Given your id's are discrete rather than continuous, if the number and size of your ranges are within the bounds of sanity, building a hash lookup for every ID in the cumulative ranges it almost trivial to code and doesn't take long to build.

That'll give you O(log2 n) queries, but there's a simple way to get to O(1). I had it mostly coded up when you posted. The code is nearly identical to yours (sick minds think alike?), so I'll provide the delta.

When reading the first datafile and preparing the data structure, instead of using a hash, use an array. 

  for ( date2offset($start) .. date2offset($end) ) {
      push @{$effective[$_]}, \$data;
  }

[download]
The probe them becomes 
  my $results = $effective[date2offset($date)];    # O(1)
  if ( defined($results) {
      # @$results is an array of all matching $data
  }

[download]
I'll leave date2offset() as an exercise, other than noting that there's a non-obvious trick you can pull with it. Instead of a 1-to-1 mapping, you can do something not-quite right, like 
  sub date2offset {
      my($yyyymmdd) = @_;
      my $year = int($yyyymmdd/ 10000) - 1970;
      my $mmdd = $yyyymmdd % 10000;
      my $month = int($mmmdd / 100) - 1;
      my $day = $mmdd % 100 - 1;
      return $year * (12 * 31) + $month * 31 + $day;
  }

[download]
This is a classic time-for-space tradeoff, simplifying the calculation by assuming all months are 31 days long, resulting in 6-7 bogus offsets per year. Note that it has no effect on probe time.

In reply to Re: Re: Code efficiency / algorithm - O(1) by dws
in thread Code efficiency / algorithm by dave8775

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