  In-memory join is an essential operator in any database engine. It has been
extensively investigated in the database literature. In this paper, we study
whether exploiting the CDF-based learned models to boost the join performance
is practical or not. To the best of our knowledge, we are the first to fill
this gap. We investigate the usage of CDF-based partitioning and learned
indexes (e.g., Recursive Model Indexes (RMI) and RadixSpline) in the three join
categories; indexed nested loop join (INLJ), sort-based joins (SJ) and
hash-based joins (HJ). Our study shows that there is a room to improve the
performance of INLJ and SJ categories through our proposed optimized learned
variants. Our experimental analysis showed that these proposed learned variants
of INLJ and SJ consistently outperform the state-of-the-art techniques.
