Technology Category
Machine Learning: Efficient ML / Green AIData Mining & Knowledge Management: Scalability, Parallel & Distributed SystemsSearch and Optimization: Evaluation and Analysis
Beyond Performance: Measuring the Environmental Impact of Analytical Databases
🤖 AI Summary
This work addresses the lack of standardized, quantitative assessment of environmental impact in database systems. We propose ATLAS, the first framework to model the full lifecycle carbon footprint of analytical databases by jointly accounting for embodied carbon from hardware manufacturing and operational energy consumption. ATLAS enables systematic environmental efficiency evaluation across four representative systems—DuckDB, MonetDB, Hyper, and StarRocks—through empirical energy-efficiency measurement, geography-aware carbon intensity modeling, and an open-source benchmark suite. Key findings include: (1) architectural design induces up to 2.8× difference in runtime power consumption; (2) deployment in high-carbon-intensity grids can negate over 37% of energy-saving gains; and (3) geographic location significantly modulates relative environmental advantages among architectures. Our contribution includes the first open-source benchmark for database environmental efficiency, providing quantifiable foundations for green database design and sustainable deployment decisions.
Technology Category
Application Category
📝 Abstract
The exponential growth of data is making query processing increasingly critical for modern computing infrastructure, yet the environmental impact of database operations remains poorly understood and largely overlooked. This paper presents ATLAS, a comprehensive methodology for measuring and quantifying the environmental footprint of analytical database systems, considering both operational impacts and manufacturing costs of hardware components. Through extensive empirical evaluation of four distinct database architectures (DuckDB, MonetDB, Hyper, and StarRocks), we uncover how fundamental architectural decisions affect environmental efficiency. Our findings reveal that environmental considerations in database operations are multifaceted, encompassing both immediate operational impacts and long-term sustainability implications. We demonstrate that architectural choices can significantly influence both power consumption and environmental sustainability, while deployment location emerges as a critical factor that can amplify or diminish these architectural advantages.
Problem
Research questions and friction points this paper is trying to address.
Measuring environmental impact of analytical databases
Quantifying operational and manufacturing footprint of database systems
Evaluating architectural effects on power consumption and sustainability
Innovation
Methods, ideas, or system contributions that make the work stand out.
ATLAS methodology measures database environmental footprint
Evaluates four architectures for environmental efficiency
Architectural choices impact power and sustainability
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