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Highlights
- Intel will present two papers on novel database system architectures and a demo on high-frequency telemetry data management at VLDB 2024 on August 26-30.
- Of the two papers, the first paper focuses on BonsaiKV, a key-value store that makes the best use of different components in a tiered memory system, and the second paper explores the opportunities and challenges of a flexible CXL memory expansion using a CXL type 3 prototype.
- The demo shows how a novel storage engine called Mach helps engineers collect and query high-frequency telemetry in near real-time when diagnosing a performance problem.
Intel will present two papers on novel database system architectures, and a demo on high-frequency telemetry data management at the 50th International Conference on Very Large Databases (VLDB 2024) on August 26-30 in Guangzhou, China. In collaboration with Nanjing University of Aeronautics and Astronautics, Hohai University, and NJU, Intel Labs presents BonsaiKV, a key-value store that makes the best use of different components in a tiered memory system. Working with SAP Labs Korea and SAP SE, Intel explores the opportunities and challenges of a flexible CXL memory expansion using a CXL type 3 prototype.
For the demo paper, a combined team from Brown University, University of Washington, Northwestern University, and Intel show how Mach, a new storage engine for collecting and querying high-frequency telemetry, supports high ingest rates and makes data immediately queryable while operating within a limited on-host resource envelope.
Nesime Tatbul, a senior research scientist at Intel Labs, contributed to the demo for Mach as well as serving as a program co-chair for the Industrial Track of the VLDB conference.
Research and Industrial Papers
BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value Stores with Tiered, Heterogeneous Memory System
Emerging NUMA/CXL-based tiered memory systems with heterogeneous memory devices such as DRAM and NVMM deliver ultrafast speed, large capacity, and data persistence all at once, offering great promise to high-performance in-memory key-value stores. To fully unleash the performance potential of such memory systems, researchers present BonsaiKV, a key-value store that makes the best use of different components in a tiered memory system. The core of BonsaiKV is a tri-layer hierarchical storage architecture that separates data indexing, persistence, and scalability from each other and realizes each of them within a specialized software-hardware layer. BonsaiKV is designed with a set of novel techniques, including collaborative tiered indexing, NVMM congestion control mechanisms, fine-grained data striping, and NUMA-aware data management, to leverage hardware strengths and tackle device deficiencies. BonsaiKV is compared with state-of-the-art NVMM-optimized key-value stores and persistent index structures using a variety of YCSB workloads. Evaluation results demonstrate that BonsaiKV outperforms others by up to 7.69×, 19.59×, and 12.86× in read-, write- and scan-intensive scenarios, respectively.
An Examination of CXL Memory Use Cases for In-Memory Database Management Systems Using SAP
Limited memory volume is always a performance bottleneck in an in-memory database management system (IMDBMS) as the data size keeps increasing. To overcome the physical memory limitation, heterogeneous and disaggregated computing platforms are proposed, such as Gen-Z, CCIX, OpenCAPI, and CXL. In this work, researchers explore the opportunities and challenges of flexible CXL memory expansion using a CXL type 3 prototype and evaluate its performance in an IMDBMS. The evaluation shows that CXL memory devices interfaced with PCIe Gen5 are appropriate for memory expansion with nearly no throughput degradation in OLTP workloads and less than 8% throughput degradation in OLAP workloads. Thus, CXL memory is a good candidate for memory expansion with lower TCO in IMDBMSs.
Demonstration Paper
Mach: Firefighting Time-Critical Issues in Complex Systems Using High-Frequency Telemetry
To understand the complex interactions in modern software, engineers often rely on high-frequency telemetry (HFT) data generated via tools like eBPF. However, today’s database systems are too slow for HFT’s rate and volume, and cannot process HFT within the limited resources available on individual host machines. Mach is a new storage engine for collecting and querying HFT. Key to Mach is the Temporal Skip Log (TSL)—a lightweight, write-optimized, log-based data structure specialized for HFT. Mach supports high ingest rates and makes data immediately queryable while operating within a limited on-host resource envelope. The demo shows how Mach helps engineers collect and query HFT in near real-time when diagnosing performance problems. In contrast, current systems and data reduction techniques fail to keep up. While a widely used time series database (InfluxDB) drops much of the HFT, the audience will see how Mach loses no data and allows them to interactively explore HFT from application and kernel events as they arrive.
Scott Bair is a Senior Technical Creative Director for Intel Labs, chartered with growing awareness for Intel’s leading-edge research activities, like AI, Neuromorphic Computing and Quantum Computing. Scott is responsible for driving marketing strategy, messaging, and asset creation for Intel Labs and its joint-research activities. In addition to his work at Intel, he has a passion for audio technology and is an active father of 5 children.
Scott has over 23 years of experience in the computing industry bringing new products and technology to market. During his 15 years at Intel, he has worked in a variety of roles from R&D, architecture, strategic planning, product marketing, and technology evangelism. Scott has an undergraduate degree in Electrical and Computer Engineering and a Masters of Business Administration from Brigham Young University.