Github user ioana-delaney commented on the issue:
https://github.com/apache/spark/pull/15363
@davies Thank you for reviewing the code! I see this work as evolving and
improving with the support of CBO. Without statistics and features such as
cardinality and selectivity, we cannot provide an optimal join reordering.
There were two types of regressions. The first type was caused by
reordering a non-selective star join. The query did not apply any local
predicate on the dimension tables and the join between two large fact tables
happen to be very selective. To fix this category of queries, the algorithm
will not attempt to reorder a non-selective join. A non-selective join is a
join that does not apply local predicates on dimension tables.
The other category of problem was caused by the more general issue of
lacking predicate selectivity. To overcome this problem, we introduced the
âpredicate selectivity hintâ feature, to allow the user to specify the
selectivity of the predicate. With that, we are able to plan selective
dimension first. The JIRA for predicate selectivity was not yet opened.
Then, to further guard against bad plans, we put the feature under the
starJoinOptimization option. I was thinking that, to be more conservatives, I
can further enforce a certain number of joins in the star. In general, a star
join consist of a fact table and at least two dimensions. I can add this
restriction to the algorithm.
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