redis_backend

Redis statistics
----------------

From version 1.1, it is also possible to specify Redis as a backend for 
statistics  and cache of learned messages. Redis is recommended for clustered
configurations  as it allows simultaneous learn and checks and, besides, is 
very  fast. To setup Redis, you could use redis backend for a classifier 
(cache  is set to the same servers accordingly).

The following configuration is a full featured example of how you can set up 
redis for the statistics. Please edit /etc/rspamd/local.d/statistic.conf and 
paste the code.

For a redis classifier, you need to set the backend to "redis". It is 
important to define the "servers" parameter, as it is not taken from a global
configuration (You might have defined redis for LUA modules). If you want to 
have bayes auto learning, you need to tell it to the configuration file (See 
below for further explanations on this parameter).

Bayes tokens can be stored per user. For this, you can define a LUA function.

The statfile parameters are used for the key names in redis. You must also 
specify, which symbol is spam and which is for ham.

At the end of this configuration file, you find a learning condition LUA 
function. It keeps track of already learned tokens.

    classifier "bayes" {
      tokenizer {
        name = "osb";
      }
    
      backend = "redis";
      servers = "127.0.0.1:6379";
      min_tokens = 11;
      min_learns = 200;
      autolearn = true;
    
      per_user = <<EOD
    return function(task)
        local rcpt = task:get_recipients(1)
    
    if rcpt then
        one_rcpt = rcpt[1]
        if one_rcpt['domain'] then
            return one_rcpt['domain']
        end
    end
    
    return nil
    end
    EOD
    
      statfile {
        symbol = "BAYES_HAM";
        spam = false;
      }
      statfile {
        symbol = "BAYES_SPAM";
        spam = true;
      }
    
      learn_condition =<<EOD
    return function(task, is_spam, is_unlearn)
      local prob = task:get_mempool():get_variable('bayes_prob', 'double')
    
      if prob then
        local in_class = false
        local cl
        if is_spam then
          cl = 'spam'
          in_class = prob >= 0.95
        else
          cl = 'ham'
          in_class = prob <= 0.05
        end
    
        if in_class then
          return false,string.format('already in class %s; probability %.2f%%',
            cl, math.abs((prob - 0.5) * 200.0))
        end
      end
    
      return true
    end
    EOD
    }

per_languages is not supported by Redis - it just stores everything in the same place. write_servers are used in the master-slave rotation by default and used for learning, whilst servers are selected randomly each time:

Supported parameters for the redis backend are:

  - tokenizer
    + leave it as shown for now. Currently only osb is supported
  - backend
    + set it to redis
  - servers
    + IP or hostname with port for the redis server. Use an IP for the 
    loopback interface, if you have defined localhost in /etc/hosts for both 
    IPv4 and IPv6, or your redis server will not be found!
  - write_servers (optional)
    + If needed, define other servers for learning
  - password (optional)
    + Password for the redis server
  - min_tokens
    + Minimum number of words required for statistics processing
  - max_tokens
    +  Minimum learn count for both spam and ham classes to perform 
    classification
  - autolearn (optional)
    + See below for details
  - per_user (optional)
    + LUA script. See above
  - statfile
    + Define keys for spam and ham mails. You must also set the spam= 
    parameter. 
  - learn_condition (optional)
    + LUA function as described above.