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author | Vsevolod Stakhov <vsevolod@highsecure.ru> | 2017-11-22 20:47:32 +0000 |
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committer | Vsevolod Stakhov <vsevolod@highsecure.ru> | 2017-11-22 20:47:32 +0000 |
commit | feb910e287c215d5a1b6a03856ad2a1cbd36a394 (patch) | |
tree | dce73ac15d0f0460e43e3b7547fcc5d4a850accb | |
parent | 4ca425a05708d58aaac2c016b391d67e8275cb56 (diff) | |
download | rspamd-feb910e287c215d5a1b6a03856ad2a1cbd36a394.tar.gz rspamd-feb910e287c215d5a1b6a03856ad2a1cbd36a394.zip |
[Conf] Default statistics is stored in Redis now
-rw-r--r-- | conf/statistic.conf | 28 |
1 files changed, 16 insertions, 12 deletions
diff --git a/conf/statistic.conf b/conf/statistic.conf index 26e73c4d2..79c3e3890 100644 --- a/conf/statistic.conf +++ b/conf/statistic.conf @@ -13,34 +13,38 @@ # # See https://rspamd.com/doc/tutorials/writing_rules.html for details -# Rspamd statistic setup -# Pre-built files could be loaded from: -# http://rspamd.com/rspamd_statistics/bayes.spam.sqlite -# - and - -# http://rspamd.com/rspamd_statistics/bayes.ham.sqlite +# Rspamd statistic setup, set up the Redis server as appropriate classifier "bayes" { tokenizer { name = "osb"; } - cache { - path = "${DBDIR}/learn_cache.sqlite"; - } + min_tokens = 11; - backend = "sqlite3"; - languages_enabled = true; + backend = "redis"; min_learns = 200; statfile { symbol = "BAYES_HAM"; - path = "${DBDIR}/bayes.ham.sqlite"; spam = false; } statfile { symbol = "BAYES_SPAM"; - path = "${DBDIR}/bayes.spam.sqlite"; spam = true; } + # Define different if needed + servers = "127.0.0.1:6379"; + # Store not only probabilities, but full tokens, false by default + #store_tokens = true; + # Use new schema (TODO: add convert tool) + #new_schema = true; + # Store bayes signatures (TODO: add some usefullnes to this feature) + #signatures = true; + # Expire bayes tokens (TODO: check for new schema, add expiration logic) + #expiry = 30d; + # Enable per user statistics (TODO: describe how to use per user + normal stats) + #per_user = true; + learn_condition =<<EOD return function(task, is_spam, is_unlearn) local prob = task:get_mempool():get_variable('bayes_prob', 'double') |