  Non-relational database systems (NRDS), such as graph, document, key-value,
and wide-column, have gained much attention in various trending (business)
application domains like smart logistics, social network analysis, and medical
applications, due to their data model variety and scalability. The broad data
variety and sheer size of datasets pose unique challenges for the system design
and runtime (incl. power consumption). While CPU performance scaling becomes
increasingly more difficult, we argue that NRDS can benefit from adding field
programmable gate arrays (FPGAs) as accelerators. However, FPGA-accelerated
NRDS have not been systematically studied, yet.
  To facilitate understanding of this emerging domain, we explore the fit of
FPGA acceleration for NRDS with a focus on data model variety. We define the
term NRDS class as a group of non-relational database systems supporting the
same data model. This survey describes and categorizes the inherent differences
and non-trivial trade-offs of relevant NRDS classes as well as their
commonalities in the context of common design decisions when building such a
system with FPGAs. For example, we found in the literature that for key-value
stores the FPGA should be placed into the system as a smart network interface
card (SmartNIC) to benefit from direct access of the FPGA to the network.
However, more complex data models and processing of other classes (e.g., graph
and document) commonly require more elaborate near-data or socket accelerator
placements where the FPGA respectively has the only or shared access to main
memory. Across the different classes, FPGAs can be used as communication layer
or for acceleration of operators and data access. We close with open research
and engineering challenges to outline the future of FPGA-accelerated NRDS.
