World: r3wp

Is this to get around rebol's current word limit ?

Only partly

but there's a compression value as well..

a value in the dictionary could be a page of rebol code (as a string) 
 .. and it could be represented with a single bit  (or in this case.. 
3 bytes)

the values never change.. they may be deleted... and their index 
re-valued.. or the dictionary may be appended..

so then my code becomes a block of 3 byte 'symbols' that I use to 
'pick' the actual values out of the dictionary with.

Is the aim to create a fixed-width number representation for the 
index ?

(I mean, a fixed-width of 3 characters.)

i would rather have it not fixed, and grow as needed

2 bytes would be plenty to start.. but would quickly grow to 3.. 
and very slowly (if ever.. but possible) grow to 4 bytes..

so 3 bytes is fine

When the dictionary grows more than the current index limit (3 bytes 
-> 4 bytes), then the entire index block would need to be reprocessed 
to add the extra byte to each index value.

so.. for compaction.. the words most commonly used should be 1 byte.. 
least common.. 3 bytes

that's fine.. the index can be rebuilt rather easily

dict: ["Rebol"  "Carl"  "isa"]    are common..  should  be  represented 
as [1  2  3 ]  .. and dict:["This is a rare string.. probably only 
used once"]   shoud be  [ ZZZ ]

Even though that last dict value is long, it only takes 3 bytes to 
take advantage of it.. see where Im going?

Im going to store the entire dict in memory as a hash!, and my code 
as a separate block of 3 byte "symbols"

Your 2 million entry index, with each index value of 3 bytes, should 
theoretically take 5.7 MB. Do you really need to reduce that with 
compaction ?

not really...again.. performance is the main thing

(They aren't symbols by the way, they're 3-byte integers.)

an integer is a symbol

Compaction makes everything more complex. You are writing more and 
more code to implement that.

the sound you utter when you say "symbol" is a symbol

a symbol is not (only) an integer

now we're talking semantics

my main concern is crawling the block of integers... looking for 
 needles in haystacks.. whatever is the FASTEST method for this is 
best.. compaction is a by-product

what's faster, looking for  "¥%R" or   23472937 ?

or 16581375 rather

The dictionary will have 2 million strings... the index would be 
much smaller

index being the DB using triples as schema

Here's a question.. when a block of integers is converted to a HASH!.. 
what actually happens to it?

Hashes, lists and blocks can contain any type of value. The values 
are not affected by the container type.

A hash just has a different way of linking the items it contains 
together, so it performs faster in some operations at the expense 
of others.

If you just need to save large arrays of integers, you can use format 
used in AS3:
 The AS3 Integer can be encoded into between 1 and 5 bytes.

    *

      if the integer is between 0×00 and 0x7F then only one byte (representing 
      the integer)
    *
      if between 0×80 and 0x3FFF then 2 bytes :
          o
            (i & 0x7F) | 0×80
          o
            (i » 7)
    *
      if between 0×4000 and 0x1FFFFF then 3 bytes :
          o
            (i & 0x7F) | 0×80
          o
            (i » 7) | 0×80
          o
            (i » 14)
    *
      if between 0×200000 and 0xFFFFFFF then 4 bytes :
          o
            (i & 0x7F) | 0×80
          o
            (i » 7) | 0×80
          o
            (i » 14) | 0×80
          o
            (i » 21)
    *
      if more or equal than 0×10000000 :
          o
            (i & 0x7F) | 0×80
          o
            (i » 7) | 0×80
          o
            (i » 14) | 0×80
          o
            (i » 21) | 0×80
          o
            (i » 28)

but if jyou need to search such an array, it's not the best for your... 
especially if there is no rebcode yet:(

That's interesting, using a bit in each byte to "escape" and open 
up the next, more significant byte. So it's a variable-length encoding 
scheme. (I guess, kind of like UTF8)

But yes, I think this adds complexity and would slow down the access 
routines.

yeah... I think the conclusion is ... don't worry about the number 
of bytes (and thus mem) when using plain integers with my index block, 
as it's much smaller than the dictionary anyways.. and unless Im 
shown otherwise, the crawling of it (find/ foreach, append) should 
be about as fast as any other method, right?

It should be faster and simpler to just use integers. When you want 
to cut down the size of your 2 million integers (=30MB), you can 
then look at implementing 3-byte integers packed in a binary.

exactly

Here's a couple of conversion routines.

struct: make struct! [int [int]] none

; convert integer -> 3-byte binary

integer-to-3-byte-binary: func [integer [integer!]][
	struct/int: integer
	copy/part third struct 3
]

; convert 3-byte binary -> integer

binary-3-byte-to-integer: func [int3 [binary!]][
	struct/int: 0 ; just make sure all bytes are zero
	change third struct int3
	struct/int
]
	
; test

my-bin: integer-to-3-byte-binary 2000000
;== #{80841E}
my-int: binary-3-byte-to-integer my-bin
;== 2000000

ok.. that's cool.. thanks

Even though each integer uses 16 bytes, there's some compaction by 
using the smallest integers with the most commonly used dictionary 
strings

I'll add these functions to the dictionary.. 

dict: [{integer-to-3-byte-binary: func [integer [integer!]][
	struct/int: integer
	copy/part third struct 3
]}]


now whenever i want to use that function.. i can represent it with 
a single integer.. ie:  do pick dict 1

Terry, I would not make own binary storage as there should be new 
datatype dictionary! in R3 which is exactly what you need... if I 
understand it well

I cannot see hash! in this list http://www.rebol.net/r3blogs/0076.html
so it will be probably replaced

and that could be reduced further.. 

dict: [

{fetch: func [index][do pick dict index]}

{integer-to-3-byte-binary: func [integer [integer!]][
	struct/int: integer
	copy/part third struct 3
]}

]

do pick dict 1
fetch 2
my-bin: integer-to-3-byte-binary 2000000

The new vector! datatype will be usefull as well to store large block 
of same data type

Im happy with R2 ;)