Abstract - Adaptive join algorithms have recently attracted a lot of attention in emerging applications that provide data through autonomous data sources in diverse network environments. Their main advantage over traditional joining technologies is that they can give the results they join as soon as the first input duplex is available, thereby improving the pipeline by joining the results and hiding the source or network delay. In this paper, we first suggest the new adaptive two-way join algorithm DINER (Dual Indexed-Loops Reactive Join) to increase the result rate. Diner combines two main components: the novel Retrofit technology, which allows algorithms to quickly switch between memory processing and a clea r flushing approach aimed at increasing the productivity of memory tuples in producing results in the stage of reaching online. We are expanding the application of specific technology for a more challenging setup: managing more than two inputs. The Multi Active Relational Join Algorithm (MARA) is a multi-path joint operator that claims its principles from DINER. Tara surpasses previous compatible joint algorithms in our experiments with real and synthetic data sets, makes the best use of available memory and produces duplicates of results at significantly higher rates. In the presence of multiple experiments, our experiments show that MARA can produce a high percentage of initial results and surpass existing technologies for adaptive multi-path joining.