A cost model that splits join selectivity into match probability and fanout, and counts redundant probes, makes join-order optimization for many-to-many joins more accurate and more robust.
Factorised Representations of Query Results
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Query tractability has been traditionally defined as a function of input database and query sizes, or of both input and output sizes, where the query result is represented as a bag of tuples. In this report, we introduce a framework that allows to investigate tractability beyond this setting. The key insight is that, although the cardinality of a query result can be exponential, its structure can be very regular and thus factorisable into a nested representation whose size is only polynomial in the size of both the input database and query. For a given query result, there may be several equivalent representations, and we quantify the regularity of the result by its readability, which is the minimum over all its representations of the maximum number of occurrences of any tuple in that representation. We give a characterisation of select-project-join queries based on the bounds on readability of their results for any input database. We complement it with an algorithm that can find asymptotically optimal upper bounds and corresponding factorised representations.
citation-role summary
citation-polarity summary
fields
cs.DB 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Optimizing Queries with Many-to-Many Joins
A cost model that splits join selectivity into match probability and fanout, and counts redundant probes, makes join-order optimization for many-to-many joins more accurate and more robust.