Raw Filtering of JSON data on FPGAs

Hahn T, Becher A, Wildermann S, Teich J (2022)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Conference Proceedings Title: Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe

Event location: Antwerpen BE

DOI: 10.23919/DATE54114.2022.9774696

Abstract

Many Big Data applications include the processing of data streams on semi-structured data formats such as JSON. A disadvantage of such formats is that an application may spend a significant amount of processing time just on unselectively parsing all data. To relax this issue, the concept of raw filtering is proposed with the idea to remove data from a stream prior to the costly parsing stage. However, as accurate filtering of raw data is often only possible after the data has been parsed, raw filters are designed to be approximate in the sense of allowing false-positives in order to be implemented efficiently.

Contrary to previously proposed CPU-based raw filtering techniques that are restricted to string matching, we present FPGA-based primitives for filtering strings, numbers and also number ranges. In addition, a primitive respecting the basic structure of JSON data is proposed that can be used to further increase the accuracy of introduced raw filters.

The proposed raw filter primitives are designed to allow for their composition according to a given filter expression of a query. Thus, complex raw filters can be created for FPGAs which enable a drastical decrease in the amount of generated false-positives, particularly for IoT workload. 

As there exists a trade-off between accuracy and resource consumption, we evaluate primitives as well as composed raw filters using different queries from the RiotBench benchmark. Our results show that up to 94.3% of the raw data can be filtered without producing any observed false-positives using only a few hundred LUTs.

Authors with CRIS profile

Tobias Hahn Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Andreas Becher Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Stefan Wildermann Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Jürgen Teich Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)

Related research project(s)

None - None Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis (ReProVide) DFG-Schwerpunktprogramm (SPP) 2037 - Skalierbares Datenmanagement für zukünftige Hardware Aug. 28, 2017 - March 1, 2023

How to cite

APA:

Hahn, T., Becher, A., Wildermann, S., & Teich, J. (2022). Raw Filtering of JSON data on FPGAs. In Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe. Antwerpen, BE.

MLA:

Hahn, Tobias, et al. "Raw Filtering of JSON data on FPGAs." Proceedings of the Design, Automation and Test in Europe Conference (DATE), Antwerpen 2022.

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