Optimizing cyclic join view maintenance over distributed data sources
Materialized views defined over distributed data sources are critical for many applications to ensure efficient access, reliable performance, and high availability. Materialized views need to be maintained upon source updates since stale view extents may not serve well or may even mislead user applications. Thus, view maintenance performance is one of the keys to the success of these applications. In this work, we investigate two maintenance strategies, extended batching and view graph transformation, for maintaining general join views where join conditions may exist between any pairs of data sources possibly with cycles. Many choices are available for maintaining cyclic join views. We thus propose a cost-driven view maintenance framework which generates optimized maintenance plans tuned to the environmental settings. The proposed framework has been implemented in the TxnWrap system. Experimental studies illustrate that our proposed optimization techniques significantly improve the view maintenance performance in a distributed environment.
view graph materialized view maintenance view maintenance optimization distributed query optimization grouping maintenance