Large-scale global Bayes tuning?

[Kris Deugau]

Anyone have any suggestions on tuning a large global Bayes db for 
stability and sanity?  I've got my fingers in the pie of a moderately 
large mail cluster, but I haven't yet found a Bayes configuration that's 
sane and stable for any extended period.  Wiping it completely about 
once a week seems to provide "acceptable" filtering performance (we have 
a number of addon rulesets), but I still see spam in my inbox with 
BAYES_00 - a sure sign of a mistuned Bayes database.

Past experience with (much) smaller systems has shown stable behaviour 
with bayes_expiry_max_db_size set to 1500000 (~40M BDB Bayes), daily 
expiry runs delete ~25-35K tokens;  mail volume ~3K/day.  However, the 
larger system (MySQL, currently set with max_db_size at 3000000, on-disk 
files running ~100M) only seems to be expiring that same 25-35K tokens 
even though autolearn is picking up ~1.5M+ from ~300K messages on a 
daily basis.  Reading through the docs on token expiry I would guess it 
should be far more aggressive than it is.  (Among other things, I really 
don't want to bump up max_db_size by two orders of magnitude;  up to ~5M 
should be fine, and I could see as high as 7.5M if really necssary.)

I'm not even really sure what questions to ask to get more detail; 
sa-learn -D doesn't really spit out *enough* detail about the expiry 
process to know for sure if something is going wrong there.

-kgd