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Thanks everyone for all the input!

To answer a few questions:
        No, no file triggers in this case.
        No, no great number of logicals. In most cases with the files
I'm concerned with, there is only 1 - occasionally 2
        I will look forward in the future to trying imbedded SQL for
this kind of work, because I am indeed changing every occurrence of
DATA1 to DATA2.
                there will be no exceptions. Right now, time prohibits
changing the programs to do that. Perhaps for next time...

I tested the READE vs READ idea on a rather large dataset. In this case,
the field being updated is also the primary key. Therefore, I could use
a READ to know when I'd reached the end of the data I wanted to change.

Working with 249,271 records, the READE loop changed those records in 70
seconds (processor time).
Working with the same number of records, using READ but leaving BLOCK as
it's default - 72 seconds
Working with the same number of records, using READ and adding
BLOCK(*NO) - 72 seconds

I ran these benchmarks on our development machine, which has a fairly
constant load. I was surprised READ didn't show me any advantage, even
though many of you suggested that it would. 

Therefore, it looks like removing old wood from these files is the best
way to boost the speed of the process, followed by parallel-processing a
few of the updates. 

Once again, thank you for all the ideas and if I get time to play with
it more I'll try to post another thread.

'Best wishes

Jim Wiant




Once a job is fouled up, anything done to improve it makes it worse. 
Finagle 


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