I am trying to compare two data sets and pull out the matches based on an id. I could use a little wisdom for this :) 

Dataset A
A   "Monday"
B   "Tuesday"
C   "Wednesday"
D   "Thursday"


Dataset B
M    252   212  "Bill"
A    325   908  "Jim"
C    426   907  "Mike"
A    423   383  "Sally"
A    993   421  "Jim"
C    737   432  "Mary"

[download]

The goal would be to merge these together such that redundant ids (first fields) are included and those not present in Dataset A are excluded. So the merging and filtering of the data would look something like this: 

A   "Monday"     325   908  "Jim"
A   "Monday"     423   383  "Sally"
A   "Monday"     993   421  "Jim"
C   "Wednesday"     737   432  "Mary"
C   "Wednesday"     737   432  "Mary"

[download]

Now, I could easily iterate through two arrays side by side and do a pattern match BUT the problem is speed. I have enormous amounts of data that I need to mine through so it needs to be pretty fast.

I started making a hash table but you need a unique id which is problematic the keys need to be unique and I am interested redundant matches. I started building a matrix using anonymous arrays but it started getting clumsy and I know there is a more elegant way to do this.

Any ideas or suggestions would be greatly appreciated!! Expo

In reply to Data Matching Challenge by expo

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