2 research outputs found
Empowering In-Memory Relational Database Engines with Native Graph Processing
The plethora of graphs and relational data give rise to many interesting
graph-relational queries in various domains, e.g., finding related proteins
satisfying relational predicates in a biological network. The maturity of
RDBMSs motivated academia and industry to invest efforts in leveraging RDBMSs
for graph processing, where efficiency is proven for vital graph queries.
However, none of these efforts process graphs natively inside the RDBMS, which
is particularly challenging due to the impedance mismatch between the
relational and the graph models. In this paper, we propose to treat graphs as
first-class citizens inside the relational engine so that operations on graphs
are executed natively inside the RDBMS. We realize our approach inside VoltDB,
an open-source in-memory relational database, and name this realization
GRFusion. The SQL and the query engine of GRFusion are empowered to
declaratively define graphs and execute cross-data-model query plans formed by
graph and relational operators, resulting in up to four orders-of-magnitude in
query-time speedup w.r.t. state-of-the-art approaches
Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Modern real-time business analytic consist of heterogeneous workloads (e.g,
database queries, graph processing, and machine learning). These analytic
applications need programming environments that can capture all aspects of the
constituent workloads (including data models they work on and movement of data
across processing engines). Polystore systems suit such applications; however,
these systems currently execute on CPUs and the slowdown of Moore's Law means
they cannot meet the performance and efficiency requirements of modern
workloads. We envision Polystore++, an architecture to accelerate existing
polystore systems using hardware accelerators (e.g, FPGAs, CGRAs, and GPUs).
Polystore++ systems can achieve high performance at low power by identifying
and offloading components of a polystore system that are amenable to
acceleration using specialized hardware. Building a Polystore++ system is
challenging and introduces new research problems motivated by the use of
hardware accelerators (e.g, optimizing and mapping query plans across
heterogeneous computing units and exploiting hardware pipelining and
parallelism to improve performance). In this paper, we discuss these challenges
in detail and list possible approaches to address these problems.Comment: 11 pages, Accepted in ICDCS 201