From: Alexander Moisseev Date: Mon, 9 Apr 2018 15:59:37 +0000 (+0300) Subject: [Feature] Add lazy expiration mode for new classifier schema X-Git-Tag: 1.7.4~110^2 X-Git-Url: https://source.dussan.org/?a=commitdiff_plain;h=9ec51344790d48a71f37c9f0a8b12d6a157c133c;p=rspamd.git [Feature] Add lazy expiration mode for new classifier schema --- diff --git a/src/plugins/lua/bayes_expiry.lua b/src/plugins/lua/bayes_expiry.lua index 7be82d600..bcf7c0864 100644 --- a/src/plugins/lua/bayes_expiry.lua +++ b/src/plugins/lua/bayes_expiry.lua @@ -31,19 +31,19 @@ local settings = { epsilon_common = 0.01, -- eliminate common if spam to ham rate is equal to this epsilon common_ttl = 10 * 86400, -- TTL of discriminated common elements significant_factor = 3.0 / 4.0, -- which tokens should we update + lazy = false, -- enable lazy expiration mode classifiers = {}, cluster_nodes = 0, } -local template = { - -} +local template = {} local function check_redis_classifier(cls, cfg) -- Skip old classifiers if cls.new_schema then local symbol_spam, symbol_ham local expiry = (cls.expiry or cls.expire) + if cls.lazy then settings.lazy = cls.lazy end -- Load symbols from statfiles local statfiles = cls.statfile for _,stf in ipairs(statfiles) do @@ -146,6 +146,7 @@ template.threshold = settings.threshold template.common_ttl = settings.common_ttl template.epsilon_common = settings.epsilon_common template.significant_factor = settings.significant_factor +template.lazy = settings.lazy for k,v in pairs(template) do template[k] = tostring(v) @@ -161,9 +162,12 @@ local expiry_script = [[ local next = ret[1] local keys = ret[2] local nelts = 0 + local significant = 0 local extended = 0 local common = 0 local discriminated = 0 + local infrequent = 0 + local ttls_set = 0 local tokens = {} local sum, sum_squares = 0, 0 @@ -192,30 +196,43 @@ local expiry_script = [[ end for key,token in pairs(tokens) do - local ham, spam, ttl = token[1], token[2], token[3] + local ham, spam, ttl = token[1], token[2], tonumber(token[3]) local threshold = mean local total = spam + ham if total >= threshold and total > 0 then if ham / total > ${significant_factor} or spam / total > ${significant_factor} then - redis.call('EXPIRE', key, math.floor(KEYS[2])) - extended = extended + 1 + significant = significant + 1 + if ${lazy} then + if ttl ~= -1 then + redis.call('PERSIST', key) + extended = extended + 1 + end + else + redis.call('EXPIRE', key, math.floor(KEYS[2])) + extended = extended + 1 + end end - end - if total == 0 or math.abs(ham - spam) <= total * ${epsilon_common} then + elseif total == 0 or math.abs(ham - spam) <= total * ${epsilon_common} then common = common + 1 - if tonumber(ttl) > ${common_ttl} then + if ttl > ${common_ttl} then discriminated = discriminated + 1 redis.call('EXPIRE', key, ${common_ttl}) end + else + infrequent = infrequent + 1 + if ${lazy} and ttl == -1 then + redis.call('EXPIRE', key, math.floor(KEYS[2])) + ttls_set = ttls_set + 1 + end end end - return {next, nelts, extended, discriminated, mean, stddev, common} + return {next, nelts, extended, discriminated, mean, stddev, common, significant, infrequent, ttls_set} ]] local cur = 0 -local c_data = {0,0,0,0,0,0}; +local c_data = {0,0,0,0,0,0,0,0,0}; local function expire_step(cls, ev_base, worker) local function redis_step_cb(err, data) @@ -235,13 +252,22 @@ local function expire_step(cls, ev_base, worker) end end - logger.infox(rspamd_config, 'executed expiry step for bayes: %s items checked, %s extended, %s common (%s discriminated), %s mean, %s std', - data[1], data[2], data[6], data[3], data[4], data[5]) - + local function log_stat(cycle) + logger.infox(rspamd_config, 'finished expiry %s%s: %s items checked, %s significant (%s %s), %s common (%s discriminated), %s infrequent%s, %s mean, %s std', + cycle and 'cycle' or 'step', + settings.lazy and ' (lazy)' or '', + c_data[1], c_data[7], c_data[2], + settings.lazy and 'made persistent' or 'extended', + c_data[6], c_data[3], data[8], + settings.lazy and ' (' .. data[9] .. ' ttls set)' or '', + cycle and math.floor(.5 + c_data[4] / c_data[1]) or data[4], + cycle and math.floor(.5 + math.sqrt(c_data[5] / c_data[1])) or data[5] + ) + end + log_stat(false) if cur == 0 then - logger.infox(rspamd_config, 'executed final expiry step for bayes, totals: %s items checked, %s extended, %s common (%s discriminated), %s mean, %s cv', - c_data[1], c_data[2], c_data[6], c_data[3], math.floor(.5 + c_data[4] / c_data[1]), math.floor(.5 + math.sqrt(c_data[5] / c_data[1]))) - c_data = {0,0,0,0,0,0}; + log_stat(true) + c_data = {0,0,0,0,0,0,0,0,0}; end end end