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A key design decision for data systems is whether they follow the row-store or the column-store paradigm. The former supports transactional workloads, while the latter is better for analytical queries. This decision has a significant impact on the entire data system architecture. The multiple-decadelong journey of these two designs has led to a new family of hybrid transactional/analytical processing (HTAP) architectures. Several efforts have been proposed to reap the benefits of both worlds by proposing systems that maintain multiple copies of data (in different physical layouts) and convert them into the desired layout as required. Due to data duplication, the additional necessary bookkeeping, and the cost of converting data between different layouts, these systems compromise between efficient analytics and data freshness. We depart from existing designs by proposing a radically new approach. We ask the question: “What if we could access any layout and ship only the relevant data through the memory hierarchy by transparently converting rows to (arbitrary groups of) columns?” To achieve this functionality, we capitalize on the reinvigorated trend of hardware specialization (that has been accelerated due to the tapering of Moore's law) to propose Relational Fabric, a near-data vertical partitioner that allows memory or storage components to perform on-the-fly transparent data transformation. By exposing an intuitive API, Relational Fabric pushes vertical partitioning to the hardware, which profoundly impacts the process of designing and building data systems. (A) There is no need for data duplication and layout conversion, making HTAP systems viable using a single layout. (B) It simplifies the memory and storage manager that needs to maintain and update a single data layout. (C) It reduces unnecessary data movement through the memory hierarchy, allowing for better hardware utilization and, ultimately, better performance. In this paper, we present Relational Fabric for both memory and storage. We present our initial results on Relational Fabric for in-memory systems and discuss the challenges of building this hardware and the opportunities it brings for simplicity and innovation in the data system software stack, including physical design, query optimization, query evaluation, and concurrency control.
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Relational Memory: Native In-Memory Accesses on Rows and Columns
Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest data in row-first form and transform it in the background to columns to facilitate future analytical queries. How will this design change if we can always efficiently access only the desired set of columns? To address this question, we present a radically new approach to data transformation from rows to columns. We build upon recent advancements in embedded platforms with re-programmable logic to design native in-memory access on rows and columns. Our approach, termed Relational Memory (RM), relies on an FPGA-based accelerator that sits between the CPU and main memory and transparently transforms base data to any group of columns with minimal overhead at runtime. This design allows accessing any group of columns as if it already exists in memory. We implement and deploy RM in real hardware, and we show that we can access the desired columns up to 1.63× faster compared to a row-wise layout, while matching the performance of pure columnar access for low projectivity, and outperforming it by up to 2.23× as projectivity (and tuple reconstruction cost) increases. Overall, RM allows the CPU to access the optimal data layout, radically reducing unnecessary data movement without high data transformation costs, thus, simplifying software complexity and physical design, while accelerating query execution.
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- Award ID(s):
- 2008799
- PAR ID:
- 10482044
- Publisher / Repository:
- OpenProceedings.org
- Date Published:
- Journal Name:
- International Conference on Extending Database Technology (EDBT'23)
- Format(s):
- Medium: X
- Location:
- Ioannina, Greece
- Sponsoring Org:
- National Science Foundation
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A key design decision for data systems is whether they follow the row-store or the column-store paradigm. The former supports transactional workloads, while the latter is better for analytical queries. This decision has a profound impact on the entire data system architecture. The multiple-decadelong journey of these two designs has led to a new family of hybrid transactional/analytical processing (HTAP) architectures. Several efforts have been proposed to reap the benefits of both worlds by proposing systems that maintain multiple copies of data (in different physical layouts) and convert them into the desired layout as required. Due to data duplication, the additional necessary bookkeeping, and the cost of converting data between different layouts, these systems compromise between efficient analytics and data freshness. We depart from existing designs by proposing a radically new approach. We ask the question: “What if we could access any layout and ship only the relevant data through the memory hierarchy by transparently converting rows to (arbitrary groups of) columns?” To achieve this functionality, we capitalize on the reinvigorated trend of hardware specialization (that has been accelerated due to the tapering of Moore’s law) to propose Relational Fabric, a near-data vertical partitioner that allows memory or storage component to perform on-the-fly transparent data transformation. By exposing an intuitive API, Relational Fabric pushes vertical partitioning to the hardware, which has a profound impact on the process of designing and building data systems. (A) There is no need for data duplication and layout conversion, making HTAP systems viable using a single layout. (B) It simplifies the memory and storage manager that needs to maintain and update a single data layout. (C) It reduces unnecessary data movement through the memory hierarchy allowing for better hardware utilization, and ultimately better performance. In this paper, we present Relational Fabric for both memory and storage. We present our initial results on Relational Fabric for in-memory systems and discuss the challenges of building this hardware, as well as the opportunities it brings for simplicity and innovation in the data system software stack, including physical design, query optimization, query evaluation, and concurrency control.more » « less
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