Re: FETCH FIRST clause PERCENT option

On 1/9/19 7:09 AM, Surafel Temesgen wrote:
>
>
> On Sun, Jan 6, 2019 at 5:51 PM Tomas Vondra
> <tomas(dot)vondra(at)2ndquadrant(dot)com <mailto:tomas(dot)vondra(at)2ndquadrant(dot)com>> wrote:
>
>
> On 1/6/19 12:40 PM, Surafel Temesgen wrote:
> >
> >
> > On Fri, Jan 4, 2019 at 5:27 PM Tomas Vondra
> > <tomas(dot)vondra(at)2ndquadrant(dot)com
> <mailto:tomas(dot)vondra(at)2ndquadrant(dot)com>
> <mailto:tomas(dot)vondra(at)2ndquadrant(dot)com
> <mailto:tomas(dot)vondra(at)2ndquadrant(dot)com>>> wrote:
> >
> >
> >     What formula? All the math remains exactly the same, you just
> need to
> >     update the number of rows to return and track how many rows
> are already
> >     returned.
> >
> >     I haven't tried doing that, but AFAICS you'd need to tweak
> how/when
> >     node->count is computed - instead of computing it only once it
> needs to
> >     be updated after fetching each row from the subplan.
> >
> >     Furthermore, you'll need to stash the subplan rows somewhere
> (into a
> >     tuplestore probably), and whenever the node->count value
> increments,
> >     you'll need to grab a row from the tuplestore and return that
> (i.e.
> >     tweak node->position and set node->subSlot).
> >
> >     I hope that makes sense. The one thing I'm not quite sure about is
> >     whether tuplestore allows adding and getting rows at the same
> time.
> >
> >     Does that make sense
> >
> >
> >
> > In current implementation in LIMIT_INITIAL state we execute outer
> > plan to the end , store the resulting tuples in tuplestore and
> > calculate limitCount in number .We are in this state only once and
> > did not return any tuple. Once we are at LIMIT_INWINDOW state and
> > inner node execution asking for tuple it return from tuple store
> > immediately.
> >
>
> Right.
>
> > Inorder to do fast startup what I was thinking was dividing the work
> > done at LIMIT_INITIAL state in to limitCount. For example we want
> > 0.5 percent of the result which means in LIMIT_INWINDOW state we
> > execute outer node 200 times ,store the result in tuplestore and
> > return the first tuple. if we don’t get that much tuple that means we
> > reach end of the limit.
> Yes, pretty much.
>
>
>
> While working on this method i found an issue that did not work will
> on percent with fractional part like 2.6 percent which means we have
> to emit per every 38.4615 tuple so it have to be round up to 39 or
> round down to 38 either way it leads to incorrect result .Even with
> integer percentage sometimes the result came out with fractional
> part and needs rounding
>

It's hard to say what exactly are you doing, because you haven't shared
any code nor examples. But it sounds as if you're computing how often to
produce an additional row at the beginning, and then simply multiplying
it. I'm not sure that's quite possible, because there will be issues due
to rounding.

Firstly, I see your last patch (from 30/11) does this:

node->count = DatumGetInt64((DatumGetFloat8(val) *
tuplestore_tuple_count(node->totalTuple)) / 100);

but the SQL standard I have says this in the <query expression> section:

If <fetch first percentage> is specified, then let FFP be the
<simple value specification> simply contained in <fetch first
percentage>, and let LOCT be a <literal> whose value is OCT.
Let FFRC be the value of

CEILING ( FFP * LOCT / 100.0E0 )

That's not what you patch does, though, because it relies on automatic
float->int conversion, which does floor() and not ceil(). FWIW the final
DatumGetInt64 seems unnecessary, because the expression is float8 and
not Datum.

The patch should do something like this instead:

node->count = ceil((DatumGetFloat8(val) *
tuplestore_tuple_count(node->totalTuple)) / 100);

Now, back to the row count issue - let's assume the user requests

FETCH FIRST 3 PERCENT ROWS ONLY

This means we should produce 1 rows every 33 rows, but only very
roughly. In reality it's a bit less, because 3% is not 1/33 exactly.
Consider this query, which computes number of rows for

select sample_cnt, l - lag(l) over (order by sample_cnt) AS d from (
select sample_cnt, min(nrows) AS l from (
select
nrows,
(nrows * 3.0 / 100.0) AS raw_sample_cnt,
ceil(nrows * 3.0 / 100.0) AS sample_cnt
from generate_series(1,10000) s(nrows)
) foo group by sample_cnt order by sample_cnt
) bar;

The inner-most query simply computes how many rows we're supposed to
return based on certain row count. Then we compute points at which the
sample_cnt actually changes. And finally we compute the distance between
those points. You should get something like

sample_cnt | d
------------+----
1 |
2 | 33
3 | 33
4 | 34
5 | 33
6 | 33
7 | 34
8 | 33
9 | 33
10 | 34
11 | 33
...

This shows that the distance oscillates between 33 and 34, so no matter
what value you pick, you can't simply multiply it to get the desired
sample size. It needs to be re-computed as you go.

regards

--
Tomas Vondra http://www.2ndQuadrant.com
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