Accelerating Cloud-Native Databases with Distributed PMem Stores

Relational databases have gone through a phase of architectural transition from a monolithic to a distributed architecture to take full advantage of cloud technology. These distributed databases can leverage remote storage to maintain larger amounts of data than monolithic databases at the cost of i...

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Veröffentlicht in:Data engineering S. 3043 - 3057
Hauptverfasser: Sun, Jason, Ma, Haoxiang, Zhang, Li, Liu, Huicong, Shi, Haiyang, Luo, Shangyu, Wu, Kai, Bruhwiler, Kevin, Zhu, Cheng, Nie, Yuanyuan, Chen, Jianjun, Zhang, Lei, Liang, Yuming
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2023
Schlagworte:
Benchmark testing
cloud computing
cloud-native database
Computer architecture
distributed database
Distributed databases
PMem
Query processing
query push-down
RDMA
Relational databases
Technological innovation
Throughput
ISSN:2375-026X
Online-Zugang:Volltext
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Zusammenfassung:Relational databases have gone through a phase of architectural transition from a monolithic to a distributed architecture to take full advantage of cloud technology. These distributed databases can leverage remote storage to maintain larger amounts of data than monolithic databases at the cost of increased latency. At ByteDance, we have built a distributed database called veDB based on the popular compute-storage separation architecture, however we have observed the system is unable to provide both low latency and high throughput required by some business critical applications, such as batched order processing.In this paper we present our novel approaches to tackle this problem. We have modified our system's storage to utilize persistent memory (PMem) coupled with a remote direct memory access (RDMA) network to reduce read/write latency and increase the throughput. We also propose a query push-down framework to push partial computations to the PMem storage layer to accelerate analytical queries and reduce the impact of the transaction workload in the computation layer. Our experiments show that our methods improve the throughput by up to 1.5× and reduce latency by up to 20× for standard benchmarks and real-world applications.
ISSN:2375-026X
DOI:10.1109/ICDE55515.2023.00233