Samples
Time bucketing with aggregates
Bucket temperature readings into 5-minute intervals and calculate statistics:Gapfilling missing data
Fill gaps in time-series data using last observation carried forward (LOCF):Analyzing data distribution
Create a histogram of response times to understand distribution patterns:Approximate row counting
Get a fast estimate of table size without scanning all data:Available functions
Time series utilities
time_bucket(): bucket rows by time intervalfirst(): get the first value ordered by another columnlast(): get the last value ordered by another columndays_in_month(): calculate days in a monthmonth_normalize(): normalize monthly metrics
Gapfilling
time_bucket_gapfill(): bucket time and fill gaps in resultslocf(): last observation carried forward for filling gapsinterpolate(): linear interpolation for filling gaps
Distribution analysis
histogram(): create histograms to visualize data distributionapproximate_row_count(): fast approximate count of rows in a table
Additional hyperfunctions
For advanced time-series analysis including statistical analysis, percentile approximation, state tracking, and more, see the API reference.Deprecated hyperfunctions
time_bucket_ng(): next generation time bucketing with additional features