Relational Memory: Native In-Memory Accesses on Rows and Columns
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by
Shahin Roozkhosh, Denis Hoornaert, Ju Hyoung Mun, Tarikul Islam Papon, Ulrich Drepper, Renato Mancuso, Manos Athanassoulis
2021
Abstract
Analytical database systems are typically designed to use a column-first data
layout that maps better to analytical queries of access patterns. This choice
is premised on the assumption that storing data in a row-first format leads to
accessing unwanted fields; moreover, transforming rows to columns at runtime is
expensive. On the other hand, new data items are constantly ingested in
row-first form and transformed in the background to columns to facilitate
future analytical queries. How will this design change if we can always access
only the desired set of columns? In this paper, to address this question, we
present a radically new approach to data transformation from rows to columns.
We build upon recent advancements in commercial embedded platforms with
tightly-coupled re-programmable logic to design native in-memory access on rows
and columns. We propose a new database management system (DBMS) architecture
that is the first hardware/software co-design. It relies on an FPGA-based
accelerator to transparently transform base data to any group of columns with
minimal overhead at runtime. This design allows the DBMS to access any group of
columns as if it already exists in memory. Our method, termed relational
memory, currently implements projection, and offers the groundwork for
implementing selection, group by, aggregation, and supporting joins in
hardware, thus, vastly simplifying the software logic and accelerating the
query execution. We present a detailed analysis of relational memory using both
synthetic benchmarks and realistic workloads. Our relational memory
implementation can convert on the fly rows to arbitrary groups of columns
without any latency penalty. Essentially, relational memory can load in cache
the desired columns from a row-oriented base data layout as fast as reading
from column-oriented base data layout by outsourcing data transformation to the
hardware.
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