experimenting with class %Library.Vector I found an unattractive way:

;; compose JSON array  >> v
USER>zw v
v=[($double(.5)),($double(1.5)),($double(2.2000000000000001776))]  ; <DYNAMIC ARRAY>
USER>set vec=##class(%Vector).OdbcToLogical(v)
 
USER>zw vec
vec={"type":"double", "count":3, "length":3, "vector":[$double(.5),$double(1.5),$double(2.2000000000000001776)]}  ; <VECTOR>

Applying OdbcToLogical  was really shocking

Hi al!,
Just returning from some private troubleshooting I'm deeply moved and thankful for this feedback.
It's once more a motivation to continue my activities.
Sometimes I'm insisting on small pieces that may bypass general attention.
Though servicing customers - and I understand you all as my customers -
requires to take care also of the small and often annoying pieces. 

Special big THANKS to the brilliant team behind the DC+OEX+GM facility.
YOU ARE GREAT. 💐🌷🌺🌸💮🌼🌻
 

you are in Caché so this might help:
http://localhost:57772/csp/docbook/DocBook.UI.Page.cls?KEY=GIOD_rmsseqfiles
USE file:position
the equivalent in %Stream,Object is MoveTo 
• method MoveTo(position As %Integer) as %Boolean

Move to this position in the stream. If this suceeds then return true, else return false. Note this implementation is not efficient because it searches from the start of the stream, it can be improved upon in specific subclasses. Note that moving to position 1 will be at the start of the stream, position 2 will be at the second character of the stream, etc.

And then you do your Read or Find..

Hi @Luis Angel Pérez Ramos
I got in fact the same values with my iris community edition.

Test Columnar vs. Row Storage
=============================
     1 - Initialize Tables
     2 - Generate Data
     3 - Compare SELECT
     4 - Loop SELECT
     5 - Auto Loop
Select Function or * to exit : 5 Loops to run :25
Set steps by loop
Records to add (1...10000)[1]:10000 
records = 15000 row = .033238 col = .044981
records = 25000 row = .007728 col = .000254
records = 35000 row = .011427 col = .000335
records = 45000 row = .014625 col = .000406
records = 55000 row = .018682 col = .000500
records = 65000 row = .023468 col = .000562
records = 75000 row = .026235 col = .000659
records = 85000 row = .029151 col = .000738
records = 95000 row = .032212 col = .000794
records = 105000 row = .035926 col = .000856
records = 115000 row = .039431 col = .000934
records = 125000 row = .043036 col = .001008
records = 135000 row = .049134 col = .001074
records = 145000 row = .050405 col = .001404
records = 155000 row = .054313 col = .001669
records = 165000 row = .058039 col = .001380
records = 175000 row = .060756 col = .001384
records = 185000 row = .064746 col = .001451
records = 195000 row = .068403 col = .001665
records = 205000 row = .070737 col = .001642
records = 215000 row = .073610 col = .001690
records = 225000 row = .078551 col = .001797
records = 235000 row = .084139 col = .001997
records = 245000 row = .087316 col = .001908
records = 255000 row = .087862 col = .002546
records = 265000 row = .090478 col = .002152