We have a database with approximately 130M rows, and we need to produce statistics (e.g. mean, standard deviation, etc.) on the data. Right now, we're generating these stats via a single SELECT, and it is extremely slow...like it can take hours to return results. The queries are variable, so there is no way I can think of to "pre-calculate" the stats.
Here is an example query...
SELECT count(mae) AS count, to_char(avg(mae), 'FM990.999') AS mae,
to_char(avg(sqrt(rms)), 'FM990.999') AS rms, to_char(avg(bias), 'FM990.999') AS bias,
to_char(max(mae), 'FM990.999') AS mae_max, to_char(min(mae), 'FM990.999') AS mae_min,
to_char(max(bias), 'FM990.999') AS bias_max, to_char(min(bias), 'FM990.999') AS bias_min,
to_char(max(sqrt(rms)), 'FM990.999') AS rms_max, to_char(min(sqrt(rms)), 'FM990.999') AS rms_min,
to_char(stddev(sqrt(rms)), 'FM990.999') AS rms_stddev,
to_char(stddev(mae), 'FM990.999') AS mae_stddev,
to_char(stddev(bias), 'FM990.999') AS bias_stddev
FROM verify_data.verification_data
WHERE model_name='foo'
AND...several other conditions...
As one might imagine, this query pounds the CPU relentlessly for hours.
I'm thinking there must be a better approach for doing large amounts of calculations on the data. Any pointers would be greatly appreciated!
Matt
