Most time-series data analysis techniques aggregate data into fixed time intervals, which smooths the data and makes it easier to interpret and analyze. When you write queries for data in this form, you need an efficient way to aggregate raw observations, which are often noisy and irregular, in to fixed time intervals. Timescale does this using time bucketing, which gives a clear picture of the important data trends using a concise, declarative SQL query.

Sorting data into time buckets works well in most cases, but problems can arise if there are gaps in the data. This can happen if you have irregular sampling intervals, or you have experienced an outage of some sort. You can use a gapfilling function to create additional rows of data in any gaps, ensuring that the returned rows are in chronological order, and contiguous.


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