Abstract:The traditional DSP and FPGA-based digital signal processing technology is more suitable for real-time signal processing. It is limited by the size and frequency resolution of the data. So it unsuitable for applications of off-line data processing, data analysis and data mining under large-scale data. Currently the industrial big data analytics platforms can use Spark as a computational engine for real-time signal processing and off-line signal processing acceleration. However, there is a lack of mathematical solutions such as digital signal processing for distributed parallel computing engines. Consequently, this paper presents a library of distributed digital signal processing algorithms based on Spark, which provides a support for the analysis of industrial big data application scenarios. This paper describes the architecture of the algorithm library design and takes the FFT algorithm and DFT algorithm as examples to introduce the distributed implementation of the traditional digital signal algorithm in Spark. Finally, this paper persents a test and analysis for this algorithm library. The results show that the algorithm library can correctly accomplish the function of digital signal processing and it can fulfill the industrial big data analysis platform for large-scale data sets for digital signal processing needs.