Well my site gets around 20,000 page views per day and so far today it has gotten over 10,000 according to Google
AdSense and I'm not seeing any problems.
Although the data table has some 150,000 rows, whomever created the original record search routine was sharp enough to use a bisection search, which allows the script to only look at a tiny fraction of the actual records.
Think of it this way, the script takes the middle record of the dataset and decides if the IP address is higher or lower than that record. If it is lower then it takes the record at the halfway point of the lower sub-set of records and again makes this determination. This continues until it gets to the correct record. This is one of the most efficient methods finding a matching record and allows the script to only need to every look at a very small fraction of the total records.
Here is what practically happens on a dataset of 100,000 records:
Check #1) 50,000 records eliminated. 50% remaining
Check #2) 25,000 records eliminated. 25% remaining
Check #3) 12,500 records eliminated. 12.5% remaining
Check #4) 6,250 records eliminated. 6.25% remaining
Check #5) 3,125 records eliminated. 3.13% remaining
Check #6) 1,562 records eliminated. 1.56% remaining
Check #7) 781 records eliminated. 0.78% remaining
Check #8) 390 records eliminated. 0.39% remaining
Check #9) 195 records eliminated. 0.20% remaining
Check #10) 98 records eliminated. 0.10% remaining
Check #11) 49 records eliminated. 0.05% remaining
Check #12) 24 records eliminated. 0.02% remaining
Check #13) 12 records eliminated. 0.01% remaining
Check #14) 6 records eliminated. 0.006% remaining
Check #15) 3 records eliminated. 0.003% remaining
Check #16) 2 records eliminated. 0.002% remaining
Check #17) 1 recored eliminated match made.
As you can see it is very very efficient.