- superloglog_counter 1.2.2
This is an implementation of SuperLogLog algorithm as described in the paper "LogLog Counting of Large Cardinalities," published by Flajolet and Durand in 2003. Generally it is an improved LogLog estimator, with truncation and restriction rules.
Contents of the extension
The extension provides the following elements
superloglog_estimator data type (may be used for columns, in PL/pgSQL)
functions to work with the superloglog_estimator data type
superloglog_add_item(counter superloglog_estimator, item anyelement)
The purpose of the functions is quite obvious from the names, alternatively consult the SQL script for more details.
where the 1-parameter version uses default error rate 2.5%. That's quite generous and it may result in unnecessarily large estimators, so if you can work with worse error rate, pass the parameter explicitly.
Using the aggregate is quite straightforward - just use it like a regular aggregate function
db=# SELECT superloglog_distinct(i, 0.01) FROM generate_series(1,100000) s(i);
and you can use it from a PL/pgSQL (or another PL) like this:
DO LANGUAGE plpgsql $$ DECLARE v_counter superloglog_estimator := superloglog_init(0.01); v_estimate real; BEGIN PERFORM superloglog_add_item(v_counter, 1); PERFORM superloglog_add_item(v_counter, 2); PERFORM superloglog_add_item(v_counter, 3); SELECT superloglog_get_estimate(v_counter) INTO v_estimate; RAISE NOTICE 'estimate = %',v_estimate; END$$;
And this can be done from a trigger (updating an estimate stored in a table).
Be careful about the implementation, as the estimators may easily occupy several kilobytes (depends on the precision etc.). Keep in mind that the PostgreSQL MVCC works so that it creates a copy of the row on update, an that may easily lead to bloat. So group the updates or something like that.
This is of course made worse by using unnecessarily large estimators, so always tune the estimator to use the lowest amount of memory.