World: r3wp

btw, in my find-fast above... when search matchs aprox 1/10 times, 
it ends up being twice as slow as the foreach... 

so speed will depend on the dataset, as always.

maxim, your find-fast returns the values, not the indices (not sure 
if that was intended)

so does terry's

his "feach" yes, but not his "ind" adaptation

I'm comparing to his feach, which was faster... but if I just returned 
the index, it would be faster still (no copy/part).

I'm trying out something else, which might be even faster...

indices?-maxim-1: func [series value /local result][
    result: make block! length? series
    until [
        not if series: find series value [
            append result index? series
            series: next series
        ]
    ]
    result
]

that's your above find, stripped of the record-length stuff, and 
returning indices instead of the actual values

btw... I discovered  //  instead   of   mod,   which is twice as 
fast..... turns out  MOD is a mezz... never noticed that.

prin "Initialising ... "
random/seed now/precise
dim1: 10'000'000
dim2: dim1 / 2
needle: random dim2
haystack: make block! dim1
loop dim1 [append haystack random dim2]
print "done"

(the loop is twice as fast, meaning // is MUCH faster than using 
 mod)

here's a tiny bit of setup code, adjust dim1/dim2 as you wish. then 
find the needles in the haystack: indices? haystack needle

I'm working on a (bigger) script which has similar setup code... 
specifically meant to compare different dataset scenarios

using find/tail instead of just find speeds up things slightly:

loop kernel becomes:

not if series: find/tail series value [
            append result (index? series) - 1
        ]

ah good idea!

now, dropping the if for an all, speeds up things minimally, but 
it clean up the code

indices?-3: func [series value /local result][
    result: make block! length? series
    until [
        not all [
            series: find/tail series value
            append result (index? series) - 1
        ]
    ]
    result
]

hehe... just what I am doing  ;-)

ladislav's original parse example is much faster for me, than pekr's 
"quote"-based one

interestingly enough, a naive c extension is only negligibly faster

indices?-null 	 0:00:00.184183
indices?-p1 	 0:00:01.082892
indices?-p2 	 0:00:01.523015
indices?-u1 	 0:00:00.347117
indices?-u2 	 0:00:00.345846
indices?-u3 	 0:00:00.346959
indices?-ext 	 0:00:00.329520

null just creates a result block, to demonstrate that 50% of runtime 
is mem allocation for the 10m result array (so that's where one should 
really spend time optimising).


p1 is ladislav's `1 1 value` parse, p2 is pekrs `quote (value)` parse, 
u1/2/3 are the until-based versions shown above, ext is a naive C 
extension.

the kernel of the C extension:

    result = RXI_MAKE_BLOCK(series_n - series_i);
    result_n = 0;
    for (; series_i < series_n; ++series_i) {
        elem_type = RXI_GET_VALUE(series, series_i, &elem);

        if (elem.int64 == value.int64 && elem_type == value_type) {
            result_v.int64 = series_i + 1;

            RXI_SET_VALUE(result, result_n++, result_v, RXT_INTEGER);
        }
    }

I've just finished my tests... I've got a keyed search func which 
returns the exact same results as feach but  20 times faster!

I'll put the whole script in the profiling group... it  has several 
dataset creations for comparison and includes a clean run-time printout.

all speeds are dependent on data... so YMMV

what is all this?  What are you guys testing?

what is all this?  What are you guys testing?

what is all this?  What are you guys testing?

weird, I only hit enter once.

looking at  possible search optimising within rebol, for big data 
sets.

you means searching a block such as [1 "this" 2 "that" 3 "more"] 
 etc..?

ideally, a large block of  key/values like ["key1" "value 1" "key 
2" "value 2"] with the ability to use pattern matching on keys or 
values... but FAST

Terry: "foreach is the winner speed wise.. as a bonus, If i use foreach, 
I don't need the index?" - unbelievable, how you compare apples and 
oranges without noticing

It's all about the goal, Lad... apples, oranges.. unripened bananas... 
i don't care

I was debating the merits of Rebol to the Redis group, and they said 
the same thing.. I said "Rebol + Cheyenne" is so much faster than 
Redis + PHP + Apache.. and they said "I'm comparing apples to oranges"

What? Apples? Oranges?  It's the RESULT i'm interested in. In that 
case it's was Redis pulling 7200 values from 100,000 keys per second 
vs Rebol pulling millions per second.

Terry: "I don't care" - you should, since you are comparing speed 
of code adhering to different specifications. If you really want 
to find the fastest code for a given specification, that is not the 
way to take.

You are certainly entitled to do whatever you like, but saying "foreach 
is the winner speed wise..." is wrong, since you did not allow parse 
to do what you allowed foreach to do.

fair enough

Interesting - "ladislav's original parse example is much faster for 
me, than pekr's "quote"-based one" - then why on my machine was it 
otherwise? What could be the technical reason?

different OS, different processor, maybe even different R3?

all my tests are being done on R2, for the record.

btw - will there be any difference between: 

result: make block! length? series
and
result: make series 0


I mean - e.g. not prealocating large enough series (make series 0) 
will be slowed down by GC constantly increasing the size?

so far, preallocating too large buffer takes much more time... but 
look at my funcs... I do even better  :-)

result: clear []


which re-uses the same block over and over, unless ladislav knows 
otherwise, the memory for that block isn't released by clear, only 
its internal size is reset to 0  which is why clear is so fast AFAIK.

so it will grow to match the needs of the dataset, optimising itself 
in size within a few searches and then not re-allocating itself too 
often.

there are many important differences:

*make series 0 does not necessarily make a block

*regarding the allocation length - if you can estimate reliably the 
necessary length, then you are better off

with 10 million records, I estimated my dense datasets at about 120000 
records and did a few tests, they wher MUCH slower than using 
result: clear [ ]

but, make block! 1000 is certainly slower than make block! 0 if it 
turns out, that you need only 10 elements, e.g.

(estimated 120000 results)