|From:||Tomas Vondra <tomas(dot)vondra(at)2ndquadrant(dot)com>|
|To:||James Coleman <jtc331(at)gmail(dot)com>|
|Subject:||Re: Binary search in ScalarArrayOpExpr for OR'd constant arrays|
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On Sat, Apr 25, 2020 at 12:21:06AM +0200, Tomas Vondra wrote:
>On Thu, Apr 23, 2020 at 04:55:51PM +0200, Tomas Vondra wrote:
>>On Thu, Apr 23, 2020 at 09:02:26AM -0400, James Coleman wrote:
>>>On Thu, Apr 23, 2020 at 8:47 AM Tomas Vondra
>>>>On Mon, Apr 20, 2020 at 09:27:34PM -0400, James Coleman wrote:
>>>>>Over in "execExprInterp() questions / How to improve scalar array op
>>>>>expr eval?"  I'd mused about how we might be able to optimized
>>>>>scalar array ops with OR'd semantics.
>>>>>This patch implements a binary search for such expressions when the
>>>>>array argument is a constant so that we can avoid needing to teach
>>>>>expression execution to cache stable values or know when a param has
>>>>>The speed-up for the target case can pretty impressive: in my
>>>>>admittedly contrived and relatively unscientific test with a query in
>>>>>select count(*) from generate_series(1,100000) n(i) where i in (<1000
>>>>>random integers in the series>)
>>>>>shows ~30ms for the patch versus ~640ms on master.
>>>>Nice improvement, although 1000 items is probably a bit unusual. The
>>>>threshold used in the patch (9 elements) seems a bit too low - what
>>>>results have you seen with smaller arrays?
>>>At least in our systems we regularly work with 1000 batches of items,
>>>which means you get IN clauses of identifiers of that size. Admittedly
>>>the most common case sees those IN clauses as simple index scans
>>>(e.g., WHERE <primary key> IN (...)), but it's also common to have a
>>>broader query that merely filters additionally on something like "...
>>>AND <some foreign key> IN (...)" where it makes sense for the rest of
>>>the quals to take precedence in generating a reasonable plan. In that
>>>case, the IN becomes a regular filter, hence the idea behind the
>>>Side note: I'd love for us to be able to treat "IN (VALUES)" the same
>>>way...but as noted in the other thread that's an extremely large
>>>amount of work, I think. But similarly you could use a hash here
>>>instead of a binary search...but this seems quite good.
>>>As to the choice of 9 elements: I just picked that as a starting
>>>point; Andres had previously commented off hand that at 8 elements
>>>serial scanning was faster, so I figured this was a reasonable
>>>starting point for discussion.
>>>Perhaps it would make sense to determine that minimum not as a pure
>>>constant but scaled based on how many rows the planner expects us to
>>>see? Of course that'd be a more invasive patch...so may or may not be
>>>as feasible as a reasonable default.
>>Not sure. That seems a bit overcomplicated and I don't think it depends
>>on the number of rows the planner expects to see very much. I think we
>>usually assume the linear search is cheaper for small arrays and then at
>>some point the binary search starts winning The question is where this
>>"break even" point is.
>>I think we usually use something like 64 or so in other places, but
>>maybe I'm wrong. The current limit 9 seems a bit too low, but I may be
>>wrong. Let's not obsess about this too much, let's do some experiments
>>and pick a value based on that.
>>>>Another idea - would a bloom filter be useful here, as a second
>>>>optimization? That is, for large arrays build s small bloom filter,
>>>>allowing us to skip even the binary search.
>>>That's an interesting idea. I actually haven't personally worked with
>>>bloom filters, so didn't think about that.
>>>Are you thinking that you'd also build the filter *and* presort the
>>>array? Or try to get away with using only the bloom filter and not
>>>expanding and sorting the array at all?
>>Yeah, something like that. My intuition is the bloom filter is useful
>>only above some number of items, and the number is higher than for the
>>binary search. So we'd end up with two thresholds, first one enabling
>>binary search, the second one enabling bloom filter.
>>Of course, the "unknown" variable here is how often we actually find the
>>value in the array. If 100% of the queries has a match, then the bloom
>>filter is a waste of time. If there are no matches, it can make a
>I did experiment with this is a bit, both to get a bit more familiar
>with this code and to see if the bloom filter might help. The short
>answer is the bloom filter does not seem to help at all, so I wouldn't
>bother about it too much.
>Attacched is an updated patch series and, script I used to collect some
>performance measurements, and a spreadsheet with results. The patch
>series is broken into four parts:
> 0001 - the original patch with binary search
> 0002 - adds GUCs to enable bin search / tweak threshold
> 0003 - allows to use bloom filter + binary search
> 0004 - try using murmurhash
>The test script runs a wide range of queries with different number
>of lookups, keys in the array, match probability (i.e. fraction of
>lookups that find a match) ranging from 1% to 100%. And of course, it
>runs this with the binsearch/bloom either enabled or disabled (the
>threshold was lowered to 1, so it's the on/off GUCs that determine
>whether the binsearch/bloom is used).
>The results are summarized in the spreadsheet, demonstrating how useless
>the bloom filter is. There's not a single case where it would beat the
>binary search. I believe this is because theaccess to bloom filter is
>random (determined by the hash function) and we don't save much compared
>to the log(K) lookups in the sorted array.
>That makes sense, I think the bloom filters are meant to be used in
>cases when the main data don't fit into memory - which is not the case
>here. But I wonder how would this change for cases with more expensive
>comparisons - this was using just integers, so maybe strings would
>result in different behavior.
OK, I tried the same test with text columns (with md5 strings), and the
results are about as I predicted - the bloom filter actually makes a
difference in this case. Depending on the number of lookups and
selectivity (i.e. how many lookups have a match in the array) it can
mean additional speedup up to ~5x compared to binary search alone.
For the case with 100% selectivity (i.e. all rows have a match) this
can't really save any time - it's usually still much faster than master,
but it's a bit slower than binary search.
So I think this might be worth investigating further, once the simple
binary search gets committed. We'll probably need to factor in the cost
of the comparison (higher cost -> BF more useful), selectivity of the
filter (fewer matches -> BF more useful) and number of lookups.
This reminds me our attempts to add bloom filters to hash joins, which I
think ran into mostly the same challenge of deciding when the bloom
filter can be useful and is worth the extra work.
Tomas Vondra http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services
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