Frequency domain data encoding in apache IoTDB
Haoyu Wang, Shaoxu Song
Abstract
Frequency domain analysis is widely conducted on time series. While online transforming from time domain to frequency domain is costly, e.g., by Fast Fourier Transform (FFT), it is highly demanded to store the frequency domain data for reuse. However, frequency domain data encoding for efficient storage is surprisingly untouched. We notice that (1) the precision of data value is unnecessarily high after transforming to frequency domain and (2) the data values are with skewed distribution leading to a very large bit width for encoding. To avoid such space waste in both precision and skewness, we devise a descending bit-packing encoding for frequency domain data. Specifically, we quantize the data values in proper precision referring to the signal-noise-ratio (SNR) in frequency domain analysis. Moreover, we sort the data values in descending order so that the bit width could be dynamically reduced in encoding. The method has been deployed in Apache IoTDB, an open-source time-series database, not only for directly encoding frequency domain data, but also as a lossy compression of the time domain data. The extensive experiments on the system demonstrate the superiority of our encoding for both frequency domain and time domain data.