ADAPTIVE JOIN OPERATORS FOR RESULT RATE MAXIMIZATION

This work is focused on how join operator works in a single, homogeneous and heterogeneous environment. Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data is provided by autonomous data sources through heterogeneous network environments. In traditional join techniques, they can start producing join results as soon as the first input tuples are available, thus improving pipelining by smoothing join result production and by masking source or network delays. In this work, Evaluation of the performance and comparison of Multiway join (MJoin), Double Index Nested Loop Reactive Join (DINER), and Multiple Index Nested Loop Reactive Join (MINER). DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results, and a novel re-entrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus better exploiting temporary delays when new data is not available. MINER outperforms in comparison with the previous join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory.