Re: reducing random_page_cost from 4 to 2 to force index scan

On Thu, May 19, 2011 at 2:39 PM, Jim Nasby <jim(at)nasby(dot)net> wrote:
> On May 19, 2011, at 9:53 AM, Robert Haas wrote:
>> On Wed, May 18, 2011 at 11:00 PM, Greg Smith <greg(at)2ndquadrant(dot)com> wrote:
>>> Jim Nasby wrote:
>>>> I think the challenge there would be how to define the scope of the
>>>> hot-spot. Is it the last X pages? Last X serial values? Something like
>>>> correlation?
>>>>
>>>> Hmm... it would be interesting if we had average relation access times for
>>>> each stats bucket on a per-column basis; that would give the planner a
>>>> better idea of how much IO overhead there would be for a given WHERE clause
>>>
>>> You've already given one reasonable first answer to your question here.  If
>>> you defined a usage counter for each histogram bucket, and incremented that
>>> each time something from it was touched, that could lead to a very rough way
>>> to determine access distribution.  Compute a ratio of the counts in those
>>> buckets, then have an estimate of the total cached percentage; multiplying
>>> the two will give you an idea how much of that specific bucket might be in
>>> memory.  It's not perfect, and you need to incorporate some sort of aging
>>> method to it (probably weighted average based), but the basic idea could
>>> work.
>>
>> Maybe I'm missing something here, but it seems like that would be
>> nightmarishly slow.  Every time you read a tuple, you'd have to look
>> at every column of the tuple and determine which histogram bucket it
>> was in (or, presumably, which MCV it is, since those aren't included
>> in working out the histogram buckets).  That seems like it would slow
>> down a sequential scan by at least 10x.
>
> You definitely couldn't do it real-time. But you might be able to copy the tuple somewhere and have a background process do the analysis.
>
> That said, it might be more productive to know what blocks are available in memory and use correlation to guesstimate whether a particular query will need hot or cold blocks. Or perhaps we create a different structure that lets you track the distribution of each column linearly through the table; something more sophisticated than just using correlation.... perhaps something like indicating which stats bucket was most prevalent in each block/range of blocks in a table. That information would allow you to estimate exactly what blocks in the table you're likely to need...

Well, all of that stuff sounds impractically expensive to me... but I
just work here.

--
Robert Haas
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