Optimization of Incremental Join Based on Auxiliary View

In the era of big data, the importance of join operation in the formation of wide tables has become increasingly prominent. Compared with full join, incremental join helps avoid the need to recalculate wide table data when the underlying base data changes, ensuring the freshness and timeliness of data in the wide tables. We propose an algorithm that utilizes auxiliary views to accelerate incremental join. With a self-maintained auxiliary mapping hash structure, the algorithm applies to any join tree. Then, we further optimize the algorithm by adjusting join order of base tables and introduces parallelism to the algorithm.