World: r3wp

I'll say that's a respectable time... and the leading contestant 
:)

:-)

now if only i was 42 again...

But wait, there's more.... 

convert dataset to hash! and run ultimate-find again!

>> ultimate-find dataset "age"  2 3 100
ultimate find():  -> 0:00   2 matches found
== ["maximn" "age" "unknown" "terry" "age" "42"]

100 iterations not even registering

1000 iterations 0.40

OMG !

exactly

but I'm getting an odd deadlock here on some tests... hum...

I'm getting extremely slow results on dense tests...

interesting... im not too worried as density isn't a big issue with 
triple stores

im off.. good luck with your optimizations

I'm talking like 100 times worse!   the larger the list the worse 
it gets... seems like an exponential issue.

that seems like an anomaly

both dense tests perform pretty much the same, the moment I convert 
it to a hash, it gets reallllly slow.

yeah, i see that too

mind you, that's pretty dense data

the strange thing is i did tests using a record size of 2, which 
wouldn't trigger strange mis aligned key/value issues.  I even removed 
the copy to make sure that wasn't the issue and one test with only 
400000 records took more than 4 minutes to complete vs .297 for the 
feach test!

I'm looking for the 6 integer.. it's still cranking and i can hear 
my system struggling..

must be a loop error

well, the results where the same at the end... pretty weird... maybe 
someone has encountered this before and can explain why this happens....

Max - just a question - wouldn't using parse be faster than find/skip?

my advice would be:

1) to test Parse as Pekr noted (traversing only the respective field)
2) to use a hash to index the respective field

I didn't do any parse test tweaks... but find/skip is very fast so 
far, we can skip over 100 million records within a millisecond.  
not sure parse can beat that

Did you find a solution to the density issue Max?

nope... I'm working on urgent stuff won't have time for a few days 
to put more time on this.

didn't tested since a while in R2, but in R3, parse is faster in 
most of the cases (if you write correctly the rules)

I'm wondering if it has something to do with recreating the hash 
each time a value is found?

Looking at Maxim's ulitmate-find (above, monday 11:32) , does anyone 
have an idea why when dealing with hash! , the more matches it finds, 
the slower it gets?

I think, that it is quite natural. You should probably generate some 
random data having (approximately) similar properties as what you 
intend to process and try some variant approaches to really find 
out, which one is best for the task. Do you know, that it is possible 
to index just a specific record field, i.e. you don't need to make 
a hash containing all the data from the database?

Yeah, i've tried some actual data finding 3270 matches out of a hash 
that is 732981 in length.. 

when it's block the search takes .033 s, and same run against has 
is 0.6

but if the matches are just a few, hash is 1000x faster

(against has = against hash)

.033 s, and same run against has is 0.6
 - do you mean 0.6s, ie. roughly 18 times slower?

yeah

that is interesting, can you post your data generator?

or, do you use the real-world data?

the only thing that I'm thinking is that when the hash index changes, 
its rehashing its content... which is strange.

it's maxim's ultimate-find above ( and im using real world data)

ladislav, there is a script earlier in this discussion which has 
a complete working example.

aha, you are using real-world data. OK, then, you should tell me 
how many matches you see

(and a revision to ultimate-find, just after it)

the example shows the problem very well.

3270 matches

the example creates several sets of data with different organizations 
and it compares all of them amongst each other.


so with that script, you should be able to do all the analysis you 
need.

495 matches against the same 732981 hash takes only .003

above I said "its rehashing its content... which is strange."

that is a guess... it should say:

its *might* be rehashing its content... which *would be* strange.

hmm, what if the hash is optimized for unique elements?

...then you are most probably out of luck trying to use hash! for 
indexing purposes

ah   yes.... ususally a hash will have to skip over  elements which 
return the same hash key.


so if your table has a few thousand similar items, you aren't benifiting 
from the hashing... and its quite possible that looking up a hash 
itself is actually longer when it has to skip over and over (comparing 
data on top of the hash).


though one could argue, that the speeds should be a bit slower than 
using a block, not this slower...  possibly related to the implementation 
itself.

my dilema is indexing triples in a key/value world