Cached plans and statement generalization

Hi hackers,

There were a lot of discussions about query plan caching in hackers
mailing list, but I failed to find some clear answer for my question and
the current consensus on this question in Postgres community. As far as
I understand current state is the following:
1. We have per-connection prepared statements.
2. Queries executed inside plpgsql code are implicitly prepared.

It is not always possible or convenient to use prepared statements.
For example, if pgbouncer is used to perform connection pooling.
Another use case (which is actually the problem I am trying to solve
now) is partitioning.
Efficient execution of query to partitioned table requires hardcoded
value for partitioning key.
Only in this case optimizer will be able to construct efficient query
plan which access only affected tables (partitions).

My small benchmark for distributed partitioned table based on pg_pathman
+ postgres_fdw shows 3 times degrade of performance in case of using
prepared statements.
But without prepared statements substantial amount of time is spent in
query compilation and planning. I was be able to speed up benchmark more
than two time by
sending prepared queries directly to the remote nodes.

So what I am thinking now is implicit query caching. If the same query
with different literal values is repeated many times, then we can try to
generalize this query and replace it with prepared query with
parameters. I am not considering now shared query cache: is seems to be
much harder to implement. But local caching of generalized queries seems
to be not so difficult to implement and requires not so much changes in
Postgres code. And it can be useful not only for sharding, but for many
other cases where prepared statements can not be used.

I wonder if such option was already considered and if it was for some
reasons rejected: can you point me at this reasons?

--
Konstantin Knizhnik
Postgres Professional: http://www.postgrespro.com
The Russian Postgres Company