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.
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Towards Physical Design Management in Storage Systems
In the post-Moore era, systems and devices with new architectures will arrive at a rapid rate with significant impacts on the software stack. Applications will not be able to fully benefit from new architectures unless they can delegate adapting to new devices in lower layers of the stack. In this paper we introduce physical design management which deals with the problem of identifying and executing transformations on physical designs of stored data, i.e. how data is mapped to storage abstractions like files, objects, or blocks, in order to improve performance. Physical design is traditionally placed with applications, access libraries, and databases, using hard- wired assumptions about underlying storage systems. Yet, storage systems increasingly not only contain multiple kinds of storage devices with vastly different performance profiles but also move data among those storage devices, thereby changing the benefit of a particular physical design. We advocate placing physical design management in storage, identify interesting research challenges, provide a brief description of a prototype implementation in Ceph, and discuss the results of initial experiments at scale that are replicable using Cloudlab. These experiments show performance and resource utilization trade-offs associated with choosing different physical designs and choosing to transform between physical designs.
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- Award ID(s):
- 1836650
- NSF-PAR ID:
- 10166880
- Date Published:
- Journal Name:
- 2019 IEEE/ACM Fourth International Parallel Data Systems Workshop (PDSW)
- Page Range / eLocation ID:
- 40 to 49
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PDSW49588.2019.00009
