Cloud Analytics Benchmark - van Renen, Alexander; Leis, Viktor (Proceedings of the VLDB Endowment, 2023)
The cloud facilitates the transition to a service-oriented perspective. This affects cloud-native data management in general, and data analytics in particular. Instead of managing a multi-node database cluster on-premise, end users simply send queries to a managed cloud data warehouse and receive results. While this is obviously very attractive for end users, database system architects still have to engineer systems for this new service model. There are currently many competing architectures ranging from self-hosted (Presto, PostgreSQL), over managed (Snowflake, Amazon Redshift) to query-as-a-service (Amazon Athena, Google BigQuery) offerings. Benchmarking these architectural approaches is currently difficult, and it is not even clear what the metrics for a comparison should be. To overcome these challenges, we first analyze a real-world query trace from Snowflake and compare its properties to that of TPC-H and TPC-DS. Doing so, we identify important differences that distinguish traditional benchmarks from real-world cloud data warehouse workloads. Based on this analysis, we propose the Cloud Analytics Benchmark (CAB). By incorporating workload fluctuations and multi-tenancy, CAB allows evaluating different designs in terms of user-centered metrics such as cost and performance.
CRIS-Team Scopus Importer:2023-04-28

Plush: A Write-Optimized Persistent Log-Structured Hash-Table - Vogel, Lukas; van Renen, Alexander; Imamura, Satoshi; Giceva, Jana; Neumann, Thomas; Kemper, Alfons (Proceedings of the VLDB Endowment, 2022)
Persistent memory (PMem) promised DRAM-like performance, byte addressability, and the persistency guarantees of conventional block storage. With the release of Intel Optane DCPMM, those expectations were dampened. While its write latency competes with DRAM, its read latency, write endurance, and especially bandwidth fall behind by up to an order of magnitude. Established PMem index structures mostly focus on lookups and cannot leverage PMem’s low write latency. For inserts, DRAM-optimized index structures are still an order of magnitude faster than their PMem counterparts despite the similar write latency. We identify the combination of PMem’s low write bandwidth and the existing solutions’ high media write amplification as the culprit. We present Plush, a write-optimized, hybrid hash table for PMem with support for variable-length keys and values. It minimizes media write and read amplification while exploiting PMem’s unique advantages, namely its low write latency and full bandwidth even for small reads and writes. On a 24-core server with 768 GB of Intel Optane DPCMM, Plush outperforms state-of-the-art PMem-optimized hash tables by up to 2.44× for inserts while only using a tiny amount of DRAM. It achieves this speedup by reducing write amplification by 80%. For lookups, its throughput is similar to that of established PMem-optimized tree-like index structures.
CRIS-Team Scopus Importer:2022-09-30

SageDB: An Instance-Optimized Data Analytics System - Ding, Jialin; Marcus, Ryan; Kipf, Andreas; Nathan, Vikram; Nrusimha, Aniruddha; Vaidya, Kapil; van Renen, Alexander; Kraska, Tim (Proceedings of the VLDB Endowment, 2022)
Moderndata systems are typically bothcomplex and general-purpose. They are complex because of the numerous internal knobs and parameters that users need to manually tune in order to achieve good performance; they are general-purpose because they are designed to handle diverse use cases, and therefore often do not achieve the best possible performance for any specific use case. A recent trend aims to tackle these pitfalls: instance-optimized systems are designed to automatically self-adjust in order to achieve the best performance for a specific use case, i.e., a dataset and query workload. Thus far, the research community has focused on creating instance-optimized database components, such as learned indexes and learned cardinality estimators, which are evaluated in isolation. However, to the best of our knowledge, there is no complete data system built with instance-optimization as a foundational design principle. In this paper, we present a progress report on SageDB, our effort towards building the first instance-optimized data system. SageDB synthesizes various instance-optimization techniques to automatically specialize for a given use case, while simultaneously exposing a simple user interface that places minimal technical burden on the user. Our prototype outperforms a commercial cloud-based analytics system by up to 3× on end-to-end query workloads and up to 250× on individual queries. SageDB is an ongoing research effort, and we highlight our lessons learned and key directions for future work.
CRIS-Team Scopus Importer:2024-12-16