Prefetching records

The predict module helps to keep the cache hot by prefetching records. It can utilize two independent mechanisms to select the records which should be refreshed: expiring records and prediction.

Expiring records

This mechanism is always active when the predict module is loaded and it is not configurable.

Any time the resolver answers with records that are about to expire, they get refreshed. (see is_expiring()) That improves latency for records which get frequently queried, relatively to their TTL.


The predict module can also learn usage patterns and repetitive queries, though this mechanism is basically a prototype.

For example, if it makes a query every day at 18:00, the resolver expects that it is needed by that time and prefetches it ahead of time. This is helpful to minimize the perceived latency and keeps the cache hot.

You can disable prediction by configuring period = 0. Otherwise it will load the required stats module if not present, and it will use its stats.frequent() table and clear it periodically.


The tracking window and period length determine memory requirements. If you have a server with relatively fast query turnover, keep the period low (hour for start) and shorter tracking window (5 minutes). For personal slower resolver, keep the tracking window longer (i.e. 30 minutes) and period longer (a day), as the habitual queries occur daily. Experiment to get the best results.

Example configuration

modules = {
        predict = {
                window = 15, -- 15 minutes sampling window
                period = 6*(60/15) -- track last 6 hours

Defaults are as above: 15 minutes window, 6 hours period.

Exported metrics

To visualize the efficiency of the predictions, the module exports following statistics.

  • predict.epoch - current prediction epoch (based on time of day and sampling window)

  • predict.queue - number of queued queries in current window

  • predict.learned - number of learned queries in current window


predict.config({ window = 15, period = 24})

Reconfigure the predictor to given tracking window and period length. Both parameters are optional. Window length is in minutes, period is a number of windows that can be kept in memory. e.g. if a window is 15 minutes, a period of “24” means 6 hours.