A Scalable and Skew-insensitive Algorithm for Join Operations using Map/Reduce Model

For over a decade, Map/Reduce has become a prominent programming model to handle vast amounts of raw data in large scale systems. This model ensures scalability, reliabil-ity and availability aspects with reasonable query processing time. However these large scale systems still face some challenges: data skew, task imbalance, high disk i/o and redistribution costs can have disastrous effects on performance. In this paper, we introduce MRFA-Join algorithm: a new Frequency Adaptive algo-rithm based on Map/Reduce Programming model and distributed histograms for join pro-cessing on large-scale datasets. A cost analysis of this algorithm shows that our approach is insensitive to data skew and ensures perfect balancing properties during all stages of join computation. Performances have been experimented on Grid’5000 infrastructure