TinyLFU-Based Semi-Stream Cache Join for Near-Real-Time Data Warehousing

Abstract Semi-stream join is an emerging research problem in the domain of near-real-time data warehousing. A semi-stream join is basically a join between a fast stream (S) and a slow disk-based relation (R). In the modern era of technology, huge amounts of data are being generated swiftly on a daily basis which needs to be instantly analyzed for making successful business decisions. Keeping this in mind, a famous algorithm called CACHEJOIN (Cache Join) was proposed. The limitation of the CACHEJOIN algorithm is that it does not deal with the frequently changing trends in a stream data efficiently. To overcome this limitation, in this paper we propose a TinyLFU-CACHEJOIN algorithm, a modified version of the original CACHEJOIN algorithm, which is designed to enhance the performance of a CACHEJOIN algorithm. TinyLFU-CACHEJOIN employs an intelligent strategy which keeps only those records of $R$ in the cache that have a high hit rate in S. This mechanism of TinyLFU-CACHEJOIN allows it to deal with the sudden and abrupt trend changes in S. We developed a cost model for our TinyLFU-CACHEJOIN algorithm and proved it empirically. We also assessed the performance of our proposed TinyLFU-CACHEJOIN algorithm with the existing CACHEJOIN algorithm on a skewed synthetic dataset. The experiments proved that TinyLFU-CACHEJOIN algorithm significantly outperforms the CACHEJOIN algorithm.