hi,

i'm not sure if i'm on the right place too ask this question , but from some previous posts i see people are explaining their reasons for doing something through complexity.

so my question is the following:

let say i have one big table in ascii format(csv) and the 5 smaller ones. what i wish to do is search the big table with each and every small one. so when i import all those tables into sql db and make i query that takes each and every small data set and selects those data from the big table, in my understanding what i'm actually doing is :

N = 5 - number of small tables
H = 100 rows -size of the small tables 
K = 10000 rows - size of the big table



for(1..5){      # of those smaller ones
  for(1..100){  # number of steps i do when iterating through the smal
+ table
    for(1..10000){ # number of steps i do when iterating through big t
+able
      //select a row 
    }
  }
}



so \theta(NHK) is the complexity for that algorithm(?)
[download]

but if i hash all the small tables in a way:

         value   table
         -----   ----- 
%hash = (str1 => 1,
         str2 => 1,
         ...
         str100 => 1,
         str101 => 2,
         str102 => 2,
         ...);
[download]

my key is the value from the the small tables and its values are pointing to from which table did i take the value. so what can i do now is to while loop through the big table and for every row evaluate directly to which group does my data belongs to. example: if a hit the row that holds the str101 i now that that data belongs to the second small table.

so my question now is: is this just a faster implementation (because it runs much more faster than with db engine) or did i change the complexity to

for (1..5){
  # hash tha data from smalo tables
}
while(1..10000){
  # crosscheck with hash
}

so what i got is 
\theta(NH+K)
[download]

so the problem is how to treat hashed data.

thank you

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