(1)
[root@ns1 ~]# sudo -H -u amavis bash -c '/usr/bin/sa-learn --dump magic'
0.000 0 3 0 non-token data: bayes db version
0.000 0 32225 0 non-token data: nspam
0.000 0 440420 0 non-token data: nham
0.000 0 159483 0 non-token data: ntokens
0.000 0 1525435204 0 non-token data: oldest atime
0.000 0 1525848687 0 non-token data: newest atime
0.000 0 0 0 non-token data: last journal
sync atime
0.000 0 1525824089 0 non-token data: last expiry atime
0.000 0 443565 0 non-token data: last expire
atime delta
0.000 0 0 0 non-token data: last expire
reduction count
(2)
as you say i think it is amavis user
(3)
your message has
X-Spam-Status: No, score=-18.15 tagged_above=-999 required=6.2
tests=[AM.WBL=-3, BAYES_00=-1.9, HEADER_FROM_DIFFERENT_DOMAINS=0.25,
MAILING_LIST_MULTI=-1, RCVD_IN_DNSWL_HI=-5, SPF_PASS=-0.001,
URIBL_BLOCKED=0.001, USER_IN_DEF_SPF_WL=-7.5]
autolearn=ham autolearn_force=no
(4)
around 50 users. they are all working in same industry
On 08/05/18 21:08, John Hardin wrote:
On Tue, 8 May 2018, Matthew Broadhead wrote:
system setup centos-release-7-4.1708.el7.centos.x86_64,
spamassassin-3.4.0-2.el7.x86_64, amavisd-new-2.11.0-3.el7.noarch
/etc/mail/spamassassin/local.cf:
required_hits 5
report_safe 0
rewrite_header Subject [SPAM]
use_bayes 1
bayes_auto_learn 1
bayes_auto_expire 1
# Store bayesian data in MySQL
bayes_store_module Mail::SpamAssassin::BayesStore::MySQL
bayes_sql_dsn DBI:mysql:sa_bayes:localhost:3306
it is storing the info to the database ok. but it doesn't seem to be
filtering any mail.
(1) What is the output of: /usr/bin/sa-learn --dump magic
(2) What user are you running sa-learn as for training, and what user
is spamd running as?
(3) Are you seeing any BAYES_nn rule hits on messages at all, on
either ham or spam?
(4) How large is your environment (rough # and diversity of users)?
I'm not familiar with SQL Bayes, others may have other
questions/recommendations.
Some general comments:
I don't recommend using auto-learn for initial bayes training at
least, particularly in smaller environments. Manual initial training
with careful review, followed by manual training of misclassifications
after review, is more reliable. Others may offer different advice,
particularly for large installs with a diverse user community (which I
don't manage).
Always keep your training corpora so that you can review and fix
training errors, and wipe and retrain from scratch if Bayes goes
completely off the rails for some reason.
If you're not auto-learning, auto-expire is not needed. If you *are*,
it's recommended to expire from a scheduled job rather than take the
hit from spamd.
