SPEAR-JSON']JSON: Selective parsing of JSON']JSON to enable accelerated stream processing on FPGAs

Big Data applications frequently involve the processing of data streams encoded in semi-structured data formats such as JSON. A major challenge here is that the parsing of such data formats is usually highly complex. Accelerating JSON parsing on FPGAs has therefore become a focus of recent research. FPGA accelerators were presented which serve as a co-processor for a CPU to convert JSON into a format that is easier for the CPU to process, e.g., Apache Arrow. However, in case the parsed data should be further processed on the FPGA, such solutions are insufficient as the format created is unsuitable for further processing on FPGAs and, above all, because the accelerators have an immense resource requirement. In this paper, we present a novel FPGA parser architecture that is able to interpret JSON data to selectively extract attributes based on a query expression into a format suitable for stream processing on FPGAs. Furthermore, it is shown how the sparsity of JSON can be used to implement a resource-efficient design, only requiring few FPGA resources. This leaves the major share of resources free for accelerating subsequent processing steps of a given application. Our experimental evaluation shows that we can achieve a throughput of 51.1 MB/s per kLUT which is about 3.8 times higher than the throughput per LUT achievable on the most efficient related approach.