BtrBlocks: Efficient Columnar Compression for Data Lakes
Maximilian Kuschewski, David Sauerwein, Adnan Alhomssi, Viktor Leis
摘要
Analytics is moving to the cloud and data is moving into data lakes. These reside on object storage services like S3 and enable seamless data sharing and system interoperability. To support this, many systems build on open storage formats like Apache Parquet. However, these formats are not optimized for remotely-accessed data lakes and today's high-throughput networks. Inefficient decompression makes scans CPU-bound and thus increases query time and cost. With this work we present BtrBlocks, an open columnar storage format designed for data lakes. BtrBlocks uses a set of lightweight encoding schemes, achieving fast and efficient decompression and high compression ratios.
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引用它的顶会 Paper27
- An Empirical Evaluation of Columnar Storage FormatsXinyu Zeng, Yulong Hui, Jiahong Shen, Andrew Pavlo 等VLDB 2024 · 被引用 59 次
- ALP: Adaptive Lossless floating-Point CompressionAzim Afroozeh, Leonardo Kuffó, Peter BonczSIGMOD 2024 · 被引用 33 次
- Two Birds With One Stone: Designing a Hybrid Cloud Storage Engine for HTAPTobias Schmidt, Dominik Durner, Viktor Leis, Thomas NeumannVLDB 2024 · 被引用 12 次
- High-Performance Query Processing with NVMe Arrays: Spilling without Killing PerformanceMaximilian Kuschewski, Jana Giceva, Thomas Neumann, Viktor LeisSIGMOD 2025 · 被引用 11 次
- F3: The Open-Source Data File Format for the FutureXinyu Zeng, Ruijun Meng, Martin Prammer, Wes McKinney 等SIGMOD 2026 · 被引用 10 次
它引用的顶会 Paper4
- Chimp: Efficient Lossless Floating Point Compression for Time Series DatabasesPanagiotis Liakos, Katia Papakonstantinopoulou, Yannis KotidisVLDB 2022 · 被引用 76 次
- Towards Cost-Optimal Query Processing in the CloudViktor Leis, Maximilian KuschewskiVLDB 2021 · 被引用 34 次
- Cloud Analytics BenchmarkAlexander van Renen, Viktor LeisVLDB 2023 · 被引用 32 次
- FSST: Fast Random Access String CompressionPeter Boncz, Thomas Neumann, Viktor LeisVLDB 2020
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