> 4. Even if we could accurately estimate the percentage of the table
> that is cached, what then? For example, suppose that a user issues a
> query which retrieves 1% of a table, and we know that 1% of that table
> is cached. How much of the data that the user asked for is cache?
FWIW, for a manual override setting, I was thinking that the % would
convert to a probability. In that way, it wouldn't be different from
the existing RPC calculation; we're just estimating how *likely* it is
that the data the user wants is cached.
> One idea Tom and I kicked around previously is to set an assumed
> caching percentage for each table based on its size relative to
> effective_cache_size - in other words, assume that the smaller a table
> is, the more of it will be cached. Consider a system with 8GB of RAM,
> and a table which is 64kB. It is probably unwise to make any plan
> based on the assumption that that table is less than fully cached. If
> it isn't before the query executes, it soon will be. Going to any
> amount of work elsewhere in the plan to avoid the work of reading that
> table in from disk is probably a dumb idea. Of course, one downside
> of this approach is that it doesn't know which tables are hot and
> which tables are cold, but it would probably still be an improvement
> over the status quo.
Actually, we *do* have some idea which tables are hot. Or at least, we
could. Currently, pg_stats for tables are "timeless"; they just
accumulate from the last reset, which has always been a problem in
general for monitoring. If we could make top-level table and index
stats time-based, even in some crude way, we would know which tables
were currently hot. That would also have the benefit of making server
performance analysis and autotuning easier.
> But DBAs
> frequently have a very good idea of which stuff is in cache - they can
> make observations over a period of time and then adjust settings and
> then observe some more and adjust some more.
-- Josh Berkus
PostgreSQL Experts Inc.
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