There's a few ways to do this. One would be to just construct a dataset of IDs that have non-matching rows, then do a merge or a SQL join and remove anything that matched this list.
However, my preferred option (partly because of speed, but also it's more straightforward once you understand how it works) is the DoW loop.
data want;
id_nonmatch = 0;
do _n_ = 1 by 1 until (last.id);
set have;
by id;
if match1 ne match2 then id_nonmatch = 1; *set the flag to 1 if we find a nonmatch;
end;
do _n_ = 1 by 1 until (last.id);
set have;
by id;
if id_nonmatch = 0 then output;
end;
run;
There are two set statements on the data step, each of which runs through the same dataset separately. If it doesn't make sense, throw a put _all_; inside each of the do loops - that will show you what it's doing. The first loop goes over all of the rows for one ID, checks if any violate the constraint, and if none do, the flag variable (id_nonmatch) stays 0. If one does, it becomes a 1 (and stays that way). Then, when it hits an ID boundary, it stops pulling records from the first set statement, and goes onto the second - re-pulling those same rows. Now, it outputs only when the flag is a zero.
This is very efficient because of buffering - unless your id groups are very large, the data step may be able to use buffers to keep the same rows in memory and not have to reread them from disk. (This will depend on your disk and buffers - and seems to help much less on flash than on physical disks [since there is not the additional benefit of the disk head not having to move] - so your mileage may vary here.)
Just to show this difference, here is a log showing that there isn't much additional time needed for the second read - when the record is reasonably sized. This benefit is less when the record is very small - I imagine there is more overhead involved. Note that the second read adds only 1/7 of the time of the first read to the total processing time!
69 data have;
70 call streaminit(7);
71 length strvar $1000;
72 do id = 1 to 100000;
73 do iter = 1 to 50;
74 x = rand('Uniform');
75 output;
76 end;
77 end;
78 run;
NOTE: Variable strvar is uninitialized.
NOTE: The data set WORK.HAVE has 5000000 observations and 4 variables.
NOTE: DATA statement used (Total process time):
real time 5.20 seconds
cpu time 5.20 seconds
79
80
81 data _null_;
82 do _n_ = 1 by 1 until (last.id);
83 set have;
84 by id;
85 end;
86 run;
NOTE: There were 5000000 observations read from the data set WORK.HAVE.
NOTE: DATA statement used (Total process time):
real time 2.37 seconds
cpu time 2.37 seconds
87
88
89 data _null_;
90 do _n_ = 1 by 1 until (last.id);
91 set have;
92 by id;
93 end;
94 do _n_ = 1 by 1 until (last.id);
95 set have;
96 by id;
97 end;
98 run;
NOTE: There were 5000000 observations read from the data set WORK.HAVE.
NOTE: There were 5000000 observations read from the data set WORK.HAVE.
NOTE: DATA statement used (Total process time):
real time 2.74 seconds
cpu time 2.73 seconds