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Abstract
It is known that optimization of join queries based on average selectivities is sub-optimal in highly correlated databases. Relations are naturally divided into partitions , each partition having substantially different statistical characteristics in such databases. It is very compelling to discover such data partitions during query optimization and create multiple plans for a given query , one plan being optimal for a particular combination of data partitions. This scenario calls for the sharing of state among plans, so that common intermediate results are not recomputed. We study this problem in a setting with a routing-based query execution engine based on eddies. Eddies naturally encapsulate horizontal partitioning and maximal state sharing across multiple plan. The purpose of this paper is to present faster execution time over traditional optimization for high correlations, while maintaining the same performance for low correlations.
Highlights
It is known that optimization of join queries based on average selectivities is sub-optimal in highly correlated databases
Relations are naturally divided into partitions, each partition having substantially different statistical characteristics in such databases
When data correlations are present, the input relations are naturally divided into partitions, each partition having completely different statistical characteristics
Summary
It is known that optimization of join queries based on average selectivities is sub-optimal in highly correlated databases. Relations are naturally divided into partitions , each partition having substantially different statistical characteristics in such databases Traditional query optimizers pick one execution plan per query, based on first-order statistics about the underlying data. The presence of data correlations does make selectivity estimation harder-it offers opportunities for more effective query optimization. When data correlations are present, the input relations are naturally divided into partitions, each partition having completely different statistical characteristics. It is very attractive to create multiple plans per query, each plan being optimized for a different combination of data partitions. The combined cost of the two resulting plans can be smaller than the cost of any possible monolithic plan
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