There must be a bug in the way the token reduction gets calculated. I see this on several of my bayes databases.
Example (excerpts): sa-learn --dump magic 0.000 0 3 0 non-token data: bayes db version 0.000 0 62759 0 non-token data: nspam 0.000 0 43000 0 non-token data: nham 0.000 0 1796131 0 non-token data: ntokens local.cf: bayes_expiry_max_db_size 1000000 sa-learn --force-expire -D [16049] dbg: bayes: expiry check keep size, 0.75 * max: 750000 [16049] dbg: bayes: token count: 0, final goal reduction size: -750000 [16049] dbg: bayes: reduction goal of -750000 is under 1,000 tokens, skipping expire [16049] dbg: bayes: expiry completed There are 1796131 tokens, but sa-learn thinks there are 0 tokens. Or do I misinterpret this? I can get it sometimes to start an expiry by changing the bayes_expiry_max_db_size to some other value. e.g. on a database with 3.5 million tokens it saw 0 tokens with a limit of 100.000, but saw the correct number of tokens when I changed the limit to 1.000.000. Unfortunately then the typical expiry failure kicks in (couldn't find a good delta atime, need more token difference, skipping expire). As this bugs me for quite some time I'm wondering in case there is a bug in the basic token count (as it seems) if there's not a chance there's also a bug in the expiry procedure? Kai -- Kai Schätzl, Berlin, Germany Get your web at Conactive Internet Services: http://www.conactive.com
