Skew-insensitive Parallel Algorithms for Relational Join

Skew-insensitive parallel algorithms for relational join

Join is the most important and expensive operation in relational databases. The parallel join operation is very sensitive to the presence of the data skew. In this paper, we present two new parallel join algorithms for coarse-grained machines, which work optimally in presence of arbitrary amount of data skew. The first algorithm is sort-based and the second is hash-based. Both of these algorith...

Skew-Insensitive Join Processing in Shared-Disk Database Systems

Skew effects are still a significant problem for efficient query processing in parallel database systems. Especially in shared-nothing environments, this problem is aggravated by the substantial cost of data redistribution. Shared-disk systems, on the other hand, promise much higher flexibility in the distribution of workload among processing nodes because all input data can be accessed by any ...

A Skew-insensitive Algorithm for Join and Multi-join Operations on Shared Nothing Machines

An Optimal Skew-insensitive Join and Multi-join Algorithm for Distributed Architectures

The development of scalable parallel database systems requires the design of efficient algorithms for the join operation which is the most frequent and expensive operation in relational database systems. The join is also the most vulnerable operation to data skew and to the high cost of communication in distributed architectures. In this paper, we present a new parallel algorithm for join and m...

Data-Parallel Spatial Join Algorithms

E cient data-parallel spatial join algorithms for pmr quadtrees and R-trees, common spatial data structures, are presented. The domain consists of planar line segment data (i.e., Bureau of the Census TIGER/Line les). Parallel algorithms for map intersection and a spatial range query are described. The algorithms are implemented using the SAM (Scan-AndMonotonic-mapping) model of parallel computa...