On 22/11/2024 22:53, Frédéric Yhuel wrote:
My colleague Christophe Courtois and I have been trying to fix a bad
plan for one of Dalibo's clients. It is a (probably well-known) problem
with skewed data and a parameterized Nested Loop with an underestimation
of the cardinality of the inner relation.
Here is a test case (the script to create and populate the two tables is
at the end):
Thanks for the case provided!
I wonder if data science has invented a statistic or selectivity
estimation technique that could tackle your case in general. As I see,
we should touch the table of products to realise which specific ID has
the product named 'Babar'.
I can imagine the trick when you build MCV on (id, name) and have a
chance to find this popular ID, cache it, and use it during join clause
estimation later. But it seems too expensive to do the same for
arbitrary incoming queries.
If you want a workaround, such cases fit query-driven techniques. Among
open-source ones, I can point your attention to the AQO extension
(honestly, designed under my command). It can save information about an
estimation error and correct the query next time.
In an enterprise-grade area, you can pick the sr_plan extension, which
is designed to store the plan for a specific query (you can choose
parameterisation on your own) and spread it globally across all
instances' backends.
--
regards, Andrei Lepikhov
