Auto-Tuning of Raw Filters for FPGAs

Many Big Data applications include the processing of data streams on semi-structured data formats such as JSON. A disadvantage of these formats, however, is that applications may require a significant portion of their processing time to unselectively parse all data. As a remedy, so-called raw filters have been introduced in the past, aiming to reduce the data load before the costly parsing stage. Since filtering unparsed data can also become very costly, raw filters can be designed to filter data approximately, in the sense that they allow false positives to occur, in order to be implemented efficiently. While previously proposed CPU-based solutions are restricted to just string filtering, FPGA approaches have recently been proposed with much more expressive raw filters, allowing also to capture numbers and structural relationships. Yet, as a consequence of the variety of filter possibilities as well as the limited amount of resources available on FPGAs, the selection of optimal filters before their deployment has been identified as a complex problem resulting in the potential need to select less expressive filters in order to consume fewer resources. Many Big Data applications (e.g., stream processing) operate on incoming real-time data over long, potentially unlimited time periods. As a consequence, the conditions for which such a filter is optimized can change over time after its deployment. In this realm, this paper presents a new methodology which automatically adapts the hardware accelerator for raw filtering by means of dynamic hardware reconfiguration. Data is sampled on-the-fly during operation and used by an optimizer-in-the-loop to select and generate a raw filter with optimized selectivity for these data samples. As the optimizer has to take into account the resource costs of the hardware accelerator, we introduce models to estimate the resource costs in order to avoid performing a full synthesis. The filter selection problem can thus be solved within a few minutes with results close to the accurate resource cost estimation. If the selectivity of a query changes over time, such as seasonal differences in the analysis of IoT data, the system can auto-tune its filter to adapt to the situation. Depending on the query and the variability of inherent data changes, significant improvements in the amount of filtered data are presented, resulting in a significant parsing speedup in comparison to a state-of-the-art non-adaptive approach.