM is unlikely to decrease in the coming years, and it is already the dominating cost factor in modern data centers. Consequently, lightweight in-memory compression techniques can reduce the memory footprint and maximize the data stored in memory. However, compressing all data, regardless of the compression algorithm’s efficiency, causes additional CPU overhead during query execution. To address this challenge, we introduce AdaCom, a novel framework that selectively applies lightweight succinct encodings only to infrequently accessed data. By doing so, we mitigate the performance overhead associated with compression. In our experimental evaluation, we demonstrate that AdaCom reduces the memory footprint by up to 40% while retaining most of the performance (≈ 95%).
AB - Efficient utilization of dynamic random access memory (DRAM) is crucial for achieving high-performance query processing in database systems, especially as data volumes continue to grow. Unfortunately, the cost of DRAM is unlikely to decrease in the coming years, and it is already the dominating cost factor in modern data centers. Consequently, lightweight in-memory compression techniques can reduce the memory footprint and maximize the data stored in memory. However, compressing all data, regardless of the compression algorithm’s efficiency, causes additional CPU overhead during query execution. To address this challenge, we introduce AdaCom, a novel framework that selectively applies lightweight succinct encodings only to infrequently accessed data. By doing so, we mitigate the performance overhead associated with compression. In our experimental evaluation, we demonstrate that AdaCom reduces the memory footprint by up to 40% while retaining most of the performance (≈ 95%).
UR - http://www.scopus.com/inward/record.url?scp=85190974726&partnerID=8YFLogxK
U2 - 10.48786/edbt.2024.13
DO - 10.48786/edbt.2024.13
M3 - Conference contribution
AN - SCOPUS:85190974726
T3 - Advances in Database Technology - EDBT
SP - 143
EP - 149
BT - Advances in Database Technology - EDBT
PB - OpenProceedings.org
T2 - 27th International Conference on Extending Database Technology, EDBT 2024
Y2 - 25 March 2024 through 28 March 2024
ER -